Different Domains Of Knowledge Research Articles

Metamorphosis of EduX: a comparative study for education in the metaverse

This research explores critical aspects of the ongoing educational transition from traditional two-dimensional digital platforms toward immersive realities in the metaverse. In this work, a networked, collaborative, ubiquitous, and immersive platform, “EduX” is developed to host different educational sessions. Several metrics are tracked and compared with parallel sessions using traditional digital platforms used by educational institutions, particularly during and after the Covid-19 pandemic, such as Zoom, MS Teams, and Black Board Ultra as the Learning Management System. Four groups of students from different domains of knowledge (n = 396) participated in the study, whereby each group was split into two subgroups, taking their session via EduX and BBUltra, followed by post-assessments and discussions. Using a mixed methods approach, quantitative and qualitative data were collected and analyzed to examine the influence of the platform on engagement, performance, and overall student achievements. The discussed results demonstrate superior usability and user experience levels of the proposed platform, with higher levels of engagement, leading to significantly improved attainments. Other than demonstrating the efficacy and feasibility of the platform, this work establishes fundamental metrics to be considered by software and hardware solution providers, academic institutions, and the research community concerned with the convergence towards immersive technologies.

A physics instructor’s enactment of three-dimensional learning: Action research

This action research study was conducted in a physics education class focusing on electricity and magnetism. The instructor aimed to integrate three-dimensional learning into curriculum, lesson planning, and instruction to understand successes and challenges of teaching through a new approach and students’ perceptions of their learning process. The data collection included instructor’s lesson planning, pre- and post-lesson reflections, student artifacts, and students’ reflections. The qualitative data were analyzed through constant comparative method to identify theory-driven and data-driven codes, determine their frequency to categorize and construct themes. The results were provided with three themes: (1) the instructor’s integration of three-dimensional learning, (2) the strengths and challenges of the implementation, and (3) students’ experiences. These findings suggested the need for focusing on developing teachers’ knowledge in different domains connected to each other such as scientific practices, crosscutting concepts, subject matter knowledge, and nature of science for student conceptions and instructional strategies.

Status and associated factors of food and nutrition literacy among young adults aged 15-44 years in Shenzhen City, China.

Food and nutrition literacy (FNL) plays an important role in young adults' dietary habits and nutrition. This study aimed to investigate FNL status and its associated factors among young adults aged 15-44 years in Shenzhen. A cross-sectional survey of 5,390 participants was conducted in June 2021. FNL was measured using the Food and Nutrition Literacy Questionnaire for Chinese Adults (FNLQ). A generalized linear model was employed to analyze the factors associated with FNL. The median FNL score (total score = 100) was 68.00, which was below the adequate level of 80. FNL was divided into the two different domains of knowledge and skills, with significantly different scoring rate of 85.30 and 67.77%, respectively. The overall proportion of respondents with adequate FNL was 19.52%. The FNL score was significantly higher among the participants who were female (β = 2.665; 95% confidence interval [CI]: 2.031-3.299) and with higher education levels (β ranging from 5.632 [CI: 3.845-7.419] to 10.756 [CI: 8.973-12.538]), healthcare-related work experience (β = 4.197; CI: 3.557-4.837) and a higher economic status (β ranging from 0.753 [CI: 0.079-1.426] to 6.217 [CI: 5.208-7.227]). Those who were divorced or with an unknown marital status (β = -8.438; CI: -9.701, -7.175), abnormal body mass index (thin [β = -2.115; CI: -3.063, -1.166], overweight [β = -1.427; CI: -2.254, -0.600]), and suffering from chronic diseases (single disease [β = -3.483; CI: -4.485, -2.480], multimorbidity [β = -5.119; CI: -5.912, -4.327]) had significantly lower FNL scores. Generally, the level of FNL among young adults in Shenzhen, China, was relatively low. Thus, nutrition education programs targeted at promoting improved FNL status call for additional emphasis, especially in subgroups with lower scores.

