Knowledge-based Components Research Articles

"Leap before you look": Conditions that suppress explicit, knowledge-based learning during visuomotor adaptation.

When learning a novel visuomotor mapping (e.g., mirror writing), accuracy can improve quickly through explicit, knowledge-based learning (e.g., aim left to go right), but after practice, implicit or procedural learning takes over, producing fast, natural movements. This procedural learning occurs automatically, whereas it has recently been found that knowledge-based learning can be suppressed by the gradual introduction of the novel mapping when participants must make fast movements and visuomotor perturbations are small (e.g., 30° rotations). We explored the range of task instructions, perturbation parameters, and feedback that preclude or encourage this suppression. Using a reaching task with a rotation between screen position and movement direction, we found that knowledge-based learning could be suppressed even for an extreme 90° rotation, but only if it was introduced gradually and only under instructions to move quickly. If the rotation was introduced abruptly or if instructions emphasized accuracy over speed, knowledge-based learning occurred. A second experiment indicated that knowledge-based learning always occurred in the absence of continuous motion feedback, evidenced by the time course of learning, the aftereffects of learning when the rotation was abruptly removed, and the outcome of formal model comparison between a dual-state (procedural and knowledge-based) versus a single-state (procedural only) learning model of the data. A third experiment replicated the findings and verified that the knowledge-based component of the dual-state model corresponded to explicit aiming, whereas the procedural component was slow to unlearn. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Semantic models and tools for designing adaptive user interfaces of intelligent systems

Objectives. Models and tools for designing adaptive user interfaces for intelligent systems are being developed. The relevance is determined by the need to reduce overhead costs and development time for user interfaces and to provide their adaptation to the specific characteristics of the user of the intelligent system.Methods. Existing approaches to designing user interfaces are being analyzed. A semantic model of an adaptive user interface for intelligent systems is proposed, implemented using a basic universal language for representing knowledge based on set theory and graph theory.Results. An adaptive user interface model for intelligent systems has been developed, which includes a knowledge base model of the user interface, an agent-oriented model of the user interface, and a library of reusable components that provide integration of the user interface into both individual intelligent systems and intelligent systems groups. A method of transferring user interface components within an intelligent systems group during the operation of an intelligent system has also been developed.Conclusion. Developed models and tools allow to simplify the reuse of user interface components together with knowledge base and problem-solving components in the design and development of individual intelligent systems, as well as in the design and development of a group of semantically compatible intelligent systems, ensuring automation of integration of user interfaces and their adaptation for each user. The developed set of user interface components has been included in a library of reusable user interface components. Further expansion of the set of components in the library and their integration into a group of semantically compatible intelligent systems is planned.

K-Synapse: Intelligent Learning System with Elements of Gamification

The purpose of the article is to solve the problem of acquiring expert knowledge in the subject area of an online course, forming a knowledge base containing educational content (elements of educational material in the chosen discipline, test questions), and game components for organizing learning processes with elements of gamification in the author’s intelligent learning system K-Synapse. The research methods are the main methodological approaches and technological tools for developing intelligent learning systems with gamification elements based on the use of an appropriate multi-level ontological model. Such methods include, in particular, systemic and comparative analyses to identify the features of information learning systems with elements of intellectualization and gamification, the method of expert evaluation, which involves the analysis of literary sources and information resources, interviews and surveys of experts in the pedagogical, psychological and information technology fields, as well as the process of developing and testing an intelligent system. The scientific novelty of the research is the development of an expert knowledge acquisition system for the K-Synapse intelligent system, which combines the formation of an expert knowledge base and game components with elements of intellectual dialogue for different age categories and self-education. This development can be applied in the field of online education and cognitive skills development using the game form. Conclusions. Developing an expert knowledge acquisition system for the K-Synapse intelligent system is an important stage in developing education and self-education using innovative technologies. The preparation of an expert knowledge base and game components with elements of intellectual dialogue for different age categories will increase the efficiency of the learning process and increase students’ motivation for self-directed education. The K-Synapse system is an intelligent learning system with gamification elements and intellectual dialogue. This system provides, in particular, support for a wide range of topics, and knowledge (competencies, skills or abilities), that a student can acquire during training (in the process of playing); a wide range of topics and levels of test questions; helps the student by providing appropriate advice, tips, explanations of complex or incomprehensible issues (questions); monitors the learning process and controls the results; increases the level of student motivation to learn through the desire to achieve better results that other students have already had (this happens in an exciting game manner, not psychologically stressful). The successful implementation of the K-Synapse intelligent learning system will give students’ learning and cognitive activities an impetus for creativity, improve the quality of the educational process, and integrate this system into many modern information learning systems. Thus, such a system development can have great potential for improving education quality and increasing students’ competence.

