bluuebunny/crossref_metadata_2025_split huggingface dataset:
Heat transfer analysis of Maxwell tri-hybridized nanofluid through Riga wedge with fuzzy volume fraction
Authors: Zulqarnain Rana Muhammad , Nadeem Muhammad , Siddique Imran , Ahmad Hijaz , Askar Sameh , Samar Mahvish 
Published: October 2023

AbstractThis contribution aims to optimize nonlinear thermal flow for Darcy-Forchheimer Maxwell fuzzy $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} + {\text{Cu }} + {\text{TiO}}_{{2}} {\text{/EO}}} \right)$$

Al
2

O
3

+

Cu

+

TiO
2

/EO

tri-hybrid nanofluid flow across a Riga wedge in the context of boundary slip. Three types of nanomaterials, $${\text{Al}}_{{2}} {\text{O}}_{{3}} ,$$

Al
2

O
3

,

Cu and $${\text{TiO}}_{2}$$

TiO
2

have been mixed into the basic fluid known as engine oil. Thermal properties with the effects of porous surface and nonlinear convection have been established for the particular combination $$\left( {{\text{Al}}_{{2}} {\text{O}}_{{3}} + {\text{Cu}} + {\text{TiO}}_{{2}} {\text{/EO}}} \right){.}$$

Al
2

O
3

+
Cu
+

TiO
2

/EO

.

Applying a set of appropriate variables, the set of equations that evaluated the energy and flow equations was transferred to the dimensionless form. For numerical computing, the MATLAB software's bvp4c function is used. The graphical display is used to demonstrate the influence of several influential parameters. It has been observed that flow rate decay with expansion in porosity parameter and nanoparticles volumetric fractions. In contrast, it rises with wedge angle, Grashof numbers, Darcy-Forchheimer, nonlinear Grashof numbers, and Maxwell fluid parameter. Thermal profiles increase with progress in the heat source, nanoparticles volumetric fractions, viscous dissipation, and nonlinear thermal radiation. The percentage increases in drag force for ternary hybrid nanofluid are 13.2 and 8.44 when the Modified Hartmann number takes input in the range $$0.1 \le {\text{Mh}} \le 0.3$$

0.1
≤
Mh
≤
0.3

and wedge angle parameters $$0.1 \le m \le 0.3$$

0.1
≤
m
≤
0.3

. For fuzzy analysis, dimensionless ODEs transformed into fuzzy differential equations and employed symmetrical triangular fuzzy numbers (TFNs). The TFN makes a triangular membership function (M.F.) that describes the fuzziness and comparison. This study compared nanofluids, hybrid nanofluids, and ternary nanofluids through triangular M.F. The boundary layer flow caused by a wedge surface plays a crucial role in heat exchanger systems and geothermal.
Skimmianine attenuates liver ischemia/reperfusion injury by regulating PI3K–AKT signaling pathway-mediated inflammation, apoptosis and oxidative stress
Authors: Huo Cheng-long , Wang Bing , Zhang Xuewen , Sun Zhen-Gang 
Published: October 2023

AbstractLiver ischemia/reperfusion (I/R) injury is a common injury after liver transplantation and hepatectomy. Skimmianine (Ski) has antibacterial, antiviral pharmacological effects. However, it is not clear whether Ski has a protective effect against liver I/R injury. In the present study, we established a mouse liver I/R model and an AML12 cell hypoxia-reoxygenation (H/R) model, both pretreated with different concentrations of Ski. Serum transaminase levels, necrotic liver area, cell viability, inflammatory factors, oxidative stress and apoptosis-related levels were measured to assess the protective effect of Ski against liver I/R injury. Western blotting was used to detect apoptosis-related proteins and PI3K–AKT pathway-related proteins. Mice and cells were also treated with PI3K inhibitor LY294002 to assess changes in indicators of liver injury. The results showed that Ski significantly reduced transaminase levels, liver necrosis area, oxidative stress, and apoptosis levels in mice with I/R. Ski also inhibited cell injury and apoptosis after H/R. Moreover, Ski activated phosphorylation of PI3K–AKT pathway-related proteins after liver I/R and cell H/R. Importantly, the PI3K inhibitor LY294002 effectively reversed the alleviation of I/R injury caused by Ski. These results confirm that Ski exerts a protective effect against liver I/R injury through activation of the PI3K–AKT pathway.
Numerical modeling of subduction and evaluation of Philippine Sea Plate tectonic history along the Nankai Trough
Authors: Moreno E. J. , Manea V. C. , Manea M. , Yoshioka S. , Suenaga N. , Bayona A. 
Published: October 2023

