Domain Knowledge Research Articles

Development of self-derivation through memory integration and relations with world knowledge

ABSTRACT Accumulating world knowledge is a major task of development and education. The productive process of self-derivation through memory integration seemingly is a valid model of the process. To test the model, we examined relations between generation and retention of new factual knowledge via self-derivation through integration and world knowledge as measured by standardised assessments. We also tested whether the productive process of self-derivation predicted world knowledge even when a measure of learning through direct instruction also was considered. Participants were 162 children ages 8–12 years (53% female; 15% Black, 6% Asian, 1% Arab, 66% White, 5% mixed race, 7% unreported; 1% Latinx). Age accounted for a maximum of 4% of variance in self-derivation and retention. In contrast, substantial individual variability related to general knowledge and content knowledge in several domains, explaining 20–40% variance. In each domain for which self-derivation performance was a unique predictor, it explained a nominally greater share of the variance than the measure of learning through direct instruction. The findings imply that individual variability in self-derivation has functional consequences for accumulation of semantic knowledge across the elementary-school years.

Neural Representations of Sensory Uncertainty and Confidence are Associated with Perceptual Curiosity.

Humans are immensely curious and motivated to reduce uncertainty, but little is known about the neural mechanisms that generate curiosity. Curiosity is inversely associated with confidence, suggesting that it is triggered by states of low confidence (subjective uncertainty). The neural mechanisms of this process, however, have been little investigated. What are the mechanisms through which uncertainty about an event gives rise to curiosity about that event? Inspired by studies of sensory uncertainty, we hypothesized that visual areas provide multivariate representations of uncertainty, which are then read out by higher-order structures to generate signals of confidence and, ultimately, trigger curiosity. During fMRI, participants (17 female, 15 male) performed a new task in which they rated their confidence in identifying distorted images of animals and objects and their curiosity to see the clear image. To link sensory certainty and curiosity, we measured the activity evoked by each image in occipitotemporal cortex (OTC) and devised a new metric of "OTC Certainty" indicating the strength of evidence this activity conveys about the animal vs. object categories. We show that, consistent with findings using trivia questions, perceptual curiosity peaked at low confidence. Moreover, OTC Certainty negatively correlated with curiosity, establishing a link between curiosity and a multivariate representation of sensory uncertainty. Finally, univariate (average) activity in two frontal areas - vmPFC and ACC - correlated positively with confidence and negatively with curiosity, and the vmPFC mediated the relationship between OTC Certainty and curiosity. The results suggest that multiple mechanisms link curiosity with representations of confidence and uncertainty.Significance Statement Curiosity motivates us to explore and learn about the world around us. Traditional perspectives hypothesize that curiosity arises from variability in confidence, but the neural mechanisms by which this occurs have been difficult to evaluate. Here, we harness the human visual system to uncover a neural mechanism of curiosity. We show that a multivariate representation of certainty in occitotemporal cortex is transformed into a univariate representation of confidence in prefrontal cortex to facilitate curiosity. Together, these results illuminate how perceptual input is transformed by successive neural representations to ultimately evoke a feeling of curiosity - elucidating how and why we become curious to learn and delve into diverse domains of knowledge.

Informating and Automating in Systems Design: Extending the Zuboffian View of Technology Ready-to-Use vs. Technology Unintuitive-to-Use

ABSTRACT Previous literature has treated software functionalities as a binary choice between informating and automating, based on Zuboff’s definitions. We suggest that the space between the role of a user engaging with a system (informate) and what the system can accomplish for the user (automate) has increasingly become blurred, moving toward a more nuanced continuum. We present a user-system interaction framework that analyzes this continuum with a focus on the user’s perspective. Using cases from archival data, this paper provides an analysis of what goes well and badly in the locus of control handoff between users and systems, highlighting the implications of software that is created in one region but used globally. The paper contributes to the literature by extending Zuboff’s informate vs. automate discussion and identifying design decisions that could help to avoid potentially catastrophic design errors. Using the interaction framework, we argue that it would be beneficial for system designers to move toward the informate end of the continuum, when designing systems for users with a high level of domain knowledge and expertise, and when there are significant differences between the environment in which the system is designed and the one in which the system is used.