Data-informed reservoir computing for efficient time-series prediction

We propose a new approach to dynamical system forecasting called data-informed-reservoir computing (DI-RC) that, while solely being based on data, yields increased accuracy, reduced computational cost, and mitigates tedious hyper-parameter optimization of the reservoir computer (RC). Our DI-RC approach is based on the recently proposed hybrid setup where a knowledge-based model is combined with a machine learning prediction system, but it replaces the knowledge-based component by a data-driven model discovery technique. As a result, our approach can be chosen when a suitable knowledge-based model is not available. We demonstrate our approach using a delay-based RC as the machine learning component in conjunction with sparse identification of nonlinear dynamical systems for the data-driven model component. We test the performance on two example systems: the Lorenz system and the Kuramoto–Sivashinsky system. Our results indicate that our proposed technique can yield an improvement in the time-series forecasting capabilities compared with both approaches applied individually, while remaining computationally cheap. The benefit of our proposed approach, compared with pure RC, is most pronounced when the reservoir parameters are not optimized, thereby reducing the need for hyperparameter optimization.

Mental health training programs for community pharmacists, pharmacy staff and students: A systematic review.

Primary care is often the first point of contact for people living with mental disorders. Community pharmacists, pharmacy staff and students are increasingly being trained to deliver mental health care. However, there is still a gap in the literature exploring the characteristics of all available mental health training programs and their components and their influence on pharmacists, pharmacy staff and students' outcomes. To summarize the evidence evaluating mental health training programs completed by community pharmacists, pharmacy staff and students. More specifically, to explore the components of mental health training programs and identify those that facilitate significant improvements in outcomes. A systematic review was conducted following the Cochrane handbook and reported according to PRISMA guidelines. A search for published literature was conducted in three databases (PubMed, Scopus, and Web of Science) in July 2021. Eligible studies were included if they described and evaluated the impact of mental health training programs delivered to community pharmacists, pharmacy staff and pharmacy students regardless of design or comparator. The methodological quality of included studies was appraised using both the NIH quality assessment, to evaluate studies with an uncontrolled pre-post design, and the Cochrane EPOC risk of bias assessment, to evaluate studies with a controlled (randomized and non-randomized) study design. Thirty-three studies were included. Most of the identified mental health training programs contained knowledge-based components and active learning activities. Changes in participants' attitudes, stigma, knowledge, confidence and skills were frequently assessed. An extensive range of self-assessment and observational instruments used to evaluate the impact of the training programs were identified. Positive improvements in participants' attitudes, knowledge and stigma were frequently identified following participation in training programs. This systematic review highlights the importance of mental health training programs in increasing pharmacists', pharmacy staff and pharmacy students' skills and confidence to deliver mental health care in community pharmacy. Future research should build upon this basis and further focus on finding the most efficient measures to evaluate these training programs and assess their long-term effectiveness, allowing comparison between programs.

Fuzzification Technique for Candidate Rating and Selection

The traditional ways of candidate selection and recruitment are prone to subjectivity, imprecision and vagueness. With a view to achieving objective and precise selection and recruitment while keeping up with technological improvement and changes, this paper discusses a fuzzification-based technique for candidate rating and selection. The technique comprises a fuzzy logic component that is an extension of Boolean logic and used for establishing accurate selection process and precise solutions to multi-variable problems. There is a knowledge base component which forms the database of multi-level information and rule base which composes a set of if-then statements for decision making. Its inference engine applies a pre-defined procedure on input from the rule base and fuzzy logic interfaces for final recommendations. The proposed methodology performs pre-defined procedures that are based on some input sets which stores multi-level information derived from several pre-specified scores. Results from the implementation of the proposed technique established its practical function.