AbstractThe subduction of the Philippine Sea (PHS) plate along the Nankai Trough in in southwest Japan is a relatively recent process compared with subduction along the Japan Trench in northeast Japan. However, the tectonic evolution of the PHS plate along the Nankai Trough is still controversial and not fully understood. There are several competing hypotheses based on different estimates for the time variations of convergence rate and plate age. Our study employs numerical modelling of subduction in order to evaluate the slab evolution for the last 15 Myr and aims to evaluate each tectonic scenario against the present-day slab geometry along a profile passing through the Shikoku and Chugoku regions. The modelling strategy involves a parameter study where subduction initiation and various subduction parameters are analyzed in terms of subduction geometry evolution. Two-dimensional visco-elasto-plastic numerical simulations of spontaneous bending subduction predict that convergence rate and plate age variations play an important role in the evolution of subduction geometry. Modeling results after 15 Myr of evolution reveal that the tectonic model based on a high convergence rate between ~ 15 Ma and ~ 3 Ma produces a slab geometry that agrees well with the observed present-day slab shape specific for the Shikoku and Chugoku regions.
Performance of administrative databases for identifying individuals with multiple sclerosis
Authors: Ducatel Pauline , Debouverie Marc , Soudant Marc , Guillemin Francis , Mathey Guillaume , Epstein Jonathan 
Published: October 2023

AbstractAdministrative databases are an alternative to disease registries as a research tool to study multiple sclerosis. However, they are not initially designed to fulfill research purposes. Therefore, an evaluation of their performance is necessary. Our objective was to assess the performance of the French administrative database comprising hospital discharge records and national health insurance databases in identifying individuals with multiple sclerosis, in comparison with a registry that exhaustively compiles resident multiple sclerosis cases in Lorraine, northeastern France, as reference. We recorded all individuals residing in the Lorraine region who were identified by the administrative database or the registry as having multiple sclerosis from 2011 to 2016. We calculated the Matthews correlation coefficient and other concordance indicators. For identifying individuals with multiple sclerosis, the Matthews correlation coefficient by the administrative database was 0.79 (95% CI 0.78–0.80), reflecting moderate performance. The mean time to identification was 5.5 years earlier with the registry than the administrative database. Administrative databases, although useful to study multiple sclerosis, should be used with caution because results of studies based on them may be biased. Our study highlights the value of regional registries that allow for a more exhaustive and rapid identification of cases.
Variable-moment fluid closures with Hamiltonian structure
Authors: Burby J. W. 
Published: October 2023

AbstractBased on ideas due to Scovel–Weinstein, I present a general framework for constructing fluid moment closures of the Vlasov–Poisson system that exactly preserve that system’s Hamiltonian structure. Notably, the technique applies in any space dimension and produces closures involving arbitrarily-large finite collections of moments. After selecting a desired collection of moments, the Poisson bracket for the closure is uniquely determined. Therefore data-driven fluid closures can be constructed by adjusting the closure Hamiltonian for compatibility with kinetic simulations.
Digital twin models of replicative ground stones: insight into simulating usage of Upper Paleolithic tools
Authors: Marulli Maria Rosaria , Sorrentino Giusi , Menna Fabio , Paggi Marco 
Published: October 2023

AbstractThis work presents the first attempt to create a physics-based digital twin model for predictive analysis of damage evolution during the use of ground stone tools (GSTs) in transformative tasks, encompassing the processing of raw resources for nutritional and non-alimentary purposes. The proposed methodology introduces a digital twin of the GSTs developed from 3D models generated using a photogrammetric technique based on Structure-from-Motion and Multi-View Stereo reconstruction. These models serve as the foundation for the development of the finite element (FE)-based digital twin model of the GSTs that exploits a contact formulation and the phase-field approach to simulate tool damage during pounding and grinding tasks. Defining the initial relative positions of the stones, their mechanical behaviour, and controlling the movement of the active stone in a way as close as possible to the real one, the digital twin model has been devised to evaluate how the surface damage is affected by perturbations in the loading conditions. The simulated damage is compared with the surface traces observed from experiments. The developed digital twin model aims at demonstrating its potentials for the GSTs investigations, as a supporting tool for experiments and for simulated tests on the archaeological records.
Abdominal organ segmentation via deep diffeomorphic mesh deformations
Authors: Bongratz Fabian , Rickmann Anne-Marie , Wachinger Christian 
Published: October 2023

