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Background: Considering the development of a knowledge-based economy in the world and Iran, identifying the effective components in the development of knowledge-based companies in the medical field is essential in line with the policy and economic development of the country. Methods: In this qualitative research, data was collected through in-depth and semi-structured interviews from faculty members, specialists and managers of organizations in charge of the country's knowledge-based ecosystem. The statistical population was selected by snowball sampling, and the scientific interviews continued until the theoretical saturation stage, and finally 19 interviews were conducted. All data were analyzed based on Brovan and Clark's six-step theme analysis method. Results: From data analysis, three main themes including the factors of " revision of the general policies of the country", "creating a suitable economic platform" and "revision of the performance of institutions in charge of knowledge-based economy" and 37 subthemes were extracted which were regarded as effective factors in the development of knowledge-based companies in the medical field. Conclusion: It is necessary to review the general and executive policies of the country for the development of knowledge-based companies, and the government must provide the basis for the development of knowledge-based companies by creating a suitable economic context, fighting economic rents, creating economic stability and reforming the banking network. It is also necessary to review the processes in the Deputy of Science, Technology and Knowledge-Based Economy and the Ministry of Health, Treatment and Medical Education.

Care planning for Looked After Children (LAC) is accepted as being critical in ensuring good outcomes for LAC, however, the available evidence base highlights that despite the introduction of key legislation across the UK, that care planning for LAC remains inconsistent. There is limited Northern Ireland (NI) specific research available that relates to care planning for LAC; with the care planning experience of LAC being referenced in the recent review of Children's services in NI, (Jones, R. (2023). The report of the independent review of Northern Ireland's children's social care services. .) which highlighted the variance in policies and procedures across the five Health and Social Care Trusts in Northern Ireland (NI), resulting in continued disparity in service delivery for children and families. The current, restricted evidence base for social work decision making in care planning, highlights practitioner's overreliance on practice wisdom rather than evidenced-based research, theory and/or service user knowledge. This mixed methods study focussed on increasing knowledge around decision making in care planning. A training package was developed and delivered to practitioners across children's services in one Trust area. A pre and post survey was completed with 74 practitioners in addition to 20 post training semi-structured interviews. The significant response rates and sample size offer validity and strength to the findings which correlate with previous studies completed across the UK that also conclude that research, theory and service user knowledge is not prominent in social worker's understanding of what informs their decision making in care planning for LAC. This information is valuable for practitioners and managers attached to social work in children's services. Recommendations include further embedding of a culture of learning and reflection to support the development of practitioner knowledge, skills and confidence; and to assist in the development of governance processes to audit the impact of practitioner training. [ABSTRACT FROM AUTHOR]

In most of the Indian low-resourced government primary schools, it is observed that a single teacher teaches across classes and subjects. Such multi-grade and multi-subject teaching are generally considered as one of the major challenges in primary education. However, it is believed that through translanguaging pedagogy these challenges can be converted to an advantage. In this context, this paper reports a study aimed to develop the 'knowledge-base' of government primary school teachers in Assam, one of the North-Eastern states in India. Multi-grade and multi-subject teaching sessions were observed to understand the current status of teaching reading and the potential of translanguaging pedagogy in reading instructions. Informal discussions were also used to get a richer picture of the reading and the possibility of translanguaging. Then, attempts were made to demonstrate how to infuse translanguaging into the structured reading lessons by using students' first language to create a translanguaging space in low-resourced classrooms, using bilingual texts, promoting flexible language use, and providing concurrent instructions in students' first language (Assamese in this context) and English. Postdemonstration informal discussions with the teachers revealed that these sessions helped in developing the teachers' knowledge-base. It is recommended that translanguaging can be included in teacher education programmes for primary school teachers working in multi-grade/subject contexts. [ABSTRACT FROM AUTHOR]

Against the backdrop of physical ageing, asset management organizations not only struggle with scattered object‐related data across incompatible software and models, but also dispersed, often missing, insights from domain experts. There is a large volume of data already available particularly at National Road Authorities i.e. object characteristics, inspection and measurement results, usage records. Yet this data is distributed, has complex and varying structures, it takes cumbersome effort to find and obtain for a particular asset management decision. Semantic knowledge models aim to capture domain information and represent it via class definitions, their attributes, relationships and in instances. Such models are used to create knowledge bases, where different technologies are utilized. In the IM‐SAFE project, a knowledge base is created based on a Semantic Wiki Platform. It captures asset information concerning their structural analysis and state on 99 civil structures across Europe. In this paper, a step‐by‐step methodology is provided to build the knowledge base. The paper involves knowledge acquisition, data collection, knowledge modelling, populating datasets and quality assurance steps. It will conclude future implications to make the base expandable and widely used for asset management. [ABSTRACT FROM AUTHOR]

