Expert Knowledge Research Articles

Health state assessment method for complex system based on multiexpert joint belief rule base

The health of complex systems continues to decline as they operate over long periods of time, so it is important to assess the health state of complex systems. Belief rule base (BRB) is widely used in the field of health state assessment of complex systems as a semi-quantitative method that can address uncertainty effectively and with interpretability. In practical engineering, BRB still has problems: the incompleteness of expert knowledge and the inconsistency of the cognitive abilities of each expert have an effect on the construction of the model and interpretability. To address this problem, a complex system health state assessment method is proposed based on a joint multiexpert belief rule base (BRB-ME). Experts first build their own models, and a new multiexpert knowledge fusion algorithm is designed for the fusion of different expert models. The ER is used as the inference machine for the model. Next, a multi-population evolution whale optimization algorithm with multiexpert knowledge constraints (C-MEWOA) is used to optimize the BRB-ME model. Finally, the effectiveness of the BRB-ME model in health state assessment is verified through case studies of lithium-ion batteries and flywheels. Comparative studies have shown that the BRB-ME model can fuse multiexpert knowledge and has advantages in terms of the stability and accuracy of assessment results.

Self-Efficacy Perceptions of Tongan Students and Their Teachers within Year 11 Business Studies

Abstract A growing number of Pasifika students are pursuing Business Studies at Secondary school. However, indigenous knowledge and epistemologies are not prominent in Business Studies. We discuss a qualitative decolonising study that explores the concept of self-efficacy in Business Studies from a Tongan perspective. We provide insights into the perceptions of Tongan student’s self-efficacy and their Business Studies teachers. Teachers’ views aligned with Western notions of self-efficacy, which conflict with Tongan perceptions, precisely the collective nature of Tongan self-efficacy. Key findings from teacher interviews and Talanoa with students provided an understanding of Pasifika students in general and experiences specific to Tongan students. Three main themes are highlighted: (a) key values for Tongan students, such as relationships, family, and identity; (b) students’ learning experiences across different contexts; and (c) framings of self-efficacy. This study’s implications for curriculum suggest how teachers can learn from and incorporate Tongan students’ values and identities, enhancing their engagement and performance in Business Studies. Extending this research to include the knowledge and understanding of Tongan families, community leaders, curriculum specialists, Business Studies educators, and researchers would create a transformational space for student engagement. Bringing together shared expert knowledge in the future would require more time for effective and meaningful Talanoa as a collective.

Usage, knowledge and attitudes towards electronic cigarettes use among nursing students in Croatia: a cross-sectional study

BackgroundEvidence shows harmful effects of e-cigarettes on health. There is limited data on the use of e-cigarettes among nursing students. This study aimed to investigate the usage, knowledge, and attitudes towards e-cigarettes among nursing students in Croatia, as well as their views on the role of nurses in counseling patients and assisting with smoking cessation.MethodsThis cross-sectional study was conducted via an online questionnaire among nursing students at 10 universities in Croatia in March/April 2024. Students were asked questions about socio-demographics, knowledge and attitudes towards e-cigarettes and their views on the role of nurses in counseling patients and assisting with smoking cessation.ResultsThe study included 1,039 participants; 89% were women, 43% were smokers; 53% used e-cigarettes and 76% used them for recreational purposes. 21% of participants agreed that e-cigarettes are an effective method for quitting smoking. More than half indicated that e-cigarettes could encourage non-smokers to start smoking. The majority (60%) reported that they had never received education on smoking cessation at university. Furthermore, 66% stated that they did not feel confident advising smokers about e-cigarettes. The majority (70%) considered physicians to be the most knowledgeable health experts to advise and educate users of e-cigarettes. There were no significant differences in attitudes between smokers and non-smokers on the use of e-cigarettes. Participants demonstrated insufficient knowledge about e-cigarettes. Non-smokers had more positive attitudes towards the role of nurses in combating smoking compared to smokers. Participants who had received education on smoking cessation were more willing to advise smokers and had clearer views on the risks and the importance of the nurse’s role in the quitting process compared to those who had not received such education. Smoking status and age were significant predictors of counseling confidence.ConclusionThe study found a high prevalence of smoking and e-cigarette use among Croatian nursing students, along with limited knowledge and generally negative attitudes towards e-cigarettes. It highlighted gaps in education regarding the health risks of e-cigarettes and the lack of practical skills needed to help smokers quit, suggesting that nursing programs should update their curricula to better prepare students in these areas.

