Use Of Expert Knowledge Research Articles

From Real-World Data to Causally Interpretable Models: A Bayesian Network to Predict Cardiovascular Diseases in Adolescents and Young Adults with Breast Cancer.

Background: In the last decades, the increasing number of adolescent and young adult (AYA) survivors of breast cancer (BC) has highlighted the cardiotoxic role of cancer therapies, making cardiovascular diseases (CVDs) among the most frequent, although rare, long-term sequalae. Leveraging innovative artificial intelligence (AI) tools and real-world data (RWD), we aimed to develop a causally interpretable model to identify young BC survivors at risk of developing CVDs. Methods: We designed and trained a Bayesian network (BN), an AI model, making use of expert knowledge and data from population-based (1036 patients) and clinical (339 patient) cohorts of female AYA (i.e., aged 18 to 39 years) 1-year survivors of BC, diagnosed in 2009-2019. The performance achieved by the BN model was validated against standard classification metrics, and two clinical applications were proposed. Results: The model showed a very good classification performance and a clear causal semantic. According to the predictions made by the model, focusing on the 25% of AYA BC survivors at higher risk of developing CVDs, we could identify 81% of the patients who would actually develop it. Moreover, a desktop-based app was implemented to calculate the individual patient's risk. Conclusions: In this study, we developed the first causal model for predicting the CVD risk in AYA survivors of BC, also proposing an innovative AI approach that could be useful for all researchers dealing with RWD. The model could be pivotal for clinicians who aim to plan personalized follow-up strategies for AYA BC survivors.

Spatial downscaling of global soil texture classes into 30 m images at the province scale

Soil categorical data is an important aspect in soil science because it effectively facilitates communication between policymakers and stakeholders. Furthermore, soil categorical data exceeds single-property data in terms of depth of information and is an essential component of various scientific disciplines such as hydrological, ecological and pollution frameworks. However, datasets containing such information are usually too coarse for local needs and regional policies. In this study, an algorithm was introduced, known as rafikisol, to spatially downscale (to a finer detail/resolution) soil texture classes from 1 km SoilGrids images into 30 m images in three environmentally diverse provinces (Gauteng, KwaZulu-Natal, and the Western Cape) in South Africa. Rafikisol surpassed the performance of another high-resolution soil dataset (Innovative Solutions for Decision Agriculture) by 9% and 27% in Gauteng and the Western Cape, respectively (accuracy ∼75% and ∼72%). Conversely, iSDAsoil outperformed rafikisol by 34% in KwaZulu-Natal (11% accuracy). The spatial soil texture class distribution predicted by rafikisol was considerably different and heavier (clayey) in Gauteng and KwaZulu-Natal but had a similar spatial distribution with lighter (sandy) soil texture in the Western Cape compared to iSDAsoil. With improvements such as the introduction of new sampling techniques, algorithm optimization and the use of expert knowledge, this method has the potential to increase the accuracy of additional modeling frameworks that require high-resolution soil information, especially in data-scarce or resource-constrained regions. This has implications for proper land-use management, affecting aspects ranging from food security and urban expansion to biodiversity.

Diagnosa Penyakit pada Jamur Tiram Putih menggunakan Metode Dempster Shafer

: Oyster mushrooms are one of the popular horticultural products in the community, with a significant increase in demand from year to year. However, farmers often face difficulties in identifying and preventing diseases that attack oyster mushroom plants, which have an impact on production stability. To overcome this problem, this study aims to design an expert system that can diagnose diseases in white oyster mushrooms using the Dempster Shafer Method. This system is designed to provide accurate diagnostic information and solutions to deal with diseases in oyster mushrooms, so as to improve the quality and quantity of production. This study also strengthens previous studies using the Certainty Factor method, with an emphasis on the use of expert knowledge to overcome disease problems in oyster mushroom cultivation. The case study was conducted at Omah Jamur Tiram Stabat, where the results of the implementation are expected to increase the effectiveness and efficiency of oyster mushroom cultivation in the area.

