Intelligent Approach Research Articles

Big Data and Oral Health Disparities: A Critical Appraisal.

Big data has emerged as a pivotal asset in addressing oral health disparities in recent years. Big data encompasses the vast pool of health care-related biomedical information sourced from diverse channels, such as claims data, patient registries, and electronic health records (EHRs). This study is a critical review that synthesizes the evidence, identifies gaps in knowledge, and discusses future implications regarding big data analytics and oral health disparities. Published reports from 2014 to 2023 that studied associations between big data, social determinants of oral health, and oral health disparities, published in English and available in electronic databases, were included. Search engines were MEDLINE via PubMed, Google Scholar, and Web of Science. A total of 23 studies were included in the review, and all were retrospective data analytics. Studies have used a variety of big data sources, including EHRs, claims, and national or regional registries. This study used a framework of data quality dimensions with intrinsic (data attributes) and contextual values (information provided by the data, in this case, oral health disparities) to critically appraise the included studies. Big data revealed disparities in oral health outcomes and dental care utilization based on race, ethnicity, socioeconomic status, geographical location, insurance category, access to care, and other barriers to care. For the intrinsic data dimension, none of the studies addressed or reported data missingness or consistency of the data. The studies clearly provided contextual data dimensions. From a value-added perspective, several studies provided novel and new information related to racial oral health inequities. Several studies used more than one oral health disparities variable or a composite variable. However, the conclusions from several studies were based on association-based analytics, and few studies used artificial intelligence approaches to understand the population's oral health inequities-gaps were seen in the study designs and causal analytics.

The Role of Artificial Intelligence and Machine Learning in Accelerating the Discovery and Development of Nanomedicine.

The unique potential of nanomedicine to address challenging health issues is rapidly advancing the field, leading to the generation of more effective products. However, these complex systems often pose several challenges with respect to their design for specific functionality, scalablemanufacturing, characterization, quality control, and clinical translation. In this regard, the application of artificial intelligence (AI) and machine learning (ML) approaches can enable faster and more accurate data assessment, identifying trends and predicting outcomes, leading to efficient nanomedicine product development. This perspective paper discusses the potential of AI and ML in nanomedicine product development with a focus on their applications in discovery, assessment, manufacturing, and clinical trials.The potential limitations of AI and ML approaches in nanomedicine product development are also covered.

Investigation and application of big data and AI-Powered provincial power grid accurate planning support platform

ABSTRACT This study presents an accurate planning assistance platform for big data and artificial intelligence-based provincial power grids. The platform greatly increases the efficiency of data integration and management by building a planning big database containing fundamental geographic information, thematic data, remote sensing image data, and planning business data. Technically speaking, this paper addresses the issues of accuracy and speed in the processing of remote sensing image data by using deep learning algorithms like convolutional neural networks (CNN) and full convolutional neural networks (FCN) to achieve automatic classification of high-resolution optical remote sensing images and residential area extraction. In the meantime, the intelligent routing approach, which is based on cost surface modelling and multi-criteria decision-making, optimises the routing of power lines by carefully taking into account a wide range of variables, including land use, accessibility for vehicles, and environmental impact. In addition to resolving the primary issues with present power grid planning, this study’s exact planning support platform for provincial power grids greatly enhances real-time, accurate, and scientific planning.

A Novel Hybrid Intelligent Approach to Assess Blasting-Induced Overbreak Incorporating Geological Conditions in Different Tunnel Sections

Overbreak induced by tunnel blasting is a harmful phenomenon. Accurate assessment of overbreak can effectively reduce investment and ensure operational safety. In this study, a hybrid intelligent model for assessing blasting-induced overbreak is proposed which can accurately predict overbreak and effectively evaluate the importance of feature parameters. To ensure accurate prediction of overbreak, hyperparameters of four machine learning algorithms are optimized using a whale optimization algorithm. Their performance is compared based on three regression metrics: R2, RMSE, and VAF. Given the limitations of traditional feature importance analysis methods, the Shapley Additive Explanation method is used in conjunction with the random forest algorithm. After accurately predicting overbreak caused by different sections of the tunnel, the impact of each input parameter on overbreak is analyzed, and recommendations for design values of certain significant parameters are provided. The research indicates that the proposed method can accurately predict overbreak caused by actual engineering blasts and provide insights into the selection of design parameter values.

