bluuebunny/crossref_metadata_2025_split huggingface dataset:
Unravelling the origin of isoprene in the human body—a forty year Odyssey
Authors: Mochalski P , King J , Unterkofler K , Mayhew C A 
Published: April 2024

Abstract
In the breath research community’s search for volatile organic compounds that can act as non-invasive biomarkers for various diseases, hundreds of endogenous volatiles have been discovered. Whilst these systemic chemicals result from normal and abnormal metabolic activities or pathological disorders, to date very few are of any use for the development of clinical breath tests that could be used for disease diagnosis or to monitor therapeutic treatments. The reasons for this lack of application are manifold and complex, and these complications either limit or ultimately inhibit the analytical application of endogenous volatiles for use in the medical sciences. One such complication is a lack of knowledge on the biological origins of the endogenous volatiles. A major exception to this is isoprene. Since 1984, i.e. for 40 years, it has been generally accepted that the pathway to the production of human isoprene, and hence the origin of isoprene in exhaled breath, is through cholesterol biosynthesis via the mevalonate (MVA) pathway within the liver. However, various studies between 2001 and 2012 provide compelling evidence that human isoprene is produced in skeletal muscle tissue. A recent multi-omic investigation of genes and metabolites has revealed that this proposal is correct by showing that human isoprene predominantly results from muscular lipolytic cholesterol metabolism. Despite the overwhelming proof for a muscular pathway to isoprene production in the human body, breath research papers still reference the hepatic MVA pathway. The major aim of this perspective is to review the evidence that leads to a correct interpretation for the origins of human isoprene, so that the major pathway to human isoprene production is understood and appropriately disseminated. This is important, because an accurate attribution to the endogenous origins of isoprene is needed if exhaled isoprene levels are to be correctly interpreted and for assessing isoprene as a clinical biomarker.
Fe3O4/CoFe2O4 core-shell nanoparticles with enhanced magnetic properties for hyperthermia application
Authors: Oanh V T K , Nguyen L H , Phong L T H , Trang M T T , Thu H P , Truong N X , Ca N X , Nam P H , Manh D H 
Published: April 2024

Abstract
Fe3O4/CoFe2O4 core/shell nanoparticles with varying shell thickness were fabricated by seed-mediated growth via thermal decomposition method. Ligand exchange process using poly(maleic anhydride-alt-1-octadecene) (PMAO) was performed to prepare the aqueous magnetic fluids from the as-synthesised nanoparticles. X-ray diffraction (XRD), transmission electron microscopy (TEM) and Quantum Design PPMS VersaLab were utilised to characterise morphological and magnetic properties of the sample. XRD results showed that all the particles were single phase with spinel structure and the average crystallite size in the range of 11–17 nm. All particles were spherical in TEM images with similar size compared to results calculated from XRD. Magnetic measurements were performed at different temperatures (50 − 300 K) at 30 kOe. The result showed that the saturation magnetisation (M
s) and coercivity (H
C) were significantly increased with the formation of hard magnetic shell with varying thickness. The dynamic light scattering (DLS) analysis presented a narrow distribution and zeta potential of −16 to −35 mV, indicating a good stability of the ferrofluids. The cytotoxicity of the FOC3/PMAO ferrofluid, which has the highest SAR value of 372.02 W g−1, was tested on Hep-G2 cell line at different concentrations from 10 to 100 μg ml−1. Less than 30% of the cell was inhibited, indicating that the FOC3/PMAO particles have low toxicity at these tested concentrations. Thus, these as-synthesised core/shell nanoparticles with uniform particle size, high saturation magnetisation, good stability and five-time increased specific absorption rate (SAR) compared to the Fe3O4 core nanoparticles are very promising in hyperthermia and magnetic resonance imaging (MRI) applications.
Characterization of banana powder-based film reinforced with CNF and Bio-mediated ZnO nanoparticles for potential food applications
Authors: Leta Tshiamo B , Adeyemi Jerry O , Fawole Olaniyi A 
Published: April 2024

