Modeling Methodology Research Articles

Methodology for Modeling and Correcting the Effects of Extension Cables for Measurements in Low Voltage Grids in the 1.1–10 MHz Frequency Band

Methodology for Modeling and Correcting the Effects of Extension Cables for Measurements in Low Voltage Grids in the 1.1–10 MHz Frequency Band

A predictive model based on the gut microbiota improves the diagnostic effect in patients with rheumatoid arthritis.

Rheumatoid arthritis (RA) is a chronic, destructive autoimmune disorder predominantly targeting the joints, with gut microbiota dysbiosis being intricately associated with its progression. The aim of the present study was to develop of effective early diagnostic methods for early RA based on gut microbiota. A cohort comprising 262 RA patients and 475 healthy controls (HCs) was recruited. Faecal samples were collected from all participants, and microbial DNA was subsequently extracted. The V3-V4 region of the 16S rRNA gene was amplified via polymerase chain reaction (PCR) and subjected to high-throughput sequencing using the Illumina MiSeq platform. Additionally, a dataset with the accession number PRJNA450340 from the European Nucleotide Archive (ENA) was incorporated into the study. The sequencing data underwent processing and analysis utilizing QIIME2. To construct microbiome-based diagnostic models, Random Forest (RF), Support Vector Machine (SVM), and Generalized Linear Model (GLM) methodologies were employed, with the self-test data functioning as the training set and the PRJNA450340 dataset serving as the validation set. The results indicated that patients with RA exhibited a significantly reduced gut microbial α-diversity compared with the HCs group. The β-diversity analysis demonstrated notable distinctions in the gut microbiota structure between RA patients and HCs. Variations in the gut microbiome composition between RA patients and HCs were evident at both the phylum and genus levels. LEfSe analysis revealed a substantial number of significantly different microbiota between RA patients and HC, and 7 key genera were obtained by intersection of the different flora in the two data sets: Ruminococcus_gnavus_group, Fusicatenibacter, Butyricicoccus, Subdoligranulum, Erysipelotrichaceae_UCG-003, Romboutsia, and Dorea. Utilizing these seven core genera, RA diagnostic models were developed employing RF, SVM, and GLM methodologies. The GLM model exhibited consistent performance, achieving an area under the curve (AUC) of 71.03% in the training set and 74.71% in the validation set. Notable differences in gut microbiota exist between RA patients and healthy individuals. Diagnostic models based on key microbial genera hold potential for aiding in the early identification of individuals at risk for developing RA, thereby suggesting new avenues for its diagnosis.

Leveraging Incremental Machine Learning for Reconfigurable Systems Modeling under Dynamic Workloads

Dynamic workload orchestration is one of the main concerns when working with heterogeneous computing infrastructures in the edge-cloud continuum. In this context, FPGA-based computing nodes can take advantage of their improved flexibility, performance and energy efficiency provided that they use proper resource management strategies. In this regard, many state-of-the-art systems rely on proactive power management techniques and task scheduling decisions, which in turn require deep knowledge about the applications to be accelerated and the actual response of the target reconfigurable fabrics when executing them. While acquiring this knowledge at design time was more or less feasible in the past, with applications mostly being static task graphs that did not change at run time, the highly dynamic nature of current workloads in the edge-cloud continuum, where tasks can be deployed on any node and at any time, has removed this possibility. As a result, being able to derive such information at run time to make informed decisions has become a must. This paper presents an infrastructure to build incremental ML models that can be used to obtain run-time power consumption and performance estimations in FPGA-based reconfigurable multi-accelerator systems operating under dynamic workloads. The proposed infrastructure features a novel stop-and-restart resource-aware mechanism to monitor and control the model training and evaluation stages during normal system operation, enabling low-overhead updates in the models to account for either unexpected acceleration requests (i.e., tasks not considered previously by the models) or model drift (e.g., fabric degradation). Experimental results show that the proposed approach induces a maximal additional error of 3.66% compared to a continuous training alternative. Furthermore, the proposed approach incurs only a 4.49% execution time overhead, compared to the 20.91% overhead induced by the continuous training alternative. The proposed modeling strategy enables innovative scheduling approaches in reconfigurable systems. This is exemplified by the conflict-aware scheduler introduced in this work, which achieves up to a 1.35 times speedup in executing the experimental workload. Additionally, the proposed approach demonstrates superior adaptability compared to other methods in the literature, particularly in response to significant changes in workload and to mitigate the effects of model overfitting. The portability of the proposed modeling methodology and monitoring infrastructure is also shown through their application to both Zynq-7000 and Zynq UltraScale+ devices.