Emerging Nanozymes in Neurological Disorder Therapeutics: Bridging Oxidoreductase Mimicry and Antioxidant Chemistry

AbstractThe prevalence of neurological dieases, including neurodegenerative, neurotraumatic disorders, and neuroinflammatory conditions, has been rising due to global population and aging demographics. A key factor in the pathogenesis of these disorders is the hyperaccumulation of reactive oxygen and nitrogen species (RONS). Nanozymes have emerged as promising candidates for neurotherapeutic applications owing to their exceptional catalytic activity and stability. Of particular note is their ability to cross the blood‐brain barrier and counteract the production of reactive oxygen species via their enzyme‐mimicking characteristics. In this review, the latest advancements and theoretical knowledge in this research domain are summarized. Using the inherent functionalities of the Web of Science and bibliometric methodologies, annual publication trends are identified and extensively explored the most researched topics and neurological disorders in this field. The antioxidant reduction chemistry of the nanozymes is discussed, highlighting their ability to mimic natural oxidoreductase activity and inhibit RONS production at the source. Moreover, this review delves into the current limitations and future prospects of these mechanisms in addressing neurological disorders. The significant benefits and recent developments in the use of RONS‐regulating nanozymes for the treatment of neurological diseases are emphasized, offering insights into their therapeutic applications and broader implications for neurology.

Women Empowerment through Self Help Groups: A Bibliometric Analysis

‘Women Empowerment’ has been regarded as an inevitable component of development. The Self Help Groups (SHGs) approach was implemented to promote savings and create social cohesiveness among women to empower them in a collective manner, especially for the rural poor. The article conducted a comprehensive bibliometric analysis to identify significant contributors, existing refinements, fields, and advocates for different directions in the study area of SHGs and women's empowerment and to ascertain the domain knowledge on developments connected to women empowerment and SHGs. The Scopus database was used to search the global literature on self-help groups and Women Empowerment. As per the findings, this field is still growing globally, both in developed and developing nations. It found significant links between SHGs and socio-economic empowerment, gender equality, and health promotion. It also shows that SHGs have been utilized as a medium at the local, national, and worldwide levels to achieve gender equality and women's empowerment in various countries.

Cognitive intelligence-enabled requirements elicitation for design optimisation of smart product-service system

The rapid development of advanced technologies provides futuristic option to enhance product value with digital services, which the concept of Smart Product-Service System (SPSS) was proposed. SPSS focuses on usage data, the whole lifecycle, and the iterative optimisation. A large amount of data and context information can be collected during the usage stage, which can be used to elicit requirements to guide further design. How to realise the requirement elicitation with cognitive intelligence based on usage data is the key step for design optimisation. Therefore, this research proposes a cognitive intelligence-enabled requirements elicitation framework to help designers promote the design optimisation process. First, the digital twin-driven data collection architecture of SPSS is proposed by considering multi-sources and multi-stages. Second, the behaviour of SPSS is modelled based on Auto-Encoder which is constructed using deep learning to cognise SPSS performance. Third, the corresponding behaviour model is interpreted based on Shapley Addictive exPlanations (SHAP) to discover key factors with cognitive intelligence. Finally, the domain knowledge graph is used to generate the optimisation requirements for further design. To demonstrate the feasibility and advantages of the proposed framework, this study adopts the case study of wind turbines for illustration and verification.

Ensemble of HMMs for Sequence Prediction on Multivariate Biomedical Data

Background: Biomedical data are usually collections of longitudinal data assessed at certain points in time. Clinical observations assess the presences and severity of symptoms, which are the basis for the description and modeling of disease progression. Deciphering potential underlying unknowns from the distinct observation would substantially improve the understanding of pathological cascades. Hidden Markov Models (HMMs) have been successfully applied to the processing of possibly noisy continuous signals. We apply ensembles of HMMs to categorically distributed multivariate time series data, leaving space for expert domain knowledge in the prediction process. Methods: We use an ensemble of HMMs to predict the loss of free walking ability as one major clinical deterioration in the most common autosomal dominantly inherited ataxia disorder worldwide. Results: We present a prediction pipeline that processes data paired with a configuration file, enabling us to train, validate and query an ensemble of HMMs. In particular, we provide a theoretical and practical framework for multivariate time-series inference based on HMMs that includes constructing multiple HMMs, each to predict a particular observable variable. Our analysis is conducted on pseudo-data, but also on biomedical data based on Spinocerebellar ataxia type 3 disease. Conclusions: We find that the model shows promising results for the data we tested. The strength of this approach is that HMMs are well understood, probabilistic and interpretable models, setting it apart from most Deep Learning approaches. We publish all code and evaluation pseudo-data in an open-source repository.

