Scientific Domains Research Articles

Optimising nanoparticles: harnessing quality by design through 3-level factorial design and box-behnken methodology

Nanoparticles (NPs) have become essential elements in a number of scientific and industrial domains, such as materials research, drug delivery, and diagnostics, because of their special qualities and potential for focused uses. An ideal formulation procedure is essential to maximising the effectiveness and efficiency of nanoparticles. In order to optimise the formulation and production processes for nanoparticles, this research investigates the application of Quality by Design (QbD) principles in conjunction with sophisticated statistical approaches, namely the 3-level factorial design and the Box-Behnken methodology. The Quality by Design methodology is a methodical approach that prioritises predetermined goals, in-depth process comprehension, and careful control grounded in reliable science and quality risk management. When QbD is incorporated into nanoparticle optimisation, reliable and repeatable formulations that adhere to specifications with little variation are guaranteed. To look into how different factors interact and affect the properties of nanoparticles, the 3-level factorial design is used. With this design approach, a thorough understanding of the process variables can be obtained by exploring a broad variety of component values. Critical quality attributes (CQAs) include important parameters such drug encapsulation efficiency, zeta potential, polydispersity index (PDI), and particle size. These parameters are methodically examined to determine the ideal amounts of each. To further refine the 3-level factorial design, the Box-Behnken methodology is applied. Without the requirement for extensive combination testing, this response surface methodology (RSM) is especially useful for building second-order polynomial models and investigating quadratic response surfaces. By enabling a more accurate comprehension of the interactions between inputs and responses, the Box-Behnken design improves the optimisation process and makes it easier to identify the ideal circumstances for nanoparticle synthesis. By utilising both approaches in tandem, this work methodically determines ideal formulation parameters that minimise nanoparticle size and polydispersity while optimising stability and drug loading efficiency. The outcomes highlight how important a QbD framework is for directing the creation of reliable nanoparticle systems that function consistently and predictably.

EURAD state-of-the-art report: development and improvement of numerical methods and tools for modeling coupled processes in the field of nuclear waste disposal

The Strategic Research Agenda (SRA; https://www.ejp-eurad.eu/publications/eurad-sra) of the European Joint Programme on Radioactive Waste Management (EURAD; https://www.ejp-eurad.eu/) describes the scientific and technical domains and sub-domains and knowledge management needs of common interest between EURAD participant organizations. Theme number 7 is entitled “Performance assessment, safety case development and safety analyses.” A list of research and development priorities and activities of common interest to be addressed within EURAD for theme 7 have been established. Amongst others, the Understanding and modelling of multi-physical Thermo-Hydro-Mechanical-Chemical coupled processes (THMC) occurring in radioactive waste disposal is a major and permanent issue to support optimization of design and safety case abstraction. To tackle this challenge a research work package entitled “DONUT: Development and improvement of numerical methods and tools for modelling coupled processes” has been conducted within the EURAD join programming initiative. The purpose of this work package is to improve/develop methods or numerical tools in order to go a step further in development of (i) relevant, performant and cutting-edge numerical methods that can easily be implemented in existing or new tools, in order to carry out high-performance computing to facilitate the study of highly coupled processes in large systems, (ii) numerical scale transition schemes for coupled processes, (iii) innovative numerical methods to carry out uncertainty and sensitivity analyses. In this paper the work carried out within the DONUT work package is put in perspective regarding the existing concept and literature on the field. It does not pretend to be exhaustive but rather to put emphasis on particular issues tackled during the project.

Mathematical Information Retrieval: A Review

Mathematical formulas are commonly used to demonstrate theories and basic fundamentals in the Science, Technology, Engineering, and Mathematics (STEM) domain. The burgeoning research in the STEM domain results in the mass production of scientific documents that contain both textual and mathematical terms. In scientific information, the definition of mathematical formulas is expressed through context and symbolic structure that adheres to strong domain-specific notions. Whereas the retrieval of textual information is well-researched, and numerous text-based search engines are present. However, textual information retrieval systems are inadequate for searching scientific information containing mathematical formulas, including simple symbols to complicated mathematical structures. The retrieval of mathematical information is infancy, and it requires the inclusion of new technologies and tools to promote the retrieval of scientific information and the management of digital libraries. This paper provides a comprehensive study of mathematical information retrieval, highlights their challenges and future opportunities.

