bluuebunny/crossref_metadata_2025_split huggingface dataset:
Correlation analysis of composition, microstructure, and final properties of porcelain tiles: an experimental study
Authors: Zamani Mohsen , Yapicioglu Haluk , Kara Alpagut , Sevik Cem 
Published: October 2024

Abstract
This study deals with the intricate relationship between composition, microstructure and the final properties of porcelain tiles namely bulk density, water absorption, thermal expansion coefficient, strength, and Young’s modulus. In order to achieve this, a comprehensive set of 16 experimental trials were carried out, involving the preparation of eight distinct formulations characterized by varying Seger values and ratios which were subsequently subjected to sintering at two distinct firing temperatures. The analysis was conducted through a comprehensive correlation matrix, revealing significant relationships between composition, phase content and the final properties. The glassy phase emerges as a key contributor, positively influencing bulk density, reducing water absorption and linear shrinkage, and enhancing Young’s modulus. The coefficient of thermal expansion correlates positively with residual quartz and inversely with mullite content, highlighting their impact on this property. Strength is positively correlated with mullite content, while quartz, albite, and anorthite phases exhibit negative correlations due to their intricate influence on the overall microstructure. These findings provide essential insights into the complex interplay between composition, microstructure and properties, contributing to the advancement of materials engineering for tailored porcelain tiles.
Physics-informed neural networks approach for pollutant dispersion flow caused by magnetic dipole
Authors: Punith Gowda R. J. , Beemkumar N. , Kulshreshta Ankur , Chohan Jasgurpreet Singh , Abdulrahman Amal , Chandan K 
Published: October 2024

Abstract
Research on the liquid flow and distribution of waste discharge concentration over cylindrical surfaces might lead to the growth of efficient waste management and pollution controlling strategies. In view of this, the pollutant concentration and magnetic dipole impact on the liquid flow through the cylinder with a porous medium are explored in the present analysis. Moreover, Fourier’s and Fick’s laws are considered to explore heat transport features. The governing partial differential equations (PDEs) of the present study are converted into non-dimensional ordinary differential equations (ODEs) utilizing similarity variables. Also, the current study proposes a novel implementation of neural networks based on physics knowledge for the liquid flow past a cylinder. The self-updating weights and bias facilitate the network to satisfy the minimization objective function and provide accurate results. Additionally, the solution obtained by Runge Kutta Fehlberg's fourth-fifth order (RKF-45) method is utilized to validate the physics-informed neural network (PINN) results, presenting mean squared errors from 10-9 to 10-11. Furthermore, it is found that the increase in temperature relaxation time parameter decreases the Nusselt number gradually at the rate of 1 to 5%. An upsurge in the values of the pollutant external source parameter and the pollutant external source variation parameter decreases the concentration profile.
Analyzing Stellar Spectra for PRV by Accurate Modeling and Retrieval of Telluric Absorption Features
Authors: Sung Keeyoon , Toon Geoffrey C. , Cale Bryson L. , Chen Pin , Vasisht Gautam , Rocha Graca M. , Gibson Rose K. , Oppenheimer Rebecca , Plavchan Peter , Beichman Charles A. 
Published: October 2024