China’s manufacturing industry accounts for the largest proportion of the economy and involves a huge scale.It is of practical significance for enterprises to gradually realize energy consumption optimization through digital technology.Taking automobile manufacturing as an example, a scheme of energy consumption optimization based on knowledge-base was proposed, including the analysis of the characteristics of energy consumption in manufacturing industry, the key technologies of energy consumption optimization and the presentation of application practice.Starting with the production equipment of the enterprise, the law affecting energy consumption was found and a knowledge-base was build through the information of manufacturing execution system and equipment operation status of the enterprise, and the optimization of energy consumption was realized through machine learning algorithms such as neural network and correlation analysis.

Decision Trees are a common approach used for classifying unseen data into defined classes. The Information Gain is usually applied as splitting criteria in the node selection process for constructing the decision tree. However, bias in selecting the multi-variation attributes is a major limitation of using this splitting condition, leading to unsatisfactory classification performance. To deal with this problem, a new decision tree algorithm called “Knowledge-Based Decision Tree (KDT)” is proposed which exploits the knowledge in an ontology to assist the decision tree construction. The novelty of the study is that an ontology is applied to determine the attribute importance values using the PageRank algorithm. These values are used to modify the Information Gain to obtain appropriate attributes to be nodes in the decision tree. Four different datasets, Soybean, Heart disease, Dengue fever, and COVID-19 dataset, were employed to evaluate the proposed approach. The experimental results show that the proposed method is superior to the other decision tree algorithms, such as the traditional ID3 and the Mutual Information Decision tree (MIDT), and also performs better than a non-decision tree algorithm, e.g., the k-Nearest Neighbors.

Copyright of Telecommunications Science is the property of Beijing Xintong Media Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Decision Trees are a common approach used for classifying unseen data into defined classes. The Information Gain is usually applied as splitting criteria in the node selection process for constructing the decision tree. However, bias in selecting the multi-variation attributes is a major limitation of using this splitting condition, leading to unsatisfactory classification performance. To deal with this problem, a new decision tree algorithm called “Knowledge-Based Decision Tree (KDT)” is proposed which exploits the knowledge in an ontology to assist the decision tree construction. The novelty of the study is that an ontology is applied to determine the attribute importance values using the PageRank algorithm. These values are used to modify the Information Gain to obtain appropriate attributes to be nodes in the decision tree. Four different datasets, Soybean, Heart disease, Dengue fever, and COVID-19 dataset, were employed to evaluate the proposed approach. The experimental results show that the proposed method is superior to the other decision tree algorithms, such as the traditional ID3 and the Mutual Information Decision tree (MIDT), and also performs better than a non-decision tree algorithm, e.g., the k-Nearest Neighbors. [ABSTRACT FROM AUTHOR]

Knowledge and experience, mediated by reflection, are essential components in teacher education and development programs. This paper discusses core elements that have guided ELT education in the last years and analyzes the case of five English language teacher educators who –in the Colombian context—reflect on their own pedagogical practices through narrative. I guide the discussion by four probing questions that generate a reflective overview of English language teachers’ knowledge-base, identity construction, and decision-making when it comes to localizing knowledge. This reflection uncovers teacher educators’ gains and challenges as it becomes input for those others who are at different stages of their teaching career.

We are witnessing a fast development of Artificial Intelligence (AI), but it becomes dramatically challenging to explain AI models in the past decade. “Explanation” has a flexible philosophical concept of “satisfying the subjective curiosity for causal information”, driving a wide spectrum of methods being invented and/or adapted from many aspects and communities, including machine learning, visual analytics, human-computer interaction and so on. Nevertheless, from the view-point of data and knowledge engineering (DKE), a best explaining practice that is cost-effective in terms of extra intelligence acquisition should exploit the causal information and explaining scenarios which is hidden richly in the data itself. In the past several years, there are plenty of works contributing in this line but there is a lack of a clear taxonomy and systematic review of the current effort. To this end, we propose this survey, reviewing and taxonomizing existing efforts from the view-point of DKE, summarizing their contribution, technical essence and comparative characteristics. Specifically, we categorize methods into data-driven methods where explanation comes from the task-related data, and knowledge-aware methods where extraneous knowledge is incorporated. Furthermore, in the light of practice, we provide survey of state-of-art evaluation metrics and deployed explanation applications in industrial practice. [ABSTRACT FROM AUTHOR]