Abstract
The formulation of a multifunctional nanocomposite packaging material with potential against agents of food deterioration, such as free radicals and microorganisms, has emerged as a solution for shelf-life extension and food security. This study developed banana powder (BP) film infused with cellulose nanofiber (CNF) and ZnO-PPW and ZnO-PSW nanoparticles (NPs) at different concentrations for food applications. The BP/CNF/ZnO films were characterized using UV–vis spectroscopy, XRD, FT-IR, and SEM techniques. The analyses confirmed the successful infusion of ZnO NPs into the BP/CNF matrix, leading to significant changes (p < 0.05) in color and appearance, enhanced UV–vis barrier properties, and increased thickness and flexibility of the films. Furthermore, the presence of ZnO in the base matrix influenced the moisture content (19%–29%), film solubility (68%–74%), and oil permeability significantly more than the control BP/CNF film. Adding ZnO significantly improved the UV barrier properties compared to the control. The nanocomposite BP/CNF/ZnO films demonstrated concentration-based antioxidant and good antimicrobial activity against five selected food pathogens (Escherichia coli, Enterococcus faecalis, Listeria monocytogenes and Staphylococcus aureus). Similarly, good antioxidant properties were reported in different assays, proving superior to the control BP/CNF. These key findings, especially those of the BP/CNF/ + 0.6% ZnO NPs films, showed that these films possess great potential for application as food packaging materials with antioxidant and antimicrobial properties.
From electronic structure to magnetism and skyrmions
Authors: Borisov Vladislav 
Published: April 2024

Abstract
Solid state theory, density functional theory and its generalizations for correlated systems together with numerical simulations on supercomputers allow nowadays to model magnetic systems realistically and in detail and can be even used to predict new materials, paving the way for more rapid material development for applications in energy storage and conversion, information technologies, sensors, actuators etc. Modeling magnets on different length scales (between a few

Å

ngström and several micrometers) requires, however, approaches with very different mathematical formulations. Parameters defining the material in each formulation can be determined either by fitting experimental data or from theoretical calculations and there exists a well-established approach for obtaining model parameters for each length scale using the information from the smaller length scale. In this review, this approach will be explained step-by-step in textbook style with examples of successful scale-bridging modeling of different classes of magnetic materials from the research literature as well as based on results newly obtained for this review.
Compact pixelated scintillator detector investigation for gamma ray detection
Authors: Wang Jin-Long , Wu Xiao-Guang , He Zi-Yang , Yang Li-Wang , Zheng Yun , Li Cong-Bo , Li Tian-Xiao , Zhao Zi-Hao 
Published: April 2024

Abstract

Objective. A scintillator is a luminescent material that converts high-energy photons into visible light and is widely used in medical imaging. Different scintillators are applied to gamma imaging in proton therapy and boron neutron capture therapy (BNCT). A pixelated scintillator is suitable for position measurement. The energy resolution, detection efficiency and position measurement of pixelated scintillators coupled to a SiPM were investigated via two kinds of detectors. The goal of this manuscript was to accurately measure the gamma source position through spectrum analysis in selection of energy windows for characteristic gamma lines. Approach. Scintillators were effectively manufactured and encapsulated before testing, especially for the easily deliquescent LaBr3. The compact front-end electronic prototype modules with 2 × 2 array SiPMs in stacked form and 1 × 10 array SiPMs in parallel form were developed for gamma ray energy resolution and efficiency measurements with a radioactive source of 22Na, which had two energy gamma lines at 511 keV and 1274 keV. Energy calibration was used for accurate energy window selection when measuring the position of the gamma source. Main results. Evident inconsistencies were present between different pixels of the same type of scintillator. Thus, an energy calibration method was needed. LaBr3 was the first candidate scintillator for the gamma ray spectrum measurement since it exhibited the best performance with an energy resolution of ∼5%. The recommended size of LaBr3 was 5 × 5 mm2, which had a higher efficiency than the 3 × 3 mm2 size. The gamma count of the multiple mode of the 2 × 2 array was much higher than that of the single mode, while the energy resolution was poorer. Thus, multiple mode was not suitable for gamma ray detection. The 1 × 10 array detector had the potential to measure the gamma ray source position and could be used for proton therapy and BNCT. A small deviation of 0.22 cm was observed in the measurement of the source center position with Energy Window 1 for 511 keV and Energy Window 2 for 1274 keV before the energy calibration. No deviation was observed after energy calibration. Thus, to achieve a higher accuracy position measurement, automatic energy calibration algorithm was coded into data acquisition software. Significance. The characteristic gamma lines produced by particle therapy are abundant and useful for imaging technology. Our developed compact pixelated scintillator detector coupled with SiPMs could measure the gamma spectrum with high resolution. The energy calibration and window selection method could measure the position of the source with high accuracy. Therefore, an advanced imaging device based on the energy spectrum for particle therapy could be potentially attainable.
Pozzolanic potential of sustainable precursors for engineered geopolymer composites (EGC)
Authors: Subramanian Saravanan , Davis Robin , Thomas Blessen Skariah 
Published: April 2024

