Integrated Knowledge Base Research Articles

Identification of Slow Learner with Proposed Innovative NDLI Resources Utilization Approach

The National Digital Library of India (NDLI) is National Mission on Education set by Ministry of Education of Govt. of India. It is developed, operated, and maintained by Indian Institute of Technology Kharagpur. It is intended to provide education for all through integrated knowledge base ranging from school level to researcher’s level considering different languages and easily available at the fingertips. The SWOT analysis of contents of NDLI portal is done in this paper. Authors proposed new pedagogy to map the contents of various engineering branches available on NDLI portal with university curriculum to achieve the course outcomes (CO). The analysis of student’s results through quizzes and tests is used for identification of slow, medium, and fast learners of different courses.

Preparing Prospective English Language Teachers for ICT Integration

This paper reports on a case study of six prospective English language teachers’ ICT training experiences and the connections between these experiences and their beliefs about how well their language teacher education program prepared them for effective technology integration in language classrooms. This study aimed at arguing the importance of the situational factors in the university coursework and fieldwork components of teacher education on future teachers’ technology-related beliefs and technology use. Data were gathered through surveys and semi-structured face-to-face interviews. The results provided some implications for curriculum updates in teacher education programs and necessary improvements in relevant policies to prepare their graduates to teach with technology-integrated classrooms. The development of a practical and integrated knowledge base of ICT use should be a visible component in teacher education programs.

MIAMI-AD (Methylation in Aging and Methylation in AD): an integrative knowledgebase that facilitates explorations of DNA methylation across sex, aging, and Alzheimer's disease.

Alzheimer's disease (AD) is a common neurodegenerative disorder with a significant impact on aging populations. DNA methylation (DNAm) alterations have been implicated in both the aging processes and the development of AD. Given that AD affects more women than men, it is also important to explore DNAm changes that occur specifically in each sex. We created MIAMI-AD, a comprehensive knowledgebase containing manually curated summary statistics from 98 published tables in 38 studies, all of which included at least 100 participants. MIAMI-AD enables easy browsing, querying, and downloading DNAm associations at multiple levels-at individual CpG, gene, genomic regions, or genome-wide, in one or multiple studies. Moreover, it also offers tools to perform integrative analyses, such as comparing DNAm associations across different phenotypes or tissues, as well as interactive visualizations. Using several use case examples, we demonstrated that MIAMI-AD facilitates our understanding of age-associated CpGs in AD and the sex-specific roles of DNAm in AD. This open-access resource is freely available to the research community, and all the underlying data can be downloaded. MIAMI-AD facilitates integrative explorations to better understand the interplay between DNAm across aging, sex, and AD. Database URL: https://miami-ad.org/.

Towards multimodal graph neural networks for surgical instrument anticipation

PurposeDecision support systems and context-aware assistance in the operating room have emerged as the key clinical applications supporting surgeons in their daily work and are generally based on single modalities. The model- and knowledge-based integration of multimodal data as a basis for decision support systems that can dynamically adapt to the surgical workflow has not yet been established. Therefore, we propose a knowledge-enhanced method for fusing multimodal data for anticipation tasks.MethodsWe developed a holistic, multimodal graph-based approach combining imaging and non-imaging information in a knowledge graph representing the intraoperative scene of a surgery. Node and edge features of the knowledge graph are extracted from suitable data sources in the operating room using machine learning. A spatiotemporal graph neural network architecture subsequently allows for interpretation of relational and temporal patterns within the knowledge graph. We apply our approach to the downstream task of instrument anticipation while presenting a suitable modeling and evaluation strategy for this task.ResultsOur approach achieves an F1 score of 66.86% in terms of instrument anticipation, allowing for a seamless surgical workflow and adding a valuable impact for surgical decision support systems. A resting recall of 63.33% indicates the non-prematurity of the anticipations.ConclusionThis work shows how multimodal data can be combined with the topological properties of an operating room in a graph-based approach. Our multimodal graph architecture serves as a basis for context-sensitive decision support systems in laparoscopic surgery considering a comprehensive intraoperative operating scene.

