Field Of Science Research Articles

A method of identifying domain-specific academic user information needs based on academic Q&A communities

Purpose To address the shortcomings of existing academic user information needs identification methods, such as low efficiency and high subjectivity, this study aims to propose an automated method of identifying online academic user information needs. Design/methodology/approach This study’s method consists of two main parts: the first is the automatic classification of academic user information needs based on the bidirectional encoder representations from transformers (BERT) model. The second is the key content extraction of academic user information needs based on the improved MDERank key phrase extraction (KPE) algorithm. Finally, the applicability and effectiveness of the method are verified by an example of identifying the information needs of academic users in the field of materials science. Findings Experimental results show that the BERT-based information needs classification model achieved the highest weighted average F1 score of 91.61%. The improved MDERank KPE algorithm achieves the highest F1 score of 61%. The empirical analysis results reveal that the information needs of the categories “methods,” “experimental phenomena” and “experimental materials” are relatively high in the materials science field. Originality/value This study provides a solution for automated identification of academic user information needs. It helps online academic resource platforms to better understand their users’ information needs, which in turn facilitates the platform’s academic resource organization and services.

Atomistic structures and stabilities of MgAl2O4 surfaces under processing conditions: A first-principles thermodynamics study

Surfaces, in contacting ambient environments, are of particular importance for controlling sintering, thin-film growth, and catalytic activity of oxides. Magnesium aluminate spinel (MgAl2O4), an essential oxide for both functional and structural ceramics, has attracted considerable attentions across diverse scientific fields. Nonetheless, a central challenge in rationally manipulating the properties of MgAl2O4 lies in the atomistic understanding of its surface structures under processing conditions. This study presents comprehensive first-principles calculations of MgAl2O4 (110) and (111) polar surfaces with/without off-stoichiometries. We predict from a thermodynamic model the dependence of surface stabilities on critical processing parameters, namely the oxygen partial pressure (PO2) and temperature (T). The findings indicate that at typical sintering temperatures, the non-stoichiometric (110) surface transits from Al/Mg-rich to O-rich with increasing PO2. In contrast, the non-stoichiometric (111) surface stably terminates in a Mg-rich configuration at low PO2, gradually shifting to Al-rich and then to Mg/O-rich as PO2 increases. The correlation between surface off-stoichiometry and stability is elucidated through electronic structure analysis. The investigation offers fundamental insights into surface structures and physicochemical behavior of MgAl2O4, thereby facilitating the rational processing control of complex oxides for applications in catalysis and electronics.

Advanced Method Optimization for Sampling and Analysis Instrumentation.

This work presents a generalized approach for analytical method optimization that branches the gap between techniques historically employed and accurate modern optimization techniques suitable for various applications. The novelty of the described strategy is the utilization of multivariate, multiobjective optimization with Karush-Kuhn-Tucker conditions to bound the optimization space to solutions within the physical limitations of instrumentation. Briefly, the basic steps outlined in this paper are to (1) determine the objective(s) that should be maximized or minimized based on the goals of the analytical application, (2) conduct a screening experiment, (3) perform ANOVA to determine the parameters which have a statistically significant effect on the objective, (4) conduct an experiment (e.g., Box-Behnken design) to collect data for fitting the objective equation, and (5) determine the physical constraints of the parameters and solve the Lagrangian to determine the optimal method parameters. A broad approach to optimization target selection allows for robust method tuning to develop improved data sets amenable for chemometrics and machine learning algorithm development. Gas chromatography-mass spectrometry was selected as a use case due to its broad use across scientific fields and time-consuming method development involving numerous parameters. This strategy can reduce the cost of research, improve data quality, and enable the rapid development of new analytical techniques.

Coupling Hyperbolic GCN with Graph Generation for Spatial Community Detection and Dynamic Evolution Analysis

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning.