Target Bioconjugation of Protein Through Chemical, Molecular Dynamics, and Artificial Intelligence Approaches

Covalent modification of proteins at specific, predetermined sites is essential for advancing biological and biopharmaceutical applications. Site-selective labeling techniques for protein modification allow us to effectively track biological function, intracellular dynamics, and localization. Despite numerous reports on modifying target proteins with functional chemical probes, unique organic reactions that achieve site-selective integration without compromising native functional properties remain a significant challenge. In this review, we delve into site-selective protein modification using synthetic probes, highlighting both chemical and computational methodologies for chemo- and regioselective modifications of naturally occurring amino acids, as well as proximity-driven protein-selective chemical modifications. We also underline recent traceless affinity labeling strategies that involve exchange/cleavage reactions and catalyst tethering modifications. The rapid development of computational infrastructure and methods has made the bioconjugation of proteins more accessible, enabling precise predictions of structural changes due to protein modifications. Hence, we discuss bioconjugational computational approaches, including molecular dynamics and artificial intelligence, underscoring their potential applications in enhancing our understanding of cellular biology and addressing current challenges in the field.

A switching based forecasting approach for forecasting sales data in supply chains

Forecasting future demand has been a challenging task for supply chain practitioners, which is further exacerbated due to the recent pandemic effects. While the literature suggests a potential for improved accuracy with ML/AI approaches compared to probabilistic distribution-based traditional forecasting methods, the extent of this enhancement may vary based on the specific case. It is recognized that traditional probabilistic forecasting approaches are often considered less accurate and may lead to errors, potentially influencing the estimation of overall business costs. Meanwhile, with the advancement of artificial intelligence (AI) approaches, such as machine learning (ML) and deep learning (DL), this misestimation of cost can be reduced by forecasting demand more accurately from historical data. Consequently, this paper applies several AI-based approaches to predict demand data. Since no fixed AI approach works best for all datasets, a switching-based forecasting approach (SBFA) is proposed to exploit the merit of different advanced ML/DL approaches for different days ahead of prediction. Based on the performance of validation data, the proposed system automatically switches between different approaches to determine a more appropriate forecasting approach. A two-echelon supply chain model with different attributes is developed to validate the proposed SBFA against a few traditional forecasting approaches. The reorder points of this supply chain model are calculated based on the predictions from conventional/ML/DL forecasting approaches. Predictions from SBFA and other approaches are analysed by calculating overall supply chain cost. Based on overall supply chain costs under static and dynamic lead time settings, the effectiveness and applicability of the proposed SBFA against traditional forecasting approaches are demonstrated.

An artificial intelligence approach to smart exam supervision using YOLOv5 and siamese network

<p>Artificial intelligence has introduced revolutionary and innovative solutions to many complex problems by automating processes or tasks that used to require human power. The limited capabilities of human efforts in real-time monitoring have led to artificial intelligence becoming increasingly popular. Artificial intelligence helps develop the monitoring process by analysing data and extracting accurate results. Artificial intelligence is also capable of providing surveillance cameras with a digital brain that analyses images and live video clips without human intervention. Deep learning models can be applied to digital images to identify and classify objects accurately. Object detection algorithms are based on deep learning algorithms in artificial intelligence. Using the deep learning algorithm, object detection is achieved with high accuracy. In this paper, a combined model of the YOLOv5 model and network Siames technology is proposed, in which the YOLOv5 algorithm detects cheating tools in classrooms, such as a cell phone or a book, in such away that the algorithm detects the student as an object and cannot recognize his face. Using the Siames network, we compare the student’s face against the data base of students in order to identify the student with cheating tools.</p>