Knowledge Base component of Intelligent ALMM System based on the ontology approach

This paper presents the implementation of a knowledge base supporting an intelligent system to solve problems of optimization especially problems of discrete production processes optimization called Intelligent Algebraic-Logical Meta-Model (ALMM) Solver. Using a unified description of selected optimization problems, an ontological knowledge base was designed, which allows for selective selection of Intelligent ALMM Solver components necessary to solve and model problems. Using the definitions of the properties of optimization problems, scalable components describing exemplary optimization jobs were selected. Ontology for this area was developed, with particular emphasis on the requirements of the ALMM Solver. Using the possibility of interactive communication with the ALMM ontology in the form of SQL queries in the experimental part of the work, exemplary queries for the designed Knowledge Base (KB) module were presented, and the response generated by the system is a scenario of intelligent selection of a set of components modeling and solving a given problem. Such an innovative approach allows for dynamic construction of algorithms solving problems of discrete optimization. The use of knowledge about the properties of the considered processes and ALMM technology universalizes the proposed KB system making it an intelligent and efficient tool for solving discrete optimization jobs. The key advantage of the proposed ontological approach is the ability to flexibly expand it and extend its use to other classes of problems which have already been described in the ALMM technology.

Exploring tertiary EFL practitioners\u2019 knowledge base component of assessment literacy: implications for teacher professional development

Language teachers’ assessment literacy has been a much debated subject in the educational arena recently. Teachers’ assessment knowledge base and skills, important aspects of their assessment literacy, have been extensively investigated at the school level; however, the research on this subject in the tertiary EFL context remains underdeveloped. This study reports findings of tertiary EFL teachers’ assessment literacy in terms of their assessment knowledge base in the context of Saudi Arabia. The study is informed by sociocultural theoretical background, with pragmatism as the philosophical underpinning. It uses an adapted instrument called Classroom Assessment Literacy Questionnaire, and the data are obtained from 80 questionnaire respondents. The statistical analysis of the data revealed that tertiary EFL teachers’ current assessment knowledge base is very limited and not consistent with the contemporary trends and approaches to educational assessment in terms of their preparation and readiness for the continuously mounting challenges posed by classroom-based assessments. These results indicating gaps and inadequacies in tertiary EFL practitioners’ assessment literacy have strong implications for teacher development in the area of assessment and testing at the level of policy, practice and professional development programmes.

Using data assimilation to train a hybrid forecast system that combines machine-learning and knowledge-based components.

We consider the problem of data-assisted forecasting of chaotic dynamical systems when the available data are in the form of noisy partial measurements of the past and present state of the dynamical system. Recently, there have been several promising data-driven approaches to forecasting of chaotic dynamical systems using machine learning. Particularly promising among these are hybrid approaches that combine machine learning with a knowledge-based model, where a machine-learning technique is used to correct the imperfections in the knowledge-based model. Such imperfections may be due to incomplete understanding and/or limited resolution of the physical processes in the underlying dynamical system, e.g., the atmosphere or the ocean. Previously proposed data-driven forecasting approaches tend to require, for training, measurements of all the variables that are intended to be forecast. We describe a way to relax this assumption by combining data assimilation with machine learning. We demonstrate this technique using the Ensemble Transform Kalman Filter to assimilate synthetic data for the three-variable Lorenz 1963 system and for the Kuramoto-Sivashinsky system, simulating a model error in each case by a misspecified parameter value. We show that by using partial measurements of the state of the dynamical system, we can train a machine-learning model to improve predictions made by an imperfect knowledge-based model.

Knowledge base formation for automation of dispatch control over power systems of the mining and metallurgical complex