A data-informed knowledge discovery framework to predict fatigue properties of additively manufactured Ti–6Al–4V, IN718 and AlSi10Mg alloys using fatigue databases

Abstract With the increasing interest in utilizing additive manufacturing (AM) for fatigue-critical applications, reliable fatigue analysis tools are increasingly necessary. Unlike traditional manufacturing processes, AM involves a greater number of process parameters and manufacturing variables which adds complexity to the development of mechanistic fatigue models. Data-driven models, to some extent, have shown predictive capabilities for the fatigue properties of AM alloys. However, no study has extensively investigated fatigue properties across different AM alloys with the aim of developing predictive models for fatigue databases. A data-informed knowledge discovery framework is developed for exploring diverse AM fatigue datasets in the literature. Characteristics of the AM fatigue datasets are first analyzed within this framework to uncover data-driven insights for machine learning modeling. Machine learning techniques are then used to solve classification and regression problems using fatigue ratio as the property of interest. k-nearest neighbors and support vector machine classifiers are used to classify process parameters and manufacturing variables to determine the class of fatigue ratio they belong to based on a pre-defined threshold. Similarly, a Gaussian process regression (GPR) model is used to predict the fatigue ratio of AM alloys with a 95% prediction interval. The Shapley Additive exPlanations (SHAP) method is used to explain the predictions of the GPR model. This framework is validated using Ti–6Al–4V, IN718, and AlSi10Mg datasets from the literature. The proposed framework efficiently maps process parameters, post-processing sequences, and AM types to the fatigue ratio, thereby providing an efficient surrogate modeling methodology for estimating fatigue properties from available datasets.

A Methodology for Online Situational Awareness Provision in a Business Entity

This paper presents a practical approach to building situational awareness at a critical infrastructure entity and its implementation in supporting security management. It outlines the main factors for achieving a high level of situational awareness and ensuring the safety and continuity of business objectives. Among other things, situational awareness requires precise identification and understanding of multilateral dependencies of infrastructure, services, and processes executed by the entity and services provided by external businesses, resulting from the strong interrelationship of the critical infrastructure sectors and the sharing of resources. Accordingly, this paper presents a cyberspace modeling methodology that supports an in-depth analysis of the causes and consequences of threat proliferation in a complex infrastructure–business environment and its implementation in a Situational Awareness Management System (SAMS). An emphasis is placed on threat propagation analysis and dynamic risk assessment mechanisms and how they are used to identify and take preemptive actions protecting or limiting the scope of the threats’ propagation. The paper concludes with insights from a pilot implementation of the system prototype and directions for further work.

Physics-Based Modelling of Plate-Fin Heat Exchangers

Aluminium plate-fin heat exchangers are widely used in automotive, aerospace, and other industrial applications. Extensive research has been conducted on these coolers, yet accurate predictive tools for their thermo-hydraulic performance are still lacking, due to the wide variety of geometric parameters and working fluids involved. This work proposes an original approach based purely on physical principles and established models, combining detailed numerical models for the extended surfaces and manifolds, with global models aimed at accurately evaluating overall head losses and heat transfer rates in plate-fin heat exchangers. Extended surfaces are studied by means of computational models of unitary fin modules under fully developed flow conditions. Entrance effects are analysed through dedicated numerical models. Numerical results on extended surfaces are extended to whole heat exchangers by global models for heat transfer and head losses, based on the ε−NTU method and the Darcy–Weisbach equation, respectively. The proposed approach is presented and validated through the analysis of a case study comprising several heat exchangers featuring different geometries and working fluids. Numerically derived heat transfer rates and head losses are compared with experimental data showing maximum deviations of ±20% for most of the tested configurations, highlighting the strength of the proposed modelling methodology.

Effect of surface roughness on pumping characteristics in solar lasers

We studied the impact of laser rod surface roughness on pumping characteristics in the context of solar lasers. We propose a new approach for modeling light reflectance and transmission from rough surfaces using normal (Gaussian) distributions. We analyzed three modeling methodologies: one representing laser rod surfaces as specular (fully polished), another as diffusive (unpolished), and a third incorporating varying degrees of roughness parameterized by the standard deviation of the normal distribution. To validate these models, an experiment was conducted measuring the transmitted laser radiation from a Ce:Nd:YAG rod. Results indicate that specular models overestimate pumping efficiency by up to 3%, while diffusive models might underestimate it by up to 3.5%. Our work emphasizes the importance of incorporating realistic surface roughness characteristics for accurate modeling of pumping efficiency in solar-pumped.