Evaluation of artificial intelligence-generated drug therapy communication skill competencies in medical education.

This study compared three artificial intelligence (AI) platforms' potential to identify drug therapy communication competencies expected of a graduating medical doctor. We presented three AI platforms, namely, Poe Assistant©, ChatGPT© and Google Bard©, with structured queries to generate communication skill competencies and case scenarios appropriate for graduating medical doctors. These case scenarios comprised 15 prototypical medical conditions that required drug prescriptions. Two authors independently evaluated the AI-enhanced clinical encounters, which integrated a diverse range of information to create patient-centred care plans. Through a consensus-based approach using a checklist, the communication components generated for each scenario were assessed. The instructions and warnings provided for each case scenario were evaluated by referencing the British National Formulary. AI platforms demonstrated overlap in competency domains generated, albeit with variations in wording. The domains of knowledge (basic and clinical pharmacology, prescribing, communication and drug safety) were unanimously recognized by all platforms. A broad consensus among Poe Assistant© and ChatGPT© on drug therapy-related communication issues specific to each case scenario was evident. The consensus primarily encompassed salutation, generic drug prescribed, treatment goals and follow-up schedules. Differences were observed in patient instruction clarity, listed side effects, warnings and patient empowerment. Google Bard did not provide guidance on patient communication issues. AI platforms recognized competencies with variations in how these were stated. Poe Assistant© and ChatGPT© exhibited alignment of communication issues. However, significant discrepancies were observed in specific skill components, indicating the necessity of human intervention to critically evaluate AI-generated outputs.

Tax aggressiveness in a scenario of economic crisis and financial difficulty: an analysis of companies listed on B3

Objective: This study aimed to examine the relationship between economic crisis scenarios, financial difficulties, and tax aggressiveness in companies listed on B3. Methodology: This descriptive and documentary study employed a quantitative approach, incorporating a sample of 326 companies. Data analysis entailed using descriptive statistics and regression analysis with panel data. Results: The findings of this study suggest that a crisis environment does not promote the adoption of aggressive tax strategies by the companies within the sample. This contradicts established literature, which has posited that there is a heightened effort to decrease tax liability during periods of crisis. Nonetheless, it was observed that companies experiencing financial difficulties tend to adopt aggressive tax strategies in an effort to alleviate their tax burdens. Study Contributions: This research aims to contribute to discussing tax aggressiveness among Brazilian companies during economic crises, pointing out discrepancies with previous studies. Additionally, it seeks to broaden the understanding of tax aggressiveness in situations of crisis and financial difficulty, thereby enhancing the body of knowledge in the tax domain.

Context-aware reinforcement learning for cooling operation of data centers with an Aquifer Thermal Energy Storage

Data centers are often equipped with multiple cooling units. Here, an aquifer thermal energy storage (ATES) system has shown to be efficient. However, the usage of hot and cold-water wells in the ATES must be balanced for legal and environmental reasons. Reinforcement Learning has been proven to be a useful tool for optimizing the cooling operation at data centers. Nonetheless, since cooling demand changes continuously, balancing the ATES usage on a yearly basis imposes an additional challenge in the form of a delayed reward. To overcome this, we formulate a return decomposition, Cool-RUDDER, which relies on simple domain knowledge and needs no training. We trained a proximal policy optimization agent to keep server temperatures steady while minimizing operational costs. Comparing the Cool-RUDDER reward signal to other ATES-associated rewards, all models kept the server temperatures steady at around 30 °C. An optimal ATES balance was defined to be 0% and a yearly imbalance of −4.9% with a confidence interval of [−6.2, −3.8]% was achieved for the Cool 2.0 reward. This outperformed a baseline ATES-associated reward of 0 at −16.3% with a confidence interval of [−17.1, −15.4]% and all other ATES-associated rewards. However, the improved ATES balance comes with a higher energy consumption cost of 12.5% when comparing the relative cost of the Cool 2.0 reward to the zero reward, resulting in a trade-off. Moreover, the method comes with limited requirements and is applicable to any long-term problem satisfying a linear state-transition system.