Mathematical Analysis of a Navier–Stokes Model with a Mittag–Leffler Kernel

In this paper, we establish the existence and uniqueness results of the fractional Navier–Stokes (N-S) evolution equation using the Banach fixed-point theorem, where the fractional order β is in the form of the Atangana–Baleanu–Caputo fractional order. The iterative method combined with the Laplace transform and Sumudu transform is employed to find the exact and approximate solutions of the fractional Navier–Stokes equation of a one-dimensional problem of unsteady flow of a viscous fluid in a tube. In the domains of science and engineering, these methods work well for solving a wide range of linear and nonlinear fractional partial differential equations and provide numerical solutions in terms of power series, with terms that are simple to compute and that quickly converge to the exact solution. After obtaining the solutions using these methods, we use Mathematica software Version 13.0.1.0 to present them graphically. We create two- and three-dimensional plots of the obtained solutions at various values of β and manipulate other variables to visualize and model relationships between the variables. We observe that as the fractional order β becomes closer to the integer order 1, the solutions approach the exact solution. Lastly, we plot a 2D graph of the first-, second-, third-, and fourth-term approximations of the series solution and observe from the graph that as the number of iterations increases, the approximate solutions become close to the series solution of the fourth-term approximation.

Imbalanced Data Parameter Optimization of Convolutional Neural Networks Based on Analysis of Variance

Classifying imbalanced data is important due to the significant practical value of accurately categorizing minority class samples, garnering considerable interest in many scientific domains. This study primarily uses analysis of variance (ANOVA) to investigate the main and interaction effects of different parameters on imbalanced data, aiming to optimize convolutional neural network (CNN) parameters to improve minority class sample recognition. The CIFAR-10 and Fashion-MNIST datasets are used to extract samples with imbalance ratios of 25:1, 15:1, and 1:1. To thoroughly assess model performance on imbalanced data, we employ various evaluation metrics, such as accuracy, recall, F1 score, P-mean, and G-mean. In highly imbalanced datasets, optimizing the learning rate significantly affects all performance metrics. The interaction between the learning rate and kernel size significantly impacts minority class samples in moderately imbalanced datasets. Through parameter optimization, the accuracy of the CNN model on the 25:1 highly imbalanced CIFAR-10 and Fashion-MNIST datasets improves by 14.20% and 5.19% compared to the default model and by 8.21% and 3.87% compared to the undersampling model, respectively, while also enhancing other evaluation metrics for minority classes.

Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses

Abstract Deep learning algorithms often are developed and trained on a training dataset and deployed on test datasets. Any systematic difference between the training and a test dataset may severely degrade the final algorithm performance on the test dataset -- what is known as the domain shift problem. This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets. Typically, the domain shift problem is solved through various domain adaptation methods. However, these methods are often tailored to a specific downstream task and may not easily generalize to different tasks. This work explores the feasibility of using an alternative way to solve the domain shift problem that is not specific to any downstream algorithm. The proposed approach relies on modern Unpaired Image-to-Image translation techniques, designed to find translations between different image domains in a fully unsupervised fashion. In this study, the approach is applied to a domain shift problem commonly encountered in Liquid Argon Time Projection Chamber detector research when seeking a way to translate samples between two differently distributed LArTPC detector datasets deterministically. This translation allows for mapping real-world data into the simulated data domain where the downstream algorithms can be run with much less domain-shift-related performance degradation. Conversely, using the translation from the simulated data in a real-world domain can increase the realism of the simulated dataset and reduce the magnitude of any systematic uncertainties. We adapted several popular UI2I translation algorithms to work on scientific data and demonstrated the viability of these techniques for solving the domain shift problem with LArTPC detector data. To facilitate further development of domain adaptation techniques for scientific datasets, the "Simple Liquid-Argon Track Samples" (SLATS) dataset used in this study also is published.

How Does a Generative Large Language Model Perform on Domain-Specific Information Extraction?─A Comparison between GPT-4 and a Rule-Based Method on Band Gap Extraction.