Copyright of HOW: A Colombian Journal for Teachers of English is the property of Asociacion Colombiana de Profesores de Ingles and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This work explores the relationship between labour productivity and skill (mis) match relying on a unique database integrating information at both the firm and the worker level. The analysis is based on a novel skill match indicator providing actual and qualitatively detailed information on the demand/supply of skills. Focusing on a sample of Italian limited liability companies observed during 2012, 2014 and 2017, we show that the ability to match their skills need via new hires is always positively correlated to companies' labour productivity. This result is robust to the inclusion of variables accounting for sectoral-level training intensity, firm-level recruitment behaviour, a capillary set of firm-level controls and across size classes. [ABSTRACT FROM AUTHOR]

This paper presents a review on selection problem of material handling equipment (MHE) and general equipment used in industry area. The issue on MHE is widely paid attention since MHE has contribution on material, good and product accomplishment. Few methods and softwares have been proposed and developed to select the most appropriate MHE for a complex selection problem. Today’s high diverisity of MHE categories and types influence the generation of many possible choices which leads to the complexity. In this paper, a further discussion in terms of MHE and equipment including three major points namely level of selection, the context of selection problem and the approaches are served to highlight the complex MHE selection according to the number of possible choices provided, to analyse the consideration for the problem context, and to reveal the superior method for complex MHE selection. Forty-two papers collected from the past study are presented asscociating each point of the discussion. [ABSTRACT FROM PUBLISHER]

The evolution of the Internet of Things (IoT) accelerates the augmentation of data present on the Internet and possibilities for connections to the more dynamic and heterogeneous devices to the Internet. Recommendation technologies have proven their capabilities of digging the personalised information by proactive filtering in many application domains and can also be a backbone platform in IoT for identifying personalised things, services and relevant artefacts by prevailing over information overload problems. This paper is a comprehensive literature review that categorises IoT recommender systems by exploring the literature’s different IoT based recommendation techniques. We conclude the paper by discussing the challenges and future scope for IoT based recommendations techniques to advancing and widening the frontiers of this research area. [ABSTRACT FROM AUTHOR]

Peer-teaching and interprofessional education can help students define individual healthcare roles, establish improved interprofessional relationships, and improve overall knowledge-base. Peer-teaching was provided by student pharmacists completing Advanced Pharmacy Practice Experiences (APPEs) for nine weeks to undergraduate student nurses. 4 student pharmacists and 25 student nurses participated in this study. Knowledge-base was measured with quizzes that were pre and post educational intervention. Confidence, self-awareness, and interprofessional perception were assessed using a modified ISVS-9A. Data were analyzed using a paired t-test. The mean difference between pre and post-knowledge-based tests averaged an improvement of 3.15 points (out of an average of 10.8 points per test) showing an overall improvement (p < 0.003). A total of 25 nursing students completed a pre and post-ISVS-9A questionnaire. The mean difference between the pre and post-questionnaire items showed an average improvement of 0.87 points (p < 0.05). Interprofessional peer-teaching showed overall improvement in student nurse knowledge-base and perceived value of and socialization to interprofessional care. This study required no financial funding, and there are no conflicts of interest to disclose. [ABSTRACT FROM AUTHOR]

In the debate about what informs social work practice, research remains the dominant discourse. However, the relationship between research and social work practice has always been an uneasy one, arguably passed from other clinical disciplines without resizing to fit social work. Even as social work research matures as a discipline it represents one element in a much broader composite which informs practice. This article takes a unique step back from the traditional research-practice discourse and examines the broader information landscape of social work practice, asking how practitioners inform their practice, rather than how research informs practice. This study explores the information needs that prompt practitioners to search for information, the strategies they employ, their acquisition of information and the uses to which the information is put. This study aims to elucidate the information behaviour with a view to improving dissemination and use. Findings demonstrate that the social work information base is substantially broader than has been suggested. Practitioners employ a pragmatic palette of strategies to navigate the breadth of information that supports practice, from research through to knowledge sharing. This article proposes that a pragmatic framework of information behaviour is required to accurately reflect the information behaviour of social workers. [ABSTRACT FROM AUTHOR]