MDDBranchNet: A Deep Learning Model for Detecting Major Depressive Disorder Using ECG Signal.

Major depressive disorder (MDD) is a chronic mental illness which affects people's well-being and is often detected at a later stage of depression with a likelihood of suicidal ideation. Early detection of MDD is thus necessary to reduce the impact, however, it requires monitoring vitals in daily living conditions. EEG is generally multi-channel and due to difficulty in signal acquisition, it is unsuitable for home-based monitoring, whereas, wearable sensors can collect single-channel ECG. Classical machine-learning based MDD detection studies commonly use various heart rate variability features. Feature generation, which requires domain knowledge, is often challenging, and requires computation power, often unsuitable for real time processing, MDDBranchNet is a proposed parallel-branch deep learning model for MDD binary classification from a single channel ECG which uses additional ECG-derived signals such as R-R signal and degree distribution time series of horizontal visibility graph. The use of derived branches was able to increase the model's accuracy by around 7%. An optimal 20-second overlapped segmentation of ECG recording was found to be beneficial with a 70% prediction threshold for maximum MDD detection with a minimum false positive rate. The proposed model evaluated MDD prediction from signal excerpts, irrespective of location (first, middle or last one-third of the recording), instead of considering the entire ECG signal with minimal performance variation stressing the idea that MDD phenomena are likely to manifest uniformly throughout the recording.

Named Entity Recognition for Chinese Texts on Marine Coral Reef Ecosystems Based on the BERT-BiGRU-Att-CRF Model

In addressing the challenges of non-standardization and limited annotation resources in Chinese marine domain texts, particularly with complex entities like long and nested entities in coral reef ecosystem-related texts, existing Named Entity Recognition (NER) methods often fail to capture deep semantic features, leading to inefficiencies and inaccuracies. This study introduces a deep learning model that integrates Bidirectional Encoder Representations from Transformers (BERT), Bidirectional Gated Recurrent Units (BiGRU), and Conditional Random Fields (CRF), enhanced by an attention mechanism, to improve the recognition of complex entity structures. The model utilizes BERT to capture context-relevant character vectors, employs BiGRU to extract global semantic features, incorporates an attention mechanism to focus on key information, and uses CRF to produce optimized label sequences. We constructed a specialized coral reef ecosystem corpus to evaluate the model’s performance through a series of experiments. The results demonstrated that our model achieved an F1 score of 86.54%, significantly outperforming existing methods. The contributions of this research are threefold: (1) We designed an efficient named entity recognition framework for marine domain texts, improving the recognition of long and nested entities. (2) By introducing the attention mechanism, we enhanced the model’s ability to recognize complex entity structures in coral reef ecosystem texts. (3) This work offers new tools and perspectives for marine domain knowledge graph construction and study, laying a foundation for future research. These advancements propel the development of marine domain text analysis technology and provide valuable references for related research fields.

Knowledge Domain of Professional Identity in Nursing.

The knowledge domain of professional identity in nursing is the analysis and application of information derived from experiences, critical reflection, and scientific discovery from nursing and other disciplines. Knowledge guides role clarity, decision-making, and advocacy. Key concepts described in this column include mindset, innovation, clinical judgment, and reflection. [J Contin Educ Nurs. 2024;55(7):328-330].