Purpose. The research is aimed at developing and introducing methods of knowledge extraction concerning online control over power systems under emergency modes and building smart complexes of automatizing managerial decision making based on incorporated ontological knowledge bases. Methodology. The authors use the calculated planned experiment method applied to building sensitivity matrices of controlled parameters of power systems in sensor points to controlled factors and introduction of sensitivity coefficients into knowledge bases. Findings. The research suggests methods for obtaining and building a knowledgebase of professional ontologies for online control over power system modes. The problem of calculating sensitivity of controlled parameters to controlling actions is solved. Calculation results for the emergency mode enable building impact functions and determining sensitivity matrix coefficients. The smart system knowledgebase is built to provide decision support for dispatch control over power system modes under standard and emergency conditions. There are obtained sets of mode data used as knowledgebase components enabling efficient assessment of the emergency mode rate and its dispatch correction. Besides calculation parameters of intensity of controlling actions, the knowledgebase also comprises linguistic concepts, facts and rules of instructive dispatch materials. A knowledge base has been built on the basis of a subset of the linguistic corpus of concepts for the professional area of emergency response in the power system. Originality. For the first time, there is suggested an approach to incorporating various linguistic knowledge forms represented by a single ontological model and numerical parameters of sensitivity of the power system mode to controlling actions into an integrated knowledgebase, which enables building effective smart systems of dispatch decision support and implementing them into the operating automatized dispatch control system. Practical value. The ontological knowledgebase of online dispatch control is built that enables realizing a software complex of a decision support system aimed at automatizing online dispatch control over standard and emergency modes of power systems. Application of the suggested approach to building the knowledgebase and its use with online dispatch personnels decision support enhance reliability and increase maximum accessible time of personnels non-stop work by 1.5 years with absolute accident elimination, thus providing a significant economic effect.

A comparative and correlation analysis of experimental work for the development of organisational and managerial competences in university teachers

This article reports on the development of a framework for university teachers’ organizational and managerial competence. The work is based on the content analysis of scientific research in new generation educational program management, ways for expanding the functions of university teachers and transforming their activities with due regard to the modern reality of higher education institutions. This competence is an integrative teacher's personality trait that reflects four structural components: value and motivation-based component, cognitive component, design�based component and knowledge-based component. Expert surveys were conducted in 2017- 2019 to provide empirical support for the structural components. The sample included over 280 respondents at Samara University. Means and methods for university teacher organizational and managerial competence formation were assessed within the scope of an advanced training program (ATP) named "Educational Program Management", implemented by the Professional Competence Development Centre of Samara University. The content of the ATP’s sections was developed, and teaching methods contributing to the development of indicators of the relevant competence components were identified. Summative and formative assessments of the level of competence development used self-evaluation by a sample of 140 teachers, organizing the educational process at 35 departments of the University. A correlation analysis of summative and formative assessment data was carried out to establish ties between the components of organizational and managerial competence.

A Structured Facial Feminization Fresh Tissue Surgical Simulation Laboratory Improves Trainee Confidence and Knowledge.

Plastic surgery has seen a dramatic rise in gender-affirming surgery over the past decade, in part because of an increase in social acceptance of transgender and gender-nonconforming identities.1 Between 2016 and 2017 alone, the American Society of Plastic Surgeons reported a 155 percent increase in gender-affirming–related operations.2 A key element of gender-affirming surgery includes facial feminization, which necessitates a detailed understanding of the anatomical features that differ between female and male bony and soft tissues.3 In this communication, we describe our experience with a structured facial feminization cadaver laboratory and evaluate its educational value in plastic surgery training. Eleven plastic and reconstructive surgery residents (postgraduate years 1 through 7) and five medical students (n = 16) participated in a structured facial feminization educational curriculum that included a lecture and facial feminization surgical cadaver laboratory. The lecture was approximately 1 hour in length and covered various topics in facial feminization surgery, including indications and surgical steps. The cadaver laboratory consisted of multiple donor heads with four participants per group. All necessary surgical instruments were provided. An anonymous, volunteer-based self-assessment and knowledge-based test was administered before and after the cadaver laboratory to all participants. (See Document, Supplemental Digital Content 1, which shows the survey and test distributed to participants before and after the cadaver laboratory, https://links.lww.com/PRS/E69.) The self-assessment component of the test included evaluation of the participant’s ability to describe the surgical options, indications, surgical steps, and overall confidence with facial feminization surgery on a five-point Likert scale (1 = strongly agree, 2 = agree, 3 = neither agree nor disagree, 4 = disagree, and 5 = strongly disagree). The knowledge-based component consisted of a series of objective-based questions testing centered on facial feminization surgical management and decision-making. The survey was piloted to a sample participant before administration. Facial feminization procedures covered in the laboratory included frontal setback, scalp advancement, brow lift, mandibular body reduction, and genioplasty. Pretest and posttest results were tabulated and statistically analyzed using a paired t test. The results are presented in Figure 1. Sixteen of 16 participants completed the pretest and posttest (100 percent response rate). A statistically significant improvement between pretest and posttest scores was observed within each self-assessment category of the test. Similarly, there was an observed increase within the knowledge-based section of the test between pretest and posttest scores (67.1 ± 22.4 percent versus 84.4 ± 11.7 percent; p = 0.011).Fig. 1.: Pretest and posttest results following a structured facial feminization cadaveric laboratory. A significant improvement was observed between pretest and posttest results in all categories. Results presented are obtained from a five-point Likert scale (1 = strongly agree, 2 = agree, 3 = neither agree nor disagree, 4 = disagree, and 5 = strongly disagree). Results presented as the mean ± standard deviation. *p < 0.05; **p < 0.01.The presented results demonstrate that a structured facial feminization curriculum can significantly improve surgical trainee understanding and knowledge. These findings are especially relevant given the imposed duty-hour restrictions for medical trainees implemented by the Accreditation Council for Graduate Medical Education. Such restrictions have pushed training programs to use alternative means of augmenting resident education, including surgical simulation models and cadaveric surgical simulation exercises. Simulation models are also valuable for less common procedures or where exposure of surgical trainees to such specialized cases may be limited but desired, as in gender-affirming surgery.5 Efforts to build on this curriculum and improve resident understanding within this relatively new and rapidly evolving field within the specialty are ongoing. DISCLOSURE None of the authors has any commercial associations or financial disclosures that would create a conflict of interest with the information in this article.