1846 A bespoke teaching programme for Hospital at Home improves staff confidence in the clinical management of HaH presentations

Abstract Background Hospital at Home (HaH) provides high acuity clinical care for patients in the community. HaH teams are varied and multidisciplinary. A successful HaH service depends upon streamlined communication between multidisciplinary team (MDT) members, facilitated by an integrated knowledge base. Whilst training protocols are under development, there are currently no published teaching programmes for HaH. We responded to this unique challenge by devising a teaching programme for the HaH team at St John’s Hospital, Livingston. Methods We identified learning needs within our team with a preliminary survey. We conducted a literature review to select four competency resources which were mapped to five domains: clinical care; pharmacy; service design and delivery; anticipatory care planning and palliative care; and ethical and legal guidance. We formalised a weekly teaching session and linked teaching topics to the core competencies. We conducted a review at 3 months and 6 months to assess the impact of the programme on staff learning and clinical confidence. Results A tailored teaching programme with domains linked to multidisciplinary competencies increases staff confidence in the clinical management of common HaH presentations. Our bespoke programme has successfully delivered teaching that caters for multiple clinical backgrounds. Conclusions HaH teams represent an opportunity to learn from MDT colleagues with diverse training backgrounds and offer a unique challenge in tailoring teaching to multiple learning needs. A formal programme with clear, identifiable domains linked to learning objectives provides an essential framework for staff to demonstrate engagement with professional development, allows staff to develop personal teaching skills and cultures a strong collaborative learning environment. Going forward, we aim to evaluate the impact on staff competence and formalise a cyclical HaH curriculum for circulation to the NHS Lothian HaH teams with scope for wider dissemination.

Natural Philosophy: On Retrieving a Lost Disciplinary Imaginary

Natural Philosophy: On Retrieving a Lost Disciplinary Imaginary

Enhancing Customer Engagement with Salesforce Communities Using LWC for Knowledge Base Integration and Collaboration

Enhancing Customer Engagement with Salesforce Communities Using LWC for Knowledge Base Integration and Collaboration

Integrative multi-omics reveals analogous developmental neurotoxicity mechanisms between perfluorobutanesulfonic acid and perfluorooctanesulfonic acid in zebrafish

The molecular mechanism of perfluorobutanesulfonic acid (PFBS), an alternative to legacy perfluorooctanesulfonic acid (PFOS), is not fully understood yet. Therefore, we conducted a developmental toxicity evaluation on zebrafish embryos exposed to PFBS and PFOS and assessed neurobehavioral changes at concentrations below each point of departure (POD) determined by embryonic mortality. Using transcriptomics, proteomics, and metabolomics, biomolecular perturbations in response to PFBS were profiled and then integrated for comparison with those for PFOS. Although PFBS (7525.47 μM POD) was approximately 700 times less toxic than PFOS (11.42 μM POD), altered neurobehavior patterns and affected kinds of endogenous neurochemicals were similar between PFBS and PFOS at the corresponding POD-based concentrations. Multi-omics analysis revealed that the PFBS neurotoxicity mechanism was associated with oxidative stress, lipid metabolism, and glycolysis/glucogenesis. The commonalities in developmental neurotoxicity-related mechanisms between PFBS and PFOS interconnected by knowledge-based integration of multi-omics included the calcium signaling pathway, lipid homeostasis, and primary bile acid biosynthesis. Despite being less toxic than PFOS, PFBS exhibited similar dysregulated molecular mechanisms, suggesting that chain length differences do not affect the intrinsic toxicity mechanism. Overall, carefully managing potential toxicity of PFBS can secure its status as an alternative to PFOS.

Information and Knowledge Management for Development.

The confluence of Information Communication Technology (ICT) and Knowledge Management for Development (KM4D) creates a robust partnership in dwelling upon development targets of a nation. While problems of digital uptake brought about by digi tal divide continue to hound information-poor nations of the Global South widening the gap mostly to the benefit of Global North countries, digitization process creates economic opportunities that alleviate poverty conditions if harnessed properly and pr opel economies much higher through continued utilization. While development equates with poverty alleviation as its main target, KM4D must propel regional economies benefiting from integrated knowledge base to a much higher economic growth after hitting poverty allevation targets. In other words, KM4D can immerse with a robust community of practice through knowledge sharing practices while adopting business strategies in order to achieve economic wealth. In principle, innovators and distributors transcend economies in a given place as a network of creators. Lastly, ICT is a viable component in economic development where its utilization and management should coincide with KM’s organizational outcomes as a form of synergy.