The electronic health record as an interactional and epistemic resource: Insights from pediatric well-child visits.

Electronic health records (EHRs) have increasingly become integral to contemporary medical consultations, including pediatric care. This study aims at exploring the interactional use of the EHR during naturally occurring pediatric well-child visits, focusing specifically on how pediatricians and parents manage knowledge concerning infants' growth inscribed in the EHR. Conversation analysis is used to analyze 23 video-recorded Italian well-child visits involving two pediatricians and twenty-two families with children aged 0-18 months. The analysis focuses on the delicate activity of assessing infants' growth, a widespread parental concern. It illustrates how a no-problem assessment is collaboratively achieved through the interactional mobilization of the EHR. While parents draw upon their experiential knowledge to assess their child's "normality" (or not), pediatricians resort to expert knowledge inscribed in the EHR (e.g., growth percentiles and growth charts), thereby making the EHR a locally and institutionally relevant agent in the interaction. A hierarchy of types and sources of knowledge is presupposed and ratified by both parents and pediatricians in these visits. Expert information inscribed in the EHR is collaboratively built as the most authoritative voice to the detriment of parent-reported experiential knowledge. While acknowledging potential risks, leveraging the EHR can be a valuable interactional and epistemic resource for healthcare professionals working in pediatric care to a) soothe parental concerns regarding infants' development, and b) offer evidential support for their evaluations, thereby displaying professional accountability.

Microsatellite/SSR dataset: characterization of apple cultivars of the German Fruit Genebank

The German Fruit Genebank is a decentralized network focused on coordinating various germplasm collections across Germany to conserve and utilize the genetic resources of native fruit species. This aim emphasizes the necessity of trueness-to-type validation of genetic resources based on pomological and molecular characteristics. Between 2009 and 2021, multiple projects were undertaken to create an inventory of the apple (Malus ssp.) network of the German Fruit Genebank. Eight partner locations across Germany participated in these projects to assess the authenticity of apple cultivars. Firstly, at least two knowledgeable experts from the German Pomological Association characterized the apple accessions pomologically. Secondly, DNA fingerprint analysis was conducted for molecular characterization. The DNA fingerprint analysis employed a set of 17 simple sequence repeat markers and 8 reference genotypes recommended by the European Cooperative Programme for Plant Genetic Resources Malus/Pyrus working group and was carried out by ecogenics GmbH (c/o Microsynth AG, Balgach, Switzerland). 8,184 samples were analysed, resulting in the compilation of 1,404 molecular groups that correspond to distinct apple cultivars.

DeepDrug as an expert guided and AI driven drug repurposing methodology for selecting the lead combination of drugs for Alzheimer’s disease

Alzheimer’s Disease (AD) significantly aggravates human dignity and quality of life. While newly approved amyloid immunotherapy has been reported, effective AD drugs remain to be identified. Here, we propose a novel AI-driven drug-repurposing method, DeepDrug, to identify a lead combination of approved drugs to treat AD patients. DeepDrug advances drug-repurposing methodology in four aspects. Firstly, it incorporates expert knowledge to extend candidate targets to include long genes, immunological and aging pathways, and somatic mutation markers that are associated with AD. Secondly, it incorporates a signed directed heterogeneous biomedical graph encompassing a rich set of nodes and edges, and node/edge weighting to capture crucial pathways associated with AD. Thirdly, it encodes the weighted biomedical graph through a Graph Neural Network into a new embedding space to capture the granular relationships across different nodes. Fourthly, it systematically selects the high-order drug combinations via diminishing return-based thresholds. A five-drug lead combination, consisting of Tofacitinib, Niraparib, Baricitinib, Empagliflozin, and Doxercalciferol, has been selected from the top drug candidates based on DeepDrug scores to achieve the maximum synergistic effect. These five drugs target neuroinflammation, mitochondrial dysfunction, and glucose metabolism, which are all related to AD pathology. DeepDrug offers a novel AI-and-big-data, expert-guided mechanism for new drug combination discovery and drug-repurposing across AD and other neuro-degenerative diseases, with immediate clinical applications.