Concept for Individual and Lifetime-Adaptive Modeling of the Dynamic Behavior of Machine Tools

The increasing demand for personalized products and the lack of skilled workers, intensified by demographic change, are major challenges for the manufacturing industry in Europe. An important framework for addressing these issues is a digital twin that represents the dynamic behavior of machine tools to support the remaining skilled workers and optimize processes in virtual space. Existing methods for modeling the dynamic behavior of machine tools rely on the use of expert knowledge and require a significant amount of manual effort. In this paper, a concept is proposed for individualized and lifetime-adaptive modeling of the dynamic behavior of machine tools with the focus on the machine’s tool center point. Therefore, existing and proven algorithms are combined and applied to this use case. Additionally, it eliminates the need for detailed information about the machine’s kinematic structure and utilizes automated data collection, which reduces the dependence on expert knowledge. In preliminary tests, the algorithm for the initial model setup shows a fit of 99.88% on simulation data. The introduced re-fit approach for online parameter actualization is promising, as in preliminary tests, an accuracy of 95.23% could be reached.

Using fuzzy logic to compare species distribution models developed on the basis of expert knowledge and sampling records

BackgroundExperts use knowledge to infer the distribution of species based on fuzzy logical assumptions about the relationship between species and the environment. Thus, expert knowledge is amenable to fuzzy logic modelling, which give to propositions a continuous truth value between 0 and 1. In species distribution modelling, fuzzy logic may also be used to model, from a number of records, the degree to which conditions are favourable to the occurrence of a species. Therefore, fuzzy logic operations can be used to compare and combine models based on expert knowledge and species records. Here, we applied fuzzy logic modelling to the distribution of amphibians in Uruguay as inferred from expert knowledge and from observed records to infer favourable locations, with favourability being the commensurable unit for both kinds of data sources. We compared the results for threatened species, species considered by experts to be ubiquitous, and non-threatened, non-ubiquitous species. We calculated the fuzzy intersection of models based on both knowledge sources to obtain a unified prediction of favourable locations.ResultsModels based on expert knowledge involved a larger number of variables and were less affected by sampling bias. Models based on experts had the same overprediction rate for the three types of species, whereas models based on species records had a lower prediction rate for ubiquitous species. Models based on expert knowledge performed equally as well or better than corresponding models based on species records for threatened species, even when they had to discriminate and classify the same set of records used to build the models based on species records. For threatened species, expert models predicted more restrictive favourable territories than those predicted based on records. Observed records generated the best-fitted models for non-threatened non-ubiquitous species, and ubiquitous species.ConclusionsFuzzy modelling permitted the objective comparison of the potential of expert knowledge and incomplete distribution records to infer the territories favourable for different species. Distribution of threatened species was able to be better explained by subjective expert knowledge, while for generalist species models based on observed data were more accurate. These results have implications for the correct use of expert knowledge in conservation planning.

Argumentative Delphi Surveys: Lessons for Sociological Research

This contribution explains new variants of Argumentative Delphi surveys that can also be used in sociological research, some examples and the learnings from and limits of argumentative surveys with feedback. Argumentative Delphi surveys are not new. As Christian Dayé explains in his book, the early expert surveys and especially the Delphi surveys used explanations and arguments for exchanging knowledge - but always without direct interation (Dayé, C. (2020). Experts, Social Scientistss and Techniques of Prognosis in cold war of America. Socio-Historical Studies of the Social and Human Sciences, palgrave McMilan, Switzerland:41, see also Cuhls, K. (1998). Technikvorausschau in Japan. Ein Rückblick auf 30 Jahre Delphi-Expertenbefragungen. Physica. [Technology Foresight in Japan]). The very first approaches of Delphi surveys did not only make use of expert knowledge in judging issues under uncertainty or were trying to make accurate predictions with statistical analysis, but there were also tests in groups of students. In some of them, the groups did not only choose and tick boxes, but gave reasons or comments for their judgments. Modern Argumentative Delphi surveys do ask for comments AND use a variety of open questions for adding information to the statistical findings. This way of performing a Delphi survey gets more and more ground and can be analysed in a fast way by new means of text mining and Delphi software tools. But they have their limits - especially as they are very demanding for the participants and the analysts. If many people participate, many arguments are given, and they can quickly go beyond the limits of the participants‘ understanding and their time availability. Some lessons learned from recent Delphi projects are reported. This is closing the cycle to Dayé’s description of expert knowledge inclusion in policy-making - a way of integration of expert opinion without direct interaction.