AbstractAbdominal organ segmentation from CT and MRI is an essential prerequisite for surgical planning and computer-aided navigation systems. It is challenging due to the high variability in the shape, size, and position of abdominal organs. Three-dimensional numeric representations of abdominal shapes with point-wise correspondence to a template are further important for quantitative and statistical analyses thereof. Recently, template-based surface extraction methods have shown promising advances for direct mesh reconstruction from volumetric scans. However, the generalization of these deep learning-based approaches to different organs and datasets, a crucial property for deployment in clinical environments, has not yet been assessed. We close this gap and employ template-based mesh reconstruction methods for joint liver, kidney, pancreas, and spleen segmentation. Our experiments on manually annotated CT and MRI data reveal limited generalization capabilities of previous methods to organs of different geometry and weak performance on small datasets. We alleviate these issues with a novel deep diffeomorphic mesh-deformation architecture and an improved training scheme. The resulting method, UNetFlow, generalizes well to all four organs and can be easily fine-tuned on new data. Moreover, we propose a simple registration-based post-processing that aligns voxel and mesh outputs to boost segmentation accuracy.
Predicting diagnosis and survival of bone metastasis in breast cancer using machine learning
Authors: Zhong Xugang , Lin Yanze , Zhang Wei , Bi Qing 
Published: October 2023

AbstractThis study aimed at establishing more accurate predictive models based on novel machine learning algorithms, with the overarching goal of providing clinicians with effective decision-making assistance. We retrospectively analyzed the breast cancer patients recorded in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2016. Multivariable logistic regression analyses were used to identify risk factors for bone metastases in breast cancer, whereas Cox proportional hazards regression analyses were used to identify prognostic factors for breast cancer with bone metastasis (BCBM). Based on the identified risk and prognostic factors, we developed diagnostic and prognostic models that incorporate six machine learning classifiers. We then used the area under the receiver operating characteristic (ROC) curve (AUC), learning curve, precision curve, calibration plot, and decision curve analysis to evaluate performance of the machine learning models. Univariable and multivariable logistic regression analyses showed that bone metastases were significantly associated with age, race, sex, grade, T stage, N stage, surgery, radiotherapy, chemotherapy, tumor size, brain metastasis, liver metastasis, lung metastasis, breast subtype, and PR. Univariate and multivariate Cox regression analyses revealed that age, race, marital status, grade, surgery, radiotherapy, chemotherapy, brain metastasis, liver metastasis, lung metastasis, breast subtype, ER, and PR were closely associated with the prognosis of BCBM. Among the six machine learning models, the XGBoost algorithm predicted the most accurate results (Diagnostic model AUC = 0.98; Prognostic model AUC = 0.88). According to the Shapley additive explanations (SHAP), the most critical feature of the diagnostic model was surgery, followed by N stage. Interestingly, surgery was also the most critical feature of prognostic model, followed by liver metastasis. Based on the XGBoost algorithm, we could effectively predict the diagnosis and survival of bone metastasis in breast cancer and provide targeted references for the treatment of BCBM patients.
Comparative GC–MS based nutrients profiling of less explored legume seeds of Melilotus, Medicago, Trifolium, and Ononis analysed using chemometric tools
Authors: Fahmy Heba A. , El-Shamy Sherine , Farag Mohamed A. 
Published: October 2023