A nuclear spiral in a dusty star-forming galaxy at z=2.78

The nuclear structure of dusty star-forming galaxies is largely unexplored but harbours critical information about their structural evolution. Here, we present long-baseline Atacama Large (sub-)Millimetre Array (ALMA) continuum observations of a gravitationally lensed dusty star-forming galaxy at z=2.78. We use a pixellated lens modelling analysis to reconstruct the rest-frame 230 dust emission with a mean resolution of ≈55 pc and demonstrate that the inferred source properties are robust to changes in lens modelling methodology. The central 1 kpc is characterised by an exponential profile, a dual spiral arm morphology and an apparent super-Eddington compact central starburst. We find tentative evidence for a nuclear bar in the central 300 pc. These features may indicate that secular dynamical processes play a role in accumulating a high concentration of cold gas that fuels the rapid formation of a compact stellar spheroid and black hole accretion. We propose that the high spatial resolution provided by long-baseline ALMA observations and strong gravitational lensing will give key insights into the formation mechanisms of massive galaxies.

A plea for modelling in ethics

We present an argument about the methodology of ethics, broadly conceived, drawing on recent research on modelling in the philosophy of science. More specifically, we argue that normative ethics should adopt the methodology of modelling. We make our case in two parts. First, despite the perhaps unfamiliar terminology, modelling already happens in ethics. We identify it, and argue that its practice could be improved by recognising that it is modelling and by adopting some methodological lessons from philosophy of science. Second, modelling should be adopted more widely within normative ethics, because it fits well with various methodological ends we shall identify. Models can be used to investigate ethical questions in a manner that is systematic but relatively free of foundational theoretical commitments in first-order ethics. Models are more local, and less ambitious, than theories. They can be used to break deadlocks, by focusing attention on the particularities of a sub-domain and by providing a common tool–the surrogate model system–which each side can use to make their principles precise, illustrate the implications of their view, and identify sources of disagreement or points of agreement. We are pluralists about method, so this is not a call to abandon other philosophical methods. It is simply a plea for modelling, motivated by the method’s independent benefits and its fruitfulness in resolving some persistent methodological problems in ethics.

Learning and playing style differences between K12 and university students related to educational video games

Purpose This paper presents an in-depth study of learners’ learning and playing styles and their implications for educational video games. This study aims to scrutinize the manifestation of learning and playing styles among learners, to identify the style predispositions and dominance and to assess the implication of these results for educational video games. Design/methodology/approach This paper proposes a structured model with a detailed methodology for evaluating and categorizing style predisposition and the learner’s dominant learning and playing style. The multimodal style predisposition aggregates several manifested styles at three distinguished levels to achieve more precise differentiation. The authors analyze the outcomes of an empirical field study of 676 K12 and university students, making an in-depth evaluation of their learning and playing styles. Findings Based on the presented model to determine the level of preferences and dominance of styles, the study reveals that multimodal style predisposition prevails for both learning and playing styles. Most learners manifest several dominant styles. While the Kinesthetic Visual, Auditory, Read/Write and Kinesthetic (VARK) learning style and Logician and Strategist playing styles prevail, the Read/Write and Competitor styles are the least preferred. Research limitations/implications The survey explores particular learning and playing styles – VARK and adaptive technology-enhanced platform for edutainment (ADOPTA). It covers only Bulgarian students, who share common national cultural predispositions. The different sizes of the samples (234 K12 and 432 university students) are also a limitation. Further studies should involve international and equal samples of participants. Practical implications Educational video games should reflect the findings about learners’ preferences and predispositions at the planning, design and practical application level. Social implications Game-based learning and gamification approaches in education should focus on aspects other than competition. Originality/value This paper presents an original methodology for modeling, evaluating and categorizing style predisposition and students’ dominant learning and playing styles and results about these styles of K12 and university students in the context of educational video games.

Predictive Modeling for Electric Vehicle Battery State of Health: A Comprehensive Literature Review

The rising adoption of electric vehicles (EVs) utilizing lithium-ion batteries necessitates a robust understanding of state-of-health (SOH) estimation. The existing literature highlights various SOH estimation models, but a comprehensive comparative analysis is lacking. This paper addresses this gap by conducting an exhaustive review of diverse SOH estimation approaches for EV battery applications, including the direct measurement method, physical-based and data-driven approaches. Results highlight that data-driven methods, particularly those utilizing machine learning techniques, offer superior accuracy and adaptability but often require extensive datasets. In contrast, physical-based approaches provide interpretable insights but are computationally intensive, and direct measurement methods, though simple, lack generalizability. In addition, this paper also systematically reviews the indicators of battery SOH, influential factors affecting battery SOH, and various datasets used for SOH modeling. Future research should focus on integrating multiple modeling methodologies to leverage their combined strengths, enhancing the collection of comprehensive battery lifecycle datasets to support robust model development, and extending the scope of SOH estimation beyond individual cells to encompass entire battery packs.