Combining machine learning with knowledge-based modeling for scalable forecasting and subgrid-scale closure of large, complex, spatiotemporal systems.

We consider the commonly encountered situation (e.g., in weather forecast) where the goal is to predict the time evolution of a large, spatiotemporally chaotic dynamical system when we have access to both time series data of previous system states and an imperfect model of the full system dynamics. Specifically, we attempt to utilize machine learning as the essential tool for integrating the use of past data into predictions. In order to facilitate scalability to the common scenario of interest where the spatiotemporally chaotic system is very large and complex, we propose combining two approaches: (i) a parallel machine learning prediction scheme and (ii) a hybrid technique for a composite prediction system composed of a knowledge-based component and a machine learning-based component. We demonstrate that not only can this method combining (i) and (ii) be scaled to give excellent performance for very large systems but also that the length of time series data needed to train our multiple, parallel machine learning components is dramatically less than that necessary without parallelization. Furthermore, considering cases where computational realization of the knowledge-based component does not resolve subgrid-scale processes, our scheme is able to use training data to incorporate the effect of the unresolved short-scale dynamics upon the resolved longer-scale dynamics (subgrid-scale closure).

Ontology‐driven perspective of CFRaaS

AbstractA Cloud Forensic Readiness as a Service (CFRaaS) model allows an environment to preemptively accumulate relevant potential digital evidence (PDE) which may be needed during a post‐event response process. The benefit of applying a CFRaaS model in a cloud environment, is that, it is designed to prevent the modification/tampering of the cloud architectures or the infrastructure during the reactive process, which if it could, may end up having far‐reaching implications. The authors of this article present the reactive process as a very costly exercise when the infrastructure must be reprogrammed every time the process is conducted. This may hamper successful investigation from the forensic experts and law enforcement agencies perspectives. The CFRaaS model, in its current state, has not been presented in a way that can help to classify or visualize the different types of potential evidence in all the cloud deployable models, and this may limit the expectations of what or how the required PDE may be collected. To address this problem, the article presents the CFRaaS from a holistic ontology‐driven perspective, which allows the forensic experts to be able to apply the CFRaaS based on its simplicity of the concepts, relationship or semantics between different form of potential evidence, as well as how the security of a digital environment being investigated could be upheld. The CFRaaS in this context follows a fundamental ontology engineering approach that is based on the classical Resource Description Framework. The proposed ontology‐driven approach to CFRaaS is, therefore, a knowledge‐base that uses layer‐dependencies, which could be an essential toolkit for digital forensic examiners and other stakeholders in cloud‐security. The implementation of this approach could further provide a platform to develop other knowledge base components for cloud forensics and security.This article is categorized under: Digital and Multimedia Science &gt; Cloud Forensics Digital and Multimedia Science &gt; Cyber Threat Intelligence Digital and Multimedia Science &gt; Multimedia Forensics