Predictive Modeling and Analysis of Cu–Be Alloys: Insights into Material Properties and Performance

Cu–Be alloys are renowned for their exceptional mechanical and electrical properties, making them highly sought after for various industrial applications. This study presents a comprehensive approach to predicting the compositions of various types of Cu–Be alloys, integrating a Random Forest Regressor within a machine learning (ML) framework to analyze an extensive dataset of chemical and thermo-mechanical parameters. The research process incorporated data preprocessing, model training and validation, and robust analysis to discern feature significance. Cluster analysis was also conducted to illuminate the data’s intrinsic groupings and to identify underlying metallurgical patterns. The model’s predictive power was confirmed by high R2 values, indicative of its capability to capture and explain the variance in the dataset for both testing (R2 = 0.99375) and training (R2 = 0.99858). Distinct groupings within the alloy data were uncovered, revealing significant correlations between composition, processing conditions, and alloy properties. The findings underscore the potential of ML techniques in advancing the material design and optimization of Cu–Be alloys, providing valuable insights for the field of material science.

15 Years of Evolution of ChemCatChem

AbstractChemCatChem continues to serve an increasingly interdisciplinary and collaborative community. In our 15th year of existence, we share with you some of the initiatives that will bring our journal closer to the global and diverse community of scientists in catalysis research and adjacent fields.

Replicating Mg scaffold via 3D printing sacrificial template

The fabrication of hierarchical and porous structures holds significant promise in biomedical science, aerospace, and related fields. Despite advancements in 3D printing technology that have enhanced the capability of complex porous structures, its application in metal fabrication remains challenging. In this paper, we introduce a method involving a 3D printed sacrificial template, created by the material extrusion process, for magnesium (Mg) infiltration. This method facilitates the production of porous Mg scaffolds, with the templates subsequently being water-leached. The morphology, shrinkage rate, compressive strength, and types of lattices of templates were observed and assessed to determine their viability. Further, comprehensive compression tests and cell adhesion experiments were conducted on the Mg scaffolds to evaluate their performance. The results suggest that the templates must possess adequate strength and continuous flow channels for successful Mg infiltration. The scaffolds experienced an average of about 30 % shrinkage from the initial design to the final structure. The porous Mg scaffolds exhibit enhanced compressive strength (47.48 ± 0.84 MPa) at lower porosity (0.52) and demonstrate satisfactory biocompatibility. This research highlights the efficacy and low cost of the template replication method in generating mechanically superior porous Mg scaffolds with customized pore sizes and structures. In future research, this method also holds promise for applications involving newly developed alloys.

Linguistic Analysis of Texts in Philological Research: The Use of Salesforce Einstein Artificial Intelligence

Artificial intelligence is beginning to spread to all fields of science, and philology, linguistics and literature are no exception. This intersection of technology and the humanities opens a new era of linguistic and literary analysis. The relevance lies in the fact that artificial intelligence offers innovative ways of understanding and interpreting texts. Linguistic analysis of texts is an important part of philological research, as it helps to uncover the meaning, style, and other elements of language use in texts. This study aims to explore the innovative technology of Salesforce Einstein Artificial Intelligence, which can be used for linguistic text analysis in philological research. Research methods include literature review, correlation analysis, social analysis, impact analysis, syntactic analysis, and factor analysis. The results of the work showed that the system deeply learns the language and learns to recognize and analyze various language features and emotions. The paper examines the technical and ethical aspects of Salesforce Einstein AI. A cross-sectional study allowed us to describe the technological capabilities of the system and to debate the problem of replacing the author with artificial intelligence from an ethical perspective. The authors conclude that linguistic text processing in philology has revolutionized the way authors interact with technology and has become a valuable tool in text analysis. Salesforce Einstein AI has opened up a whole new world of opportunities for the field of philology.

Citizen science initiatives for soil literacy

Citizen science initiatives for soil literacy Alba Peiro and Francisco Sanz from the Ibercivis Foundation, Claudia Cappello and Tanja Mimmo from the Free University of Bolzano, guide us through citizen science initiatives for soil literacy that are integral to the ECHO project, Citizen science projects offer an effective framework for increasing participants’ knowledge in specific scientific fields. They also play a crucial role in reshaping participants’ perspectives on the scientific process and attitudes towards science and environmental issues. To ensure a significant increase in literacy, citizen science initiatives require meticulous design that extends beyond merely involving participants in scientific studies. This involves facilitating interactions with scientists and providing high-quality educational materials (Bonney et al., 2009).