The paper studied small and medium manufacturing enterprises in 12 South Asian developing and neighboring countries: Bangladesh, Cambodia, Lao, Myanmar, Nepal, Pakistan, Sri Lanka, India, Indonesia, the Philippines, Thailand, and Vietnam. It has seen the intensity of manufacturing enterprises and their production and export performance. The paper found that Bangladesh, Nepal, Myanmar, Pakistan, Lao, and Cambodia are poor performers; Thailand, India, Vietnam, the Philippines, and Indonesia are the high performers; and Sri Lanka stands in between. The paper used the data for 2018 in most of the cases. The data were available from national and international organizations directly relevant to SMEs. The main contribution of the paper is the explaining of the role of knowledge-base such as belief and knowledge, faith and reason, and art and aesthetics on SME and entrepreneurship development. R&D, creativity rank, human development rank, entrepreneurship development rank, and government expenditure on religious affairs gave some evidence in support of the claim. [ABSTRACT FROM AUTHOR]

The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we discuss how analytical games can be exploited as knowledge acquisition techniques in order to collect information useful to intelligent systems design. More specifically, we introduce a recently developed method, called the MARISA (MARItime Surveillance knowledge Acquisition) Game. The aim of this game is to ease the elicitation from domain experts of a considerable amount of conditional probabilities to be encoded into a maritime behavioral analysis service based on a multi-source dynamic Bayesian network. The game has been deployed in two experiments. The main objectives of such experiments are the validation of the network structure, the acquisition of the conditional probabilities for the network, and the overall validation of the game method. The results of the experiment show that the objectives have been met and that the MARISA Game proved to be an effective and efficient approach. [ABSTRACT FROM AUTHOR]

We propose an efficient and novel architecture for 3D articulated human pose retrieval and reconstruction from 2D landmarks extracted from a 2D synthetic image, an annotated 2D image, an in-the-wild real RGB image or even a hand-drawn sketch. Given 2D joint positions in a single image, we devise a data-driven framework to infer the corresponding 3D human pose. To this end, we first normalize 3D human poses from Motion Capture (MoCap) dataset by eliminating translation, orientation, and the skeleton size discrepancies from the poses and then build a knowledge-base by projecting a subset of joints of the normalized 3D poses onto 2D image-planes by fully exploiting a variety of virtual cameras. With this approach, we not only transform 3D pose space to the normalized 2D pose space but also resolve the 2D-3D cross-domain retrieval task efficiently. The proposed architecture searches for poses from a MoCap dataset that are near to a given 2D query pose in a definite feature space made up of specific joint sets. These retrieved poses are then used to construct a weak perspective camera and a final 3D posture under the camera model that minimizes the reconstruction error. To estimate unknown camera parameters, we introduce a nonlinear, two-fold method. We exploit the retrieved similar poses and the viewing directions at which the MoCap dataset was sampled to minimize the projection error. Finally, we evaluate our approach thoroughly on a large number of heterogeneous 2D examples generated synthetically, 2D images with ground-truth, a variety of real in-the-wild internet images, and a proof of concept using 2D hand-drawn sketches of human poses. We conduct a pool of experiments to perform a quantitative study on PARSE dataset. We also show that the proposed system yields competitive, convincing results in comparison to other state-of-the-art methods.

Nowadays, Human-Machines interfaces are improved by Natural Language Processing (NLP) techniques in order to reduce the users tasks. The proposed paper describes the implementation of an intelligent Multi-Agent System that receives requests from NLP module and consults its Collective User Knowledge-Base and GPS Database in order to provide accurate information to the user. [ABSTRACT FROM AUTHOR]

This study investigated the knowledge base of pre-service and in-service teacher education in Iran. In so doing, a teacher education knowledge base, and a knowledge base effectiveness questionnaire, developed by Jadidi and Bagheri (2014) were used. The participants selected through convenient sampling were 140 student teachers, the pre-service group, and 160 practicing teachers, the in-service group. The respondents were asked to indicate the extent to which the topics are included in pre and in-service programs as well as the extent to which such areas are effective for teaching. The findings showed that among the seven components extracted from the questionnaires, knowledge areas like material development, post-method indicators and research methods are not adequately covered in preservice programs with their means 1.46,1.54, and 1.62 respectively. Similarly, for the in-service teachers, issues like, teacher awareness, post-method and research methods had the lowest means, as 1.64, 1.66, and 1.71 respectively. Instead, language concepts and theoretical principles are covered the most. The results also indicated that from the perspective of pre-service teachers, learners' individual characteristics, language acquisition, and research methods are the most effective areas for teaching with their means 2.82, 2.71, and 2.45 respectively while material development, post-method indicators, and teacher awareness are the least useful areas with their means 1.65, 1.73, and 2.35 respectively. [ABSTRACT FROM AUTHOR]