Version [formula omitted]- [pymcdm – The universal library for solving multi-criteria decision-making problems

This paper looks into the extension of the pymcdm library, focusing on reference point-based techniques. It introduces the implementations of methods such as the Reference Ideal Method (RIM), Preference Ranking On the Basis of Ideal-average Distance (PROBID), and Election based on Relative Value Distances (ERVD). This update is intended to meet the increasing demand for solutions tailored to decision makers’ expertise and experience. Furthermore, it introduces techniques related to sensitivity analysis and weight comparison factors of criteria. By expanding this library, the range of MCDA/MCDM tools is broadened and further progress is encouraged in the use of expert knowledge and the implementation of compromise methods.

Epistemic othering: the interplay of knowledges in legislative drafting

AbstractIn this article, we use the concept of epistemic othering to describe the subjectivation of people who experience debt problems in the legislative drafting process, and argue that the evidence‐based policy paradigm, together with its participatory dimension, produce a potentially harmful subject position for people who are considered vulnerable and irrational. By analysing the preparatory material of Finnish interest rate cap laws, we explore what is constructed as rational and possible in the legislative process, and how these modalities frame the use of expert knowledge. We argue that what is considered rational is constructed in terms of market logic, and what is construed as possible is heavily framed by law‐as‐knowledge. Together, market logic and law‐as‐knowledge form the preconditions for the use of expert knowledge. Ultimately, the way in which these three types of knowledge interact contributes to the epistemic othering of people who experience debt problems.

Analysis of performance indicators of computer systems software on objects of special importance

Objective. The purpose of the article is to study the performance indicators of software aimed at improving the level of security at objects of particular importance. To date, the issue of improving the hardware and software components of the security system at a particularly important facility, taking into account its specifics and ensuring anti-terrorist measures, is quite urgent. There is an urgent task of evaluating the software of the physical protection system (SFZ) of an object of particular importance, while one of the main questions is which quality indicators to take into account and which methods to use when obtaining objective data, taking into account the specifics of the object of research and the need to increase anti-terrorist measures. In this article, it is proposed to analyze the performance indicators of computer systems software on objects of special importance.Method. To achieve this goal, such methods as system analysis, computational and expert statistical are used.Result. The paper presents an alternative approach to the analysis of the parameters of the effectiveness of software in relation to the components of the integrated complex (IC) SFZ, which consists in the transition from quantitative to qualitative assessments, the use of expert knowledge and statistical processing of information received from them. The proposed approach makes it possible to use a single measurement scale, as well as simplify the procedure for measuring the values of individual performance indicators of the software of the subsystems of the IC SFZ during operation at an object of particular importance.Conclusion. The results obtained can be used for a comprehensive assessment of the operation of software at facilities of particular importance, as well as to increase its security level.

Feature engineering methodology for congestion forecasting

Short-term traffic forecasting is a key element in proactive traffic management, e.g., mitigating the negative effect of impending congestion through appropriate capacity allocation at signalized intersections. In this study, we develop a data-driven methodology for reliably and robustly predicting impending stable congestion. By incorporating feature engineering techniques into an iterative machine learning process, we develop a prediction model that can be intuitively understood by traffic experts and is amenable to diagnostics during implementation. Our iterative machine learning process combines the embedded and filter approaches for feature selection with the use of expert knowledge to create aggregative input variables. The embedded approach is represented by application of a decision tree algorithm, while the filter approach is reflected in use of the mean decrease in accuracy output of a random forest algorithm for identifying expressive variables. We tested the methodology by applying it to field data from a sub-network in Tel Aviv. We demonstrated a reduction in the number of decision tree input variables from 66 raw variables to the five most effective aggregative ones, while achieving statistically significant improvement in all performance indicators. The identification rate of stable congestion increased from 65% to 74% while the robustness of the results was enhanced: the standard deviations of the identification and false alarm rates fell from 8% to 3%, respectively, to 5% and 2%. • Short-term traffic forecasting is a key element in mitigating the negative effect of impending urban congestion. • A machine learning process for reliably predicting impending congestion was developed by using feature engineering. • Aggregative input variables were created by combining embedded and filter approaches with the use of expert knowledge. • The proposed methodology significantly increases both the congestion identification rate and the robustness of the results. • The final prediction model can be intuitively understood by traffic experts.

