Expert Knowledge Research Articles

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.

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.

Modelling the effect of base component properties and processing conditions on mixture products using probabilistic, knowledge-guided neural networks

Development of materials by mixing different base components is a widespread methodology to create materials with improved properties compared to those of its base components. However, efficient determination of the properties of mixture-based materials during design remains challenging without prior knowledge of the underlying physical phenomena. In this work a new data-based methodology is proposed involving the use of probabilistic, knowledge-guided artificial neural networks to jointly model the properties of the base components, the proportions in which they are mixed and the processing conditions used during manufacture to predict properties of final products. The method proposed does not involve any assumptions in terms of ideal mixing rules of the base components, and allows for estimation of aleatoric uncertainty in the prediction. Additionally, an extension is presented that incorporates expert knowledge into the model by the implementation of monotonicity constraints between certain inputs and outputs. The methodology is illustrated with a case study involving the formulation of drug products using direct compression. The model is used to predict pharmaceutical tablets’ quality attributes (mass variation, tensile strength, disintegration time, friability and ejection force), showing that the method is able to predict properties of the final product overcoming gaps currently present in previous modelling approaches.

EMSPLA for accurate feature molecular extraction from protein-ligand interactions

Protein-ligand interactions are fundamental in various biological and medical fields, influencing drug discovery and therapeutic development. In recent years, deep learning (DL) has revolutionized the study of these interactions, but significant challenges remain in accurately representing molecular structures for DL models. Traditional featurization techniques often depend on handcrafted features, requiring expert knowledge and potentially missing crucial molecular aspects. This work addresses these challenges by developing and evaluating a novel protein-ligand feature extraction system using an enhanced molecular similarity protein-ligand aligner (EMSPLA). The primary objective is to leverage EMSPLA for similarity matching in protein-ligand interactions, improving predictive model accuracy. The methodology combines convolutional neural networks (CNN) for local feature extraction with an attention module to capture long-distance dependencies, enhancing binding site predictions. Using the PDBbind v.2020 dataset, the EMSPLA model demonstrated superior performance with a root mean square error (RMSE) of 0.67, surpassing current state-of-the-art models. These findings highlight the system’s potential for efficient deployment and scalability, positioning it as a powerful tool in computational biology and drug discovery, ultimately advancing our understanding of protein-ligand interactions.

Barriers, Potential Use, Competitiveness, and Implementation Framework of Building Information Modeling (BIM) in the Construction Industry of Pakistan

The use of building information modeling (BIM) in construction industry is believed to help decision makers throughout the project. The building sector in Pakistan is trying to adopt BIM as a tool to enhance the efficiency of projects. However, its actual implementation is being hampered by a number of barriers. Therefore, the purpose of this study is to identify those barriers and propose a framework to ensure its implementation. Data was collected through a questionnaire. Forty-nine (49) responses were collected and data was then analyzed using ANOVA to identify any significant differences in the opinions of different experts. Mean score and relative importance indices of all variables were determined. It is found that the major barriers include lack of expert knowledge, outdated contractual system and use of traditional tools for drafting. Further, the potential use of BIM is found to cause a reduction in rework as well as tool flexibility to allow different people to work at the same time. Moreover, a framework for BIM implementation on building projects include adding clauses in contracts, enhancing the awareness among industry stakeholders, and providing training and financial support. So, the findings of the current study will be helpful to pave the way for technological advancements in building projects in Pakistan. Keywords: Building information modeling (BIM), Potential benefits, Competitiveness, Barriers, Implementation framework.

Clustering- and statistic-based approach for detection and impact evaluation of faults in end-user substations of thermal energy systems

In response to climate change mitigation efforts, improving the efficiency of heat networks is becoming increasingly important. An efficient operation of energy systems depends on faultless performance. Following the need for effective fault detection and elimination methods, this study suggests a three-step workflow for increasing automation in managing defective substations on the user level within heat networks. The work focuses on a model region in northern Germany. The local heat network provides data in roughly hourly intervals, including the supply and return temperatures and the volume flow of the substations. Firstly, this study identifies common indicators of faults using k-means clustering analysis of the temperature data and expert knowledge: an exceeded return temperature level, very low cooling, and inverted temperature readings. With these indicators, the subsequent statistical identification approach confirms the successful detection of affected substations, with common diagnoses including disabled return temperature limitation units, defective motoric valves, and faults in the storage control. Lastly, the study evaluates the impact of faults on the system efficiency. Combining the temperature and the volume flow data, the workflow quantifies the negative influence of a fault, enabling the prioritization of fault elimination measures in practical application to enhance the overall system efficiency.

Intellectual Capital and Business Performance Management - Knowledge Management and the Value of Knowledge

Research background: In a global environment characterized by increasing competition, entities from various industrial sectors and geographical areas face a tremendous challenge to achieve sustainable performance improvement. We highlight the importance and relevance of addressing this issue in relation to gaining a competitive advantage and achieving enterprise success. Particularly significant is its connection with the key performance indicators of an organization. In the literature review and overview of expert knowledge, we present the opinions of selected authors and their areas of research in relation to the issue at hand. Purpose of the article: The research aim was to explore the use of performance evaluation systems through key indicators from the perspective of intellectual capital. Methods: Based on the research questions and formulated hypotheses related to performance specifically, qualifications, education, self-development, and employee recruitment - we highlighted the interdependence of these phenomena and their mutual connection to employee satisfaction and engagement. For qualifications, a linear model resulted in a determination coefficient of 0.093. indicating a 9.3% degree of variability in relation to employee recruitment. For education, it was 2.7% in relation to satisfaction, and for engagement, it was as low as 1.64%. Findings & Value added: Based on the research findings and hypothesis testing, we can conclude that in industrial enterprises within the service sector, sufficient attention is not yet paid to working with intellectual capital, and the hypotheses related to this area of research were not confirmed. There is recommended intensifying work with intellectual capital and building learning and growth processes on enhancing these potentials in line with modern trends. This requires further scientific research in this area and close collaboration with practice.

Reliability modeling and simulation: advancements with data-driven techniques and expert knowledge integration

Reliability of a system defines the likelihood that a system would operate as expected for a predefined length of time. There have been numerous notable efforts to model systems with the goal of analyzing their reliability and possibly quantifying it. Simulation as a technique has shown a great capacity to address the challenge of quantitatively evaluating system’s reliability. Traditionally, expert knowledge has been the gold standard for modeling and analyzing system reliability. However, the prevalence of data that stems from systems of interest, enabled through the easily available Internet of Things devices, coupled with simulation, presents a potential to change the way reliability of systems is being evaluated. Namely, data provides an opportunity for both automated modeling and continuous reliability assessment of systems through simulation. This article presents an overview of reliability modeling and simulation for cyber-physical systems, highlighting opportunities arising from data availability and its integration with expert knowledge. We use case studies from our research to illustrate these opportunities and advancements.