IoT-based Ubiquitous Healthcare System with Intelligent Approach to an Epidemic

Background:: The recent pandemic has shown its different shades across various solicitations, especially in the healthcare sector. It has a great impact on transforming the traditional healthcare architecture, which is based on the physical approaching model, into the modern or remote healthcare system. The remote healthcare approach is quite achievable now by utilizing multiple modern technological paradigms like AI, Cloud Computing, Feature Learning, the Internet of Things, etc. Accordingly, the pharmaceutical section is the most fascinating province to be inspected by medical experts in restoring the evolutionary healthcare approaches. COVID-19 has created chaos in the society for which many unexpected deaths occur due to delays in medication and the improper prognosis at an irreverent plan. As medical management applications have become ubiquitous in nature and technology-oriented, patient monitoring systems are getting more popular among medical actors. Method: The Internet of Things (IoT) has achieved the solution criteria for providing such a huge service across the globe at any time and in any place. A quite feasible and approachable framework has evolved through this work regarding hardware development and predictive patent analysis. The desired model illustrates various approaches to the development of a wearable sensor medium that will be directly attached to the body of the patients. These sensor mediums are mostly accountable for observing body parameters like blood pressure, heart rate, temperature, etc., and transmit these data to the cloud storage via various intermediate steps. The storage medium in the cloud will be storing the sensor-acquired data in a time-to-time manner for a detailed analysis. Further, the stored data will be normalized and processed across various predictive models. Results and Conclusion: The model with the best accuracy will be treated as the resultant model among the numerous predictive models deployed in the cloud. During the hardware development process, several hardware modules are discussed. After receiving sensor-acquired data, it will be processed by the cloud's multiple machine-learning models. Finally, thorough analytics will be developed based on a meticulous examination of the patients' cardinal, essential, and fundamental data and communicated to the appropriate physicians for action. This model will then be used for the data dissemination procedure, in which an alarm message will be issued to the appropriate authorities.

Automated Segmentation of Fetal Intracranial Volume in Three-Dimensional Ultrasound Using Deep Learning: Identifying Sex Differences in Prenatal Brain Development.

The human brain undergoes major developmental changes during pregnancy. Three-dimensional (3D) ultrasound images allow for the opportunity to investigate typical prenatal brain development on a large scale. Transabdominal ultrasound can be challenging due to the small fetal brain and its movement, as well as multiple sweeps that may not yield high-quality images, especially when brain structures are unclear. By applying the latest developments in artificial intelligence for automated image processing allowing automated training of brain anatomy in these images retrieving reliable quantitative brain measurements becomes possible at a large scale. Here, we developed a convolutional neural network (CNN) model for automated segmentation of fetal intracranial volume (ICV) from 3D ultrasound. We applied the trained model in a large longitudinal population sample from the YOUth Baby and Child cohort measured at 20- and 30-week of gestational age to investigate biological sex differences in fetal ICV as a proof-of-principle and validation for our automated method (N = 2235 individuals with 43492 ultrasounds). A total of 168 annotated, randomly selected, good quality 3D ultrasound whole-brain images were included to train a 3D CNN for automated fetal ICV segmentation. A data augmentation strategy provided physical variation to train the network. K-fold cross-validation and Bayesian optimization were used for network selection and the ensemble-based system combined multiple networks to form the final ensemble network. The final ensemble network produced consistent and high-quality segmentations of ICV (Dice Similarity Coefficient (DSC) > 0.93, Hausdorff Distance (HD): HDvoxel < 4.6 voxels, and HDphysical < 1.4 mm). In addition, we developed an automated quality control procedure to include the ultrasound scans that successfully predicted ICV from all 43492 3D ultrasounds available in all individuals, no longer requiring manual selection of the best scan for analysis. Our trained model automatically retrieved ultrasounds with brain data and estimated ICV and ICV growth in 7672 (18%) of ultrasounds in 1762 participants that passed the automatic quality control procedure. Boys had significantly larger ICV at 20-weeks (81.7 ± 0.4 mL vs. 80.8 ± 0.5 mL; B = 2.86; p = 5.7e-14) and 30-weeks (257.0 ± 0.9 mL vs. 245.1 ± 0.9 mL; B = 12.35; p = 8.2e-27) of pregnancy, and more pronounced ICV growth than girls (delta growth 0.12 mL/day; p = 1.8e-5). Our automated artificial intelligence approach provides an opportunity to investigate fetal brain development on a much larger scale and to answer fundamental questions related to prenatal brain development.

In Silico Tools to Score and Predict Cholesterol-Protein Interactions.