AbstractExploring novel sources of plant protein for nutrition of both humans and animals is motivated mainly by its growing demand worldwide, besides identifying healthy alternatives for animal protein. The present study evaluates metabolome diversity within 15 legume seed species. The examined samples comprised threeMelilotus, fourMedicago, fourTrifolium, and fourOnonisseed species. A holistic approach for metabolites profiling using gas chromatography-mass spectrometry (GC–MS) led to the annotation and quantification of 87 metabolites comprising alcohols, free amino acids, aromatics, fatty acids/esters, nitrogenous compounds, organic acids, sugar alcohols, sugars, terpenes, and steroids. Fatty acids represented the major metabolite class represented by palmitic, stearic, oleic, linoleic, and linolenic acids. Sucrose and pinitol were the major sugars and sugar alcohols among seeds.Ononisseeds (OR, OS and OA) were the most abundant in fatty acids, sugars, sugar alcohols, and free amino acids, whereasMelilotusspecies (MO and MS) were least enriched in these key nutrients posingOnonisas potential food source for humans and animals. The examined seeds were generally low in sulfur-containing free amino acids and lacking many of the essential free amino acids. Multivariate data analysis aided in the identification ofOnonismetabolite markers belonging to various classesi.e., (alcohol) glycerol, (sugar) allofuranose, and (sugar alcohol) pinitol, although the differentiation betweenMedicago,Melilotus, andTrifoliumgenera was not attained suggestive for other analytical platforms for its classification.
Removal of Cr6+ ions and mordant violet 40 dye from liquid media using Pterocladia capillacea red algae derived activated carbon-iron oxides
Authors: Mohamed Soha Mahrous Ismail , Güner Eda Keleş , Yılmaz Murat , El Nemr Ahmed 
Published: October 2023

AbstractIn recent years, water pollution has become one of the most dangerous problems facing the world. Pollution of water with heavy metals and different dyes has caused many harmful effects on human health, living organisms and our environment. In this study, iron oxide nanomagnetic composite from Pterocladia Capillacea red algae-derived activated carbon (PCAC-IO) was synthesized by co-precipitation method using different iron salts and different base solutions. The synthesized nanocomposite was investigated with various characterization techniques such as FTIR, BET, SEM-EDX, TEM, XRD, and VSM. The obtained PCAC-IO adsorbent was used for Cr6+ ions and Mordant Violet 40 (MV40) dye removal. The adsorption mechanism of Cr6+ ions and MV40 dye on PCAC-IO was examined using several adsorption and kinetic isotherm models. Langmuir and Freundlich models were investigated using experimental data. Pseudo-first-order (PFO), Pseudo-second-order (PSO) and intraparticle diffusion models (IPDM) were applied to identify the adsorption mechanism. It has shown that the PSO kinetic model fits better with the experimental data obtained from PCAC-IO. This result can be interpreted as the adsorption of the adsorbate on the nanocomposite as chemical adsorption. The optimum conditions for maximum Cr6+ ions removal (96.88%) with PCAC-IO adsorbent occur at room temperature, 5 g L−1 adsorbent concentration, 100 mg L−1 initial pollutant concentration, pH 1 and at the end of 180 min, while maximum MV40 dye removal (99.76%), other conditions being the same, unlikely it occurred at pH 2.06 and after 45 min. The most suitable model for Cr6+ ions removal under the conditions of 1 L−1 g adsorbent concentration and 400 mg L−1 adsorbate concentration was Langmuir (Qmax = 151.52 mg g−1), while for MV40 removal it was Freundlich (Qmax = 303.03 mg g−1). We propose the use of activated carbon-supported iron oxide prepared from bio-waste material, especially from Pterocladia Capillacea red algae, as a promising adsorbent with high efficiency in the removal of Cr6+ ions and MV40 dye from aqueous media.
Comparing effectiveness of image perturbation and test retest imaging in improving radiomic model reliability
Authors: Zhang Jiang , Teng Xinzhi , Zhang Xinyu , Lam Sai-Kit , Lin Zhongshi , Liang Yongyi , Yu Hao , Siu Steven Wai Kwan , Chang Amy Tien Yee , Zhang Hua , Kong Feng-Ming , Yang Ruijie , Cai Jing 
Published: October 2023