Integrating Domain Knowledge in Multi-Source Classification Tasks

This work presents an extended investigation into multi-domain learning techniques within the context of image and audio classification, with a focus on the latter. In machine learning, collections of data obtained or generated under similar conditions are referred to as domains or data sources. However, the distinct acquisition or generation conditions of these data sources are often overlooked, despite their potential to significantly impact model generalization. Multi-domain learning addresses this challenge by seeking effective methods to train models to perform adequately across all domains seen during the training process. Our study explores a range of model-agnostic multi-domain learning techniques that leverage explicit domain information alongside class labels. Specifically, we delve into three distinct methodologies: a general approach termed Stew, which involves mixing all available data indiscriminately; and two batch domain-regularization methods: Balanced Domains and Loss Sum. These methods are evaluated through several experiments conducted on datasets featuring multiple data sources for audio and image classification tasks. Our findings underscore the importance of considering domain-specific information during the training process. We demonstrate that the application of the Loss Sum method yields notable improvements in model performance (0.79 F1-Score) compared to conventional approaches that blend data from all available domains (0.62 F1-Score). By examining the impact of different multi-domain learning techniques on classification tasks, this study contributes to a deeper understanding of effective strategies for leveraging domain knowledge in machine learning model training.

Medical-informed machine learning: integrating prior knowledge into medical decision systems

BackgroundClinical medicine offers a promising arena for applying Machine Learning (ML) models. However, despite numerous studies employing ML in medical data analysis, only a fraction have impacted clinical care. This article underscores the importance of utilising ML in medical data analysis, recognising that ML alone may not adequately capture the full complexity of clinical data, thereby advocating for the integration of medical domain knowledge in ML.MethodsThe study conducts a comprehensive review of prior efforts in integrating medical knowledge into ML and maps these integration strategies onto the phases of the ML pipeline, encompassing data pre-processing, feature engineering, model training, and output evaluation. The study further explores the significance and impact of such integration through a case study on diabetes prediction. Here, clinical knowledge, encompassing rules, causal networks, intervals, and formulas, is integrated at each stage of the ML pipeline, resulting in a spectrum of integrated models.ResultsThe findings highlight the benefits of integration in terms of accuracy, interpretability, data efficiency, and adherence to clinical guidelines. In several cases, integrated models outperformed purely data-driven approaches, underscoring the potential for domain knowledge to enhance ML models through improved generalisation. In other cases, the integration was instrumental in enhancing model interpretability and ensuring conformity with established clinical guidelines. Notably, knowledge integration also proved effective in maintaining performance under limited data scenarios.ConclusionsBy illustrating various integration strategies through a clinical case study, this work provides guidance to inspire and facilitate future integration efforts. Furthermore, the study identifies the need to refine domain knowledge representation and fine-tune its contribution to the ML model as the two main challenges to integration and aims to stimulate further research in this direction.

Leveraging Large Language Models for Knowledge-free Weak Supervision in Clinical Natural Language Processing.

The performance of deep learning-based natural language processing systems is based on large amounts of labeled training data which, in the clinical domain, are not easily available or affordable. Weak supervision and in-context learning offer partial solutions to this issue, particularly using large language models (LLMs), but their performance still trails traditional supervised methods with moderate amounts of gold-standard data. In particular, inferencing with LLMs is computationally heavy. We propose an approach leveraging fine-tuning LLMs and weak supervision with virtually no domain knowledge that still achieves consistently dominant performance. Using a prompt-based approach, the LLM is used to generate weakly-labeled data for training a downstream BERT model. The weakly supervised model is then further fine-tuned on small amounts of gold standard data. We evaluate this approach using Llama2 on three different n2c2 datasets. With no more than 10 gold standard notes, our final BERT models weakly supervised by fine-tuned Llama2-13B consistently outperformed out-of-the-box PubMedBERT by 4.7-47.9% in F1 scores. With only 50 gold standard notes, our models achieved close performance to fully fine-tuned systems.

A Bibliometric Analysis of Suspension System Development for Enhancing Comfort in Family Passenger Vehicles

In daily-used family passenger cars, passenger comfort is crucial. Vehicle suspension is a major factor in passenger comfort. A detailed suspension domain knowledge evaluation is necessary. Despite continual comfort technology advances, studies have yet to use bibliometric analysis to track suspension system history and expansion. This study analyzes keywords, citations, h-index, publishing years, journals, affiliations, nations, authors, and review articles in a novel way. Based on Scopus, the analysis is performed in CSV format and incorporated into bibliometric.org's Biblioshiny, VOSviewer, and R-studio apps. The US leads this study, followed by India and Canada. Indian publications are the most prolific. For suspension technology makers, this research may help them discover rivals and predict future trends. This study promotes suspending technology development through educated decision-making, strategic planning, and innovation.