Abstract
Ground-based Precision Radial Velocity (PRV) measurements are inevitably impeded by contamination from telluric absorption features, particularly in the infrared region. Thus, it is crucial to improve modeling of the telluric absorption features down to the spectral noise level. As part of the efforts towards improved PRV measurements, we have taken an existing atmospheric trace gas retrieval algorithm (GFIT) and have successfully adapted it to fit the telluric absorption features in stellar spectra down to the spectral noise level (typically ∼1%). We have established a stellar spectral fitting processing pipeline, Stellar-GFIT, to analyze a series of stellar spectra observed by two spectrographs, PARVI (1.1–1.76 μm) commissioned at the Palomar Observatory (Palomar Mountain, CA) and iSHELL (1–5 μm) deployed on the IRTF (Mauna Kea, HI). For this, we have (1) implemented a Gaussian instrumental line shape function, (2) generated atmospheric models (consisting of temperature, pressure, and volume mixing ratios of all the known trace gases) for the particular observation sites and times, (3) employed the most up-to-date spectroscopic parameters in the target spectral regions, and finally (4) developed a series of spectral fitting intervals of ∼60 cm−1 width, i.e., micro-windows, customized to the individual orders of each spectrograph. Stellar-GFIT is also capable of handling non-telluric features, such as transitions from a gas cell placed in the starlight beam and stellar features if a model spectrum template is available for the target star. We present spectrum fits from the observations of various target stars and discuss the performance and advantages of our novel approach. One of the major strengths of Stellar-GFIT is an ability to adjust the abundance of atmospheric trace gases simultaneously with determining the stellar doppler shift, mitigating any adverse impacts of short-timescale variations of water vapor.
Activation thresholds for electrical phrenic nerve stimulation at the neck: evaluation of stimulation pulse parameters in a simulation study
Authors: Wegert Laureen , Ziolkowski Marek , Kalla Tim , Lange Irene , Haueisen Jens , Hunold Alexander 
Published: October 2024

Abstract

Objective. Phrenic nerve stimulation reduces ventilator-induced-diaphragmatic-dysfunction, which is a potential complication of mechanical ventilation. Electromagnetic simulations provide valuable information about the effects of the stimulation and are used to determine appropriate stimulation parameters and evaluate possible co-activation. Approach. Using a multiscale approach, we built a novel detailed anatomical model of the neck and the phrenic nerve. The model consisted of a macroscale volume conduction model of the neck with 13 tissues, a mesoscale volume conduction model of the phrenic nerve with three tissues, and a microscale biophysiological model of axons with diameters ranging from 5 to 14 µm based on the McIntyre–Richardson–Grill-model for myelinated axons. This multiscale model was used to quantify activation thresholds of phrenic nerve fibers using different stimulation pulse parameters (pulse width, interphase delay, asymmetry of biphasic pulses, pulse polarity, and rise time) during non-invasive electrical stimulation. Electric field strength was used to evaluate co-activation of the other nerves in the neck. Main results. For monophasic pulses with a pulse width of 150 µs, the activation threshold depended on the fiber diameter and ranged from 20 to 156 mA, with highest activation threshold for the smallest fiber diameter. The relationship was approximated using a power fit function x
−3. Biphasic (symmetric) pulses increased the activation threshold by 25 to 30 %. The use of asymmetric biphasic pulses or an interphase delay lowered the threshold close to the monophasic threshold. Possible co-activated nerves were the more superficial nerves and included the transverse cervical nerve, the supraclavicular nerve, the great auricular nerve, the cervical plexus, the brachial plexus, and the long thoracic nerve. Significance. Our multiscale model and electromagnetic simulations provided insight into phrenic nerve activation and possible co-activation by non-invasive electrical stimulation and provided guidance on the use of stimulation pulse types with minimal activation threshold.
Gyrokinetic prediction of core tungsten peaking in a WEST plasma with nitrogen impurities
Authors: Dominski J. , Maget P. , Manas P. , Morales J. , Ku S. , Scheinberg A. , Chang C.S. , Hager R. , O’Mullane M. ,   the WEST team
Published: October 2024

Abstract
Tungsten peaking is predicted in the core of a WEST plasma with total-f gyrokinetic simulations, including both collisional and turbulent transport. This prediction is validated with a synthetic diagnostic of the bolometry. Although nitrogen impurities are shown to reduce the neoclassical peaking of tungsten on-axis, the overall tungsten peaking increases when nitrogen impurities are present, as they reduce the turbulence screening off-axis. This finding is important for the plasma current ramp-up phase of ITER, where light impurities seeding will be desirable to achieve low temperatures at the plasma-facing components and reduce tungsten sputtering. It provides further argument for applying early ECRH heating to maintain margins on the core power balance. The neoclassical peaking factor is cross-verified between XGC and FACIT. The heat flux at separatrix and the heat load width are modeled by XGC and compared to WEST data.
Future drought overestimations due to no constraints of CO2 physiological effect and land-atmosphere coupling on potential evapotranspiration
Authors: An Xuehua , Zhang Yifang , Sun Shanlei , Chai Rongfan , Bi Zaoying , Li Jinjian , Zhou Yang , Zhou Botao , Chen Haishan 
Published: October 2024