AbstractImage perturbation is a promising technique to assess radiomic feature repeatability, but whether it can achieve the same effect as test–retest imaging on model reliability is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by the two methods using four different classifiers. A 191-patient public breast cancer dataset with 71 test–retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and manual tumor segmentations for radiomic feature extraction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to filter high repeatable features. We evaluated model reliability in both internal generalizability and robustness, which were quantified by training and testing AUC and prediction ICC. Higher testing performance was found at higher feature ICC thresholds, but it dropped significantly at ICC = 0.95 for the test–retest model. Similar optimal reliability can be achieved with testing AUC = 0.7–0.8 and prediction ICC > 0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test–retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria.
GC–MS, quantum mechanics calculation and the antifungal activity of river red gum essential oil when applied to four natural textiles
Authors: Taha Ayman S. , Ibrahim Ibrahim H. M. , Abo-Elgat Wael A. A. , Abdel-Megeed Ahmed , Salem Mohamed Z. M. , El-Kareem Mamoun S. M. Abd 
Published: October 2023

AbstractThe most important uses of old fabrics include clothing, mummification, and bookbinding. However, because they are predominantly constructed of natural materials, they are particularly susceptible to physical and chemical deterioration brought on by fungi. The treatments that are typically used to preserve old textiles focus on the use of synthetic fungicides, which have the potential to be dangerous for both human health and the environment. Essential oils (EOs), which are safe for the environment and have no negative effects on human health, have been widely advocated as an alternative to conventional antifungals. Four natural fabrics—linen, cotton, wool, and silk—were utilized in the current work. The extracted EO from leaves of river red gum (Eucalyptus camaldulensis Dehnh.) were prepared at 125, 250, and 500 µL/L. Aspergillus flavus, Fusarium culmorum and Aspergillus niger were inoculated separately into the treated four fabrics with the EO at concentrations of 125, 250, and 500 µL/L or the main compounds (spathulenol and eucalyptol) at the concentrations of 6, 12, 25, and 50 µL/L and were then compared to the un-treated samples. GC–MS was used to analyze the EO chemical composition, while visual observations and scanning electron microscopic (SEM) were used to study the fungal growth inhibition. Spathulenol (26.56%), eucalyptol (14.91%), and p-cymene (12.40%) were the principal chemical components found in E. camaldulensis EO by GC–MS. Spathulenol molecule displayed the highest electrostatic potential (ESP) compared with the other primary compound, as calculated by quantum mechanics. In the untreated textile samples, SEM analysis revealed substantial proliferation of hyphae from A. flavus, F. culmorum, and A. niger. The fungal growth was completely inhibited at a concentration of 500 µL/L from the EO. Both eucalyptol and spathulenol completely inhibited the formation of the fungal spores at a concentration of 50 µL/L, although eucalyptol was more effective than spathulenol across the board for all four textiles. The results support E. camaldulensis EO functionalized textiles as an effective active antifungal agent.
Dynamic and rapid deep synthesis of chemical exchange saturation transfer and semisolid magnetization transfer MRI signals
Authors: Nagar Dinor , Vladimirov Nikita , Farrar Christian T. , Perlman Or 
Published: October 2023

AbstractModel-driven analysis of biophysical phenomena is gaining increased attention and utility for medical imaging applications. In magnetic resonance imaging (MRI), the availability of well-established models for describing the relations between the nuclear magnetization, tissue properties, and the externally applied magnetic fields has enabled the prediction of image contrast and served as a powerful tool for designing the imaging protocols that are now routinely used in the clinic. Recently, various advanced imaging techniques have relied on these models for image reconstruction, quantitative tissue parameter extraction, and automatic optimization of acquisition protocols. In molecular MRI, however, the increased complexity of the imaging scenario, where the signals from various chemical compounds and multiple proton pools must be accounted for, results in exceedingly long model simulation times, severely hindering the progress of this approach and its dissemination for various clinical applications. Here, we show that a deep-learning-based system can capture the nonlinear relations embedded in the molecular MRI Bloch–McConnell model, enabling a rapid and accurate generation of biologically realistic synthetic data. The applicability of this simulated data for in-silico, in-vitro, and in-vivo imaging applications is then demonstrated for chemical exchange saturation transfer (CEST) and semisolid macromolecule magnetization transfer (MT) analysis and quantification. The proposed approach yielded 63–99% acceleration in data synthesis time while retaining excellent agreement with the ground truth (Pearson’s r > 0.99, p < 0.0001, normalized root mean square error < 3%).