Different Domains Of Knowledge Research Articles

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.

Assessment of factual recall and higher-order cognitive domains in an open-book medical school examination

Open-book examinations (OBEs) will likely become increasingly important assessment tools. We investigated how access to open-book resources affected questions testing factual recall, which might be easy to look-up, versus questions testing higher-order cognitive domains. Few studies have investigated OBEs using modern Internet resources or as summative assessments. We compared performance on an examination conducted as a traditional closed-book exam (CBE) in 2019 (N = 320) and a remote OBE with free access to Internet resources in 2020 (N = 337) due to COVID-19. This summative, end-of-year assessment focused on basic science for second-year medical students. We categorized questions by Bloom’s taxonomy (‘Remember’, versus ‘Understand/Apply’). We predicted higher performance on the OBE, driven by higher performance on ‘Remember’ questions. We used an item-centric analysis by using performance per item over all examinees as the outcome variable in logistic regression, with terms ‘Open-Book, ‘Bloom Category’ and their interaction. Performance was higher on OBE questions than CBE questions (OR 2.2, 95% CI: 2.14–2.39), and higher on ‘Remember’ than ‘Understand/Apply’ questions (OR 1.13, 95% CI: 1.09–1.19). The difference in performance between ‘Remember’ and ‘Understand/Apply’ questions was greater in the OBE than the CBE (‘Open-Book’ * ‘Bloom Category’ interaction: OR 1.2, 95% CI: 1.19–1.37). Access to open-book resources had a greater effect on performance on factual recall questions than higher-order questions, though performance was higher in the OBE overall. OBE design must consider how searching for information affects performance, particularly on questions measuring different domains of knowledge.

Combined Use of EMG and EEG Techniques for Neuromotor Assessment in Rehabilitative Applications: A Systematic Review

Electroencephalography (EEG) and electromyography (EMG) are widespread and well-known quantitative techniques used for gathering biological signals at cortical and muscular levels, respectively. Indeed, they provide relevant insights for increasing knowledge in different domains, such as physical and cognitive, and research fields, including neuromotor rehabilitation. So far, EEG and EMG techniques have been independently exploited to guide or assess the outcome of the rehabilitation, preferring one technique over the other according to the aim of the investigation. More recently, the combination of EEG and EMG started to be considered as a potential breakthrough approach to improve rehabilitation effectiveness. However, since it is a relatively recent research field, we observed that no comprehensive reviews available nor standard procedures and setups for simultaneous acquisitions and processing have been identified. Consequently, this paper presents a systematic review of EEG and EMG applications specifically aimed at evaluating and assessing neuromotor performance, focusing on cortico-muscular interactions in the rehabilitation field. A total of 213 articles were identified from scientific databases, and, following rigorous scrutiny, 55 were analyzed in detail in this review. Most of the applications are focused on the study of stroke patients, and the rehabilitation target is usually on the upper or lower limbs. Regarding the methodological approaches used to acquire and process data, our results show that a simultaneous EEG and EMG acquisition is quite common in the field, but it is mostly performed with EMG as a support technique for more specific EEG approaches. Non-specific processing methods such as EEG-EMG coherence are used to provide combined EEG/EMG signal analysis, but rarely both signals are analyzed using state-of-the-art techniques that are gold-standard in each of the two domains. Future directions may be oriented toward multi-domain approaches able to exploit the full potential of combined EEG and EMG, for example targeting a wider range of pathologies and implementing more structured clinical trials to confirm the results of the current pilot studies.

How care holds humanity: the myth of Cura and theories of care

Modern medicine has often struggled to grasp the cultural aspects of interpersonal care. The medical humanities, on the other hand, have struggled to grasp the embodied, intimate character of care....

Multi visual and textual embedding on visual question answering for blind people

Visual impairment community, especially blind people have a thirst for assistance from advanced technologies for understanding and answering the image. Through the development and intersection between vision and language, Visual Question Answering (VQA) is to predict an answer from a textual question on an image. It is essential and ideal to help blind people with capturing the image and answering their questions automatically. Traditional approaches often utilize the strength of convolution and recurrent networks, which requires a great effort for learning and optimizing. A key challenge in VQA is finding an effective way to extract and combine textual and visual features. To take advantage of previous knowledge in different domains, we propose BERT-RG, the delicate integration of pre-trained models into feature extractors, which relies on the interaction between residual and global features in the image and linguistic features in the question. Moreover, our architecture integrates a stacked attention mechanism that exploits the relationship between textual and visual objects. Specifically, the partial regions of images interact with partial keywords in question to enhance the text-vision representation. Besides, we also propose a novel perspective by considering a specific question type in VQA. Our proposal is significantly meaningful enough to develop a specialized system instead of putting forth the effort to dig for unlimited and unrealistic approaches. Experiments on VizWiz-VQA, a practical benchmark dataset, show that our proposed model outperforms existing models on the VizWiz VQA dataset in the Yes/No question type.