Bodies and Bites: a medical school program that teaches anatomy, physiology, and nutrition to elementary school kids

Undergraduate medical students who participate in community outreach programs gain a multitude of benefits that impact not only their professional development but also the well-being of the communities they serve. At the Virginia Tech Carilion School of Medicine (VTCSOM), students have the opportunity to volunteer in the “Bodies and Bites” program at the West End Center for Youth, an after-school educational center that serves K-12 children in Roanoke, Virginia. The purpose of Bodies and Bites is to teach elementary school children in 2nd to 5th grade how their bodies work and how to keep them healthy through good nutrition and exercise. All sessions are led by VTCSOM medical students and graduate students from our partnering academic institution, the Fralin Biomedical Research Institute (FBRI). Each week, the children and Health Professions students explore a different topic related to human anatomy and physiology using anatomical models, small group discussions, and hands-on activities. At the end of each session, the participants create a healthy snack related to the day’s topic. The overall goal of the present study was to assess the perception of the Bodies and Bites program from the view of our student volunteers, and the 4th and 5th graders who attend the West End Center. Now in its 6th year, Bodies and Bites continues to be popular as a voluntary program among our Health Professions students, and is well received by the West End Center and the elementary school children they serve. Our students and community mutually benefit from this program, with the former having an opportunity to briefly disengage from the rigors of their studies while gaining valuable skills in science communication and inspiring children to pursue fields in Science, Technology, Engineering, Math, and Medicine (STEMM), and the latter having fun while learning about their bodies and discovering ways to improve their health.

Geometrical Analysis of Spacelike and Timelike Rectifying Curves and Their Applications

In the light of great importance of curves and their frames in many different branches of science, especially differential geometry as well as geometric properties and their uses in various fields, we are interested here to study a special kind of curves called rectifying curves. We consider some characterizations of a non-lightlike curve has a spacelike or timelike rectifying plane in pseudo-Euclidean space E13. Then, we demonstrate that the proportion of curvatures of any spacelike or timelike rectifying curve is a non-constant linear function of the arc length parameter s. Finally, we defray a computational example to support our main findings.

A noise‐tolerant fuzzy‐type zeroing neural network for robust synchronization of chaotic systems

SummaryAs a significant research issue in control and science field, chaos synchronization has attracted wide attention in recent years. However, it is difficult for traditional control methods to realize synchronization in predefined time and resist external interference effectively. Inspired by the excellent performance of zeroing neural network (ZNN) and the wide application of fuzzy logic system (FLS), a noise‐tolerant fuzzy‐type zeroing neural network (NTFTZNN) with fuzzy time‐varying convergent parameter is proposed for the synchronization of chaotic systems in this paper. Notably the fuzzy parameter generated from FLS combined with traditional convergent parameter embedded into this NTFTZNN can adjust the convergence rate according to the synchronization errors. For the sake of emphasizing the advantages of NTFTZNN model, other three sets of contrast models (FTZNN, VPZNN, and PTZNN) are constructed for the purpose of comparison. Besides, the predefined‐time convergence and noise‐tolerant ability of NTFTZNN model are distinctly demonstrated by detailed theoretical analysis. Furthermore, synchronization simulation experiments including two chaotic systems with different dimensions are provided to verify the related mathematical theories. Finally, the schematic of NTFTZNN model for chaos synchronization is accomplished completely through Simulink, further accentuating its effectiveness and potentials in practical applications.

Artificial intelligence for library and information science education: using mnemonics to improve learning and retention

Purpose This paper aims to familiarize students and instructors in the field of library and information science (LIS) with some features and benefits of mnemonics in the context of learning artificial intelligence (AI). Design/methodology/approach A narrative review among literature exists on mnemonics, AI and library services. Findings In the field of LIS, the use of mnemonics can be highly beneficial for both students and instructors. Mnemonics can aid in the retention and recall of complex concepts, classification systems, information retrieval strategies, jargon learning and library memory that are fundamental to the field. In the field of AI, mnemonics can be a valuable tool for students learning complex algorithms, concepts and techniques. Mnemonics can help students remember key components of AI models, understand the underlying principles and apply them effectively in problem-solving. Originality/value To the best of the authors’ knowledge, this paper is among the first ones exploring the use of mnemonics for learning subject areas related to artificial intelligence in LIS.