Abstract
Various offline drought indices have been widely used to project dryness/wetness and drought changes. However, the results derived from these indices often differ from or even contradict observations and direct projections made by coupled climate models. Therefore, it is crucial to investigate this scientific debate thoroughly and identify the potential causes. This study adopts a water demand-side perspective, focusing on potential evapotranspiration (PET), to address such controversy. Employing the Standardized Precipitation-Evapotranspiration Index (SPEI), three PET models including the Food and Agriculture Organization of the United Nations’ report 56 (FAO-56) Penman–Monteith (PM) model, a corrected FAO-56 PM model incorporating CO2 physiological effect (PMCO2), and a land-atmosphere coupled PET model (PET-LAC) are further compared. Despite projected increases in PET across most land areas, the PM shows the most pronounced increases among these PET models. Compared to PMCO2 and PET-LAC, the PM model predicts the most significant drying, with the 3-month SPEI decreasing by 0.50 ± 0.23 yr−1. Additionally, it projects the most substantial drought intensification, with increases in areas, intensity, and duration of 28 ± 6.9%, 0.70 ± 0.20 yr−1, and 2.90 ± 0.83 month yr−1, respectively. Meanwhile, these projections correspond to the most extensive area percentages, with 78.5 ± 10% for drying, 94.8 ± 7.2% for drought intensity, and 93.6 ± 7.9% for drought duration. These findings imply that the commonly used PM model overestimates future drought conditions. Differences and contradictions between the drought projections from PM-based offline indices and direct climate model outputs can be partly attributed to the omission of CO2 physiological effect and land-atmosphere coupling constraints in the PM model. This study highlights the importance of improving PET models by incorporating these constraints, thereby providing valuable insights for enhancing the accuracy of future drought assessments.
On the suitability of rigorous coupled-wave analysis for fast optical force simulations
Authors: Gao Bo , Gersen Henkjan , Hanna Simon 
Published: October 2024

Abstract
Optical force responses underpin nanophotonic actuator design, which requires a large number of force simulations to optimize structures. Commonly used computation methods, such as the finite-difference time-domain method, and finite element methods, are resource intensive and require large amounts of calculation time when multiple structures need to be compared during optimization. This research demonstrates that performing optical force calculations on 2D-periodic structures using the rigorous coupled-wave analysis method is typically on the order of 10 times faster than other approaches and with sufficient accuracy to suit optical design purposes. Moreover, this speed increase is available on consumer grade laptops, avoiding the need for a high performance computing resource.
Classification of faults in friction stir processed composites using a machine learning and ensemble learning approach
Authors: Saxena Pragya , Bongale Arunkumar 
Published: October 2024