We present a knowledge-base to represent collated infectious disease risk (IDR) knowledge. The knowledge is about personal and contextual risk of contracting an infectious disease obtained from declarative sources (e.g. Atlas of Human Infectious Diseases). Automated prediction requires encoding this knowledge in a form that can produce risk probabilities (e.g. Bayesian Network - BN). The knowledge-base presented in this paper feeds an algorithm that can autogenerate the BN. The knowledge from 234 infectious diseases was compiled. From this compilation, we designed an ontology and five rule types for modelling IDR knowledge in general. The evaluation aims to assess whether the knowledge-base structure, and its application to three disease-country contexts, meets the needs of personalized IDR prediction system. From the evaluation results, the knowledgebase conforms to the system's purpose: personalization of infectious disease risk. [ABSTRACT FROM AUTHOR]

The use of Bayesian networks for behavioral analysis is gaining attention. The design of such algorithms often makes use of expert knowledge. The knowledge is collected and organized during the knowledge acquisition design task. In this paper, we discuss how analytical games can be exploited as knowledge acquisition techniques in order to collect information useful to intelligent systems design. More specifically, we introduce a recently developed method, called the MARISA (MARItime Surveillance knowledge Acquisition) Game. The aim of this game is to ease the elicitation from domain experts of a considerable amount of conditional probabilities to be encoded into a maritime behavioral analysis service based on a multi-source dynamic Bayesian network. The game has been deployed in two experiments. The main objectives of such experiments are the validation of the network structure, the acquisition of the conditional probabilities for the network, and the overall validation of the game method. The results of the experiment show that the objectives have been met and that the MARISA Game proved to be an effective and efficient approach.

This paper provides formalized, machine-tractable representations of two broad kinds of constructional configuration, argument-structure and implicational constructions, on the basis of previous linguistic analyses. It discusses computational implementation requirements on constructional description. In this respect, the paper argues that the Goldbergian approach (cf. Goldberg, 2006) provides the best fit for the implementation of implicational constructions, while a "mini-constructionist" account (cf. Boas, 2014) is suitable for argumentstructure constructions. Because of their representativeness, we have chosen to illustrate our discussion by making reference to the family of English resultatives, which are argumentstructure constructions, and to the family of Wh-attitudinal constructions, which are implicational. Computational implementation demands that the members of the family of the resultative be split into mini-constructions, while the complexity of implicational configurations requires that different formal variants be grouped together under one single computational representation. The paper further makes explicit proposals for the machine tractability of lexical-constructional integration and of meaning implications that have reached constructional status through entrenchment, two problems that remain unsolved within standard computational approaches to language processing. [ABSTRACT FROM AUTHOR]

Scientific discovery progresses by exploring new and uncharted territory. More specifically, it advances by a process of transforming unknown unknowns first into known unknowns, and then into knowns. Over the last few decades, researchers have developed many knowledge bases to capture and connect the knowns, which has enabled topic exploration and contextualization of experimental results. But recognizing the unknowns is also critical for finding the most pertinent questions and their answers. Prior work on known unknowns has sought to understand them, annotate them, and automate their identification. However, no knowledge-bases yet exist to capture these unknowns, and little work has focused on how scientists might use them to trace a given topic or experimental result in search of open questions and new avenues for exploration. We show here that a knowledge base of unknowns can be connected to ontologically grounded biomedical knowledge to accelerate research in the field of prenatal nutrition. We present the first ignorance-base, a knowledge-base created by combining classifiers to recognize ignorance statements (statements of missing or incomplete knowledge that imply a goal for knowledge) and biomedical concepts over the prenatal nutrition literature. This knowledge-base places biomedical concepts mentioned in the literature in context with the ignorance statements authors have made about them. Using our system, researchers interested in the topic of vitamin D and prenatal health were able to uncover three new avenues for exploration (immune system, respiratory system, and brain development) by searching for concepts enriched in ignorance statements. These were buried among the many standard enriched concepts. Additionally, we used the ignorance-base to enrich concepts connected to a gene list associated with vitamin D and spontaneous preterm birth and found an emerging topic of study (brain development) in an implied field (neuroscience). The researchers could look to the field of neuroscience for potential answers to the ignorance statements. Our goal is to help students, researchers, funders, and publishers better understand the state of our collective scientific ignorance (known unknowns) in order to help accelerate research through the continued illumination of and focus on the known unknowns and their respective goals for scientific knowledge. [Display omitted] • We created the first ignorance-base (knowledge-base) to capture goals for scientific knowledge. • Our exploration methods provide analyses, summaries, and visualizations based on a query. • Ignorance enrichment provided fruitful avenues for future research. • Exploration by topic in vitamin D found three avenues to explore. • Exploration by experimental results for vitamin D and preterm birth found an emerging topic. [ABSTRACT FROM AUTHOR]