A simple approach for sensing and accurate prediction of multiple organic vapors by sensors based on CuO nanowires

• CuO nanowires have been synthesized using a facile solution phase method. • The room-temperature operability of CuO sensors were developed for methanol, acetone and acetonitrile and might aid in reducing the power requirements of the sensors. • A simple algorithm was developed in this research that could predict the vapors fairly accurately. • The prediction accuracy of the algorithm was improved when high-temperature data was included with room temperature data. • This work includes integration and application of knowledge of different domains to aid in prototype and product development. Development of sensors that can sense volatile organic compounds (VOCs) efficiently is imperative for different real-life applications. This paper reports synthesis of CuO nanowires using a facile solution-phase technique. The samples were found to have monoclinic polycrystalline structure when characterized using an X-ray diffractometer (XRD). The morphology was confirmed to be nanowires with diameter of approximately 10 nm when studied using field emission scanning electron microscope (FESEM), and the bandgap of the CuO nanowires was found to be 1.9 eV when characterized using UV–vis spectroscopy. The CuO nanowires based sensors were tested for five different concentrations (500–7000 ppm) of methanol, acetone, and acetonitrile at room temperature (25 °C) and four different concentrations (500–5000 ppm) of the three vapors at 200 °C. At room temperature, the response of the sensor ranged between 4.3 % and 29.9 %, 0.83 % and 14.7 %, and 1 % and 7 % for 500–7000 ppm methanol, acetonitrile, and acetone respectively. The response of the sensor was observed to be ranging between 13 % and 70 %, 150 % and 700 %, and 610 % and 2300 % for 500–5000 ppm of methanol, acetonitrile, and acetone respectively when tested at 200 °C. A novel and simple algorithm was developed that considered the initial 60 s responses of CuO nanowires exhibited for methanol, acetone, and acetonitrile at room temperature and at 200 °C and could predict the vapors along with their concentrations accurately.

Pierre Larousse, lexicographe et vulgarisateur engagé

The Grand Dictionnaire universel du xixe siècle (Great Universal Dictionary of the Nineteenth Century) by Pierre Larousse (1817-1875) is considered to be the reference dictionary of its century. Larousse had a profoundly didactic aim and spent the better part of his life encapsulating human knowledge in one single but monumental fifteen volume work (1866-1876), brought up to date after his death with two supplements (1878 and 1890). His aim was to address as broad a readership as possible with the different domains of knowledge in view to shaping a new concept of citizenship. Starting from an analysis of the “Préface”, this article sets out to analyse the aims and means that Larousse, as an encyclopædist and lexicographer, set himself to take up the challenge of a new form of popularisation, more closely adapted to the reader of the nineteenth century, following the motto “teach everyone about everything.&#8221

A Personality-Driven Recommender System for Cross-Domain Learning Based on Holland Code Assessments

Over the past few decades, AI has been widely used in the field of education. However, very little attention has been paid to the use of AI for enhancing the quality of cross-domain learning. College/university students are often interested in different domains of knowledge but may be unaware of how to choose relevant cross-domain courses. Therefore, this paper presents a personality-driven recommender system that suggests cross-domain courses and related jobs by computing personality similarities and probable course grades. In this study, 710 students from 12 departments in a Taiwanese university conducted Holland code assessments. Based on the assessments, a comprehensive empirical study, including objective and subjective evaluations, was performed. The results reveal that (1) the recommender system shows very promising performances in predicting course grades (objective evaluations), (2) most of the student testers had encountered difficulties in selecting cross-domain courses and needed the further support of a recommender system, and (3) most of the student testers positively rated the proposed system (subjective evaluations). In summary, Holland code assessments are useful for connecting personalities, interests and learning styles, and the proposed system provides helpful information that supports good decision-making when choosing cross-domain courses.