Abstract
Aluminium alloy-based surface composites with hard reinforcement particles have a wide scope in the aerospace and automobile manufacturing industries. In this paper, the aluminium composites, manufactured by friction stir processing (FSP) with varying parameters are investigated for the faults that occurred during the fabrication process. In this study, Al6061 alloy surface composites reinforced with copper and graphene are fabricated using Friction Stir Processing (FSP) on a CNC milling machine. The process involves heat generation through friction, leading to a uniform distribution of reinforcement particles in the matrix. Various sensors (vibration, current, and dynamometer) are used to monitor specific parameters during the FSP experimentation. The Taguchi L27 orthogonal array is employed to design experiments for analyzing sensor data from vibration sensors, current sensors, and dynamometers during composite fabrication. Data is collected in the time domain and processed using Python libraries. Also, it explores a machine-learning approach to detect defects of surface hybrid composites with an Al6061 alloy matrix, reinforced with copper and graphene nano-powders, using friction stir processing and a tungsten carbide tool on a milling machine. Multi-sensor time series data (vibration, force, and current) collected during fabrication, is preprocessed and labelled with normal and defective categories (e.g., pin break, brazing break, rough surface, no composite) using visual inspection. The important time domain and frequency domain features are extracted using different libraries in python. Then, various types of feature selection techniques, viz filter, wrapper, and embedded methods are implemented to select the most relevant features. The selected subset of features from all selection methods used are applied to different machine learning and ensemble learning classifiers, and their performances are evaluated. The optimal combinations of the type of feature selection method and classifier used, are obtained for efficient classification of surface defects in composited formed by FSP. The impact of feature selection on classification accuracy for defect identification in FSP-fabricated composites using multisensory data is conducted. Among filter methods, the Chi-square feature selection combined with the Gradient Boosting algorithm delivers the best performance. Forward feature selection with the Random Forest classifier achieves high accuracy in wrapper methods. Additionally, the Random Forest importance selection technique paired with the Random Forest classifier provides the highest accuracy among embedded feature selection methods. The real-time monitoring and defect detection system can be developed in the future for the composites developed by FSP using the developed models.
Optimizing pulmonary chest x-ray classification with stacked feature ensemble and swin transformer integration
Authors: Mohanty Manas Ranjan , Mallick Pradeep Kumar , Reddy Annapareddy V N 
Published: October 2024

Abstract
This research presents an integrated framework designed to automate the classification of pulmonary chest x-ray images. Leveraging convolutional neural networks (CNNs) with a focus on transformer architectures, the aim is to improve both the accuracy and efficiency of pulmonary chest x-ray image analysis. A central aspect of this approach involves utilizing pre-trained networks such as VGG16, ResNet50, and MobileNetV2 to create a feature ensemble. A notable innovation is the adoption of a stacked ensemble technique, which combines outputs from multiple pre-trained models to generate a comprehensive feature representation. In the feature ensemble approach, each image undergoes individual processing through the three pre-trained networks, and pooled images are extracted just before the flatten layer of each model. Consequently, three pooled images in 2D grayscale format are obtained for each original image. These pooled images serve as samples for creating 3D images resembling RGB images through stacking, intended for classifier input in subsequent analysis stages. By incorporating stacked pooling layers to facilitate feature ensemble, a broader range of features is utilized while effectively managing complexities associated with processing the augmented feature pool. Moreover, the study incorporates the Swin Transformer architecture, known for effectively capturing both local and global features. The Swin Transformer architecture is further optimized using the artificial hummingbird algorithm (AHA). By fine-tuning hyperparameters such as patch size, multi-layer perceptron (MLP) ratio, and channel numbers, the AHA optimization technique aims to maximize classification accuracy. The proposed integrated framework, featuring the AHA-optimized Swin Transformer classifier utilizing stacked features, is evaluated using three diverse chest x-ray datasets—VinDr-CXR, PediCXR, and MIMIC-CXR. The observed accuracies of 98.874%, 98.528%, and 98.958% respectively, underscore the robustness and generalizability of the developed model across various clinical scenarios and imaging conditions.
Aligning the Western Balkans power sectors with the European Green Deal
Authors: Fejzić Emir , Niet Taco , Wade Cameron , Usher Will 
Published: October 2024