Cholesterol is structurally distinct from other lipids, which confers it with singular roles in membrane organization and protein function. As a signaling molecule, cholesterol engages in discrete interactions with transmembrane, peripheral, and certain soluble proteins to control cellular responses. Accordingly, the cholesterol-protein interface is central to cholesterol-related diseases and is an essential consideration in drug design. However, cholesterol's hydrophobic, un-drug-like nature presents a unique challenge to traditional in silico analyses. In this Perspective, we survey a collection of tools designed to predict and evaluate cholesterol binding sites in proteins, including classical sequence motifs, molecular docking, template-based strategies, molecular dynamics simulations, and recent artificial intelligence approaches. We then comment on contemporary tools to evaluate ligand-protein interactions, their applicability to cholesterol, and the yet-untapped potential of cholesterol-protein interactions in human health and disease.

Long-term deep reinforcement learning for real-time economic generation control of cloud energy storage systems with varying structures

Energy storage systems play a crucial role in modern power systems. Consequently, a mixed cloud energy storage (CES) system is proposed. The mixed CES system comprises consumers and prosumers. The consumers can only consume energy. The prosumers can either produce or consume energy at different time intervals. The proposed mixed CES system is designed for investigating the generation control challenges in mixed interconnected power systems. To optimize the active power balance and economic efficiency of the mixed CES system, a long-term deep reinforcement learning (LDRL) artificial intelligence approach is proposed as the real-time economic generation controller to control the mixed CES system. The LDRL consists of a reinforcement mechanism and two models: a long short-term memory model for economic dispatch and a deep neural networks model for smart generation control. The reinforcement framework updates policies for the prosumers. The efficacy of proposed method is validated across three mixed systems, i.e., the improved IEEE 300-bus, Polish 2383-bus, and mixed systems with varying structures. The numerical simulations verify that the LDRL method can efficiently and economically control the mixed CES systems with diverse structures.

Advancements in Machine Learning for Pipeline Integrity Management: A Comprehensive Review of Predictive and Optimization Techniques

Oil and gas pipeline networks are crucial component of energy infrastructure. However, as the integral elements of the energy transportation system are evolving towards Industry 4.0 and Smart Manufacturing, pipelines network are progressively shifting towards intelligence and digitization. The fields of asset integrity management, operation inspection, corrosion prevention, leak and intrusion detection, and flow assurance are just a few of the areas where machine learning and artificial intelligence (AI) algorithms are becoming more and more important. The predictive and optimization capabilities of these Models/ Algorithms have been leveraged by various Pipeline Network Integrity Management systems to avert pipeline failure, reduce environmental damage, and effectively allocate resources. This study reviews and gives a summary of current advancements in intelligent techniques, encompassing heuristic computations, mathematical programming, and machine learning techniques used in a pipeline network integrity management to enhance various operational and maintenance aspects. This work makes two intellectual accomplishments. Firstly, it offers a technical review of literatures, systematically synthesizing different computational intelligence and machine learning approaches previously applied, along with their methodologies, contributions, drawbacks, and comparisons. Secondly, the study recommends some nature-inspired meta-heuristic algorithms, which encompasses Evolutionary and the Swarm Intelligence Algorithms to be applied for future directions and challenges. Finally, the study underscores the potential of computational intelligence and algorithms for learning methods in predicting and optimizing the efficiency of pipeline network operation and management, emphasizing the necessity for further development of models/algorithms and application to enhance more intricate interactions between pipeline infrastructure monitoring, maintenance, and management.

Smart Strategies in the Digital Age: Conventional Retail Facing the Revolution E-Commerce

The aim of this research is to see how e-commerce trends impact traditional retail marketing strategies. The study emphasize the use of new technologies such as augmented reality (AR), artificial intelligence (AI), and omnichannel approaches as a way to maintain traditional retail competitiveness. This was done using the PRISMA framework and SWOT analysis. Data was collected from eleven relevant articles over the past three years (2022-2024) using searches in ScienceDirect and Google Scholar. Results show that the use of technology in omnichannel strategies can improve customer experience and operational efficiency. However, the main obstacles remain infrastructure limitations, high investment costs and data security threats. To address this issue, it is recommended to use mitigation strategis that prioritize technology partnerships and focus on specific market segments. This research provides parctical guidelines for retail partitioners to adjust to the dynamics of the e-commerce market. In addition, this research offers a perspective for academics in studying retail transformation in the digital era.