The Rassemblement National and COVID-19: How Nativism, Authoritarianism and Expert Populism Did Not Pay Off during the Pandemic

AbstractThe article examines the response of the French National Rally (Rassemblement National – RN) to COVID-19. It combines computer-based and qualitative content analysis of Facebook posts and press releases to uncover the salience and frames of the infection. I find that the RN used an ambivalent strategy to respond to the pandemic, linking an unexpected problem to its core ideological tenets, while also adapting its programmatic profile. Specifically, the RN interpreted COVID-19 in terms of immigration and defence. Diagnostic frames drew on nativist, authoritarian and populist tenets to identify the origin of the infection (migrants), and to attribute blame for its spread (lack of border controls). Moreover, prognostic frames used disagreement within the scientific community to criticize governments' policies and propose alternatives. While this strategy allowed the RN to address an issue outside its ‘comfort zone’, its support base remained stable. These results point at the radical right's strategic use of expert knowledge and populist logics to try to improve its credibility in mainstream public debates.

Integrating stakeholder preferences into ecosystem services mapping in Yala wetland, Kenya

ABSTRACT Wetlands such as the Yala swamp in Kenya are among the most important and increasingly threatened ecosystems globally due to their ecological significance and complexity, and the importance of the ecosystem services (ES) they provide to wetland communities. Appropriate governance and management of wetlands thus require the use of interdisciplinary tools that take into account both ecological and social considerations. This study used the matrix model combining social preferences with GIS-based maps of land use/land cover (LULC) to analyse the capacity of the Yala swamp to supply ES (flows). We engaged a total of 132 participants who manage and use natural resources in the wetland through a participatory process to identify ES, map LULC, and score the flow of ES on a scale of 0 to 5 using the matrix model. We also analysed the impacts of stakeholder characteristics (gender, environmental expertise, and location) on the scoring of the matrix. Results showed high average scores (score of 4) for trees and shrubs, papyrus, and water bodies across a range of provisioning, regulating and cultural services. The study found that gender and location had little influence on the respondents’ scores, while environmental conservation experts provided scores significantly higher than local resource users (farmers/fishermen) across the ES types. Overall, the study contributes to understanding: 1) the importance of linking LULC with ES provision to inform landscape management and 2) the need to incorporate a range of stakeholder perspectives in studies making use of expert knowledge and preferences, for inclusive management.

Performance assessment of food safety management systems in Australian apple packhouses in view of microbial control

Australian apples significantly contribute to the Australian economy and dietary requirements. Their safety is ensured by food safety management systems (FSMS) audited by commercial entities. Production environments, systems and apparent microbial risk vary, resulting in company-specific food safety practices. Holistic, objective measurement of how a company's FSMS is performing could determine if these practices are appropriate for the level of risk of contamination with foodborne pathogens. In this study, the performance of FSMS was measured in seven apple packhouses using a modified FSMS diagnostic tool (FSMS-DT). Results were compared with a previous observational study that found variable application of food safety controls and low assurance activities, to investigate the potential for improving FSMS assessment and enhancing more consistent application of controls. Overall, packhouses demonstrated high FSMS performance. Higher performance was measured in packhouses with better-designed controls and where there was higher food safety knowledge among employees. Comprehensive measurement of food safety management better identified strengths such as supportive management, systematic use of feedback to modify the FSMS, and a low level of nonconforming product. Common system weaknesses included low requirement for food safety knowledge among workers, limited validation of preventive controls, and limited use of expert knowledge for risk assessment. Results confirmed the findings in the observational study but assessment using the FSMS-DT better examined the details of the FSMS and the connections between its components, thus enabling continuous improvement. The diagnostic tool could assist the industry assess their FSMS and identify opportunities for improvement of control and assurance activities. • A modified diagnostic tool measured food safety management performance. • Australian apple packhouses showed good overall food safety management performance. • High and low scores indicated strengths and weaknesses in control respectively. • Usage of the tool could enhance internal and external audits. • Wide use of the tool could encourage consistent application of food safety controls.