Awareness and attitude of healthcare workers towards Telehealth in Cairo, Egypt

Telehealth is delivering health care services remote from health-care facilities using telecommunications. Egypt is aiming for Universal Health Coverage; this increases the demand for telehealth in routine health services. To measure the awareness of telehealth health care workers of primary health care units and to evaluate the effect of an orientation programme on primary health care workers' awareness and acceptance of the application of telehealth. This was an intervention study amongst health care workers. A self-administrated valid questionnaire for health care workers was designed, the questionnaire consists of different domains of knowledge in addition to advantages, disadvantages, security and necessity perceptions. Each domain consists of Likert scale questions of 5 points. The questions were scored as the worst answer (1) and the best (5). A total of 109 questionnaires were filled by participants who spent at least 6months in primary health care units. Then the health care workers attended an orientation programme and the questionnaire was re-filled once more. A total of 104 was recollected. Ethical issues were considered. 50.5% of the health care workers were aware of telehealth; 66.7% of health care workers with master's degree were aware of telehealth in comparison to 31.8% amongst those with a diploma, 64.3% of physicians were aware of telehealth, while 29.6 of nurses were aware [Correction added on 08 April 2021 after first online publication: '9.6%' has been amended to '29.6%' in the preceding sentence]. The score of knowledge and attitude increased from 130±23.538 to 156.49±18.456 after the educational programme. Half of the health care workers were aware of telehealth; the orientation improved the Healthcare Workers' knowledge and attitude.

Conceptualising and measuring domain‐specific content knowledge of early childhood educators: A systematic review

The role of domain‐specific content knowledge is discussed controversially for the early childhood context. Therefore, this review aims at untangling the research on domain‐specific content knowledge for early childhood educators by systematically reviewing the conceptual and operational definition of and results on early childhood educators’ content knowledge in different domains. Using the scientific databases ERIC, PsycInfo and Web of Sciences, we identified 36 studies on early childhood educators’ domain‐specific content knowledge. By comparing these studies, we found that conceptualizations of early childhood educators’ content knowledge move on a continuum between a scientific related perspective and a practice related perspective. The scientific related perspective defines content knowledge as the knowledge of key concepts, facts and rules of the domain integrating knowledge taught in primary, secondary or upper secondary school. The practice related perspective includes knowledge of key concepts, facts and rules of the domain limited to the knowledge explicitly relevant for teaching in early childhood education as well as selected domain‐specific knowledge of children and teaching. Our review shows that the results and implications drawn by the study authors depend on how these authors conceptualize early childhood educators’ content knowledge on this continuum. Further research, therefore, needs to consider carefully how early childhood educators’ content knowledge is conceptualized. The paper further discusses gaps in this research field, such as validating methods for measuring early childhood educators’ content knowledge or implementing more rigorous experimental designs to examine effects of early childhood educators’ content knowledge.

Semantic Web Ontology Technology and Its Impact on E-Learning

This paper aims at portraying the imparts of integrating semantic web ontology technology into conventional online learning platform. This semantic e-learning system will have all the features of existing e-learning system as well as additional feature of semantic web ontology technology, which provides an adaptable, personalized and intelligent learning environment. With the incorporation of this technology, students can access the learning materials uploaded on the learning environment by the instructors as well as engage on self-directed learning by searching the ontology backbone of the semantic web. The ontology repository within the e-learning platform helps to maintain learners’ personalization details, as well as learning resources pertaining to different domains of knowledge and with the help of intelligent search engine, learners can semantically search through the ontology for learning materials based on their preferences, thus they are not placed at the mercy of the learning materials provided by the instructors. This semantic web ontology will be very helpful in promoting customized learning, thereby replacing the existing instructor centric e-learning systems. This paper has been able to show the tremendous improvement in electronic learning, following the integration of semantic web ontology approach and it will be very helpful to schools and other learning institutions in providing a sophisticated and adaptable learning environment for their students or learners.