In a globalizing economy, competitiveness means information and know-how rather than capital and physical assets. The function of knowledge management is therefore to allow organizations to leverage their information resources and knowledge assets by remembering and applying experience. An organization’s ability to compete on the market is increasingly seen as depending on the skills and knowledge of its employees, regarded as intellectual capital, and on its capacity to preserve and use as much as possible of this knowledge in knowledge-bases and expert-systems. However, knowledge evolves rapidly, due to the continuous changes of the business environment, and the useful life span of the organizational skills is decreasing, which means the survival and competitiveness of an organization is linked to its ability to produce and use knowledge as well as to include the results of the learning process in organizational competences and virtual products.

The choice of a proper computer system is not an easy task for a decider. One reason could be the present market development of computer systems applied in business. The big number of the Romanian market players determines a big number of computerized products, with a multitude of various properties. Our proposal tries to optimize and facilitate this decisional process within an e-shop where are sold IT packets applied in business, building an online decisional assistant, a special component conceived to facilitate the decision making needed for the selection of the pertinent IT package that fits the requirements of one certain business, described by the decider. The user interacts with the system as an online buyer that visit an e-shop where are sold IT package applied in economy.

Many international standards exist in the field of IT security. This research is based on the ISO/IEC 15408, 15446, 19791, 13335 and 17799 standards. In this paper, we propose a knowledge base comprising a threat countermeasure model based on international standards for identifying and specifying threats which affect IT environments. In addition, the proposed knowledge base system aims at fusing similar security control policies and objectives in order to create effective security guidelines for specific IT environments. As a result, a knowledge base of security objectives was developed on the basis of the relationships inside the standards as well as the relationships between different standards. In addition, a web application was developed which displays details about the most common threats to information systems, and for each threat presents a set of related security control policies from different international standards, including ISO/IEC 27002.

For decades, teacher educators have struggled to better prepare teachers who critically understand and utilize second language acquisition (SLA) theory and research. Using a sociocultural theoretical framework that positions teacher-learners as active users and producers of SLA theory, this study explores how teacher-learners in two sections of one capstone SLA course (offered in a graduate-level TESOL/World Language teacher education program) construct their SLA knowledge and praxis. We used thematic content analysis to examine our data set which included written artifacts teacher-learners produced during their course assignments, online discussion posts, observations of class discussions over sixteen weeks, and three questionnaires (completed before, in the middle, and at the end of the course). Findings revealed how teacher-learners externalized their understanding of SLA, struggled with contrasting perspectives, and resituated theory in light of past, present and future teaching. This study has implications for L2 teacher education by shedding light on processes of teacher learning and by offering an inquiry based model for SLA courses that prepare teacher-learners as transformative intellectuals and lifelong learners. [ABSTRACT FROM AUTHOR]

Health Information technology (HIT) played important role in wide range of healthcare settings. The strategy of HIT is to reduce cost and increase efficiency. The aim of cardiac implantable devices is to reduce the effect and risk of cardiovascular diseases. The number of patients that benefits from these devices are growing rapidly. Cardiac rehabilitation program improves the ability of cardiac patient to restore his health to an optimal level and reduce his stress and depression, especially after the cardiovascular surgeries. The need for the use of ontologies in cardiac rehabilitation domain has emerged over the last years, in order to improve the standardization of cardiac rehabilitation's practice worldwide. In this study, ontology-based system for managing the cardiac rehabilitation program has been developed. The ontology presents the required functionalities to implement the program management that helps to enhance the quality of cardiac rehabilitation program. Moreover, proposed ontology contributes to enhance the understanding, reusability, sharing and transferring knowledge in the domain of cardiac rehabilitation. [ABSTRACT FROM AUTHOR]