Rice and its Multiple Identities: A Discursive Study of Paddy Cultivation in Nepal and Himalaya

This paper encounters the problem of the identity of rice and paddy farmers in Nepal and in the Himalayan region. It examines discursive practices in three different regimes of knowledge that constitute rice and paddy cultivation in Nepal and the Himalayas; mythological, socio-cultural, and Techno-political. For the analysis, it has brought descriptions of materials from the comprehensive compendium on ‘Rice Sciences and Technology in Nepal’, (2017). The compendium is the major source of materials or ‘corpus of the statement’ in relation to each of the domains of knowledge in paddy cultivation for analysis. The analysis exposes the meanings and identities of rice and paddy farmers and farmers’ positions in each of the regimes. This paper argues that the contemporary discourse of rice/paddy cultivation is dominated by the techno-political regime of knowledge which has specific implications for the production of the identity of rice and paddy farmers. Furthermore, this mediation reproduces rice as a commodity to sell in the open market and paddy farmers as the passive recipients of the information and skills from the modern agriculture agencies like the state, scientists, media, and agro-companies. It utilizes Foucauldian Discourse Analysis (FDA) method while approaching the different domains of knowledge. The argument is based on the ‘non-essentialist’ theory within the anthropological framework of political ecology as proposed by a senior post-modern anthropologist Arturo Escobar.

Entropy and Entropic Differences in the Work of Michel Serres

Michel Serres’s philosophy of entropy takes what he famously calls the ‘Northwest Passage’ between the sciences and the humanities. By contextualizing his approach to entropy and affirming the role of a philosophy of difference, this paper explores Serres’s approach by means of ‘entropic differences’. It claims that entropy – or rather, entropies – provide Serres with a paradigmatic case for critical translations between different domains of knowledge. From his early Hermès series, through to The Birth of Physics and later writings on social and ethical themes, he keeps thermodynamical and informational – or ‘hard’ and ‘soft’ – understandings of entropy apart, while simultaneously exploring their relation. By focusing on the systematic significance of Serres’s ‘entropic difference’, this paper shows how it unfolds not necessarily as an ontological difference but as an operative function between the history and philosophy of science, epistemology, and a theory of negentropic (inter)subjectivity.

Hierarchical Domain Adaptation Projective Dictionary Pair Learning Model for EEG Classification in IoMT Systems

Epilepsy recognition based on electroencephalogram (EEG) and artificial intelligence technology is the main tool of health analysis and diagnosis in Internet of medical things (IoMT). As a distributed learning framework, federated learning can train a shared model from multiple independent edge nodes using local data, which has greatly promoted the development of IoMT. One of the main challenges of EEG-based epilepsy recognition in IoMT is that EEG records show varying distributions in different devices, different times, and different people. This nonstationary characteristic of EEG reduces the accuracy of the recognition model. To improve the classification performance in IoMT, a hierarchical domain adaptation projective dictionary pair learning (HDA-PDPL) model is developed in the study. HDA-PDPL integrates EEG signals from different domains (person, edge nodes, devices, etc.) into a set of hierarchical subspace and simultaneously learns synthesis and analysis dictionary pairs in each layer. Specifically, a nonlinear transform function is introduced to seek hierarchical feature projection. The domain adaptation term on sparse coding builds a connection between different domains. Thus, the shared synthesis and analysis dictionaries can encode domain-invariant representation and discrimination knowledge from different domains. Besides, the local preserved term of projective codes is introduced to capture the potential discriminative local structures of samples. The experimental results on two EEG epilepsy classifications verified that the HDA-PDPL model can outperform other comparisons by utilizing more shared knowledge of different domains.