Towards a pedagogy for noticing: Piloting a novel intervention for health professions students

Teaching students to ‘notice’ what is happening around them, to be more attuned to what is going on within themselves, and nurturing self-inquiry into one’s practice is desirable yet difficult to achieve. We sought to teach the metacognitive skill of ‘noticing’ to pre-registration health professions students in the context of interprofessional collaborative practice (IPCP). A three-part curriculum was designed: an e-module focused on ‘noticing’ in IPCP; a team-based workplace learning observation and interprofessional debrief; and a written reflective assignment. We found that students concentrated on the disciplinary content of IPCP in the assignments, which ‘overshadowed’ the metacognitive content. We learned that: we had underestimated the challenges of retrofitting new content into existing curricula; that we had not paid enough attention to students’ perceptions about what they want to learn; and working with a large and diverse group of educators requires adequate preparation. The next iteration of this program will improve the constructive alignment between learning outcomes and assessments and provide better support for educators. In the future we will temper decisions to act quickly to implement curricular innovations. More broadly, we suggest that educational design that seeks to take account of qualitatively different but intersecting knowledge domains, such as IPCP and ‘noticing’, is worthy of further study.

Free-listing and Semantic Knowledge: A Tool for Detecting Alzheimer Disease?

Impairment in semantic knowledge contributes to Alzheimer disease (AD)-related decline. However, the particulars of the impact AD has on specific domains of knowledge remain debatable. To investigate the impact of AD on specific semantic categories that are integral to daily functions-living things and man-made objects. We administered a free-listing task (written version) to 19 individuals with AD and 15 cognitively normal older adults and assessed the task's relationship with other cognitive and functional tests in clinical use. We compared the contents of the lists of salient concepts generated by the AD and control groups. Group membership (AD or control), after controlling for age, sex, formal education, and an estimate of premorbid intellectual ability, predicted the groups' performance on the free-listing task across two categories. Functional status was inversely related to performance on the free-listing task, holding demographic variables constant. Based on a comparison of the contents of the free lists that were generated by the two groups, it was possible to conclude that, in individuals with AD, conceptual knowledge central to the respective categories was well preserved, whereas the peripheral conceptual material showed evidence of degradation. The free-listing task, which is an easy-to-administer and cost-effective tool, could aid in the preliminary detection of semantic knowledge dysfunction, revealing concepts that are better preserved and, possibly, the characterization of AD. Cognitive assessment tools that can be applied across cultures are needed, and the free-listing task has the potential to address this gap.

Automatically Inspecting Thousands of Static Bug Warnings with Large Language Model: How Far Are We?

Static analysis tools for capturing bugs and vulnerabilities in software programs are widely employed in practice, as they have the unique advantages of high coverage and independence from the execution environment. However, existing tools for analyzing large codebases often produce a great deal of false warnings over genuine bug reports. As a result, developers are required to manually inspect and confirm each warning, a challenging, time-consuming, and automation-essential task. This article advocates a fast, general, and easily extensible approach called Llm4sa that automatically inspects a sheer volume of static warnings by harnessing (some of) the powers of Large Language Models (LLMs). Our key insight is that LLMs have advanced program understanding capabilities, enabling them to effectively act as human experts in conducting manual inspections on bug warnings with their relevant code snippets. In this spirit, we propose a static analysis to effectively extract the relevant code snippets via program dependence traversal guided by the bug warning reports themselves. Then, by formulating customized questions that are enriched with domain knowledge and representative cases to query LLMs, Llm4sa can remove a great deal of false warnings and facilitate bug discovery significantly. Our experiments demonstrate that Llm4sa is practical in automatically inspecting thousands of static warnings from Juliet benchmark programs and 11 real-world C/C++ projects, showcasing a high precision (81.13%) and a recall rate (94.64%) for a total of 9,547 bug warnings. Our research introduces new opportunities and methodologies for using the LLMs to reduce human labor costs, improve the precision of static analyzers, and ensure software trustworthiness