The advent of generative Large Language Models (LLMs) has greatly impacted the field of Natural Language Processing. However, it is inconclusive how generative LLMs perform on domain-specific information extraction tasks. This study compares the performance of GPT-4 and a rule-based information extraction method based on ChemDataExtractor on band gap information extraction, a task that has important implications for the materials science domain. No training data is required for either method, which is desirable because there is a lack of training data in the materials science domain compared with a variety of material information that is of interest. Manual evaluation on 415 randomly selected articles showed that the GPT-4 model achieved a higher level of accuracy in extracting materials' band gap information than the rule-based method (Correctness 87.95% vs 51.08%, Partial correctness 11.33% vs 36.87%, incorrectness 0.72% vs 12.05%). Further analysis of the errors reveals the strengths and weaknesses of the GPT-4 model compared to the rule-based method. The GPT-4 model shows stronger performance in interdependency resolution and complicated material name recognition, while it also has weaknesses in hallucination, identifying band gap values, and identifying band gap types. Revised prompt based on the error analysis leads to improved accuracy for GPT-4. To the best of our knowledge, this study is the first to compare the GPT-4 model and ChemDataExtractor for the band gap extraction task. This study provides evidence to support using generative LLMs for domain-specific information extraction tasks.

Binary imidazolium based aqueous ionic liquid droplet on a rough surface: a molecular dynamics study

Ionic liquids (ILs) have garnered immense attention due to their tunable physicochemical properties. In this study, we delve into the wetting behaviour and interfacial properties of aqueous binary IL mixtures containing [EMIM] [BF4] and [HMIM] [BF4] on both smooth and rough graphite surfaces. Employing classical molecular dynamics simulations, we systematically explored the effects of IL concentration and surface morphology on wetting phenomena. To ensure the equilibrium of the systems, we began by calculating the interaction energies between the components. Subsequently, contact angles were computed, shedding light on the wetting characteristics of the aqueous binary IL mixtures. We meticulously examined the density profiles of the IL systems from the substrate to unravel the dominance of one IL over the other, exploiting the hydrophilic nature of [EMIM] [BF4] and the hydrophobic nature of [HMIM] [BF4]. A comprehensive analysis of hydrogen bond distribution allowed us to discern the intricate nature of these aqueous binary IL droplets. We probed the changes in intermolecular forces within the aqueous IL solution by investigating surface tensions. The tunability of these ILs emerges as a significant aspect of our study, paving the way for their tailored application in engineering and scientific domains. Schematic diagram showing the density distribution of hydrophilic and hydrophobic ILs ([EMIM] [BF4] and [HMIM] [BF4]) in water and also the hydrogen bond (HB) distribution of anion ([BF4]−) water inside the droplet on a pillared surface with a surface fraction ( f ) of 0.4 and pillar height of H = 4 .

Fractal Analysis and Fractal Dimension in Materials Chemistry

Using fractal analysis and computing fractal dimension to understand materials chemistry and solve problems such as catalysis, chemical reactions, fuel storage, and material design is an important scientific research domain [...]

Effect of concentration and electrical charge of maltodextrin, and packing factor on electrospinning processing

The power law ηrel ∼ Ca of maltodextrins with higher entanglement concentrations (Ce) of dextrose equivalent (DE) 4, 10, and 18 exhibits an unusually high exponent a, ranging from 10.34 to 14.38 in congested solutions where particles occupy significant space. This contrasts with the dilute exponents (a ∼2) typically observed in polymer–solvent systems. To address this issue, several viscosity factors were evaluated using the Einstein–Roscoe and Mooney equations, which demonstrated viscosity dependence due to volume fraction (ϕ) > 0.35, shape, crowding factor (k) packing factor (1/k∼0.5) and centered cubic cell (CC), particularly for larger chain sizes (>20 glucoses) in DE-4. Chain electrical charges significantly stiffen the chain and increase its length beyond the theoretically predicted Kuhn persistent length (Lp), which seems to be responsible for the exponential increase in viscosity. Normalizing the viscosity/charge data revealed a log–log plot consistent with the exponent a of the general power law theory. In addition to concentration, electrospinning processing is influenced by strong particle–particle charge interactions, (ϕ), packing factor (1/k), CC, and chain sizes. The Maxwell model demonstrated poor interaction for DE-4 (modulus ∼286 Pa at maximum ϕ due to loose packing of CC, in contrast to DE-18, which exhibited a modulus of 994 Pa for a solution with maximum ϕ> 0.5 and a congested solution of 52% w/v, packing factor of 0.68, and high particle–particle interaction of the body-centered cubic (BCC) cell, favoring electrospun fiber formation. The packing factor, ϕ, and cell type, are highly sensitive and have potential applications in food systems, 3D bioprinting, among many other scientific domains.