Abstract
Confirming the pozzolanic activity is crucial to ensure their compatibility and performance in geopolymer composite (GC) applications, as it improves the geopolymerization process and optimizes the strength characteristics of GCs. This work evaluates the pozzolanic properties of Fly ash (FA), Basic Oxygen Furnace (BOF) slag, and Iron Ore Tailings (IOT) for their potential use in the development of Engineered Geopolymer Composites (EGC). IOT partially substitutes fine aggregate, while FA and BOF slag are the major precursors. Pozzolanic properties of the aforementioned materials were assessed through the Frattini, saturated lime test (SLT), and strength activity index (SAI). The Frattini test values recorded were 90, 47, and 30% of CaO removal, denoting their degree of pozzolanicity respectively for BOF Slag, FA, and IOT. In the SLT, the formation of stable calcium silicate hydrates and aluminates are verified by the reaction of the test pozzolans with lime, thereby conforming their pozzolanicity. The results from the Frattini and SAI tests showed a significant correlation, indicating an effective pozzolanicity measure of the test materials. However, the results from the SLT did not align with the outcomes from the Frattini and SAI tests. This contradiction suggests that the SLT is ineffective compared to the other two test methods in measuring the pozzolanic activity of the test materials. The research findings provide valuable insights into the potential usage of these materials (pozzolans) as sustainable building materials in the construction industry.
Multi-head CNN-based activity recognition and its application on chest-mounted sensor-belt
Authors: Verma Updesh , Tyagi Pratibha , Aneja Manpreet Kaur 
Published: April 2024

Abstract
In recent years, a great deal of research has been done on the identification, monitoring, and classification of human activities. Human activity recognition (HAR) is a term commonly used to describe the automatic identification of physical activities. For activity recognition, there are primarily vision-based and sensor-based methods available. The computer vision-based method is generally effective in lab settings, but because of clutter, fluctuating light levels, and contrast, it may not perform well in real-world scenarios. Continuous monitoring and analysis of physiological signals obtained from heterogeneous sensors attached to an individual’s body is required to realise sensor-based HAR systems. Most of the previous research in human activity recognition (HAR) is biased along with feature engineering and pre-processing which requires a good amount of domain knowledge. Application-specific modelling and time-taking methods are involved in these approaches. In this work, the multi-head convolutional neural network-based human activity recognition framework is proposed where automatic feature extraction and classification are involved in the form of an end-to-end classification approach. Experiments of this approach are performed by taking raw wearable sensor data with few pre-processing steps and without the involvement of a handcrafted feature extraction technique. 99.23% and 93.55% accuracy are obtained on the WISDM and UCI-HAR datasets which denoted the much improvement in the assessment of HAR over other similar approaches. The model is also tested on locally collected data from a chest mounted belt with fabric sensors and an accuracy of 87.14% has been achieved on that data.
Viscoelastic behaviour of yttrium oxide and reduced graphene oxide embedded epoxy nanocomposite
Authors: Singh Pooja , Sharma Swati , Kumar Kaushal , Iyer Ganesh , Lal Sohan , Kumar Arun 
Published: April 2024

