Scientific Domains Research Articles

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.

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.

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.

Development of GIS-based framework for reservoir data management and quality evaluation

Abstract Accurately measuring and understanding reservoirs is critical for ensuring successful reservoir development, reservoir development evaluation have become an increasingly important issue in reservoir development engineering and present many challenges, particularly when attempting to integrate and visualize multiple types of data. This paper presents the methodologies, techniques and integrated services adopted for the design and implementation of A quantitative oil and gas reservoir data management and evaluation system. The system is an integrated application based on petroleum geology and GIS technology for managing, displaying, administering and analyzing petroleum geological data. In addition, the system is capable of evaluating reservoir quality and input data quality which provides effective recommendations for the design and preparation of reservoir development programs. In this study, a GIS-based quantitative evaluation module was developed considering both reservoir and input data quality. In this case, the reservoir quality index is determined by five major elements, and the input data quality is determined by the quality of six data sources. The weights of each element are determined by principal component analysis (PCA). Developed a raw data output processing software in C# using ArcGIS engine functions, which automatically generates visual maps and reservoir quality assessment information. The feasibility of the platform is verified by using the geological data of Linfen gas field Formation in the eastern slope of Shanxi. The integrated framework system proposed in this study enables decision-makers and the publics to quickly response to oil reservoir development events and allows an easy adaptation to other regional and scientific domains related to real-time monitoring and evaluating.

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.

Quantum Computing and Cloud Technologies: The Next Frontier in Computing Power

Abstract: This article explores the convergence of quantum computing and cloud technologies, representing a pivotal advancement in computational power with far-reaching implications across various industries and scientific domains. It examines the current state of quantum computing, elucidating its fundamental principles and recent hardware advancements, while comparing its capabilities to classical computing systems. The integration of quantum computing with cloud platforms is analyzed, highlighting the emergence of cloud-based quantum services and their advantages in democratizing access to this cutting-edge technology. The article delves into potential applications of quantum cloud computing, including cryptography, material science, artificial intelligence, and financial modeling, showcasing its transformative potential. Critical challenges facing quantum cloud computing are addressed, such as error correction, scalability issues, and the development of quantumsafe encryption methods. Looking ahead, the article discusses prospects and research directions, including anticipated breakthroughs in quantum hardware, emerging quantum cloud architectures, and the potential impact on various industries. By synthesizing current research and industry developments, this comprehensive overview provides insights into the revolutionary potential of quantum cloud computing and its role in shaping the future of computational capabilities.

New Applications of “Abeer-AL-Tememe” Transformation in partial differential equations

The Introduction of integral transforms as a partial differential equation solution technique results from the tremendous significance of differential equations and integral transforms in scientific domains. This study proposes a new integral transform Abeer Al- Tememe transform. The suggested transform applications has demonstrated its capacity to resolve partial differential equations.

Static Magnetic Field Focusing With Neodymium Magnets For Wound Healing: A Numerical Study

Static magnetic fields (SMFs) find widespread applications in diverse scientific, technological, and medical domains. This study explores the potential of neodymium permanent magnets in focusing and controlling SMFs, specifically emphasizing wound healing applications. Numerical simulations using COMSOL Multiphysics create a uniform static magnetic field for wound healing. The study systematically increases the number of neodymium magnets, demonstrating enhanced magnetic flux density and a focused magnetic field. The results affirm the efficacy of neodymium magnets in generating a uniform static magnetic field between 160-600 m Tesla. This research proposes neodymium permanent magnets as a promising tool for wound healing applications, offering a non-invasive and focused therapeutic approach. While the study provides valuable insights, further experimental and clinical validations are necessary to establish the real-world efficacy of this method. The work contributes to the evolving understanding of static magnetic fields as a viable therapeutic modality for various medical conditions, particularly in the context of wound healing.