Abstract
Located in Southern Europe, the Drina River Basin is shared between Bosnia and Herzegovina, Montenegro, and Serbia. The power sectors of the three countries have an exceptionally high dependence on coal for power generation. In this paper, we analyse different development pathways for achieving climate neutrality in these countries and explore the potential of variable renewable energy (VRE) and its role in power sector decarbonization. We investigate whether hydro and non-hydro renewables can enable a net-zero transition by 2050 and how VRE might affect the hydropower cascade shared by the three countries. The Open-Source Energy Modelling System (OSeMOSYS) was used to develop a model representation of the countries’ power sectors. Findings show that the renewable potential of the countries is a significant 94.4 GW. This potential is 68% higher than previous assessments have shown. Under an Emission Limit scenario assuming net zero by 2050, 17% of this VRE potential is utilized to support the decarbonization of the power sectors. Additional findings show a limited impact of VRE technologies on total power generation output from the hydropower cascade. However, increased solar deployment shifts the operation of the cascade to increased short-term balancing, moving from baseload to more responsive power generation patterns. Prolonged use of thermal power plants is observed under scenarios assuming high wholesale electricity prices, leading to increased emissions. Results from scenarios with low cost of electricity trade suggest power sector developments that lead to decreased energy security.
Fate of surface gaps in magnetic topological insulators
Authors: Rostami Habib , Moghaddam Ali G 
Published: October 2024

Abstract
In magnetic topological insulators, the surface states can exhibit a gap due to the breaking of time-reversal symmetry. Various experiments, while suggesting the existence of the surface gap, have raised questions about its underlying mechanism in the presence of different magnetic orderings. Here, we demonstrate that magnon-mediated electron-electron interactions, whose effects are not limited to the surfaces perpendicular to the magnetic ordering, can significantly influence surface states and their effective gaps. On the surfaces perpendicular to the spin quantization axis, many-body interactions can enhance the band gap to a degree that surpasses the non-interacting scenario. Then, on surfaces parallel to the magnetic ordering, we find that strong magnon-induced fermionic interactions can lead to features resembling a massless-like gap. These remarkable results largely stem from the fact that magnon-mediated interactions exhibit considerable long-range behavior compared to direct Coulomb interactions among electrons, thereby dominating the many-body properties at the surface of magnetic topological insulators.
Reimagining anti-inflammatory drugs delivery: the integration of ordered mesoporous silica and MOF materials for enhanced therapeutic outcomes
Authors: Galarda Aleksandra , Panek Rafal , Goscianska Joanna 
Published: October 2024

Abstract
Migraine, one of the neurological conditions, affects approximately 15% of the global population. It is characterized by intense headaches accompanied by nausea, vomiting, and heightened sensitivity to light. The first line of drugs for treating migraine are non-steroidal anti-inflammatory drugs. Unfortunately, these medications suffer from poor solubility in water, uncontrolled release, and numerous adverse side effects. In order to maximize their therapeutic effect by preventing premature release and degradation, novel drug delivery systems based on composites are being dynamically developed. Herein, the biocompatible ketoprofen (K), naproxen sodium (NS), and diclofenac sodium (DS) vehicles integrating ordered mesoporous silica (SBA-16) with Fe-based metal–organic frameworks (MIL-101(Fe)) were synthesized via the solvothermal method. The composites were characterized by different percentages of MIL-101(Fe) (25 and 50 wt.%), which had a significant impact on their porosity, structure, and number of functional groups. The SBA-16@MIL-101(Fe)-25 and SBA-16@MIL-101(Fe)-50 samples exhibited BET surface areas of 768 and 324 m2 g−1, respectively. Their sorption capacities towards selected anti-inflammatory drugs were in the range of 141–318 mg g−1 for K, 481–490 mg g−1 for NS, and 246–589 mg g−1 for DS, notably exceeding the values obtained for pure mesoporous silica (5–9 mg g−1). Morphological defects and specific functional groups, derived from SBA-16 and MIL-101(Fe), contributed to generating new adsorption sites in composites, enhancing host-guest interactions. The drug release profiles were determined by the carrier porosity, surface charge, and the presence of functional groups. The diffusion of K and DS from the composites into the phosphate buffer (pH 7.7), mimicking rectal fluid, occurred in a more controlled manner compared to pristine silica. The SBA-16@MIL-101(Fe)-50 carrier released 82% of K and 90% of DS over 24 h.