Abstract
In this work, two different types of nanofillers yttrium oxide (Y2O3) or yttria and reduced graphene oxide (RGO) are used to reinforce with epoxy polymer for the synthesis of epoxy nanocomposites by using the ultrasonication dual mixing approach. Raman spectroscopy has been done to analyze the microstructure of yttria and conformation of the formed RGO from Graphene oxide. The viscoelastic behavior of epoxy nanocomposites is evaluated with the help of the dynamic mechanical analyzer. Further Storage modulus is used to determine the cross-link density ‘νe’, coefficient of effectiveness ‘C factor’, and reinforcement efficiency factor ‘r factor’. The damping coefficient (Tan δ) helps in determining the glass transition temperature of the epoxy and its nanocomposites. The results demonstrate that at 1.6 wt% Y2O3 & 0.8 wt% RGO reinforcement in pure epoxy matrix showed maximum enhancement in terms of storage modulus, loss modulus, and glass transition temperature. The semi-circular shape that resulted from the study of the Cole-Cole plot of all the nanocomposites demonstrated the homogenous dispersion of nanoparticles and efficient bonding between epoxy and nanoparticles. Field Emission Scanning Electron Microscopy (FESEM) has been used to identify the interfacial interaction between nanoparticle and epoxy matrix through tensile fracture surfaces.
A methodological integration of fisher score technique with intelligent machine learning methods for ball bearing fault investigation
Authors: Kumar Rajeev , Anand R S 
Published: April 2024

Abstract
This paper presents an efficient approach to detect, diagnose and estimate the severity of failures in various components of bearings in induction motors using vibration signature analysis. This automated method integrates the Fisher Score feature selection method and an efficient hyperparameter tuning model with machine learning models, including Support Vector Machine (SVM), k-Nearest Neighbor (k-NN) and Decision Tree (DT), to accurately classify defects in bearings. This approach ensures accurate classification of bearing defects within less computation time. This work is carried out with vibration signals, recorded from a laboratory experimental setup on Machine Fault Simulator (MFS), focusing on ball bearing with defects in inner race, outer race and ball itself. Time and Frequency domain analysis are employed to compute the features for fault investigation in ball bearings using machine learning models. The computed results demonstrate that the proposed feature selection method with hyperparameter tuning achieved remarkable maximum accuracy among X, Y and XY combinations of datasets, with 97% in DT, 94% in SVM and 95.23% in k-NN models during the frequency domain analysis. Notably, these model accuracies improved to 99.04% in DT, 98% in SVM and 98% in k-NN during further analysis with Fisher Score technique. Consequently, the testing loss using this hyperparameter tuning function remains very low. Overall, this paper compares the results of time and frequency domain analysis and introduces a promising approach for both efficient and accurate fault detection and severity estimation in bearings of induction motors, potentially reducing the need for extensive manpower and sensor usage.
Optimal coordination of directional overcurrent protection relays using hybrid optimizer
Authors: Habib Kashif , Wadood Abdul , Wei Tang , Khan Shahbaz , Xing Xu 
Published: April 2024

Abstract
In order to minimize or maximize an objective function, electrical engineering problems might benefit from optimization methodologies inspired by nature. This study employs a hybrid particle swarm optimizer (HPSO) to get rid of local optimum solutions and find the best global one for directional overcurrent protection relays (DOPR) protection. DOPR coordination is a highly constrained, mixed-integral nonlinear optimization problem. An adaptive defense method has been created to counteract the intentionally challenging design. Therefore, design variables such as time multiplier setting (TMS) and plug setting (PS) are utilized for each relay in the circuit. The goal function is to reduce the amount of time spent to trace the fault by all the relays. Reducing the time required for each relay to trace the fault is the aim of function. After analyzing and comparing two test studies of the IEEE benchmark using various optimization strategies, we found that the results for the IEEE 15-bus system has minimum time of 8.9 s, which is improved by 22.92% to 82.89%, and those for the IEEE 30-bus system minimum time of 22.45 s that is improved by 4.41% to 39.6%. In terms of overall DOPR operation and computational time needed to get the global optimum solution, the acquired results are demonstrably superior to those obtained using conventional and state-of-the-art methods.
A multiscale and multicriteria generative adversarial network to synthesize 1-dimensional turbulent fields
Authors: Granero Belinchon Carlos , Cabeza Gallucci Manuel 
Published: April 2024