Bläckfiskar

Cephalopods. On staged animals and popular science conventions at public aquariums.
 Lars Kaijser
 Keywords: Aquarium, cephalopod, popular science
 
 The article discusses the way animals are portrayed in popular science, using cephalopods at public aquariums as the basis. Public aquariums tend to display a set of animals that could be described as flagship species. These are animals reoccurring as live examples in tanks, used in marketing and sold as toys in the gift shop.
 Often, the presentations of these animals are enhanced with scientific facts that are combined with popular cultural stories and well-known iconographies. Together they form what could be labelled as popular science animals, easily recognisable animals with charismatic features. At aquariums, this generally refers to sharks, jellyfish, penguins, frogs or clownfish. The focus is on the biology of the animals, but equal importance is placed on the stories and popular-cultural association frameworks. The popular science animal is comprised of elements from different domains of knowledge, ranging from biology and cultural history to folklore and popular culture. These insights can be contradictory, and one important feature of presentations at aquariums is the endeavour to distinguish between fact and myth. At the same time, the myth is an important resource when curating exhibits and attracting attention. It is part of the convention that the selection of knowledge is not only chosen to inform people about animals but also to entertain and surprise them. The cephalopod is at the crossroad of research and imagination, viewed as enigmatic, fascinating, and fearful. It is characterised as smart, a superhero and a marker for both discoveries and knowledge gaps. The cephalopod, especially the octopus, is an animal that in many ways represents modernity.

Incremental learning based multi-domain adaptation for object detection

Cross-domain object detection uses knowledge from source domain tasks to enhance the object detection in target domain. It can reduce the workload of data annotations in the new domain and significantly improve the adaptation ability of the network. We consider a more realistic transfer scenario, that is, our target domain samples cannot be obtained at the same time so that learners cannot forget the old domain knowledge while learning the new domain knowledge, which is consistent with the basic assumption of incremental learning. However, the existing methods do not consider the sequential learning process of the multiple target domains which may cause performance degradation. To address this issue, we propose a novel multi-domain adaptation method for object detection based on incremental learning. Specifically, the incremental learning network saves the knowledge of multiple domains and makes the model to fuse the knowledge of different domains during the training effectively. To make it work better, the progressive training strategy is proposed to make the model gradually adapt to multiple domains. Moreover, we use a multi-level feature alignment module to ensure that domain alignment is realized on features at various levels. We perform experiments on two sets of 6 datasets, which demonstrate that our model can effectively solve the problem of domain knowledge forgetting in multi-target domain adaptation and significantly improve the detection accuracy in each domain.

Children’s perception of social robots as a source of information across different domains of knowledge

This study explores children’s perception of social robots as a source of information across different domains of knowledge. Three- to 6-year-old children (N = 80) chose between a robot and either a cartoon character or an adult to learn new information in five different domains. Regardless of the opponent, children preferred to ask questions to the robot most about machines, and least about biology and psychology. In all domains, children’s judgments of robots as animate beings affected their preferences. Specifically, the more perceptual abilities children attributed to robots, the more they were likely to choose the robot over the adult to learn new information. Our findings suggest that children’s perception of social robots as knowledgeable informants depends on the subject and their animacy attribution to robots.

A Mobile Application for Early Prediction of Student Performance Using Fuzzy Logic and Artificial Neural Networks

<p class="Abstract">Identifying students at risk or potentials excellent students is increasingly important for higher education institutions to meet the needs of the students and develop efficient learning strategy. Early stage prediction can give an indication of the students’ performance during their study years. This helps tailoring an appropriate learning strategy for different groups.</p><p class="Abstract">This work develops a novel framework for a mobile app to predict the students’ performance before starting the Universities’ education. The framework is built on a University’s students data from year 2009-2017. It has three main components, namely, a neural network model that predicts the GPA, a mobile App that tests basic knowledge in different domains, and a fuzzy model that estimates the future students’ performance. </p>

Transepistemic abduction: reasoning across epistemic domains

Abstract Frequently people draw on different domains of knowledge to reach a conclusion that seems reasonable despite being difficult to justify from the perspective of a single domain. For example, there appears to be no reason for ethics to involve mathematics, nor is there a mechanism in mathematics to embrace moral questions; however, both ethics and mathematics are likely to be involved in resolving questions about how an autonomous vehicle should make decisions in a social context. In this paper, we present a specialized mode of reasoning, transepistemic abduction (TeA), which establishes how two agents, in order to satisfactorily explain a phenomenon, reason across two epistemic domains despite each agent being ignorant of the other’s domain knowledge. We formalize TeA with epistemic logic and provide a naturalized example that brings together a psychosocial agent and a computational agent for the analysis of subjective text. We find that interaction between agents is critical to the process of TeA and that there are limits to the formalization while remaining true to naturalized reasoning. We conclude with some important implications of these findings for future work in this area.