Real-time event detection in social media streams through semantic analysis of noisy terms

Interactions via social media platforms have made it possible for anyone, irrespective of physical location, to gain access to quick information on events taking place all over the globe. However, the semantic processing of social media data is complicated due to challenges such as language complexity, unstructured data, and ambiguity. In this paper, we proposed the Social Media Analysis Framework for Event Detection (SMAFED). SMAFED aims to facilitate improved semantic analysis of noisy terms in social media streams, improved representation/embedding of social media stream content, and improved summarization of event clusters in social media streams. For this, we employed key concepts such as integrated knowledge base, resolving ambiguity, semantic representation of social media streams, and Semantic Histogram-based Incremental Clustering based on semantic relatedness. Two evaluation experiments were conducted to validate the approach. First, we evaluated the impact of the data enrichment layer of SMAFED. We found that SMAFED outperformed other pre-processing frameworks with a lower loss function of 0.15 on the first dataset and 0.05 on the second dataset. Second, we determined the accuracy of SMAFED at detecting events from social media streams. The result of this second experiment showed that SMAFED outperformed existing event detection approaches with better Precision (0.922), Recall (0.793), and F-Measure (0.853) metric scores. The findings of the study present SMAFED as a more efficient approach to event detection in social media.

Automated Rule Checking for MEP Systems Based on BIM and KBMS

Due to the growing complexity of mechanical, electrical, and plumbing (MEP) designs and the rules that govern them, performing rule checks manually has become expensive. However, MEP-based rule checking has not received adequate attention compared to automated rule checking in other domains. Based on Knowledge Management and Building Information Modeling (BIM), an automated rule checking system integrated knowledge base management system (KBMS) for model information expansion, information extraction, system integrity checking, and element spacing checking was developed. MEP rules for automated rule checking were collected, optimized, and stored in the MEP knowledge base. The KBMS facilitates the management of MEP rules in the knowledge base. A Revit plug-in of MEP rule checking system was developed including functions of KBMS, Model Integrity Checking, Elements Space Checking, and Locating the non-compliant element in model view. This study integrated both KBMS and BIM technologies to achieve automated rule checking for MEP. This simplifies the process of rule checking of MEP systems in an automated manner.

A Digital Advisor Twin for Crop Nitrogen Management

Farmers and consultants face an unmanageable amount of diverse knowledge and information for crop management decisions. To determine optimal actions, decision makers require knowledge-based support. In this way, decisions can be improved and heuristics can be replaced over time. The study presents a digital knowledge base with an integrated decision support system (DSS), using the example of nutrient supply, specifically nitrogen (N), fertilization. Therefore, the requirements of farmers and crop consultants for DSS to inform fertilization decisions for winter wheat (Triticum aestivum L.) were elaborated using surveys, expert interviews, and a prototype test. Semantic knowledge was enriched by expert knowledge and combined in a web application, the Crop Portal. To map regional and personal decision making patterns and experiences, the tacit knowledge on the complex advisory problem of N fertilization is made digitally usable. For this purpose, 16 fuzzy variables were specified and formalized. Individual decision trees and their interactions with an integrative knowledge base were used to multiply the consulting reach of experts. Using three consultants and nine model farms from different soil–climate areas in Germany, the Crop Portal was tested under practical conditions and the perceived pragmatic and hedonic quality of the system was evaluated using a standardized questionnaire. The field test showed that the variation in fertilizer recommendations from the ‘digital advisor twin’ ranged from 5 kg N ha−1 to 16 kg N ha−1 when compared with the decisions of the experts in the field. The study presents the participatory development and evaluation of a rule-based DSS prototype in agricultural practice.

Knowledge Rocks: Adding Knowledge Assistance to Visualization Systems.

