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Research Directions for Ontology-Guided Domain-Specific Knowledge Graph Population Using LLMs
No full textComputer-aided research techniques for accelerating scientific discovery in polymer (and materials) science has continued to grow in both utilization and access. There remain limitations, however, especially in the curation of data. Currently, data is primarily extracted and compiled from publications and other forms of text manually. This process can be time-consuming; and many existing forms of representation are rigid, unable to account for the evolution of data. Knowledge graphs – and ontology – provide a representation that allows for the complex nature of polymer data but still need to be populated with data from literature. Given the recent successes of large language models in interpreting massive corpora, we propose a pipeline for populating a modular knowledge graph that captures state-of-the-art polymer characterizations in combination with experimental metadata and methodology. In this work, we present different variations of this pipeline, demonstrating which configurations yield desirable and undesirable results
An Ontology To Support Hearing Protection Device Selection and Reduce Noise-Induced Hearing Loss
No full textNoise-induced hearing loss (NIHL) is widespread. Hearing protection devices (HPDs) are intended to reduce noise exposure and limit NIHL. However, rigorous HPD selection is complex as HPDs often impede auditory functions necessary to maintain situation awareness, and selection is dependent upon diverse user needs, environmental contexts, and behavioral factors. This research employs a modular ontology method to develop a knowledge representation to unify conceptual understanding and the availability of passive HPD data among industrial hygienists and experts across the United States Department of Defense and the National Institute of Occupational Safety and Health. The objective of the study was to harmonize standard terminology, identify a comprehensive set of metrics, and create a searchable repository to facilitate HPD selection. By capturing human, environmental, and device-related attributes in a structured, machine-readable format, the ontology lays the foundation for a sharable repository to support user-centered, and context-specific HPD selection. Unfortunately, detailed technical data to support selection is not readily available. Data collection efforts are recommended to populate the ontology, providing practitioners with access to metrics and data to support analytic HPD selection
Pharmacokinetic Profiles of Sertraline in Pregnancy as a Predictor of Postpartum Depressive Symptoms
No full textAim: To characterize pharmacokinetic changes of sertraline and its metabolite during pregnancy and postpartum, and their relationship to maternal postpartum depressive symptoms. Methods: This was a prospective observational, longitudinal study of pregnant women with a major depressive disorder treated with sertraline (N = 185 women, 205 pregnancies). Women were enrolled at \u3c16 weeks\u27 gestation and followed at 4-8 week intervals throughout pregnancy and the first postpartum year. Baseline measures included structured clinical interviews and demographic information. Drug and metabolite concentrations and psychometric measures (study outcomes) (ie, Hamilton Rating Scale for depression - 17 item, Beck Depression Inventory, Edinburgh Postnatal Depression Scale [EPDS], Clinical Global Impression [CGI]) were measured at follow-up visits. Serum sertraline and N-desmethylsertraline exposure were reported asstandardized 24-h concentration-to-dose (C/D) and parent to metabolite (P/M) ratios. Linear mixed-effects and latent trajectory models were used to characterize longitudinal patterns in concentration measures across pregnancy and postpartum, and their association with study outcomes. Results: Mean 24-h C/D ratios showed high variability throughout pregnancy and postpartuM.T.hat were characterized by three trajectories for sertraline and five for N-desmethylsertraline and P/M ratio corresponding to different sertraline pharmacokinetic profiles. At postpartum, sertraline drug exposure was inversely associated with higher EPDS score (P \u3c .05), while N-desmethylsertraline exposure was associated with higher scores for all measured depression scales (P \u3c .001). Higher P/M ratios had higher CGI scores (P \u3c .05) postpartum. Conclusion: Sertraline pharmacokinetic profiles varied across pregnant women and were associated with postpartum depressive symptoms. The use of therapeutic monitoring may provide clinical insight that can be useful for identifying patients with a potential toward depressive symptoms
Exploring the Role of Multidisciplinary Simulation-Based Surgical Education in Preclinical Training
No full textThe KnowWhereGraph: A Large-Scale Geo-Knowledge Graph for Interdisciplinary Knowledge Discovery and Geo-Enrichment
No full textGlobal challenges such as food supply chain disruptions, public health crises, and natural hazard responses require access to and integration of diverse datasets, many of which are geospatial. Over the past few years, a growing number of (geo)portals have been developed to address this need. However, most existing (geo)portals are stacked by separated or sparsely connected data “silos” impeding effective data consolidation. A new way of sharing and reusing geospatial data is therefore urgently needed. In this work, we introduce KnowWhereGraph, a knowledge graph-based data integration, enrichment, and synthesis framework that not only includes schemas and data related to human and environmental systems but also provides a suite of supporting tools for accessing this information. The KnowWhereGraph aims to address the challenge of data integration by building a large-scale, cross-domain, preintegrated, FAIR-principles-based, and AI-ready data warehouse rooted in knowledge graphs. We highlight the design principles of KnowWhereGraph, emphasizing the roles of space, place, and time in bridging various data “silos.” Additionally, we demonstrate multiple use cases where the proposed geospatial knowledge graph and its associated tools empower decision-makers to uncover insights that are often hidden within complex and poorly interoperable datasets
Experiments in Graph Structure and Knowledge Graph Embeddings