A new model based on belief rule base and membership function (BRB-MF) for health state prediction in sensor

Health state prediction is an effective way to improve the reliability for sensors. In the process of sensor degradation, it is difficult to obtain more effective monitoring data. And in the classification of health states, how to identify the adjacent state is also a problem. This paper proposed a health state prediction model based on belief rule base (BRB) and membership function (MF), which is called BRB-MF. In the model, BRB can make full use of expert knowledge and poor effective data. In the prediction results of BRB, it may be not completely logical or not entirely appropriate facing adjacent states of sensor. In order to solve the problem, MF is used to continue the analysis of the predicted results of BRB. In the BRB-MF model, the covariance matrix adaptation evolutionary strategies (CMA-ES) optimization algorithm is used to update the model parameters to make up for the uncertainty of expert knowledge. In the end, the brightness sensor of the rail vehicle LED lighting system is taken as a case study. The results show that the BRB-MF model can predict the health state of sensor with a high accuracy and a reasonable state.

Developing a Special Theory of Expert Knowledge Use with a view to Ensuring Information and Worldview Security in the Digital Environment

This paper substantiates the need to develop a new special theory of the use of expert knowledge in the framework of ensuring information and worldview security in the digital environment, aimed at analyzing and generalizing the possibilities of using a range of expert knowledge for the study of criminogenic information disseminated within the digital environment. For these purposes, conceptual frameworks have been developed, including the subject and the system of new expert theory, as well as other terms that are important for a holistic understanding of this area (such as "digital environment", "information (worldview) security", "value basis"). The involvement of persons with special knowledge is important due to the peculiarities of information disseminated in the digital environment (such as electronic form, hypertextuality, etc.). In this case, the question often arises about the need for a comprehensive expert analysis (for example, the use of special knowledge in the field of forensic speech and forensic psychology, special computer-technical knowledge, etc.). The result of the study is conclusion on the need to develop a new private expert theory; the paper defines its conceptual foundations in the form of a consideration of its subject and system.

Application of the D-A-(C) index as a\xa0simple tool for microbial-ecological characterization and assessment of groundwater ecosystems\u2014a\xa0case study of the Mur River Valley, Austria

The assessment and monitoring of the ecological quality and status of groundwater is a timely issue. At present, various assessment tools have been developed that now await application and validation. One of these, the D‑A‑C index, evaluates the microbiological-ecological quality of groundwater based on of prokaryotic cell counts, microbial activity measurements, and the qualitative characterization of dissolved organic carbon (DOM). The purpose of this paper is to illustrate the different ways of application of the D‑A-(C) index making use of a recently collected data set (n = 61) from the river Mur valley, Austria. First, we present an extension of the D‑A-(C) index by including measurements of dissolved organic matter quality (DOM) derived from fluorescence spectroscopy as additional variables to supplement the analysis of microbial cell density and activity levels. Second, we illustrate how the definition of a reference status for a ‘good’ microbiological-ecological state can improve the analysis and allow for a more sensitive and accurate detection of impacts on groundwater ecosystems. Based on our results, we advocate that the analysis be performed by making use of expert knowledge for the definition of reference sites to which target sites are to be compared.

Bayesian Network Applications for Sustainable Holistic Water Resources Management: Modeling Opportunities for South Africa.