Intelligent hybrid approaches utilizing time series forecasting error for enhanced structural health monitoring

Over the past decade, the growing importance of machine learning-based structural health monitoring (SHM) for early-stage damage detection has become evident. Time series forecasting, using deep learning, has emerged as a key focus, significantly contributing to improving damage detection, localization, and quantification processes. Researchers in SHM have conducted numerous studies utilizing neural networks based on time series forecasting, grounded in traditional methods. This study diverges from existing research by directly incorporating neural network prediction errors in time series for detecting, localizing, and quantifying damage. The proposed methods are well-suited for online structural monitoring. They eliminate the need for data classification methods and damage-sensitive feature extraction techniques by relying solely on training the neural network with data from structurally sound conditions. However, the testing process does require data from damaged conditions. To address the non-linear and non-stationary characteristics of the signals, the Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) method is applied for signal processing. This method processes response signals (i.e., time series) from three well-known benchmark structures: the University of Central Florida structure, the Qatar University Grandstand Simulator, and the Z24 Bridge. Subsequently, the first intrinsic mode function (IMF) obtained from signal decomposition is independently input into Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) neural networks for time series prediction. Optimal parameter values for the LSTM and GRU neural networks are chosen using the Bayesian Optimization (BO) algorithm before the prediction process. By introducing three indices—Statistical Distance Function (SDF), error index, and accuracy index—the evaluation not only emphasizes the accuracy of the methods but also explores the localization and quantification of damage. The results demonstrate that both ICEEMDAN-BO-LSTM-SDF and ICEEMDAN-BO-GRU-SDF methods have successfully achieved accurate detection, localization, and quantification without the need for data classification and damage-sensitive feature extraction methods, and merely by utilizing data from healthy states for neural network training.

A Deep Intelligent Ant Colony-Based Approach to Personalized and Customized Route Optimization for Smart Tourism

A Deep Intelligent Ant Colony-Based Approach to Personalized and Customized Route Optimization for Smart Tourism

Multi-criteria sensitivity study and optimization of a two-stage preheated SOFC system considering thermal characteristics

At present, the pivotal challenges hindering the commercialization of the Solid Oxide Fuel Cell (SOFC) lie in system design and matching, thermal management safety, and performance optimization. To address these issues, a SOFC system-level model for the optimal control is proposed, integrating a two-stage preheater design, the electrochemical reaction mechanisms, and the influence of thermoelectric coupling effects on electrode temperature distributions. Furthermore, a comprehensive multi-criteria sensitivity analysis is conducted, incorporating thermodynamic, economic, and environmental indicators. The research demonstrates that there exist distinct mechanisms underlying the impact of varying trends in fuel utilization ratio and excess air ratio on the performance indicators of the SOFC system. Then, a detailed sensitivity analysis is undertaken, exploring the response trends of performance indicators under varying parameters. To validate the dynamic behavior of the objective functions, an intelligent learning approach based on the Artificial Neural Network is proposed. Additionally, a Multi-Objective Grey Wolf Optimization process is introduced, aimed at enhancing the comprehensive performance of the SOFC system. The results of this study demonstrate that the proposed multi-objective optimization strategy achieves a reduction of 12.12% in maximum temperature gradient, 28.00% in Levelized Cost of Energy, and 4.76% in Mass Specific Emission at the optimal operating point. This underscores the effectiveness of the approach in balancing and optimizing the trade-offs between thermal stability, economic feasibility, and environmental impact of the SOFC system.

Intelligent control system for technological complexes of aluminum industry enterprises

Relevance. Aluminum production is one of the most important industries all over the world. It has a high environmental load, resource and energy intensity. To reduce the negative impact of aluminum production, efforts are underway to improve its technological processes, aimed at increasing aluminum recovery rates, reducing waste volumes, and lowering energy consumption. The reduction of energy consumption can be achieved, among others, through implementing control systems that provide energy management. Due to the complexity and multiplicity of technological processes in aluminum production, the use of traditional linear approaches for such control systems is ineffective, and adequate mathematical models are required. At the same time, for the production level, the use of mathematical models based on a phenomenological description of the ongoing physical processes overly complicates the control task. As a result, the use of intelligent and adaptive approaches to both the mathematical description of technological processes and the optimal energy consumption management is relevant. Objects. Technological complexes of aluminum production, which have the properties of inertia, nonlinearity, and closedness; and the control system based on artificial intelligence methods. Alumina production is chosen as an illustration. Aim. To develop mathematical models capable of adequately describing the interrelated processes in the technological complexes under consideration, as well as the control system that allows optimal energy consumption management. Methods. For the mathematical model based on balance equations under uncertainty, the fuzzy-set theory was used along with the gradient descent method to identify the model parameters; for the optimization task, the genetic algorithm method was used. Results. The mass balance model and the process conditions changing model have been developed to determine the energy consumption for the technological complexes of aluminum production with continuous inertial nonlinear closed production. Based on these models, the dynamic characteristics of energy consumption and the parameters of technological processes were determined depending on the main controlled parameters, allowing us to predict emergencies. Considering technological parameters and cost factors, the optimization task for energy consumption management was solved.