SURVEYING RESEARCH LANDSCAPES: REVEALING ATTRIBUTES IN THE PHYSICAL SCIENCES OF KAZAKHSTAN

This article presents a comprehensive survey-based analysis of the current research landscape within Kazakhstan’s academic community. Drawing from responses collected from 265 researchers and academics across various universities, the study unveils insightful demographic profiles and research attributes across four distinct subject areas. While encompassing a broad spectrum of disciplines, particular attention is directed towards the characteristics and trends within the physical sciences domain. The survey explores Kazakhstan’s physical sciences research landscape, revealing a focus on international collaborations, particularly with Western academics, to enhance research visibility. Challenges include limited emphasis on partnerships with governmental or nongovernmental organizations, adverse working conditions, and disparities in resource access, notably in highperformance computing facilities. This research provides a detailed understanding of the current state of academic research within the country and offers a nuanced perspective that can inform strategic planning, policy formulation, and future research endeavors.

Utilization of Polymers for the Development of Nanomaterials

AbstractThis study highlights the significance of polymers in the progress of nanoparticles across various areas. The molecules of polymers are highly regarded for their ability to adapt and self‐assemble, making them essential components in the creation of nanomaterials. The variety of polymers include natural polymers such as chitosan, synthetic polymers like polyethylene, and biodegradable polymers like poly(lactic‐co‐glycolic acid) (PLGA). These kinds of polymers possess distinct advantages such as high strength, environmental sustainability, and biocompatibility. Incorporation of nanoscale fillers into polymer matrices, which enhances the mechanical, thermal, and electrical properties of materials, is crucial for the development of nanocomposites. Illustration instances encompass carbon nanotube‐polymer composites and polymer clay hybrids, which find application in the construction, automotive, and aerospace sectors. Composites can employ many synthetic methods to generate nanostructures. Nanofibers have utility in tissue engineering, whereas polymer nanoparticles function as carriers for medical delivery. Also, polymers enhance nanomaterials by modifying their surfaces, a crucial factor for their application in membrane technology, catalysis, and sensing. A collaborative synergy between polymers and nanoparticles fosters a wide range of applications, showcasing the versatility and potential of polymers in altering the characteristics of nanomaterials. The resulting partnership continues to generate pioneering breakthroughs that address complex challenges and unveil unprecedented prospects in the domains of science, technology, and business.

Interdisciplinarity through internationality: Results from a US-Mexico graduate course bridging computational and plant science.

Interdisciplinarity is used to integrate and synthesize new research directions between scientific domains, but it is not the only means by which to generate novelty by bringing diverse perspectives together. Internationality draws upon cultural and linguistic diversity that can potentially impact interdisciplinarity as well. We created an interdisciplinary class originally intended to bridge computational and plant science that eventually became international in scope, including students from the United States and Mexico. We administered a survey over 4 years designed to evaluate student expertise. The first year of the survey included only US students and demonstrated that biology and computational student groups have distinct expertise but can learn the skills of the other group over the course of a semester. Modeling of survey responses shows that biological and computational science expertise is equally distributed between US and Mexico student groups, but that nonetheless, these groups can be predicted based on survey responses due to subspecialization within each domain. Unlike interdisciplinarity, differences arising from internationality are mostly static and do not change with educational intervention and include unique skills such as working across languages. We end by discussing a distinct form of interdisciplinarity that arises through internationality and the implications of globalizing research and education efforts.