Using expert knowledge and peer review to create a reproducible process for the NAHRS Nursing Essential Resources List (NNERL)

Background: Librarians have relied on resource lists for developing nursing collections, but these lists are usually in static or subscription-based formats. An example of this is the 26th edition of the Essential Nursing Resources last published in 2012. The Nursing and Allied Health Resources and Services (NAHRS) Caucus Nursing Essential Resources List (NNERL) Task Force has been working on a new list since Fall 2020. The goal of the Task Force is to create a nursing resource list that represents current materials and formats, uses a selection process that is transparent and reproducible, and will be available to a broad audience. Case Presentation: Working from the Essential Nursing Resources 26th edition, the NNERL Task Force updated the purpose statement then began reviewing the resources on the list. Two working groups were formed: 1) an evaluation rubric working group developed a tool to evaluate the resources and 2) a tagging work group developed guidelines for creating metadata and “tags.” Volunteers were recruited from the NAHRS Caucus to tag the resources. Lastly, the Task Force finalized the list of resources in the NNERL then cleaned and reconciled the data. Conclusions: The final version of the NNERL will be published in Airtable, a cloud-based project management product, that will include metadata for every item on the list. The NNERL will be copyrighted to the NAHRS NNERL Task Force and made available through the Open Science Framework (OSF) under an Attribution-NonCommercial-NoDerivatives 4.0 International Creative Commons License.

Identifying Key Nodes and Enhancing Resilience in Grain Supply Chains Under Drought Conditions

Grain supply chains remain stable in the face of natural disasters, and the resilience of the grain supply chain plays an important role. In a complex scenario of exposure to shocks, it is significant to identify the critical nodes of the grain supply chain and propose countermeasures accordingly to enhance the resilience of the grain supply chain. In this paper’s study, firstly, a triangular model of contradictory events is used to describe complex scenarios and obtain Bayesian network nodes. Secondly, the fragmentation of the scenario is based on the description of the scene, the scene stream is constructed, the event network is obtained, and the Bayesian network structure is built on the basis. Then, combining expert knowledge and D–S evidence theory, the Bayesian network parameters are determined, and the Bayesian network model is built. Finally, the key nodes of the grain supply chain are identified in the context of the 2022 drought data in the Yangtze River Basin in China, and, accordingly, a strategy for improving the resilience of the grain supply chain is proposed in stages. This study provides a new research perspective on issues related to grain supply-chain resilience and enriches the theoretical foundation of research related to supply-chain resilience.

Synthetic satellite telemetry data for machine learning

AbstractFor many machine learning tasks, labeled data are crucial. Even though there are methods that can be trained with data with only few labels, most of the tasks require many labels. In satellite operations, a huge amount of data are generated by the telemetry parameters of a satellite that keep track of its status. Modern satellites collect telemetry data of thousands of parameters. For example, the GRACE Follow-On satellites, operated by the German Space Operations Center (GSOC) at the German Aerospace Center (DLR), define about 80,000 unique housekeeping parameters each. However, all these telemetry data lack a complete/holistic set of labels. These data are usually unpredictable, hard to reproduce, and very diverse. As a consequence, expert knowledge is necessary to label these data, e.g., with anomalies. Moreover, labeling data by hand can be very time-consuming and, therefore, expensive. To overcome these obstacles, we implemented a synthetic satellite telemetry data library that is able to (a) generate a large variety of telemetry-like data, (b) add a plethora of well-defined anomalies to these data, and (c) deliver the labels for these injected anomalies. With these data, we are now able to train, validate, and test our machine learning models. Furthermore, we can compare different models with reproducible data. Since satellite telemetry data are often strictly confidential, we can share these synthetic data easily with our research partners.

Steering the future: expert knowledge and stakeholder voices in autonomous vehicle policy reports

Abstract The anticipated arrival of autonomous vehicles has created considerable uncertainty for US states because they govern roads. In response, states activated their policy advisory systems. While policy advising at the national level has been studied, less is known about the sub-national level. Similarly, more is known about the use of scientific knowledge by policymakers than about the full range of knowledge deployed in policy advising. This study analyzes reports written for states to help them make sense of an emerging technology in preparation for governance. Committees, university researchers, staff at Department of Transportation, and legislative staff produced different types of reports, for example, more and less academic, focused more or less on topics associated with governance or engineering. Our analysis reveals that state policy advisory systems used two types of processes—convening and expert—and employed three types of expertise—academic, practical, and political—to help prepare to govern this emerging technology. The study provides insight into how states mobilized expertise to address uncertainty around an emerging technology, showing how different actors balanced the need for credible technical knowledge with legitimate stakeholder engagement.

