Areas Of Science Research Articles

Approach to automating the construction and completion of ontologies in a scientific subject field

Domain ontologies facilitate the organization, sharing, and reuse of subject areas. Building a software ontology is labor-intensive and time-consuming. In the process of obtaining a software ontology, it is required to analyze a huge number of scientific publications relevant to the software being modeled. The process of ontology replenishing with information from a huge number of scientific publications can be facilitated and accelerated through the use of lexical-syntactic patterns of ontological design. In this paper, we consider the possibility of automated construction of scientific subject area ontologies based on a heterogeneous patterns system of ontological design. This system includes ontological design patterns and is intended for ontology developers. System also includes automatically built lexical and syntactic patterns, which help to automatic replenishment of the ontology with information extracted from natural language texts.

Existence and uniqueness of well-posed fractional boundary value problem.

In this paper, the existence and uniqueness of solution for a fractional differential model involving well-posed boundary conditions and implicit fractional differential equation is considered. The desired goals are achieved by using Banach contraction principle and Scheafer's fixed point theorem. To show the results applicability some examples are presented. The basic mathematical concept of well-posed fractional boundary value issues is investigated in this study. It dives into the existence and uniqueness of these difficulties, offering light on the conditions that allow for both practical and singular solutions. This study contributes to a better knowledge of fractional calculus and its applications in a variety of scientific and technical areas, giving significant insights for both scholars and practitioners.

Tuning Lubrication Performance of Phase‐changing Emulsion‐filled Gels by Sugar alcohols

Lubrication by hydrogels is proving to be an emerging area in colloid science. Although oil‐driven friction reduction is predominant in literature, emulsion gel‐based lubrication has attracted very little attention to date. We hypothesize that an interplay of viscolelasticity and oil volume fraction may modulate lubricity of emulsion‐filled gels. Herein, phase‐change gelatin‐based emulsion‐filled gels with different sugar alcohol and oil droplet concentrations (0‐30 wt%) were tested for their lubrication performance. The rheological and tribological tests were analysed in conjunction with spectroscopic and structural characterizations in order to reveal structure‐function relationship. Structural characterizations demonstrated that hydrogen bonding was enhanced in pure gelatin gels with the increase of sugar alcohol (glycerol)‐replacement time, which enhanced the storage modulus (G') of the gel networks. Emulsified droplets served as active fillers further strengthening the G’ and strikingly the presence of glycerol reduced the thermo‐responsiveness of the emulsion‐filled gels. Emulsion‐filled gels containing sugar alcohols, irrespective of their type, can greatly reduce the friction coefficients (μ) between hydrophobic surfaces in the boundary and mixed regimes versus the systems without sugar alcohols. In the hydrodynamic regime, the friction coefficient of the system was proportional to the second plateau shear viscosity, regulated by the oil content.This article is protected by copyright. All rights reserved.

CONSUMER COMFORT IN SOCIAL SCIENCES: A BIBLIOMETRIC ANALYSIS

This research aims to analyze consumer comfort studies in the research areas of business, behavioral sciences, and social sciences using performance and science mapping analysis, which are part of the bibliometric analysis method. This paper uses the Web of Science database to examine the most significant authors, journals, keywords, theories, countries, and their citations. This research uses VOSviewer software, specifically for citation analysis, co-citation analysis, bibliographic coupling, co-word analysis, co-authorship analysis, keyword clustering, and graphical visualization. Additionally, the research employs the SPAR-4-SLR protocol for conducting a bibliometric analysis. According to findings, the studies of consumer comfort are mostly influenced by Fornell, J. F., Spake, D. F., Parasuraman, A., and Patterson, P. G. Also, Butcher et al., (2001), Riley et al., (2000), Dobrow (2013), and Joffe et al. (2003) are the most cited articles. Facilities journal has the highest number of studies. The United States is the most frequently cited country. The most used keywords are client relationships, interpersonal communication, repurchase intention. Additionally, comfort theory is commonly used followed by the signaling theory, social identity theory, and self-efficacy theory. The research offers guidance for future research, identifying potential areas of study and contributing to the progression and enhancement of research on consumer comfort. To the best of the authors’ knowledge, this paper represents the first bibliometric evaluation of consumer comfort. An investigation of studies on consumer comfort is expected to provide precious guidelines for future researchers, managers, and entrepreneurs.