Using perceptual classes to dream policies in open-ended learning robotics

Achieving Lifelong Open-ended Learning Autonomy (LOLA) is a key challenge in the field of robotics to advance to a new level of intelligent response. Robots should be capable of discovering goals and learn skills in specific domains that permit achieving the general objectives the designer establishes for them. In addition, robots should reuse previously learnt knowledge in different domains to facilitate learning and adaptation in new ones. To this end, cognitive architectures have arisen which encompass different components to support LOLA. A key feature of these architectures is to implement a proper balance between deliberative and reactive processes that allows for efficient real time operation and knowledge acquisition, but this is still an open issue. First, objectives must be defined in a domain-independent representation that allows for the autonomous determination of domain-dependent goals. Second, as no explicit reward function is available, a method to determine expected utility must also be developed. Finally, policy learning may happen in an internal deliberative scale (dreaming), so it is necessary to provide an efficient way to infer relevant and reliable data for dreaming to be meaningful. The first two aspects have already been addressed in the realm of the e-MDB cognitive architecture. For the third one, this work proposes Perceptual Classes (P-nodes) as a metacognitive structure that permits generating relevant “dreamt” data points that allow creating “imagined” trajectories for deliberative policy learning in a very efficient way. The proposed structure has been tested by means of an experiment with a real robot in LOLA settings, where it has been shown how policy dreaming is possible in such a challenging realm.

A Hybrid Integrated Model for Big Data Applications Based on Association Rules and Fuzzy Logic: A Review

There is a real increasing in the generating of data from different sources. Data mining (DM) is a useful method to elicit valuable information. Association rule mining (ARM) can assist in finding patterns and trends in big data. Also fuzzy logic plays a main role as assistance technique in handling big data issues. This review paper produces recent literature on hybridization regarding the association rule mining or other DM methods such as classification and clustering and fuzzy logic techniques in big data. Whereas a hybrid model of association rule and fuzzy logic is suggested to get a valuable knowledge for big data applications at good accuracy and less time, with the aid of distributed framework for big data handling (Hadoop, Spark and MapReduce). Different techniques and algorithms were used in these works and evaluated according to accuracy, sensitivity, recall and run time with a various result as Specificity = 86%, Sensitivity = 80% and F-measure = 2.5, or achieving high accuracy and shorter runtime compared to other methods and 98.5accuraccy of fitness function in pruning redundant rules. At the end of the paper we present the most used and prominent techniques that assist in providing a useful and valuable knowledge in different domains from a huge, unstructured and even heterogeneous data. The paper will be beneficial to the researches who interesting in the field of mining big data.

Research on digital characterization and identification process model of functional genes for intelligent innovative design

The inheritance and variation of functional knowledge is an important way to achieve product innovation. Analogous biological genetic inheritance can solve the problems with the inheritance and variation of functional knowledge. However, the different expressions of functional knowledge in different domains lead designers to consuming a lot of time for functional knowledge characterization and identification, which reduces the efficiency of functional knowledge application. Therefore, in order to solve the problem of inefficient application of functional knowledge in the genetic process caused by irregular expression of functional knowledge, the paper proposes a digital characterization and identification process model of functional genes for product intelligent innovative design. The process model enables product intelligent innovative design through digital characterization of functional genes and rapid identification of functional genes using computer assistance. The process model consists of four following steps. First, extract functional knowledge from patents and perform preliminary functional gene characterization. Second, characterize functional genes digitally by using computerized digital coding techniques. Third, identify the required functional genes by using image similarity matching algorithm. It assists designers to quickly identify the functional genes required for the target system. Fourth, obtain the target product design scheme. By “transcribing” and “translating” the required functional genes and using TRIZ tools to solve the translated scheme, the product innovative design scheme is finally obtained. The feasibility and effectiveness of the model are verified by the intelligent innovative design of shared bicycle parking management device.

Philosophie der Bionik: Das Komponieren von bio-robotischen Formen

Abstract In this paper, I explore how bio-hybrid forms can be created and combined starting from organic forms. The thesis put forward is epistemological: the combinatorial practice of bionics, biomimetics, biorobotics, and all design strategies inspired by nature is not based on a kind of biomimetic inspiration, i. e., on a kind of imitation of nature, but on a practice of translation. To develop this thesis, I focus on the practices of contemporary biorobotics, first examining the practice of translating natural forms into technical artefacts as developed by Raoul Heinrich Francé at the beginning of the 20th century, secondly, analysing the production of robots capable of replicating complex locomotion systems and, finally, investigating the interaction between robots and living organisms (fish). In the last part of the paper, I reflect on the philosophical payoff and broader conditions of possibility for this translational practice. I discuss when and to what extent a translation of biological forms into biotechnical ones is acceptable and point out the notion of form that underlies this practice. Moreover, I draw attention to the need to philosophically investigate what happens between different domains of knowledge – and especially between science and technology. This article is thus an invitation to philosophers to develop a philosophy in the interstices of knowledge production.