Rogue-wave structures for a generalized (3+1)-dimensional nonlinear wave equation in liquid with gas bubbles

This study explores the behavior of higher-order rogue waves within a (3+1)-dimensional generalized nonlinear wave equation in liquid-containing gas bubbles. It creates the investigated equation’s Hirota D-operator bilinear form. We employ a generalized formula with real parameters to obtain the rogue waves up to the third order using the direct symbolic technique. The analysis reveals that the second and third-order rogue solutions produce two and three-waves, respectively. To gain deeper insights, we use the Cole-Hopf transformation on the transformed variables ξ and η to produce a bilinear equation. Using the system software Mathematica, the dynamic analysis presents the graphics for the obtained solutions in transformed ξ, η, and original spatial-temporal coordinates x, y, z, t. These visualizations reveal rogue waves’ intricate structure and evolution, capturing their localized interactions and significant presence within nonlinear systems. We demonstrate that rogue waves, characterized by their substantial height and sudden appearance, are prevalent in various nonlinear events. The equation examined in this study offers valuable insights into the evolution of longer waves with smaller amplitudes, which is particularly relevant in fields such as fluid dynamics, dispersive media, and plasmas. The implications of this research extend across multiple scientific domains, including fiber optics, oceanography, dusty plasma, and nonlinear systems, where understanding the behavior of rogue waves is crucial for both theoretical and practical applications.

DETERMINING STUDENT'S ONLINE ACADEMIC PERFORMANCE USING MACHINE LEARNING TECHNIQUES

Predicting student's academic performance during online learning has been considered a major task during the pandemic period. During the online mode of learning, academic activities have been affected in such a way that the management of educational institutions has planned to design support systems for predicting the student's performance to reduce the dropout ratio of the students and bring improvement in academic activities. During COVID-19, the main challenge is maintaining student's grades by predicting their academic performance using different techniques such as Education Data Mining and Learning Analytics. Different features have been identified related to the teaching mechanisms in online learning, which have a great impact on the improvement of academic performance. A high-quality dataset helps us to generate productive results, which in turn helps us to make effective decisions for promoting high-quality education. In this research, five prediction models for predicting academic performance have been proposed by collecting an imbalanced dataset of 350 students from the same computer science domain. After applying pre-processing techniques for cleaning the data, machine learning models have been applied, including K-Nearest Neighbor Classifier, Decision Tree, Random Forest, Support Vector Classifier, and Gaussian Naive Bayes. Results have been predicted for an imbalanced and balanced dataset after feature selection. Support Vector classifier has produced the best results in a balanced dataset with selected features by giving an accuracy of 96.89%.

Do Changes in Journal Rank Influence Publication Output? Evidence from China

AbstractDespite considerable progress in understanding the journal evaluation system in China, empirical evidence remains limited regarding the impact of changes in journal rank (CJR) on scientific output. By employing the difference-in-differences (DID) framework, we exploit panel data from 2015 to 2019 to examine the effect of changes in journal ranks on the number of publications by Chinese researchers. Our analysis involves comparing two groups—journals that experienced a change in ranking and journals that did not—before and after the change in ranking. Our analysis reveals a statistically significant negative effect. The results suggest that CJR has led to a 14.81% decrease in the number of publications per 100, relative to the sample mean value. The observed negative impact is consistently confirmed through robustness tests that involve excluding journals that do not adhere to best practices, removing retracted publications from the calculation of publication numbers, and randomly selecting journals with changed ranks for estimation. We also observed that CJR exhibits a widespread but unequal effect. The negative effect is particularly pronounced in the academic domains of life sciences and physical sciences, in journals that experience declines in rank, and in less-prestigious universities. We contribute to the literature on how changes in journal rankings affect researchers’ academic behavior and the determinants of scholarly publication productivity.

Assessing semantic interoperability in environmental sciences: variety of approaches and semantic artefacts

The integration and reuse of digital research products can be only ensured through the adoption of machine-actionable (meta)data standards enriched with semantic artefacts. This study compiles 540 semantic artefacts in environmental sciences to: i. examine their coverage in scientific domains and topics; ii. assess key aspects of their FAIRness; and iii. evaluate management and governance concerns. The analyses showed that the majority of semantic artefacts concern the terrestrial biosphere domain, and that a small portion of the total failed to meet the FAIR principles. For example, 5.5% of semantic artefacts were not available in semantic catalogues, 8% were not built with standard model languages and formats, 24.6% were published without usage licences and 22.4% without version information or with divergent versions across catalogues in which they were available. This investigation discusses common semantic practices, outlines existing gaps and suggests potential solutions to address semantic interoperability challenges in some of the resources originally designed to guarantee it.