DNA aptamer-functionalized PDA nanoparticles: from colloidal chemistry to biosensor applications

Polydopamine nanoparticles (PDA NPs) are widely utilized in the field of biomedical science for surface functionalization because of their unique characteristics, such as simple and low-cost preparation methods, good adhesive properties, and ability to incorporate amine and oxygen-rich chemical groups. However, challenges in the application of PDA NPs as surface coatings on electrode surfaces and in conjugation with biomolecules for electrochemical sensors still exist. In this work, we aimed to develop an electrochemical interface based on PDA NPs conjugated with a DNA aptamer for the detection of glycated albumin (GA) and to study DNA aptamers on the surfaces of PDA NPs to understand the aptamer-PDA surface interactions using molecular dynamics (MD) simulation. PDA NPs were synthesized by the oxidation of dopamine in Tris buffer at pH 10.5, conjugated with DNA aptamers specific to GA at different concentrations (0.05, 0.5, and 5 μM), and deposited on screen-printed carbon electrodes (SPCEs). The charge transfer resistance of the PDA NP-coated SPCEs decreased, indicating that the PDA NP composite is a conductive bioorganic material. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) confirmed that the PDA NPs were spherical, and dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy data indicated the successful conjugation of the aptamers on the PDA NPs. The as-prepared electrochemical interface was employed for the detection of GA. The detection limit was 0.17 μg/mL. For MD simulation, anti-GA aptamer through the 5′terminal end in a single-stranded DNA-aptamer structure and NH2 linker showed a stable structure with its axis perpendicular to the PDA surface. These findings provide insights into improved biosensor design and have demonstrated the potential for employing electrochemical PDA NP interfaces in point-of-care applications.

DNA aptamer-functionalized PDA nanoparticles: from colloidal chemistry to biosensor applications

Polydopamine nanoparticles (PDA NPs) are widely utilized in the field of biomedical science for surface functionalization because of their unique characteristics, such as simple and low-cost preparation methods, good adhesive properties, and ability to incorporate amine and oxygen-rich chemical groups. However, challenges in the application of PDA NPs as surface coatings on electrode surfaces and in conjugation with biomolecules for electrochemical sensors still exist. In this work, we aimed to develop an electrochemical interface based on PDA NPs conjugated with a DNA aptamer for the detection of glycated albumin (GA) and to study DNA aptamers on the surfaces of PDA NPs to understand the aptamer-PDA surface interactions using molecular dynamics (MD) simulation. PDA NPs were synthesized by the oxidation of dopamine in Tris buffer at pH 10.5, conjugated with DNA aptamers specific to GA at different concentrations (0.05, 0.5, and 5 μM), and deposited on screen-printed carbon electrodes (SPCEs). The charge transfer resistance of the PDA NP-coated SPCEs decreased, indicating that the PDA NP composite is a conductive bioorganic material. Transmission electron microscopy (TEM) and scanning electron microscopy (SEM) confirmed that the PDA NPs were spherical, and dynamic light scattering (DLS), Fourier transform infrared spectroscopy (FTIR), and Raman spectroscopy data indicated the successful conjugation of the aptamers on the PDA NPs. The as-prepared electrochemical interface was employed for the detection of GA. The detection limit was 0.17 μg/mL. For MD simulation, anti-GA aptamer through the 5′terminal end in a single-stranded DNA-aptamer structure and NH2 linker showed a stable structure with its axis perpendicular to the PDA surface. These findings provide insights into improved biosensor design and have demonstrated the potential for employing electrochemical PDA NP interfaces in point-of-care applications.

Technologies for investigating single-molecule chemical reactions

Abstract Single molecules, the smallest independently stable units in the material world, serve as the fundamental building blocks of matters. Among different branches of single-molecule sciences, single-molecule chemical reactions, by revealing the behavior and properties of individual molecules at the molecular scale, are particularly attractive because they can advance the understanding of chemical reaction mechanisms and help to address key scientific problems in broad fields such as physics, chemistry, biology, and materials science. This review provides a timely, comprehensive overview of single-molecule chemical reactions based on various technical platforms such as scanning probe microscopy, single-molecule junction, single nanostructure, single-molecule fluorescence detection, and crossed molecular beam. We present multidimensional analyses of single-molecule chemical reactions, offering new perspectives for research in different areas like photocatalysis/electrocatalysis, organic reactions, surface reactions, and biological reactions. Finally, we discuss the opportunities and challenges in this thriving field of single-molecule chemical reaction.