We present Knowledge Rocks, an implementation strategy and guideline for augmenting visualization systems to knowledge-assisted visualization systems, as defined by the KAVA model. Visualization systems become more and more sophisticated. Hence, it is increasingly important to support users with an integrated knowledge base in making constructive choices and drawing the right conclusions. We support the effective reactivation of visualization software resources by augmenting them with knowledge-assistance. To provide a general and yet supportive implementation strategy, we propose an implementation process that bases on an application-agnostic architecture. This architecture is derived from existing knowledge-assisted visualization systems and the KAVA model. Its centerpiece is an ontology that is able to automatically analyze and classify input data, linked to a database to store classified instances. We discuss design decisions and advantages of the KR framework and illustrate its broad area of application in diverse integration possibilities of this architecture into an existing visualization system. In addition, we provide a detailed case study by augmenting an it-security system with knowledge-assistance facilities.

Розробка та випробування алгоритмічного забезпечення експертної системи вибору оптимального складу багатокомпонентної доменної шихти у конкретних умовах роботи доменних печей

The aim of the work is to develop and test for specific conditions of the blast furnace algorithmic means of the Expert system for selecting the optimal composition of the multicomponent charge, which provides the necessary technical and economic indicators of melting in variable raw material and energy conditions. The features of algorithmic means of the system for selecting the optimal composition of blast furnace charge, which are developed using methods of physicochemical and mathematical modeling of the processes of distribution and melting of iron ore materials in different zones of the furnace for the development of scientifically based recommendations for adjusting the composition of the charge, taking into account the available raw materials and energy resources. The system implements a set of physicochemical models and criteria for describing the processes of distribution of iron ore materials along the blast furnace radius, their phase transformations in high-temperature zones and directed formation of melts, which are regulated by a system of reasonable technological constraints. The information basis for the creation of predictive models for calculating the physical and chemical properties of metallurgical melts is the information accumulated in the databases "Iron ore materials", "Slag" and "Metal-slag" created in the Institute of Ferrous Metallurgy of the integrated Knowledge Base "Metallurgy". The solution of the optimization problem is carried out on the basis of expert evaluation of the forecasting of melting indicators and calculation of the optimal composition of the charge using vector optimization methods by finding a compromise between the accepted optimality criteria and technological constraints. The Expert system has been tested and recommendations on the component composition of blast charge for specific blast furnace operating conditions have been developed.

Knowledge-Based Integration of Product Data in IoT-Platforms to Optimize Resource Efficiency

In times of a global climate crisis, the industrial digitalization offers potential to economize on resources. PDM systems are a viable solution to organize product data in a single, central integration platform. These systems enhance the human workforce by managing product data and integrating modern CAx tools during the product development. Industrial IoT platforms on the other hand gain more significance in the integration and management of smart production processes expanding the data flow over later phases of the product lifecycle.However, between development stages and different data systems the loss of information due to media breaks in the value chain impedes the usage of a holistic data model. The underlying goal is to develop a complete digital representation during all phases of the product development. To expand the digital data model over the whole product lifecycle, the data exchange processes, and the path to a single true data model are the focus of method addressed in this paper.The collection and analysis of data from multiple sources in the production and usage phase have the potential to significantly optimize the resource efficiency over the whole product lifecycle by providing relevant product information at key stages of the product lifecycle. This method aims to close the gap between the two named data systems to expand the digital product representation employing a knowledge-based integration of information.The method develops a well-structured, generic approach to render users independent of provider-specific solutions. The analysis framework includes the phases of product development and production between which the potential for data exchange is shown.