A new generalized beta function associated with statistical distribution and fractional kinetic equation

Several authors have extensively investigated beta function, hypergeometric function and confluent hypergeometric function, their extensions and generalizations due to their several application in many areas of engineering, probability theory and science. The main purpose of this paper is to present a new generalization of extended beta function, hypergeometric function and confluent hypergeometric function with the help of m-parameter Mittag-Leffler function, as well as examine some important properties like integral representations, differential formula and summation formulas. We also examine generalized Caputo fractional derivative operator with the help of m-parameter Mittag-Leffler function with associated properties using the generalized beta function. We define a new beta distribution involving the new generalized beta function. The mean, variance, coefficient of variance, moment generating function and characteristic function and cumulative distribution are derived. Further, we derive the solution of fractional Kinetic equation involving generalized hypergeometric function.

Multifunctional Applications Enabled by Fluorination of Hexagonal Boron Nitride.

2D materials exhibit exceptional properties as compared to their macroscopic counterparts, with promising applications in nearly every area of science and technology. To unlock further functionality, the chemical functionalization of 2D structures is a powerful technique that enables tunability and new properties within these materials. Here, the successful effort to chemically functionalize hexagonal boron nitride (hBN), a chemically inert 2D ceramic with weak interlayer forces, using a gas-phase fluorination process is exploited. The fluorine functionalization guides interlayer expansion and increased polar surface charges on the hBN sheets resulting in a number of vastly improved applications. Specifically, the F-hBN exhibits enhanced dispersibility and thermal conductivity at higher temperatures by more than 75% offering exceptional performance as a thermofluid additive. Dispersion of low volumes of F-hBN in lubricating oils also offers marked improvements in lubrication and wear resistance for steel tribological contacts decreasing friction by 31% and wear by 71%. Additionally, incorporating numerous negatively charged fluorine atoms on hBN induces a permanent dipole moment, demonstrating its applicability in microelectronic device applications. The findings suggest that anchoring chemical functionalities to hBN moieties improves a variety of properties for h-BN, making it suitable for numerous other applications such as fillers or reinforcement agents and developing high-performance composite structures.

Agree to agree: correcting acquiescence bias in the case of fully unbalanced scales with application to UK measurements of political beliefs

AbstractA methodologically important area in political science is measuring the ideology of voters. This task can be difficult, and researchers often rely on ‘off the shelf’ datasets. Many of these datasets contain unbalanced Likert scales, which risk acquiescence bias. This paper proposes a strategy for dealing with this issue. I first demonstrate using two comparable datasets from the UK how unbalanced scales produce distorted distributions and can affect regression results. Then, building on past research that utilises factor analysis to eliminate the influence of acquiescence bias, I demonstrate how researchers can utilise a person intercept confirmatory factor analysis model to obtain factor scores corrected for acquiescence in the case of fully unbalanced scales. I conclude with practical recommendations for researchers and survey designers moving forward.

Cognitive pairwise comparison forward feature selection with deep learning for astronomical object classification with sloan digital sky survey

This paper proposes a hybrid approach integrating the expert knowledge judgment approach using the Cognitive Pairwise Comparison (CPC) to the Deep Learning, a modern classification approach, for astronomic object classification. The astronomic data with ten thousand samples retrieved from Sloan Digital Sky Survey Sky Server Data Release 15 (SDSS SkyServer DR 15) are used for this study. The CPC is an approach to elicit and encode expert knowledge in the format of a Pairwise Opposite Matrix (POM) to evaluate expert preferences for the features. A forward feature selection algorithm taking the expert choices using CPC for the ordered features is used for the feature selection for the deep learning algorithm to build a heuristic training model based on the astronomic data. Whilst the accuracy of the case of improper feature selection is just 37.1%, the proposed hybrid approach can obtain a very high accuracy of 97.9% for the classification of the astronomic object using the eight scaled features (u, g, r, i, z redshift, ra, dec). To extend this research, the proposed CPC can be used as a human-centered tool to be applied to other areas of data sciences.