Troops Management Systems And The Difference Of Cultures. Part Ii (Variants and Lessons of Russian, Soviet and Other Models for Us)

Due to the absence of domestic military science as a separate branch of science, the topic touched on by the USSR’s GS and management culture, the largest bearer of the German model after the FWW, remains an open topic. The Russian and especially the Soviet model was based on the Prussian model and even more today continues to bear the influence of the latter, but in a more refined form. The Russian army was one of the first in the world to follow Prussian innovations. The British model was and still is unacceptable to the Russian mentality and value system. The problem is that liberalism has always been punished in this country, serfdom is still very deep in public perception. It should also be noted that the influence of the Soviet model among the post-Soviet countries was quite large and is still large. But in the second decade of the 20th century, a number of post-Soviet countries began to abandon this model one after another. Although the Soviet model seems to be the main one among the CSTO countries, nevertheless, as a result of the reforms taking place in the Armenian Army after the 44- Day War of 2020, there are tendencies to abandon that model. We hope that they will have a fundamental nature

Troops Management Systems And The Difference Of Cultures. Part Ii (Variants and Lessons of Russian, Soviet and Other Models for Us)

Due to the absence of domestic military science as a separate branch of science, the topic touched on by the USSR’s GS and management culture, the largest bearer of the German model after the FWW, remains an open topic. The Russian and especially the Soviet model was based on the Prussian model and even more today continues to bear the influence of the latter, but in a more refined form. The Russian army was one of the first in the world to follow Prussian innovations. The British model was and still is unacceptable to the Russian mentality and value system. The problem is that liberalism has always been punished in this country, serfdom is still very deep in public perception. It should also be noted that the influence of the Soviet model among the post-Soviet countries was quite large and is still large. But in the second decade of the 20th century, a number of post-Soviet countries began to abandon this model one after another. Although the Soviet model seems to be the main one among the CSTO countries, nevertheless, as a result of the reforms taking place in the Armenian Army after the 44- Day War of 2020, there are tendencies to abandon that model. We hope that they will have a fundamental nature

THE MODERN TEACHER AS A SUBJECT IN THE PROCESS OF FORMING FUTURE PHD`S CULTURE OF ACADEMIC INTEGRITY: TEMPORA MUTANTUR, ET NOS MUTAMUR IN ILLIS

The article is aimed to characterize the skills and qualities of a modern university teacher in Ukrainian higher education as a subject in the process of forming the culture of academic integrity for the future Doctor of Philosophy. The instructor must showcase a profound grasp of the various scientific fields they teach, possessing the foundational knowledge to tackle diverse socio-economic, production, and cultural challenges. Furthermore, it is crucial for the instructor to remain abreast of new research, discoveries, and hypotheses, anticipating the evolving landscape of the science they teach and fostering a culture of scientific and pedagogical thought. Effective communication skills are essential for instructors, facilitating lucid and accessible explanations of intricate concepts and enhancing students’ comprehension of the material. The instructor’s capacity to stimulate the intellectual growth of PhD candidates, nurture creative and critical thinking, and employ innovative teaching methodologies delineates a high level of professionalism. An instructor should also be committed to encouraging the personal development of students, fostering their critical thinking, creative autonomy, and self-expression. All these efforts contribute to the cultivation of academic integrity among PhD candidates. In essence, these characteristics converge to transform the instructor into not just a conduit of knowledge but also a mentor who nurtures the scholarly potential of future PhDs.

Optimizing data acquisition: a Bayesian approach for efficient machine learning model training

Abstract Acquiring a substantial number of data points for training accurate machine learning (ML) models is a big challenge in scientific fields where data collection is resource-intensive. Here, we propose a novel approach for constructing a minimal yet highly informative database for training ML models in complex multi-dimensional parameter spaces. To achieve this, we mimic the underlying relation between the output and input parameters using Gaussian process regression (GPR). Using a set of known data, GPR provides predictive means and standard deviation for the unknown data. Given the predicted standard deviation by GPR, we select data points using Bayesian optimization to obtain an efficient database for training ML models. We compare the performance of ML models trained on databases obtained through this method, with databases obtained using traditional approaches. Our results demonstrate that the ML models trained on the database obtained using Bayesian optimization approach consistently outperform the other two databases, achieving high accuracy with a significantly smaller number of data points. Our work contributes to the resource-efficient collection of data in high-dimensional complex parameter spaces, to achieve high precision machine learning predictions.