Developing a high-fidelity knowledge base for improvements in the non-destructive testing of advanced composite material products

With the application of composites driven by an industry need to replace conventional materials, products must be designed to a lightweight criterion with safety margins essential to guarantee consumer safety. Non-destructive testing is used to ensure that safety and confirm that a component is fit for purpose. To improve inspection processes, the National Composites Centre, UK, aims to address gaps in understanding the state-of-the-art of inspection methods in the composites industry.This paper describes an opportunity to further inspection understanding through the development of a novel high-fidelity knowledge base mapping material, component, and defect configuration to capabilities and limitations of selected detection methods. Using the proposed methodology, component and method criteria are used to establish a capability matrix. This matrix captures the evaluation of first iteration experimental testing of reference components. Deployment is expected to provide validated applicability data to support knowledge-based decision making for a range of inspection applications.

A Comprehensive Diagnosis of Components of Pedagogical Students’ Research Competency

Introduction. Nowadays, the aspects of development of research competencies in future experts belong to the most relevant topics, which are widely being discussed both in Russian and in foreign scientific community. One of burning issues in this thematic block is diagnosis of such competencies without which their successful development is impossible. Today, we are witnessing the disparity between a large number of the methods presented in scientific sources and technologies of the formation of students’ research competency and limitation of its criteria and indicators. Moreover, there is lack of adequate methodological tools to estimate objectively the level and quality of scientific literacy and degree of students’ research activity.The aim of the research was to define the content of a comprehensive diagnosis of the components of pedagogical students’ research competency and to justify the criteria, indicators and methods of its evaluation.Methodology and research methods. The methods of analysis, synthesis, generalisation and modelling were employed. The systematic approach was applied, taking into account the provisions of plurality, integrity and structurisation.Results and scientific novelty. The authors presented and described the model of formation of pedagogical students’ practice-focused research competency. The knowledge-based, value-oriented and practical components of the model are correlated to the methods of psycho-pedagogical monitoring, which are relevant to the content of these components. The complex of particular techniques for comprehensive, precise and complete assessment of future teachers’ research competency is designed. The complexity of measurement tools is provided with homogeneity between a theoretical construct (research competency) and operationalised constructs (psychological phenomena estimated in diagnostic techniques). Cumulative application of the selected diagnostic procedures and techniques allows: objective and subjective indicators of formation of research abilities and skills to be compared; motivation and readiness of students for implementation of independent research activity to be defined, taking into account social and professional contexts of its realisation.Practical significance. The authors of the present publication propose to introduce into practice of the higher school the approaches to contents, structure and organisation of the psychological diagnostics of level of students’ research competency, which will give the chance not only to carry out the internal regular and continuous monitoring of formation of one of key characteristics of the identity of the modern expert in educational institutions, but also to optimise the process of multilevel high school preparation, having adjusted work on early identification of its problems and crisis stages to take timely effective correctional measures.

OGER++: hybrid multi-type entity recognition

BackgroundWe present a text-mining tool for recognizing biomedical entities in scientific literature. OGER++ is a hybrid system for named entity recognition and concept recognition (linking), which combines a dictionary-based annotator with a corpus-based disambiguation component. The annotator uses an efficient look-up strategy combined with a normalization method for matching spelling variants. The disambiguation classifier is implemented as a feed-forward neural network which acts as a postfilter to the previous step.ResultsWe evaluated the system in terms of processing speed and annotation quality. In the speed benchmarks, the OGER++ web service processes 9.7 abstracts or 0.9 full-text documents per second. On the CRAFT corpus, we achieved 71.4% and 56.7% F1 for named entity recognition and concept recognition, respectively.ConclusionsCombining knowledge-based and data-driven components allows creating a system with competitive performance in biomedical text mining.