Variance Analysis and Handling of Clinical Pathway: An Overview of the State of Knowledge

Clinical pathway is a multi-disciplinary treatment plan and work mode, which is favorable for improving healthcare service quality and reducing medical costs. Most of references demonstrate that variance analysis and handling is the key to clinical pathway management. Thus, the clinical pathway variance has become the focus of scholars. This paper uses the text mining technique to present a literature review of 496 academic articles in the field of clinical pathway variance analysis and handling, which published between 1994 and 2018. Moreover, this paper conducts a bibliometric analysis to visualize the clinical pathway variance research. In variance analysis and handling, there are a lot of imprecise knowledge and fuzzy relations to be reasoned with knowledge of different domains. In this study, methods of clinical pathway variance analysis and handling are illustrated. In addition, this paper points out the limitations of each method. Based on the results, the future prospects of clinical pathway variance analysis and handling research is proposed.

Sequential Association Rule Mining for Autonomously Extracting Hierarchical Task Structures in Reinforcement Learning

Reinforcement learning (RL) techniques, while often powerful, can suffer from slow learning speeds, particularly in high dimensional spaces or in environments with sparse rewards. The decomposition of tasks into a hierarchical structure holds the potential to significantly speed up learning, generalization, and transfer learning. However, the current task decomposition techniques often cannot extract hierarchical task structures without relying on high-level knowledge provided by an expert (e.g., using dynamic Bayesian networks (DBNs) in factored Markov decision processes), which is not necessarily available in autonomous systems. In this paper, we propose a novel method based on Sequential Association Rule Mining that can extract Hierarchical Structure of Tasks in Reinforcement Learning ( SARM-HSTRL ) in an autonomous manner for both Markov decision processes (MDPs) and factored MDPs. The proposed method leverages association rule mining to discover the causal and temporal relationships among states in different trajectories and extracts a task hierarchy that captures these relationships among sub-goals as termination conditions of different sub-tasks. We prove that the extracted hierarchical policy offers a hierarchically optimal policy in MDPs and factored MDPs. It should be noted that SARM-HSTRL extracts this hierarchical optimal policy without having dynamic Bayesian networks in scenarios with a single task trajectory and also with multiple tasks’ trajectories. Furthermore, we show theoretically and empirically that the extracted hierarchical task structure is consistent with trajectories and provides the most efficient, reliable, and compact structure under appropriate assumptions. The numerical results compare the performance of the proposed SARM-HSTRL method with conventional HRL algorithms in terms of the accuracy in detecting the sub-goals, the validity of the extracted hierarchies, and the speed of learning in several testbeds. The key capabilities of SARM-HSTRL including handling multiple tasks and autonomous hierarchical task extraction can lead to the application of this HRL method in reusing, transferring, and generalization of knowledge in different domains.

Comparing Financial Socialization and Formal Financial Education: Building Financial Capability

Many individuals learn financial knowledge and skills in school (namely formal financial education) and through friends and family (e.g., family financial socialization). While the two channels have distinctive merits and limitations, little is known about how formal financial education and family financial socialization differ and interact when it comes to helping people gain financial knowledge and skills. Using data from the 2015 National Financial Capability Study, we examined the association between formal financial education, family financial socialization, and financial knowledge. Results from regression estimates with interaction terms included indicated that both channels had positive associations with increased financial knowledge levels with different impact magnitudes, while together they seemed to have negative associations with increased financial knowledge. Our study suggests that each channel likely provides financial knowledge in different domains and has implications for future research and financial education policy.

Caracterización geográfica de la vivienda en México: un enfoque de Ciencia de Datos

Una tendencia global en los primeros años del siglo XXI ha sido la generación de plataformas y datos abiertos por parte de entidades gubernamentales para la consul- ta pública. Estos datos permiten a los ciudadanos generar nuevas técnicas y meca- nismos para su análisis con la intención de lograr mejoras en el sistema político, social y económico de un país. En esta investigación, proponemos una metodología de Ciencia de Datos centrada en la caracterización geográfica de la vivienda mexi- cana, utilizando datos abiertos del período 2014-2017 y aplicando técnicas de aprendizaje automatizado no supervisado. La aplicación de esta metodología permi- tió descubrir, que son 7 los tipos de viviendas en México, todos ellos distribuidos a lo largo y ancho del territorio mexicano. Los resultados son desplegados en mapas que podrán ser de gran utilidad en ciencia aplicada, para el estudio de fenómenos humanos y geográficos en diferentes dominios del conocimiento, tales como: segu- ridad, transporte, salud, economía, industria, agricultura, entre otras.