The joint extraction of fact-condition statement and super relation in scientific text with table filling method

The fact-condition statements are of great significance in scientific text, via which the natural phenomenon and its precondition are detailly recorded. In previous study, the extraction of fact-condition statement and their relation (super relation) from scientific text is designed as a pipeline that the fact-condition statement and super relation are extracted successively, which leads to the error propagation and lowers the accuracy. To solve this problem, the table filling method is firstly adopted for joint extraction of fact-condition statement and super relation, and the Biaffine Convolution Neural Network model (BCNN) is proposed to complete the task. In the BCNN, the pretrained language model and Biaffine Neural Network work as the encoder, while the Convolution Neural Network is added into the model as the decoder that enhances the local semantic information. Benefiting from the local semantic enhancement, the BCNN achieves the best F1 score with different pretrained language models in comparison with other baselines. Its F1 scores in GeothCF (geological text) reach 73.17% and 71.04% with BERT and SciBERT as pretrained language model, respectively. Moreover, the local semantic enhancement also increases its training efficiency, via which the tags’ distribution can be more easily learned by the model. Besides, the BCNN trained with GeothCF also exhibits the best performance in BioCF (biomedical text), which indicates that it can be widely applied for the information extraction in all scientific domains. Finally, the geological fact-condition knowledge graph is built with BCNN, showing a new pipeline for construction of scientific fact-condition knowledge graph.

Cilia-assisted flow of electrically conducting micropolar fluid in a heated curved channel under Soret and Dufour effects

Cilia flow plays a crucial role in biological systems such as the movement of mucus in the respiratory tract, circulation of cerebrospinal fluid in the brain, and propulsion of sperm cells. Understanding the cilia flow of viscous fluids is essential for elucidating the biomechanics of these processes and their implications for health and disease. Motivated by such numerous biomedical applications, this article aims to exhibit the influence of heat and mass transfer on the ciliary flow of electrical conducting micropolar fluid in a curved channel. The energy and concentration equations are modulated with viscous dissipation, Soret, and Dufour effects. The constitutive equations are simplified by the hypothesis of lubrication approximation theory and then solved numerically using the implicit finite difference method (FDM). Results for velocity, pumping phenomenon, concentration, thermal field, rate of heat transfer, streamlines, skin friction coefficient, Nusselt number, and Sherwood number are analyzed subject to pertinent parameters. The study reveals that velocity is enhanced via both the micropolar parameter and curvature parameter. Temperature enhances for larger values of Dufour and Brickman numbers. Furthermore, the radial magnetic field plays a resistive role in the trapped bolus. The findings presented in this study should prove beneficial for researchers in the domains of medicine, engineering, science, and fluid mechanics. Further, it is found that the micropolar fluid model is more suitable for biofluids like blood.

Development and performance evaluation of a thin GAGG:Ce scintillator plate for high resolution synchrotron radiation X-ray imaging

Scintillator-based X-ray imaging detectors are pivotal in numerous scientific and practical domains, including medical imaging, food and device inspection, and security monitoring. Recent advancements have spurred interest in 4D X-ray imaging using synchrotron radiation, necessitating higher temporal resolutions. Consequently, this places stringent demands on X-ray detector technology, especially when X-ray energy exceeds 20 keV. The selection of a suitable scintillator material is crucial for achieving optimal timing resolution, yet it poses a significant challenge in dynamic X-ray imaging. This study delves into the optimization of scintillator properties and their impact on spatial resolution and light output, elucidating the performance of Ce-doped Gd3Ga3Al2O12 (GAGG:Ce) scintillators for X-ray imaging applications. We developed a micro X-ray imaging detector using a 100 μm-thick GAGG:Ce scintillator plate and conducted X-ray imaging tests at the Aichi SR facility. The results demonstrated that the resolution, quantified as the chart slit width at a contrast transfer function (CTF) value of 10%, reached 2 ∼ 3 μm with a 4× lens, 0.52 μm± 0.03 μm with a 20× lens, and 0.42 μm± 0.01 μm with a 40× lens. Although the results of this study did not achieve a spatial resolution nearing the effective pixel size of the 40× lens, the text also elucidates the underlying reasons for this limitation. Furthermore, we compared the X-ray sensitivity of our GAGG:Ce scintillator plate with that of a commercial LuAG:Ce scintillator, revealing an approximately 1.5-fold increase in light output. As a demonstration, transmission images of dried small fish were captured using the GAGG:Ce scintillator plate and the developed X-ray imaging system. These findings highlight the potential of the X-ray imaging detector devised in this study for future generations of X-ray imaging applications.