Anthropogenic transformation of land globally is threatening water resources in terms of quality and availability. Managing water resources to ensure sustainable utilization is important for a semiarid country such as South Africa. Bayesian networks (BNs) are probabilistic graphical models that have been applied globally to a range of water resources management studies; however, there has been very limited application of BNs to similar studies in South Africa. This article explores the benefits and challenges of BN application in the context of water resources management, specifically in relation to South Africa. A brief overview describes BNs, followed by details of some of the possible opportunities for BNs to benefit water resources management. These include the ability to use quantitative and qualitative information, data, and expert knowledge. BN models can be integrated into geographic information systems and predict impact of ecosystem services and sustainability indicators. With additional data and information, BNs can be updated, allowing for integration into an adaptive management process. Challenges in the application of BNs include oversimplification of complex systems, constraints of BNs with categorical nodes for continuous variables, unclear use of expert knowledge, and treatment of uncertainty. BNs have tremendous potential to guide decision making by providing a holistic approach to water resources management.

Experimental Study of Parabolic Trough Receiver with Perforated Twisted Tape Insert Using Fuzzy Model Analysis

A solar water heating system has been fabricated and tested to analyze the thermal performance of Parabolic Trough Solar Collector (PTSC) using twisted tape insert inside absorber tube with twisted ratio about TR=y/w=1.33. The performance of PTSC system was evaluated by using three main important indicators: water outlet temperature (Tout), useful energy and thermal efficiency (ηth) under the effect of mass flow rate (ṁ) ranges between 0.02 and 0.04 Kg/s with the corresponding of Reynolds number (Re) range (2000 to 4000). In a parallel, a fuzzy-logic model was proposed to predict the thermal efficiency (ηth) and Nusselt number (Nu) of PTSC depending on the experimental results. The fuzzy model consists of five input and two output parameters. The input parameters include: solar intensity (I), receiver temperature (Tr), water inlet temperature (Tin), water outlet temperature (Tout) and water mass flow ( ) while, the output include the thermal efficiency (ηth) and Nu. The final results indicate that, owing to the mixture of the swirling flow of the perforated twisted-tape insert, the perforated twist tape insert enhances the heat transfer characteristics and the thermal efficiency of the PTSC system. More specifically, the use of perforate twist tape inserts enhanced the thermal efficiency by 4% to 4.5% higher than smooth absorber tube. Also, the predicted values were found to be in close agreement with the experimental counterparts with accuracy of ~92 %. So, the suggested Fuzzy model system would have high validity and precision in forecasting the success of a PTSC system compared to that of the traditional model. Pace, versatility, and the use of expert knowledge for estimation relative to those of the traditional model are the advantages of this approach

Who needs lobbying? The usefulness of interest representation in the European Union from the European Commission's point of view

The article addresses issues related to the use of cooperation with lobbying groups for the European Commission. The main subject of the paper is the representation of interests in the European Union arena and the Commission’s exercise of its treaty competences. The analysis focuses on the use of expert knowledge and legitimacy provided by lobbying groups in the context of their usefulness in the process of fulfilling the Commission’s legal obligations. The arguments are classified into four groups: legislative, executive and supervisory, organisational, and legitimacy. In each of the groups, the Commission’s Treaty obligations are analysed, as well as the use of particular goods provided by lobbying groups in the course of their implementation, which allows to determine of the scope of benefits resulting from such cooperation. The purpose of this article is to examine the usefulness of lobbying in fulfilling the Commission’s Treaty functions.

Intensive Parenting in High-Resource Multi-Child Families

Current views about parenting involves the use of expert knowledge in the field of education and upbringing. The available variety of information and opportunities in this area requires parents to engage actively not only at the stage of choosing an educational trajectory for their children, but also in the process of their implementation. Large families with a fairly high income and education set a high educational level for their children, embarking on the path of modern intensive parenting, they use their own resources to achieve results. However, it is obvious that large families, taking into account the number of children and the accumulated experience of parents, have some features in this area. Thus, the work shows that parenting experience in such families allows a more rational approach to the choice of activities and their number, while carefully selecting educational institutions for their children, often depending on the abilities and needs of each child, creating individual educational trajectories. It is also noted that an important component of the educational process is the formation of an intra-family educational environment, which in large families has the ability to self-regulate and recreate. The chosen strategy of intensive parenting, which involves a significant amount of investment in the educational and upbringing process of children, requires focusing resources on children, including through the underutilization of the human capital of parents in the labor market (usually mothers). However, this strategy is considered as the most rational by parents.