Cov19VaxKB: A web-based integrative COVID-19 vaccine knowledge base

The development of SARS-CoV-2 vaccines during the COVID-19 pandemic has prompted the emergence of COVID-19 vaccine data. Timely access to COVID-19 vaccine information is crucial to researchers and public. To support more comprehensive annotation, integration, and analysis of COVID-19 vaccine information, we have developed Cov19VaxKB, a knowledge-focused COVID-19 vaccine database (http://www.violinet.org/cov19vaxkb/). Cov19VaxKB features comprehensive lists of COVID-19 vaccines, vaccine formulations, clinical trials, publications, news articles, and vaccine adverse event case reports. A web-based query interface enables comparison of product information and host responses among various vaccines. The knowledge base also includes a vaccine design tool for predicting vaccine targets and a statistical analysis tool that identifies enriched adverse events for FDA-authorized COVID-19 vaccines based on VAERS case report data. To support data exchange, Cov19VaxKB is synchronized with Vaccine Ontology and the Vaccine Investigation and Online Information Network (VIOLIN) database. The data integration and analytical features of Cov19VaxKB can facilitate vaccine research and development while also serving as a useful reference for the public.

An Intelligent Transient Stability Assessment Framework With Continual Learning Ability

Data driven method based on big data and deep model is an effective tool to solve the problem of online power system transient stability assessment. Nevertheless, when the system suffers from some large disturbances such as the changes of topology, there is no proper way for a model to supplement the original knowledge base but to retrain it from scratch. Meanwhile, with the continuous emergence of new scenarios, data management has become a new dilemma reflected in the increase of data accumulation and retraining time. To address these issues, a novel TSA framework with continual learning ability is proposed. First, the improved convolutional neural network-based orthogonal weight modification algorithm is selected as a transient stability predictor. Then, large disturbance is regarded as a new recognition task which can be continuously learned by the predictor. Through the proposed updating process, the original knowledge base can be updated and supplemented only by the data corresponding to new scenario. The test results on two power systems show that the proposed scheme can realize the tradeoff between the integrity of knowledge base and the update speed without losing too much accuracy in a limited capacity.

Student teachers’ knowledge integration across conceptual borders: the role of study approaches, learning strategies, beliefs, and motivation

There is widespread agreement that student teachers need to construct an integrated knowledge base across multiple domains. This study examined the contributions of intraindividual factors of self-regulated learning to explaining student teachers’ (a) integration of knowledge across topics and domains (i.e., integrative learning) and (b) disjointed processing of potentially domain-specific learning content (i.e., separative learning). The factors considered were study approaches; cognitive, metacognitive, and resource-related learning strategy use; epistemological and pedagogical beliefs; and career choice motivation. The study applied a cross-sectional survey design and examined separative and integrative learning in N = 103 student teachers by way of multiple regression analyses with backward eliminations. A key finding is that deep and strategic study approaches and certain cognitive learning strategies contributed significantly to explaining integrative learning in student teachers. Epistemological and pedagogical beliefs were not able to predict integrative learning. Regarding separative learning, the study identified the surface study approach, specific epistemological and pedagogical beliefs, and the “usefulness” motive for career choice as positive predictors and critical thinking as a negative predictor. The study demonstrates differences in how integrative and separative learning are shaped by distinct intraindividual factors. Implications are discussed with regard to student teachers’ self-regulated learning and pre-service teacher education.

A Systematic Review of Factors Affecting Food Loss and Waste and Sustainable Mitigation Strategies: A Logistics Service Providers’ Perspective

This study investigates the causes of food loss and waste (FLW) from the perspective of logistics service providers (LSPs) and provides sustainable options for the Chinese market. To this end, this study reviews the literature on FLW and cold chain logistics published from 2008–2021. Until recently, little attention has been paid to understanding FLW drivers from the LSP perspective. This critical systematic literature review (SLR) aims to identify the potential drivers of FLW and provide a coherent and integrated knowledge base regarding these factors. A configurative SLR was performed, and after a filtering process, 43 articles were analyzed. Potential factors were identified and categorized into four groups: (i) poor management, (ii) inappropriate operational practices, (iii) high cost, and (iv) restrictions. The results reveal that technical inefficiency and facility costs are the most serious risks, and the lack of legislation and standards constitutes the second most serious risk for FLW. Sustainable solutions are recommended to address these risks. Finally, the study findings provide guidance for LSPs to achieve sustainability in social, economic, and ecological dimensions.

Ontology-based Integration of Knowledge Base for Building an Intelligent Searching Chatbot

Ontology-based Integration of Knowledge Base for Building an Intelligent Searching Chatbot