Abstract
This article introduces a new neural network stochastic model to generate a 1-dimensional stochastic field with turbulent velocity statistics. Both the model architecture and training procedure ground on the Kolmogorov and Obukhov statistical theories of fully developed turbulence, so guaranteeing descriptions of (1) energy distribution, (2) energy cascade and (3) intermittency across scales in agreement with experimental observations. The model is a generative adversarial network (GAN) with multiple multiscale optimization criteria. First, we use three physics-based criteria: the variance, skewness and flatness of the increments of the generated field, that retrieve respectively the turbulent energy distribution, energy cascade and intermittency across scales. Second, the GAN criterion, based on reproducing statistical distributions, is used on segments of different length of the generated field. Furthermore, to mimic multiscale decompositions frequently used in turbulence’s studies, the model architecture is fully convolutional with kernel sizes varying along the multiple layers of the model. To train our model, we use turbulent velocity signals from grid turbulence at Modane wind tunnel.
Approaches to medication history taking in different hospital settings: A scoping review
Authors: Terstegen Theresa , Niestroj Christina , Stangl Julia , Scherkl Camilo , Morath Benedict , Haefeli Walter E , Seidling Hanna M 
Published: April 2024

Abstract

Purpose
A comprehensive medication history can contribute to safe therapy. Many approaches aiming to improve medication history taking require significant human resources. To design an efficient process that delivers high-quality medication histories, the individual requirements and resources of a given setting need to be considered. We aimed to provide an overview of existing approaches to medication history taking and their performance in different settings to potentially support the selection of an appropriate procedure.

Methods
We searched 3 literature databases (PubMed/MEDLINE, CINAHL, PsycINFO) for publications on approaches to medication history taking and analyzed them with regard to their key components as well as the setting, patient population, assessed outcomes, and efficacy.

Results
In total, 65 publications were included and analyzed. The majority of the reported approaches relied on involvement of dedicated staff (n = 43), followed by process-oriented interventions (eg, checklists; n = 15) and information technology (IT)–guided interventions (n = 11). A mean (SD) of 6 (2.9) outcomes were described in each study. Medication discrepancies were reported in 89% of all studies, yet about 75 different descriptions of this outcome were used, making it difficult to compare study results. Only 11 studies applied a sample size calculation and statistical tests. Of those, 10 reported a positive effect of their respective intervention on the quality of medication histories.

Conclusion
Most approaches focused on pharmacy staff, which are associated with considerable cost and resources. Therefore, IT-based approaches and patient engagement should be investigated as cost-effective alternatives and tested for superiority in the same setting. Reporting guidelines and standardized methodology are needed to improve the comparability of such studies.
BTR: a bioinformatics tool recommendation system
Authors: Green Ryan , Qu Xufeng , Liu Jinze , Yu Tingting 
Published: April 2024

Abstract

Motivation
The rapid expansion of Bioinformatics research has led to a proliferation of computational tools for scientific analysis pipelines. However, constructing these pipelines is a demanding task, requiring extensive domain knowledge and careful consideration. As the Bioinformatics landscape evolves, researchers, both novice and expert, may feel overwhelmed in unfamiliar fields, potentially leading to the selection of unsuitable tools during workflow development.

Results
In this article, we introduce the Bioinformatics Tool Recommendation system (BTR), a deep learning model designed to recommend suitable tools for a given workflow-in-progress. BTR leverages recent advances in graph neural network technology, representing the workflow as a graph to capture essential context. Natural language processing techniques enhance tool recommendations by analyzing associated tool descriptions. Experiments demonstrate that BTR outperforms the existing Galaxy tool recommendation system, showcasing its potential to streamline scientific workflow construction.

Availability and implementation
The Python source code is available at https://github.com/ryangreenj/bioinformatics_tool_recommendation.