Understanding and Assessing Scientific Wisdom

ABSTRACT We applied a balance theory of wisdom to thinking in the domain of science. In two studies, we administered maximum-performance scales measuring Scientific Wisdom, Scientific Reasoning, Scientific Creativity, and fluid and crystallized intelligence, and a typical-performance scale of self-assessed wisdom. Our Scientific Wisdom scale, along with the Scientific Reasoning scales and a Scientific Creativity scale, loaded on one clear factor separate from the factor(s) on which the psychometric tests of intelligence, SAT and ACT scores, and GPA loaded. These results suggested that the scientific tests were assessing abilities that were different from g, as well as what the SAT/ACT and academic GPA were assessing. The studies are merely a starting point for a relatively new idea—that schools should consider teaching for and assessing scientific wisdom, and that a full analysis of scientific giftedness should include scientific wisdom.

Academic entrepreneurship in the humanities and social sciences: a systematic literature review and research agenda

The predominant empirical focus of academic entrepreneurship research has been in the science, technology and engineering domains. To date there has been a limited research and empirical focus on academic entrepreneurship in the humanities, arts and social sciences (HASS). This is against a background of the growing societal and policy recognition of the important contributions and impact of HASS based research. The purpose of this paper through a systematic literature review is to examine the current state of academic entrepreneurship research in HASS. Our systematic literature identified four key themes namely, actors,barriers,mechanismsandmeasurementandevaluation. From this, we develop and outline a research agenda for academic entrepreneurship in HASS.

Examining Dropout Among Graduate and Undergraduate Public Affairs and Political Science Students

Within the domain of public affairs and political science (PAPS), student dropout emerges as a critical concern. This study delves into the factors influencing the decisions of undergraduate and graduate PAPS students to discontinue their programs. Employing a comprehensive mixed-methods approach, we surveyed 384 at-risk undergraduates and conducted in-depth interviews with 18 former graduate students. Thematic analysis of these interviews revealed six pivotal factors influencing graduate student dropout: the financial burden of study (including tuition and limited financial aid), program curricular challenges (such as demanding thesis requirements and inadequate practical components), the delicate balance of multiple responsibilities (work, study, and family), health-related issues (including anxiety and stress), poor academic performance, and personal difficulties (such as lack of family support). At the undergraduate level, descriptive analyses indicated that 38% of PAPS students contemplated dropping out, with 30% considering changing their major. Exploratory Factor Analysis and Multiple Regression highlighted the significance of factors such as family and societal influences, economic considerations, program and curriculum structure, institutional dynamics, academic performance, personal attitudes, and student health in shaping these contemplations. This research not only expands the application of integration and engagement theory and the pull, push, and failing out theory but also validates their effectiveness in predicting dropout within PAPS programs. The study offers valuable insights for both researchers and policymakers, underscoring the imperative for more supportive, flexible, and financially viable educational structures to enhance student retention in the realm of public affairs and political science.

Complexity and phase transitions in citation networks: insights from artificial intelligence research.

In this study, we analyze the changes over time in the complexity and structure of words used in article titles and the connections between articles in citation networks, focusing on the topic of artificial intelligence (AI) up to 2020. By measuring unpredictability in word usage and changes in the connections between articles, we gain insights into shifts in research focus and diversity of themes. Our investigation reveals correspondence between fluctuations in word complexity and changes in the structure of citation networks, highlighting links between thematic evolution and network dynamics. This approach not only enhances our understanding of scientific progress but also may help in anticipating emerging fields and fostering innovation, providing a quantitative lens for studying scientific domains beyond AI.

Influence of external magnetic field on nanofluid dynamics using a two-phase Lattice Boltzmann Mixture Model at low Reynolds numbers