Pursuing the greenery: assessing plant-related content in the new Spanish curricula for compulsory education

ABSTRACT Plants constitute a highly diverse taxonomic group with fundamental ecological functions. However, plant biodiversity loss is currently a dramatic problem. It is essential to promote knowledge and appreciation of plants through formal education, particularly during educational stages that are compulsory for all citizens. This study presents an analysis of the new (2022) national curricula for primary (6–12 years) and compulsory secondary education (12–16 years) in Spain, and of the regional curricula for its largest region, Castilla y León. The analysis was conducted by identifying all plant-related text fragments, and classifying them into an inductive-deductive system of categories. These competence-based curricula are strongly grounded in the 2030 Agenda and the Sustainable Development Goals, since content related to the interactions between the social and the ecological spheres quantitatively prevails over scientific content on plants. However, some areas of plant science are under-represented, appearing only through general statements in which the use of scientific terminology is absent. Opportunities to promote first-hand experience of edible and useful plants are provided by the use of learning situations encouraged in the curricula. Biodiversity is identified as a key issue to be reinforced; despite being included, the curricula fail to associate it with knowledge on plant taxonomy.

Assessing emerging sustainability-oriented technologies: the case of precision agriculture

AbstractThe pressing need for a transition towards a more sustainable economy has given rise to sustainability-oriented innovations (SOIs). The development of SOIs involves a wide range of emerging technologies, some of which are highly uncertain and may have the potential to transform an existing industry. Thus, the identification and assessment of emerging technologies is pivotal for established companies to evaluate business opportunities as well as for researchers and policy makers to support the transition to a more sustainable economy. Therefore, we aim to contribute by offering a new approach that seeks to assess such emerging technologies that contribute to sustainability transition (i.e., sustainability-oriented technologies (SOTs)) by means of spectral cluster analysis based on the semantic similarities of scientific research articles in the field of precision agriculture (PA); a case example where multiple SOIs occur. Our analyses reveal that spectral cluster analysis is a suitable approach for assessing emerging SOTs. In addition, multi-perspective assessments based on, inter alia, related United Nations Sustainable Development Goals, Web of Science subject categories, strategic diagrams, and business models allows to render a holistic assessment of a scientific research area (in this case PA) and its emerging SOTs as well as their evolution into commercial SOIs.

Causality for Trustworthy Artificial Intelligence: Status, Challenges and Perspectives

Causal inference is the idea of cause-and-effect; this fundamental area of sciences can be applied to problem space associated with Newton’s laws or the devastating COVID-19 pandemic. The cause explains the “why” whereas the effect describes the “what”. The domain itself encompasses a plethora of disciplines from statistics and computer science to economics and philosophy. Recent advancements in machine learning (ML) and artificial intelligence (AI) systems, have nourished a renewed interest in identifying and estimating the cause-and-effect relationship from the substantial amount of available observational data. This has resulted in various new studies aimed at providing novel methods for identifying and estimating causal inference. We include a detailed taxonomy of causal inference frameworks, methods, and evaluation. An overview of causality for security is also provided. Open challenges are detailed, and approaches for evaluating the robustness of causal inference methods are described. This paper aims to provide a comprehensive survey on such studies of causality. We provide an in-depth review of causality frameworks, and describe the different methods.

Teachers’ Variables and Academic Achievement of Community Health Students in Basic Science in Cross River State

Many factors are attributable to the academic achievements of students among which Teachers’ variable is a major attribute. The limitations of the teacher as a supporting agent in the area of Basic Science hinder academic achievement of students in learning of Basic Sciences. The quality of instructions from the teacher at all levels of education determines the acquisition of competencies necessary for self-reliance and effective service delivery. The teacher’s attitude to work, knowledge of subject matter and instructional competence have a relationship with students’ academic achievements. The poor quality of students’ academic quality among community health students’ in Basic Sciences in CrossRiver State may be as a result of these variables. This study therefore examined Teachers’ variable and academic achievement of community health students’ in Basic Sciences in Cross River State. To achieve the purpose of this study, four (4) specific objectives were raised to guide the study, which were then converted to four (4) research questions and transformed to research hypotheses. Related literatures we reviewed in line with the variables of the study. The study adopted correlational research design, which is a non-experimental approach between a set of variables and naturally cannot be manipulated. The research area is Cross River State, Nigeria. The target population of study was year 1 Community Health students. Sample of study was415 students randomly selected from ten schools for the 2023/2024 academic session. The instrument employed was questionnaire designed by the researcher. Cronbach Alpha reliability method was used to determine the reliability estimate of the instrument which range from 0.73 to 0.81. Data was analysed using Multiple Linear regression. The findings of the study revealed that all teachers’ variables had significant positive relationship with students’ academic performance in Basic Sciences in Cross River State. It was recommended among others that teachers should adopt positive attitude towards their teaching profession especially in secondary schools.