Enhanced bearing RUL prediction based on dynamic temporal attention and mixed MLP

Bearings are critical components in machinery, and accurately predicting their remaining useful life (RUL) is essential for effective predictive maintenance. Traditional RUL prediction methods often rely on manual feature extraction and expert knowledge, which face specific challenges such as handling non-stationary data and avoiding overfitting due to the inclusion of numerous irrelevant features. This paper presents an approach that leverages Continuous Wavelet Transform (CWT) for feature extraction, a Channel-Temporal Mixed MLP (CT-MLP) layer for capturing intricate dependencies, and a dynamic attention mechanism to adjust its focus based on the temporal importance of features within the time series. The dynamic attention mechanism integrates multi-head attention with innovative enhancements, making it particularly effective for datasets exhibiting non-stationary behaviour. An experimental study using the XJTU-SY rolling bearings dataset and the PRONOSTIA bearing dataset revealed that the proposed deep learning algorithm significantly outperforms other state-of-the-art algorithms in terms of RMSE and MAE, demonstrating its robustness and accuracy.

Infusing Illinois Educator Preparation for the Education of Multilingual Learner

A longstanding opportunity gap persists between multilingual learners and their English proficient peers. This gap has been characterized as “a persistent and seemingly intractable educational problem” (Leider et al., 2021, p. 3). In response, this article outlines how a Midwestern educator preparation program has infused mainstream teacher preparation with the minimum content knowledge and pedagogical expertise necessary to meet the needs of multilingual learners. Curriculum infusion includes theoretical grounding, literacy instruction and assessment strategies, and field experiences nurturing culturally responsive dispositions. Implications for policy and practice are discussed, including integration of Culturally Responsive Teaching and Learning Standards (Illinois State Board of Education, n.d.) and the Illinois Comprehensive Literacy Plan (https://www.isbe.net/Documents/IL-Comp-Literacy-Plan-2024.pdf).

A Novel Hyper-Heuristic Algorithm with Soft and Hard Constraints for Causal Discovery Using a Linear Structural Equation Model.

Artificial intelligence plays an indispensable role in improving productivity and promoting social development, and causal discovery is one of the extremely important research directions in this field. Acyclic directed graphs (DAGs) are the most commonly used tool in causal modeling because of their excellent interpretability and structural properties. However, in the face of insufficient data, the accuracy and efficiency of DAGs learning are greatly reduced, resulting in a false perception of causality. As intuitive expert knowledge, structural constraints control DAG learning by limiting the causal relationship between variables, which is expected to solve the above-mentioned problem. However, it is often impossible to build a DAG by relying on expert knowledge alone. To solve this problem, we propose the use of expert knowledge as a hard constraint and the structural prior gained via data learning as a soft constraint. In this paper, we propose a fitness-rate-rank-based multiarmed bandit (FRRMAB) hyper-heuristic that integrates soft and hard constraints into the DAG learning process. For a linear structural equation model (SEM), soft constraints are obtained via partial correlation analysis. The experimental results on different networks show that the proposed method has higher scalability and accuracy.

An Improved Method for Human Activity Detection with High-Resolution Images by Fusing Pooling Enhancement and Multi-Task Learning

Deep learning has garnered increasing attention in human activity detection due to its advantages, such as not relying on expert knowledge and automatic feature extraction. However, the existing deep learning-based approaches are primarily confined to recognizing specific types of human activities, which hinders scientific decision-making and comprehensive environmental protection. Therefore, there is an urgent need to develop a deep learning model to address multiple-type human activity detection with finer-resolution images. In this study, we proposed a new multi-task learning model (named PE-MLNet) to simultaneously achieve change detection and land use classification in GF-6 bitemporal images. Meanwhile, we also designed a pooling enhancement module (PEM) to accurately capture multi-scale change details from the bitemporal feature maps through combining differencing and concatenating branches. An independent annotated dataset at Yellow River Delta was taken to examine the effectiveness of PE-MLNet. The results showed that PE-MLNet exhibited obvious improvements in both detection accuracy and detail handling compared with other existing methods. Further analysis uncovered that the areas of buildings, roads, and oil depots has obviously increased, while the farmland and wetland areas largely decreased over the five years, indicating an expansion of human activities and their increased impacts on natural environments.