No full textKnowledge graphs (KGs) are an established paradigm for integrating heterogeneous data and representing knowledge. As such, there are many different methodologies for producing KGs, which span notions of expressivity, and are tailored for different use-cases and domains. Now, as neurosymbolic methods rise in prominence, it is important to understand how the development of KGs according to these methodologies impact downstream tasks, such as link prediction using KG embeddings (KGEs). In this article, we examine how various perturbations of graph structures impact downstream tasks. These perturbations are sourced from how various methodologies (or design practices) would impact the model, starting with simple inclusions of schema and basic reification constructions. We assess these changes across synthetic graphs and FB15k-237, a common benchmark. We provide visualizations, graph metrics, and performance on the link prediction task as exploration results using various KGE models
Knowledge Conceptualization Impacts RAG Efficacy
No full textInterpretability and adaptability are cornerstones of frontier and next-generation artificial intelligence (AI) systems. This is especially true in recent systems, such as large language models (LLMs), and more broadly, generative AI. As such, we are interested in how we can merge these efforts, that is, investigate the design of explainable and adaptable neurosymbolic AI systems. Specifically, we focus on a class of systems referred to as “Agentic Retrieval-Augmented Generation” systems, which actively select, interpret, and query knowledge sources in response to natural language prompts. In this paper, we systematically evaluate how different conceptualizations and representations of knowledge, particularly the structure and complexity, impact an LLM agent in effectively querying a triplestore. Our results show an impact from both approaches, and we discuss them and their implications
Impact of Optimized Irrigation Scheduling on Wheat Growth, Yield and Water Use Efficiency under Arid Conditions
No full textImproving water use efficiency is crucial since, in Pakistan, water is the main resource that limits crop productivity. A field study was conducted using a Randomized Complete Block Design (RCBD) with four replicates at the research farm of the Arid Zone Research Centre, Dera Ismail Khan, to determine the effects of altered irrigation schedules on wheat growth, yield and water use efficiency (WUE). LI1 and LI2 (90 and 60 mm irrigation) at crown root, tillering and milking stages, LI3 (60 mm irrigation) at crown root and tillering stages and (90 mm irrigation) at milking stage, and LI4 (90 mm irrigation) at crown root stage and (60 mm irrigation) at tillering and milking stages, respectively, were the four irrigation levels used in the study. The results showed a non-significant impact of varying watering schedules on plant height, but a significant impact on growth and yield metrics was observed. The largest numbers of total tillers (634), grains per spike (49.65), grain yield (5.58 t ha-1), and WUE (25.62 kg ha-1 mm-1) were observed in the LI4 treatment. The most important stage for plants to withstand water stress is the crown root stage. Compared to earlier growth stages, it is imperative to apply irrigation water at crown root stage in arid climate to maximize productivity and WUE
Economic Analysis and Technical Efficiency of Cassava Production in Rural Households of Yewa South Local Government Ogun State
No full textThe study examined economic analysis and technical efficiency of cassava production in rural household in Yewa South local Government area of Ogun state. Structure questionnaires were administered to one hundred registered cassava farmers within the ten (10) villages. Descriptive and inferential statistics was used to analyze the data; results revealed that the age of the cassava farmers ranged between (25 – 45) years. Only 55.0 % of the farmers were married, having both primary and secondary education respectively, majority (63.0 %) having 11-15 years of experience. The result of the frontier on the efficiency of cassava production of farmers showed that quantity of labour; herbicides, fertilizer and land were significant variables that significantly influenced the technical efficiency of the cassava farmers in the study area. The in-efficiency variables that affect the efficiency of cassava production are age, education and gender. The mean efficiency of the farmers is 0.9869 while the minimum and maximum production efficiencies are 0.1000 and 0.9921 correspondingly. It was showed that the mean gross margin was estimated to be N82, 100.00 mean net profit was estimated to be N57, 891.00 and the mean total revenue was approximated to be N154, 919.33while the mean total cost was N97, 028.33. The mean total variable cost was estimated to be N72, 819.33 and the mean total fixed cost was estimated to be N24, 209.00. It was recommended that to increase the efficiency of farmer should be given improve seeds, farm implement and Agro-chemical
Beyond Silence: A Review- Exploring Sensory Intelligence, Perception and Adaptive Behaviour in Plants
Get PDFPlants, traditionally viewed as passive organisms, exhibit sensory and adaptive abilities that challenge conventional notions of plant biology. All organisms evolve ways to sense and interact with their surroundings, and plants, although lacking eyes, ears, or brains, perceive the world in sophisticated ways. Despite lacking a nervous system and other features typically associated with animals, plants can see, smell, hear, feel, learn, remember, and act similarly to animals. This review examines how plants perceive and respond to their environment using mechanisms analogous to vision, hearing, smell, taste, and touch, adjusting their morphology, physiology, and phenotype accordingly. Drawing from plant physiology, biochemistry, and neurobiology research, the study explores plant perception and adaptive behaviour. A comprehensive literature review was conducted using academic platforms such as PubMed, Scopus, Google Scholar, and Web of Science, focusing on plant communication, pain perception, and memory. Despite lacking nervous systems or sensory organs, results reveal that plants demonstrate sensory intelligence by detecting light, sound, chemicals, and mechanical stimuli. This enables them to optimize growth, defence, and survival strategies. Plants use photoreceptors to sense light, respond to sound vibrations, and release volatile compounds when threatened. They can recognize kin and retain the memory of past experiences, indicating learning and adaptation. These findings underscore that plants are active participants in their environments, capable of complex decision-making and behaviours. The review emphasizes the importance of understanding plant perception for advancements in agriculture, conservation, and environmental management, and calls for further research into plant cognition to address global ecological challenges