2D Materials‐Based Pulsed Solid‐State Laser: Status and Prospect

AbstractPulsed solid‐state lasers comprise 2D materials as saturable absorbers that contain transparent windows of the atmosphere and characteristic fingerprint spectra of several vital molecules that are significant in various applications and research. Over the past few decades, significant progress has been made in the development of narrow pulse width, high energy, high average output power, high efficiency, and simple construction of passively Q‐switched and mode‐locked lasers with 2D materials as saturable absorbers. This review summarizes the development of 2D materials, including graphene, transition metal dichalcogenides, black phosphorus, topological insulators, and MXenes, as modulator devices for solid‐state lasers owing to their broadband operation, excellent nonlinear optical response, low recovery time, ultrafast dynamic processing, and easy fabrication. Then, some new emerging and representative applications of pulsed solid‐state lasers are introduced and illustrated such as laser surgery, material processing, and lidar. Finally, future challenges and perspectives of pulsed solid‐state lasers with 2D materials‐based saturable absorbers are analyzed and addressed. The rapid development of pulsed solid‐state lasers with the continuous improvement of modulation technology is expected to expand opportunities for application in industry, scientific, medical, and other areas.

Teacher educators and environmental justice: conversations about education for environmental justice between science and geography teacher educators based in England and Brazil

AbstractWhile environmental education has been present in the field of education for decades now, only recently our particular subject areas of science and geography have started to pay more critical attention to specific concerns surrounding the intersection of environmental issues and social justice (also known as environmental justice) within the context of formal secondary education, including in secondary teacher education programmes. Drawing on scholarship, policy landscapes and socio-environmental concerns from both the global South and the global North, and on a methodological approach based on transnational collective reflection and collaborative-dialogic writing, in this article we delve into our different cultural, geographical and disciplinary contexts, views and experiences as four teacher educators from Brazil and England who have been working at this intersection between environmental justice and Science and Geography teacher education programmes for secondary formal education. Here we will argue that environmental justice needs to have a central role in such teacher education programmes if we aim to support young people and their teachers in navigating the spatially diverse and unequal impacts of environmental emergencies in global North and South communities. We also consider future directions for research and collaboration across national and disciplinary boundaries within the landscape of environmental education for environmental justice, reflecting on the future of teacher education across the global North and the global South when facing more frequent and severe environmental emergencies.

Roadmap on nanoscale magnetic resonance imaging.

The field of nanoscale magnetic resonance imaging (NanoMRI) was started 30 years ago. It was motivated by the desire to image single molecules and molecular assemblies, such as proteins and virus particles, with near-atomic spatial resolution and on a length scale of 100 nm. Over the years, the NanoMRI field has also expanded to include the goal of useful high-resolution nuclear magnetic resonance (NMR) spectroscopy of molecules under ambient conditions, including samples up to the micron-scale. The realization of these goals requires the development of spin detection techniques that are many orders of magnitude more sensitive than conventional NMR and MRI, capable of detecting and controlling nanoscale ensembles of spins. Over the years, a number of different technical approaches to NanoMRI have emerged, each possessing a distinct set of capabilities for basic and applied areas of science. The goal of this roadmap article is to report the current state of the art in NanoMRI technologies, outline the areas where they are poised to have impact, identify the challenges that lie ahead, and propose methods to meet these challenges. This roadmap also shows how developments in NanoMRI techniques can lead to breakthroughs in emerging quantum science and technology applications.&#xD.