Declarative ship arenas under favourable conditions

According to maritime regulations, a collision-avoidance action shall be taken at an “ample time” while strict interpretation of this term is ambiguous. Evasive manoeuvres, executed by marine navigators on a daily basis, are usually carried out well in advance, while the distance at which they decide to perform such a manoeuvre is mostly subjective and results, e.g., from the navigator's seagoing experience. A proper understanding of the decision-maker's behaviour under favourable conditions, when time pressure does not exist, seems to be essential for the future of maritime safety. This could enable the translation and quantification of seafarers' routine actions, taken many times a day, into collision-avoidance algorithms suitable for Decision Support Systems (DSS) or Maritime Autonomous Surface Ships (MASS). The literature lacks extensive research on this subject, as it focuses mainly on safety-critical actions, which are important but rare events. Therefore, this study aims to fill this gap by surveying practitioners and extracting their expert knowledge. Based on an online survey, the declarative ship arenas, reflecting the distance of evasive manoeuvre initiation, were determined and analysed. The findings revealed that, depending on the participants' profiles, a range of responses among the groups reaches up to 2 NM. The results indicated that navigators become less consistent with growing experience. Determined declarative arenas were consequently incorporated into a simulation-based case study of a passenger ship. The conducted simulations indicated that for several scenarios, the passing distances resulting from the execution of an evasive manoeuvre as per declarative arena were less than 0.5 NM, potentially leading to dangerous situations at close range. This results most likely from an overall problem of translating the distance of manoeuvre initiation into the final passing distance. The findings of this research may be found interesting by shipping companies preparing bridge procedures or for scholars and industry representatives preparing intelligent collision-avoidance solutions for maritime transportation.

Scientific exploration with expert knowledge (SEEK) in autonomous scanning probe microscopy with active learning

This work introduces a knowledge-informed framework that integrates human expertise and prior interest into active learning-driven autonomous experimentation, which enhances the exploration efficiency through more targeted experimentation.

Mapping hotspots of zoonotic pathogen emergence: an integrated model-based and participatory-based approach.

An increase in pandemics of zoonotic origin has led to a growing interest in using statistical prediction to identify hotspots of zoonotic emergence. However, the rare nature of pathogen emergence requires modellers to impose simplifying assumptions, which limit the model's validity. We present a novel approach to hotspot mapping that aims to improve validity by combining model-based insights with expert knowledge. We conducted a systematic literature review to identify predictors for zoonotic emergence events in three priority virus families (Filoviridae, Coronaviridae, and Paramyxoviridae). We searched PubMed, Web of Science, Agricola, medRxiv, bioRxiv, Embase, CAB Global Health, and Google Scholar on Oct 14-28, 2021, with no restrictions on language or the date of publication. Articles suggested by subject matter experts and those identified by a review of reference lists were also included. We used regularised regression to fit a model to the data extracted from the literature and produced maps of ranked risk. In a series of workshops in five countries (Kenya, Peru, Senegal, Thailand, and Viet Nam), experts in zoonotic diseases produced qualitative hotspot maps based on their expertise, which were compared with the model-derived maps. 425 articles were analysed, from which 19 predictors and 1068 outcome events were identified. The in-sample misclassification error was 0·365, and 89% of participant-selected zones were ranked as moderate or high risk by the model. Participant-selected zones were too large to be actionable without further refinement. Discordance was probably due to missing predictors for which no valid data exist, and homogeneity imposed by our global model. Concordance between the two sets of maps supports the validity of each. Because model-based and participatory strategies have non-overlapping limitations, the results can be harmonised to minimise bias, and model-based results could be used to refine participant-selected zones. This approach shows potential for refining deployment of countermeasures to prevent future pandemics. US Agency for International Development.

University trauma surgery and orthopedics: where is the journey going to?