Studying argumentation and education in South America: what has been advanced and what lies ahead

ABSTRACT With this paper, we aim to present what has been developed so far in the field of argumentation and education in South America to the best of our knowledge. However, we also aim to anticipate new trends that will arise from the challenges South American democracies face right now, and the role of collective reasoned discussion in tackling them. Traditionally argumentation studies have been proposed within three broad areas of research and practice. First, argue to learn, as the uses of argumentation practices to develop conceptual knowledge in different domains of education. Second, learn to argue by promoting argumentative practices to develop argumentative and thinking skills. Third, teacher professional development enables teachers to enact argumentation in their classrooms. From this scenario, we argue that three interrelated branches of research are the future ahead of us: studies of argumentation and citizen education using deliberative practices, cultivating virtuous intellectual habits, and the rise of deliberative teaching focused on collaborative settings designed to empower democratic values (diversity, equality, and participation). We conclude by stating that South America has helped the advancement of argumentation theory, but we still have a road ahead in developing more spaces for democratic participation.

A unified framework of temporal information expression in geosciences knowledge system

Time is an essential reference system for recording objects, events, and processes in the field of geosciences. There are currently various time references, such as solar calendar, geological time, and regional calendar, to represent the knowledge in different domains and regions, which subsequently entails a time conversion process required to interpret temporal information under different time references. However, the current time conversion method is limited by the application scope of existing time ontologies (e.g., “Jurassic” is a period in geological ontology, but a point value in calendar ontology) and the reliance on experience in conversion processes. These issues restrict accurate and efficient calculation of temporal information across different time references. To address these issues, this paper proposes a Unified Time Framework (UTF) in the geosciences knowledge system. According to a systematic time element parsing from massive time references, the proposed UTF designs an independent time root node to get rid of irrelevant nodes when accessing different time types and to adapt to the time expression of different geoscience disciplines. Furthermore, this UTF carries out several designs: to ensure the accuracy of time expressions by designing quantitative relationship definitions; to enable time calculations across different time elements by designing unified time nodes and structures, and to link to the required external ontologies by designing adequate interfaces. By comparing the time conversion methods, the experiment proves the UTF greatly supports accurate and efficient calculation of temporal information across different time references in SPARQL queries. Moreover, it shows a higher and more stable performance of temporal information queries than the time conversion method. With the advent of the Big Data era in the geosciences, the UTF can be used more widely to discover new geosciences knowledge across different time references.

Dynamic transfer partial least squares for domain adaptive regression

The traditional soft sensor models are based on the independent and identical distribution assumption, which are difficult to adapt to changes in data distribution under multiple operating conditions, resulting in model performance deterioration. The domain adaptive transfer learning methods learn knowledge in different domains by means of distribution alignment, which can reduce the impact of data distribution differences, and effectively improve the generalization ability of the model. However, most of the existing models established by domain adaptation methods are static models, which cannot reflect the dynamic characteristics of the system, and have limited prediction accuracy when applied to dynamic system modeling under multiple operating conditions. The dynamic system modeling methods can effectively extract the dynamic characteristics of the data, but they cannot deal with the concept drift problem caused by the change of data distribution. This paper proposes a new dynamic transfer partial least squares method, which maps the high-dimensional process data into the low-dimensional latent variable subspace, establishes the dynamic regression relationship between the latent variables and the labels, and realizes the systematic dynamic modeling, at the same time, the model adds regular terms for distribution alignment and structure preservation, which realizes dynamic alignment of data distribution difference. The effectiveness of the proposed method is validated on three publicly available industrial process datasets.

A New Belief-Based Bidirectional Transfer Classification Method.