This work examines the behavior of an external magnetic field-induced Al2O3-water nanofluid flow between two parallel plates, with a particular emphasis on low Reynolds number scenarios. Understanding the dynamics of magnetohydrodynamic (MHD) flows in applications including energy systems, cooling systems, and medicinal devices depends on solving this problem. For the first time, a new two-phase lattice Boltzmann method was used to simulate the flow in conjunction with the mixture model. This methodology was specifically designed for nanofluid flow in the presence of magnetic field. Three coupled transport equations for flow velocity, concentration of nanoparticles, and magnetic field intensity are solved using this method. Three various cases were examined numerically. By comparing the simulated velocity profiles with analytical solutions in the first case, model validation was accomplished, demonstrating a strong agreement with less than 1 % variance. The impact of different inflow nanoparticle volume fractions (0.05 and 0.01) on the velocity field at various Reynolds numbers (0.5, 1, 10) and Hartmann numbers (0, 10, 20) was investigated in the second case. The findings showed that for Reynolds numbers of 0.5, 1, and 10, respectively, the velocity magnitude dramatically fell by approximately 75 %, 68 %, and 30 % as the Hartmann number grew from 0 to 20. Alongside this decrease, vortices began to form at Hartmann numbers of 10 and 20. Furthermore, the influence of the magnetic field diminished as the Reynolds number increased from 0.5 to 10, resulting in a noticeable reduction in vortex intensity. In the third case, where the nanoparticle volume fractions at the inlet were closer (e.g., 0.03 and 0.02), the intensity of the vortices decreased compared to the second case. The study demonstrates the robustness of the proposed model and its applicability across scientific and engineering domains. The novelty lies in quantifying MHD effects on nanofluid flow and particle distribution using a two-phase lattice Boltzmann approach, offering more precise and efficient simulations than existing methods. The findings provide new insights into the interaction between magnetic fields and nanofluids, especially at low Reynolds numbers, and emphasize the critical role of nanoparticle distribution in flow dynamics.

Investigation of Chrysene heterodimers complexes potential energy surface using ab initio computational methods

Numerous chemical and biological entities are greatly impacted by non-covalent interactions in terms of their stability and structure. One such example is the interaction of aromatic rings. These interactions are highly valued in the domains of astrochemistry, biology, chemistry, biochemistry, and material science. The main goals of this study are to explore the Potential Energy Surface (PES) of Chrysene (Chy) heterodimers, identify the most stable configurations among the Chy-Bz, Chy-Np, and Chy-Anth heterodimer complexes, and analyze the inter-molecular interactions between these molecules. This analysis was conducted utilizing ab initio computational techniques. On their PES, the Chy-Np heterodimer exhibited four minima, while the Chy-Bz and Chy-Anth heterodimer complexes showed three. Compared to conformers oriented perpendicularly, co-facial arrangement conformers in Chy heterodimer complexes have stable structures. The global minimum structure of Chy-Bz has been determined to be the face isomer, while Chy-Np and Chy-Anth have global minimum structures of the cross isomer. The binding energies of the structures generated by MP2 are higher than those of DFT-D3, DFT-D4, and SCS-MP2. Optimized geometries and binding energies of larger hydrocarbon aromatic systems are explained in detail by B3LYP-D3 and the recently created B3LYP-D4.

Extension of Meir-Keeler-Khan (ψ − α) Type Contraction in Partial Metric Space

In numerous scientific and engineering domains, fractional-order derivatives and integral operators are frequently used to represent many complex phenomena. They also have numerous practical applications in the area of fixed point iteration. In this article, we introduce the notion of generalized Meir-Keeler-Khan-Rational type (ψ−α)-contraction mapping and propose fixed point results in partial metric spaces. Our proposed results extend, unify, and generalize existing findings in the literature. In regards to applicability, we provide evidence for the existence of a solution for the fractional-order differential operator. In addition, the solution of the integral equation and its uniqueness are also discussed. Finally, we conclude that our results are superior and generalized as compared to the existing ones.

Fast Computer Model Calibration using Annealed and Transformed Variational Inference

Computer models play a crucial role in numerous scientific and engineering domains. To ensure the accuracy of simulations, it is essential to properly calibrate the input parameters of these models through statistical inference. While Bayesian inference is the standard approach for this task, employing Markov chain Monte Carlo methods often encounters computational hurdles due to the costly evaluation of likelihood functions and slow mixing rates. Although variational inference (VI) can be a fast alternative to traditional Bayesian approaches, VI has limited applicability due to boundary issues and local optima problems. To address these challenges, we propose flexible VI methods based on deep generative models that do not require parametric assumptions on the variational distribution. We embed a surjective transformation in our framework to avoid posterior truncation at the boundary. Additionally, we provide theoretical conditions that guarantee the success of the algorithm. Furthermore, our temperature annealing scheme and fine-tuning can prevent being trapped in local optima through a series of intermediate posteriors and weight adjustment. We apply our method to infectious disease models and a geophysical model, illustrating that the proposed method can provide fast and accurate inference compared to its competitors.