A Pilot Education Program to Train and Empower Radiation Oncology Medical Assistants

Medical assistants (MAs) lack formal oncologic subspecialty training but frequently play the frontline role in patient care and assessment. We sought to develop an oncologic training curriculum for MAs. A needs assessment was conducted among MAs within the Radiation Oncology Department. A resident-led curriculum was designed to improve MA knowledge base, with pre- and post-surveys using 5-point Likert scales: strongly disagree (1), disagree (2), neutral (3), agree (4), and strongly agree (5). A first quarter survey was administered after three monthly lectures to assess educational and clinical impact. Wilcoxon matched-pair sign rank tests were used to compare pre- and post-survey scores. Three MAs (60%) responded to the needs assessment survey. MAs received zero hours per month of formal lecture on clinical oncology and zero hours of educational material directed toward their scope of practice, but were unanimously interested in topics ranging from oncologic medications to the role of radiation in treating cancer to nutrition support during therapy. We secured one hour per month of protected time and designed a curriculum with the following priorities: applicability to daily work, elevating responsibilities within scope of practice, expanding clinical knowledge base, and increasing intellectual curiosity. Overall, MAs reported higher post-session comfort level with each of the covered topics (p<0.01). The greatest score increases (more than 2 points on the Likert scale) were in understanding how to maximize responsibilities within scope of practice (e.g. the MA role in a code blue and patient medication management), expansion of clinical knowledge base (components of a prescription, common therapeutic and symptom-directed medications among cancer patients), and identification of available resources for further learning. All five MAs (100%) responded to the first quarter review. Education sessions were felt to be clear, comprehensive, relevant, informative, and empowering, with each category scoring a median of 4 (agree) or 5 (strongly agree). Prior to initiating education sessions, MAs felt neutral about their knowledge base and confidence at work, with each category scoring a median of 3 (neutral). After the first quarter, MAs reported increased empathy, confidence, job satisfaction, knowledge base regarding daily practice and general oncology, and access to education resources, with each category scoring a median of 4 (agree) or 5 (strongly agree). Monthly MA didactics can be feasibly implemented to fill a needed gap in education. Education sessions prioritizing applicability to the MA workflow can improve staff confidence, empowerment, and job satisfaction. Future work will focus on measuring MA-initiated improvements in patient care and explore opportunities to enhance interprofessional education within our Department.

Intelligent Modeling for Nuclear Power Plant Accident Management

This paper explores the viability of using counterfactual reasoning for impact analyses when understanding and responding to “beyond-design-basis” nuclear power plant accidents. Currently, when a severe nuclear power plant accident occurs, plant operators rely on Severe Accident Management Guidelines. However, the current guidelines are limited in scope and depth: for certain types of accidents, plant operators would have to work to mitigate the damage with limited experience and guidance for the particular situation. We aim to fill the need for comprehensive accident support by using a dynamic Bayesian network to aid in the diagnosis of a nuclear reactor's state and to analyze the impact of possible response measures. The dynamic Bayesian network, DBN, offers an expressive representation of the components and relationships that make up a complex causal system. For this reason, and for its tractable reasoning, the DBN supports a functional model for the intricate operations of nuclear power plants. In this domain, it is also pertinent that a Bayesian network can be composed of both probabilistic and knowledge-based components. Though probabilities can be calculated from simulated models, the structure of the network, as well as the value of some parameters, must be assigned by human experts. Since dynamic Bayesian network-based systems are capable of running better-than-real-time situation analyses, they can support both current event and alternate scenario impact analyses.

Underwater Photo-Elicitation: A New Experiential Marine Education Technique

AbstractUnderwater photo-elicitation is a novel experiential marine education technique that combines direct experience in the marine environment with the use of digital underwater cameras. A program called Show Us Your Ocean! (SUYO!) was created, utilising a mixed methodology (qualitative and quantitative methods) to test the efficacy of this technique. Participants (adults and high school students) snorkelled, explored the ocean, and captured images both in and out of the water. Underwater photo-elicitation was proven effective at increasing awareness, eliciting emotional reactions, and fostering a sense of connection to the ocean. However, it was not necessarily effective for raising awareness of environmental issues nor for motivating pro-environmental behaviour. Interestingly, while some attitudes about and behaviours toward the ocean did become more environmentally sensitive, others appeared to shift unexpectedly away from environmental concern. This result was perhaps due to the lack in the program of an ecological knowledge-based component. Participants enjoying the beauty of the ocean did not realise the threats facing it or what to do about these. If the goal is to achieve significant attitude change and cultivate pro-environmental behaviour, this new approach should be supplemented with critical thinking/action competence and ecological/ocean literacy.