In pattern classification, we may have a few labeled data points in the target domain, but a number of labeled samples are available in another related domain (called the source domain). Transfer learning can solve such classification problems via the knowledge transfer from source to target domains. The source and target domains can be represented by heterogeneous features. There may exist uncertainty in domain transformation, and such uncertainty is not good for classification. The effective management of uncertainty is important for improving classification accuracy. So, a new belief-based bidirectional transfer classification (BDTC) method is proposed. In BDTC, the intraclass transformation matrix is estimated at first for mapping the patterns from source to target domains, and this matrix can be learned using the labeled patterns of the same class represented by heterogeneous domains (features). The labeled patterns in the source domain are transferred to the target domain by the corresponding transformation matrix. Then, we learn a classifier using all the labeled patterns in the target domain to classify the objects. In order to take full advantage of the complementary knowledge of different domains, we transfer the query patterns from target to source domains using the K-NN technique and do the classification task in the source domain. Thus, two pieces of classification results can be obtained for each query pattern in the source and target domains, but the classification results may have different reliabilities/weights. A weighted combination rule is developed to combine the two classification results based on the belief functions theory, which is an expert at dealing with uncertain information. We can efficiently reduce the uncertainty of transfer classification via the combination strategy. Experiments on some domain adaptation benchmarks show that our method can effectively improve classification accuracy compared with other related methods.

Evidence-Based Leadership Development: Do We ‘Walk the Talk’ and How Can We Get Better at Doing That

Academic institutions are guided by science, with scholars and educators seeking ways to extend knowledge in different domains and establish their work on evidence. Yet, despite the fact that leadership development is at the core of many business schools’ mission statements, leadership development programs (LDPs) are not as evidence based as one might expect. Research shows that a variety of reasons is behind this inconsistency, such as relative scarcity and availability of leadership development research, low accessibility of existing work, challenges of conducting research to examine the effects of LDPs, and conflicting priorities in business schools themselves. This panel symposium will examine the main challenges academics and practitioners face in their efforts to make leadership development programs more evidence-based. It will further explore ways in which we can all work together to overcome these challenges and create more evidence-based LDPs that are crucial to equip leaders of today and tomorrow with the knowledge, tools, competencies, and abilities to effectively lead organizations and society as a whole.

Structuring Circular Objectives and Design Strategies for the Circular Economy: A Multi-Hierarchical Theoretical Framework

Most frameworks for dealing with the complexity of designing for the circular economy have limitations in terms of correlating different domains of knowledge, correlating highly complex design strategies, and facilitating the process of design strategies’ discovery and development. This paper discusses how managers and designers can create products that can be circulated for several life cycles by considering five different circular objectives (i.e., maintenance/longevity, reuse, refurbishment, remanufacture and/or recycling). To achieve one or more of these objectives, multiple design strategies can be used at various phases of each product life cycle and throughout the product’s lifetime. A literature review is used in this article to evaluate how circular objectives and design strategies are classified in terms of relevance, product life cycle phases, and product life cycles. The three classifications are merged to create a novel conceptual framework, which is then tested through the use of four circular case studies to map out life cycles, circular objectives, and design strategies. The framework may help managers and designers better understand how their businesses and products interact along the supply chain, allowing them to establish more effective product lifetime plans.

Navigation ability in young, middle-aged and older adults: Different domains of knowledge and their relationship with visuospatial factors

Spatial navigation skills used in daily life are liable to decline with aging. They rely on a knowledge of landmarks, their locations (from egocentric and allocentric perspectives) and paths (learned in route and survey modes). This study investigated this knowledge in young, middle-aged and older people, also considering the role of individual visuospatial factors. Eighty-seven people in three age groups (20–30, 55–64, 65–75 years) were assessed on their visuospatial factors, and learned a path by walking in a real garden. Then their knowledge of landmarks (recognition task) and their positions (egocentric and allocentric pointing tasks), and the path (route repetition, order of landmarks, path finding and map drawing tasks) was tested. After controlling for sex and years of education, middle-aged and older adults performed similarly, and less well than young adults in allocentric pointing and map drawing tasks. Visuospatial working memory and sense of direction were the most influential individual factors, with specificity for each knowledge domain. In conclusion, the ability to use mental representations of environments learned from navigation relates to the domain of knowledge involved, age, and individual visuospatial factors.