Teaching of Physics in Engineering from Problem-Based and Project-Based Learning Approaches

Within the scope of this research, an evaluation was conducted on the problem-based learning (PBL) and project-based learning (PrBL) methods in teaching physics to mechanical and electrical engineering students at a public university in Peru. Two groups, each consisting of 15 and 16 students, were carefully selected to ensure uniformity in academic, socioeconomic, and emotional aspects. The measurement of academic performance was conducted both at the beginning and the end of the period. The statistical analysis using the t-student test revealed that the PrBL method was significantly more effective than PBL. These findings underscore the association between the choice of learning methods and the specific scientific area being taught, emphasizing the superiority of the PrBL approach in this educational context. This discovery suggests that implementing learning strategies that incorporate a project-based approach can be a valuable option to enhance academic performance in scientific disciplines.

Spearheading a new era in complex colloid synthesis with TPM and other silanes.

Colloid science has recently grown substantially owing to the innovative use of silane coupling agents (SCAs), especially 3-trimethoxysilylpropyl methacrylate(TPM). SCAs were previously used mainly as modifying agents, but their ability to form droplets and condense onto pre-existing structures has enabled their use as a versatile and powerful tool to create novel anisotropic colloids with increasing complexity. In this Review, we highlight the advances in complex colloid synthesis facilitated by the use of TPM and show how this has driven remarkable new applications. The focus is on TPM as the current state-of-the-art in colloid science, but we also discuss other silanes and their potential to make an impact. We outline the remarkable properties of TPM colloids and their synthesis strategies, and discuss areas of soft matter science that have benefited from TPM and other SCAs.

Smart Sustainable Cities: a Meta-Analysis of Concepts and Discourses in the Literature

Objective: Establish a descriptive correlation between different concepts of Smart and Sustainable Cities, in terms of theoretical and empirical foundations and assumptions. Method: The analysis is mixed, using a Systematic Literature Review and the PSALSAR methodology - Protocol, Search, Appraisal, Syntesis, Analysis and Results. The searches were carried out on four international scientific databases and one national database, with an interdisciplinary focus, involving the areas of Social Sciences, Environmental Sciences, Computer Sciences, Engineering and Energy. Research Significance: The idea of making cities smart and sustainable arises from looking for solutions in the places where the majority of the population is concentrated, along with the opportunities and threats to provide sustainable development. However, the concepts and structures relating to these cities are unclear, and there is still divergent discussion about which technologies have a positive impact on society and what their effects are on the sustainability of the planet. Results and Discussion: The approaches and discourses are aligned with the concept of sustainable development, but implementation in practice depends on government policies. Several counterpoints were found in the discourses analysed, including the fact that smart cities are not necessarily sustainable, and even in cities labelled as smart and sustainable, the lack of an environmental focus was highlighted.

Control of Qubit Dynamics Using Reinforcement Learning

The progress in machine learning during the last decade has had a considerable impact on many areas of science and technology, including quantum technology. This work explores the application of reinforcement learning (RL) methods to the quantum control problem of state transfer in a single qubit. The goal is to create an RL agent that learns an optimal policy and thus discovers optimal pulses to control the qubit. The most crucial step is to mathematically formulate the problem of interest as a Markov decision process (MDP). This enables the use of RL algorithms to solve the quantum control problem. Deep learning and the use of deep neural networks provide the freedom to employ continuous action and state spaces, offering the expressivity and generalization of the process. This flexibility helps to formulate the quantum state transfer problem as an MDP in several different ways. All the developed methodologies are applied to the fundamental problem of population inversion in a qubit. In most cases, the derived optimal pulses achieve fidelity equal to or higher than 0.9999, as required by quantum computing applications. The present methods can be easily extended to quantum systems with more energy levels and may be used for the efficient control of collections of qubits and to counteract the effect of noise, which are important topics for quantum sensing applications.

SEMI GROUP IDENTITIES WITH APPLICATIONS TO SEMI GROUPS

This research paper explores the rich and intriguing world of semi-group identities, their properties, and their applications to various types of semi-groups. Semi-groups are algebraic structures that generalize the notion of groups, allowing for non-invertible elements. Despite their broader scope, semi-groups retain many important features found in group theory. This study investigates the identities that hold true in the context of semi-group algebra and sheds light on the underlying mathematical structures and relationships among these identities. By delving into specific applications, we illustrate the significance of these findings to various types of semi-groups, such as monoids, semigroups with zero, and cancellative semi-groups. Ultimately, our results not only deepen our understanding of the fundamental properties of semi-groups. But also provide valuable insights for researchers in the areas of algebraic structures, combinatorics, and theoretical computer science.