PSF calibration of DAMPE for gamma-ray observations

The DArk Matter Particle Explorer (DAMPE) is dedicated to exploring critical scientific domains including the indirect detection of dark matter, cosmic ray physics, and gamma ray astronomy. This study introduces a novel method for calibrating the Point Spread Function (PSF) of DAMPE, specifically designed to enhance the accuracy of gamma-ray observations. By leveraging data from regions near pulsars and bright Active Galactic Nuclei (AGNs), we have refined the PSF calibration process, resulting in an improved angular resolution that closely matches our observational data. This advancement significantly boosts the precision of gamma-ray detection by DAMPE, thereby contributing to its mission objectives in dark matter detection and gamma ray astronomy.

The Upside of Teachers’ Technostress: Adaptation and Validation of a Techno-eustress Scale

Digital education transformation introduced a new source of stress for higher education teachers (HETs). Research has studied the effects of technostress, or technology-induced stress, focusing on its harmful consequences (techno-distress). Recent literature further suggests that technostress can also benefit individuals and organizations (techno-eustress), contributing to wellbeing, effectiveness, and performance. However, techno-eustress measurements are scarce. This study aimed to adapt and validate the Techno-eustress Scale for HETs. The scale was translated, adapted, and tested in a nationwide sample of 1,107 Portuguese HETs. Both exploratory and confirmatory factor analyses were used. Results led to a brief 5-item Portuguese version of the techno-eustress scale, exhibiting a one-dimensional structure and robust psychometric qualities, evidencing reliability, construct validity, and strict measurement invariance. The study provided a parsimonious techno-eustress assessment that assists future research and practice. Our work extended technostress research and crosses scientific domains such as education, psychology, information systems, and management. This brief measurement underpins positive organizational studies as it opposes the eternalization of techno(di)stress measurements, in a technology-based era when wellbeing, notably for HETs, is a precious asset.

Coconut Libtool: Bridging Textual Analysis Gaps for Non‐Programmers

ABSTRACTIn the era of big and ubiquitous data, professionals and students alike are finding themselves needing to perform a number of textual analysis tasks. Historically, the general lack of statistical expertise and programming skills has stopped many with humanities or social sciences backgrounds from performing and fully benefiting from such analyses. Thus, we introduce Coconut Libtool (www.coconut-libtool.com/), an open‐source, web‐based application that utilizes state‐of‐the‐art natural language processing (NLP) technologies. Coconut Libtool analyzes text data from customized files and bibliographic databases such as Web of Science, Scopus, and Lens. Users can verify which functions can be performed with the data they have. Coconut Libtool deploys multiple algorithmic NLP techniques at the backend, including topic modeling (LDA, Biterm, and BERTopic algorithms), network graph visualization, keyword lemmatization, and sunburst visualization. Coconut Libtool is the people‐first web application designed to be used by professionals, researchers, and students in the information sciences, digital humanities, and computational social sciences domains to promote transparency, reproducibility, accessibility, reciprocity, and responsibility in research practices.

Progressive Analytical Techniques Utilized for the Detection of Contaminants Attributed to Food Safety and Security

Food analysis and authentication are very crucial to assure safety, quality, and integrity of food and food-related products. Analysis of food products not only ensures the quality of food but also gives information about composition, physical characteristics, flavor, shelf-life, safety, microstructure, and processibility. Consequently, food is now regarded as an inexpensive means of preventing disease while also serving as a source of energy. Typically, a wide range of distinct chemical components make up the chemical composition of food. Furthermore, during processing or storage, food is always susceptible to structural alterations. Therefore, to assess the nutritional value and the quality of food products, food components must be analyzed and quantified. Food analysis mainly includes the preparation, examination, and identification of major food components, such as dietary fiber, phenolic compounds, carbohydrates, proteins, amino acids, peptides, enzymes, phospholipids, pesticides, alcohols, fat-soluble and water-soluble vitamins, organic acids, organic bases, bittering agents, pigments, and aroma compounds, as well as minor components including preservatives, antioxidants, colorants. Therefore, there are several advanced analytical techniques, including spectroscopy, mass spectrometry, chromatography, NMR, HSI, MALDI-TOF, RT-PCR, biosensors, etc. have been vital in the food science domain due to their high selectivity and sensitivity. This review emphasizes implementing various analytical techniques to analyze food safety and security.