A novel forget-update module for few-shot domain generalization

Existing Few-Shot Learning (FSL) methods learn and recognize new classes with the help of prior knowledge. However, they cannot handle this task well in a cross-domain scenario when training and testing sets are from different domains, since the fact that prior knowledge in different domains often varies greatly. To solve this problem, in this paper, we propose a few-shot domain generalization method, which is designed to extract relationship embeddings using Forget-Update Modules named FUM. The relationship embedding considers valuable relational information between samples in a specific task, and the forget-update module takes into account differences between domains and adjusts the distribution of relational embeddings through forgetting and updating mechanisms based on specific tasks. To evaluate the few-shot domain generalization ability of FUM, extensive experiments on eight cross-domain scenarios and six same-domain scenarios are conducted, and the results show that FUM achieves superior performances compared to recent few-shot learning methods. Visualization results also show that the distribution of the relationship embeddings extracted by FUM has stronger few-shot domain generalization ability than the feature embeddings used in the existing FSL methods.

Ontological model for the acoustic management in a smart environment

Purpose The Reflective Middleware for Acoustic Management (ReM-AM), based on the Middleware for Cloud Learning Environments (AmICL), aims to improve the interaction between users and agents in a Smart Environment (SE) using acoustic services, in order to consider the unpredictable situations due to the sounds and vibrations. The middleware allows observing, analyzing, modifying and interacting in every state of a SE from the acoustics. This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. Design/methodology/approach This work details an extension of the ReM-AM using the ontology-driven architecture (ODA) paradigm for acoustic management. In this paper are defined the different domains of knowledge required for the management of the sounds in SEs, which are modeled using ontologies. Findings This work proposes an acoustics and sound ontology, a service-oriented architecture (SOA) ontology, and a data analytics and autonomic computing ontology, which work together. Finally, the paper presents three case studies in the context of smart workplace (SWP), ambient-assisted living (AAL) and Smart Cities (SC). Research limitations/implications Future works will be based on the development of algorithms for classification and analysis of sound events, to help with emotion recognition not only from speech but also from random and separate sound events. Also, other works will be about the definition of the implementation requirements, and the definition of the real context modeling requirements to develop a real prototype. Practical implications In the case studies is possible to observe the flexibility that the ReM-AM middleware based on the ODA paradigm has by being aware of different contexts and acquire information of each, using this information to adapt itself to the environment and improve it using the autonomic cycles. To achieve this, the middleware integrates the classes and relations in its ontologies naturally in the autonomic cycles. Originality/value The main contribution of this work is the description of the ontologies required for future works about acoustic management in SE, considering that what has been studied by other works is the utilization of ontologies for sound event recognition but not have been expanded like knowledge source in an SE middleware. Specifically, this paper presents the theoretical framework of this work composed of the AmICL middleware, ReM-AM middleware and the ODA paradigm.

Talen en spiralen: kennis en besluitvorming in anticonceptieconsultaties met een taalbarrière

Abstract Misconception and contraception: knowledge and decision-making in contraceptive consultations with a language barrier Just like in general medical consultations, different domains of knowledge come together and are negotiated in contraceptive consultations, followed by decision-making on the contraceptive method. Research shows that a language barrier can hamper knowledge negotiation and decision-making in medical consultations. Our paper contributes to those findings by focussing on contraceptive counselling as a specific and underexplored consultation type. We gathered our data in a Belgian abortion clinic, where contraception is discussed during the consultations, and where an important part of the consultations are characterised by a language barrier. We adopt a Bourdieusian view on language as capital, and use a linguistic ethnographic and interactional sociolinguistic approach, complemented with analytical tools from conversation analysis on epistemics and deontics. The analysis of data fragments, ranging from a limited to a double language barrier, shows that this barrier is connected in various ways with how, and how much, knowledge is negotiated. Incomplete renditions, interruptions, epistemic and deontic claims from non-professional interpreters, along with a lack of shared contextualisation, impede clients to gather information and therefore influence decision-making. We conclude that a language barrier involves a potential risk for knowledge negotiation and decision-making in contraceptive consultations. More attention from healthcare professionals to language barriers could empower women in their sexual and reproductive health choices.