Intelligent decision-making approach for rapid optimization of double-wall cooling structures under varying cooling demands

Double-wall structures are widely employed for cooling turbine blades. When manufacturing materials and application conditions vary, the design of double-wall structures should be adapted to meet different cooling demands. However, because of inevitable repetitive iterations, conventional evolutionary algorithms exhibit low efficiency in optimizing double-wall structures when cooling demands change. This paper presents a reinforcement learning-based method to optimize double-wall cooling structures facing various cooling demands rapidly. A decision network established a forward mapping from cooling demands to optimized structural designs and was trained using historical decision-making experiences generated from numerical simulations. The historical experiences were quantified uniformly based on the cooling performance gains or losses resulting from changes in geometric parameters. The physical understanding that cooling performance evaluations differ with varying cooling demands was incorporated into the training dataset. The performance of the trained decision network was validated by varying the cooling optimization objectives. Results indicate that the decision network can achieve optimal double-wall cooling units that meet given objectives with a single decision. The decision process took 5 ms, nearly 90 times faster than traditional genetic algorithms. Moreover, a partitioning, multi-cycle application strategy for the decision network was employed to optimize double-wall cooling plates under non-uniform inlet temperature distributions, resulting in a 13.7K reduction in overall average temperature and improved radial temperature uniformity.

Integrative Computational Intelligence Techniques for Insomnia and Sleep Stage Recognition from ECG Data

Abstract—Insomnia is a common sleep disorder that can lead to serious health problems, including cardiovascular issues. Early and accurate detection is essential for effective treatment, but traditional diagnostic methods are often costly, time-consuming, and prone to human error. To address these challenges, this paper introduces a novel hybrid artificial intelligence (AI) approach for automatic insomnia detection using electrocardiogram (ECG) signals. The method utilizes heart rate variability (HRV) and power spectral density (PSD) analysis in various classification scenarios, including subject- based classification (normal vs. insomnia), sleep stage-based classification (REM vs. wake), and a combined classification approach using both subject and sleep stage features. Ensemble learning techniques, such as random forest (RF) and decision tree (DT) classifiers, are used in the first two scenarios, while linear discriminant analysis (LDA) is applied in the combined classification. The approach includes ECG signal collection, HRV feature extraction, PSD estimation, and classification. Evaluation using the publicly available PhysioNet dataset shows strong performance across all scenarios, with high sensitivity, specificity, and accuracy. The combined classification scenario, in particular, achieves the highest detection accuracy with LDA. Keywords—HRV Extraction, Sleep Stage, Ensemble, LDA

Human-CoMo: A Combination of Cognitive Knowledge Engineering and Data Modelling for Human-Cyber-Physical Systems in Intelligent Manufacturing

Many cyber-physical systems face the challenge of appropriately integrating domain-specific human expert knowledge into the cyber part to create a shared sphere of knowledge and intelligent interactions between humans and the semi-autonomous technical system. Cognitive engineering contributes methods and insights into higher-order cognition that help to embed human knowledge in an appropriate way. The original research introduces a novel transdisciplinary framework called Human-CoMo, which demonstrates a systematic modelling process, different human perspectives, and the integration of expert knowledge at multiple hierarchical levels. Fundamental principles inspired by human cognition, such as conceptual chunking and knowledge precision, are characterised. Furthermore, it is shown how knowledge hierarchies can be methodically reflected in appropriate data analysis and modelling levels for small and big data applications including artificial intelligence approaches. Combined knowledge- and data-based modelling approaches offer more flexibility to integrate the strengths of humans and technology in a complementary way. The cognitive foundations and their computational reflections are outlined for the technical example process electroplating from the field of materials and surface engineering. The possibilities and limitations of integrating human knowledge through formalisation and implications for future forms of human-machine interaction are discussed.