In the field of tension between generalists and specialists, in modern university trauma and orthopedic surgery the question arises as to which expertise has the greatest importance for the future of clinical care. University medicine consists of three large pillars: Research, teaching and treatment of patients. Each of these pillars is faced with the challenge of increasing specialization; however, generalists are extremely important for the future of university trauma surgery and orthopedics. Generalists promote interdisciplinary collaboration through their broad training and are versatile in acute care situations. Specialists are characterized by in-depth specialist knowledge and technological expertise in specific areas of surgery. This article analyses the changing requirements in trauma surgical and orthopedic care in the context of innovation, technologization, digitalization and increasing specialization. It also examines how structural reforms, such as the Hospital Structure Act and the new licensing regulations, are influencing the role of generalists and specialists in university training and clinical practice. The article provides adifferentiated view of which specialist skills will be necessary in hospitals in the coming years and discusses whether abalance between generalism and specialization is the solution for high-quality patient-centered care.

Commodity risk assessment of Alnus cordata, Alnus glutinosa and Alnus incana plants from the UK.

The European Commission requested the EFSA Panel on Plant Health to prepare and deliver risk assessments for commodities listed in Commission Implementing Regulation (EU) 2018/2019 as 'high-risk plants, plant products and other objects'. Taking into account the available scientific information, including the technical information provided by the applicant country, this Scientific Opinion covers the plant health risks posed by the following commodities: Alnus cordata, A. glutinosa and A. incana graftwood, bare-root plants and rooted plants in pots up to 7 years old imported into the EU from the UK. A list of pests potentially associated with the commodities was compiled. The relevance of each pest was assessed based on evidence following defined criteria. Two EU-quarantine pests (Entoleuca mammata, Phytophthora ramorum (non-EU isolates)) and one non-quarantine pest (Phytophthora siskiyouensis) were selected for further evaluation. For the selected pests, the risk mitigation measures implemented in the UK and specified in the technical dossier were evaluated taking into account the factors reducing their efficacy. For these pests, an expert judgement is given on the likelihood of pest freedom taking into consideration the risk mitigation measures acting on the pest, including uncertainties associated with the assessment. The degree of pest freedom varies between the pests evaluated, with E. mammata being the pest most frequently expected on imported Alnus spp. small trees. Expert knowledge elicitation indicated, with 95% certainty, that between 9927 and 10,000 per 10,000 Alnus spp. small trees (bare-root plants or rooted plants in pots up to 7 years old) would be free from E. mammata.

Commodity risk assessment of debarked conifer wood chips fumigated with sulfuryl fluoride from the US.

The European Commission requested the EFSA Panel on Plant Health to deliver a risk assessment on the likelihood of pest freedom from regulated EU quarantine pests, with emphasis on Bursaphelenchus xylophilus and its vectors Monochamus spp. of debarked conifer wood chips fumigated with sulfuryl fluoride as proposed by the United States (US) and as outlined in ISPM 28 - PT23 of sulfuryl fluoride (SF) fumigation treatment for nematodes and insects in debarked wood. The assessment considered the different phases in the wood chips' production, with special emphasis on the SF treatment. In addition to B. xylophilus and its vectors Monochamus spp., 22 EU quarantine pests and protected zone quarantine pests, some of which are regulated as groups of pests by the Commission Implementing Regulation (EU) 2019/2072, are present in the US and are potentially associated with the commodity. For these pests an expert judgement is given on the likelihood of pest freedom taking into consideration the available scientific information and technical information provided by the US, including uncertainties associated with the assessment. The likelihood of pest freedom varies among the pests evaluated, with B. xylophilus being the pest most frequently expected on the commodity. The Expert Knowledge Elicitation (EKE) indicated with 95% certainty that between 9491 and 10,000 m3 of debarked conifer wood chips treated with SF per 10,000 m3 will be free from B. xylophilus, and that between 9987 and 10,000 m3 of wood chips per 10,000 m3 will be free from Monochamus spp. Technical elements which are critical for a successful treatment and for minimising the presence of Union quarantine pests on the commodity are identified and described in the opinion. In particular, it is important to note that SF treatments are generally less effective in eliminating fungi than insects, the required parameters of the fumigation should be met at all points of the pile of wood chips and the time of storage of wood chips before treatment should be kept as short as possible because B. xylophilus can easily reproduce and spread throughout the pile under conducive conditions.