Unveiling and Evaluating Vulnerabilities in Branch Predictors via a Three-Step Modeling Methodology

With the emergence and proliferation of microarchitectural attacks targeting branch predictors, the once-established security boundary in computer systems and architectures is facing unprecedented challenges. This paper introduces an innovative branch predictor modeling methodology that abstractly characterizes 19 states and 53 operations of branch predictors, aiming to assist hardware designers in addressing overlooked security concerns during the microarchitecture design phase. Building upon this modeling discipline, we develop a symbolic execution-based framework to analyze and derive potential vulnerabilities in branch predictors. This framework finally yields 156 valid three-step attack patterns against branch predictors, including 89 novel variants not discovered in previous work. Subsequently, we extend the framework to automatically generate a benchmark suite for assessing the practical feasibility of derived attacks in real-world scenarios. Evaluation across five commercial Intel processors underscores the substantial threat posed by branch predictor attacks, with 130 of the 156 derived attacks proving viable on at least one processor. Finally, we theoretically model and evaluate 12 secure designs related to branch predictors. The evaluation results demonstrate that existing secure branch predictors can offer better security guarantees than secure speculation schemes, indicating that secure branch predictor designs are promising solutions to maintain the confidentiality and integrity of computer systems.

The Double-Layer Digital Needs Model in Marketing for Sustainable Digital Development

Background: Over the past several years, researchers and industry experts have observed significant shifts in consumer behaviour within digital environments. However, a gap remains in the systematic and critical understanding of these digital consumer needs, particularly in developing customer-oriented, sustainable digital products and services. Objectives: By integrating sustainable development goals (SDGs), the study aims to develop a comprehensive framework for the double-layer digital model, categorizing digital consumer needs into problem-solving and experiential layers. Methodology: This study approach uses a system modelling methodology grounded in the Zachman Framework, adapting it to illustrate the application of various marketing functions throughout the digital product life cycle. Results: The double-layer digital needs model identifies the critical tasks that digital marketers must address to meet diverse consumer needs across product life cycle stages. Additionally, we introduce modified Zachman matrices that align with the SDGs, facilitating the creation of sustainable marketing strategies and consumer-focused digital offerings. Novelty: This research uniquely contributes to marketing theory and practice by presenting a structured model emphasizing integrating sustainable development principles into digital product marketing. The model promotes a shift towards sustainability by empowering digital consumers as co-creators in a way that reinforces responsible consumption, enhances digital literacy, and advances sustainable digital development.

Correlating Fire Incidents with Meteorological Variables in Dry Temperate Forest

Forest fires pose a significant ecological threat, particularly in the Diamer District, Gilgit-Baltistan, Pakistan, where climatic factors combined with human activities have resulted in severe fire incidents. The present study sought to investigate the correlation between the incidence of forest fires and critical meteorological elements, including temperature, humidity, precipitation, and wind speed, over a period of 25 years, from 1998 to 2023. We analyzed 169 recorded fire events, collectively burning approximately 109,400 hectares of forest land. Employing sophisticated machine learning algorithms, Random Forest (RF), and Gradient Boosting Machine (GBM) revealed that temperature and relative humidity during the critical fire season, which spans May through July, are key factors influencing fire activity. Conversely, wind speed was found to have a negligible impact. The RF model demonstrated superior predictive accuracy compared to the GBM model, achieving an RMSE of 5803.69 and accounting for 49.47% of the variance in the burned area. This study presents a novel methodology for predictive fire risk modeling under climate change scenarios in the region, offering significant insights into fire management strategies. Our results underscore the necessity for real-time early warning systems and adaptive management strategies to mitigate the frequency and intensity of escalating forest fires driven by climate change.

An Effective Methodology for Diabetes Prediction in the Case of Class Imbalance.

Diabetes causes an increase in the level of blood sugar, which leads to damage to various parts of the human body. Diabetes data are used not only for providing a deeper understanding of the treatment mechanisms but also for predicting the probability that one might become sick. This paper proposes a novel methodology to perform classification in the case of heavy class imbalance, as observed in the PIMA diabetes dataset. The proposed methodology uses two novel steps, namely resampling and random shuffling prior to defining the classification model. The methodology is tested with two versions of cross validation that are appropriate in cases of class imbalance-k-fold cross validation and stratified k-fold cross validation. Our findings suggest that when having imbalanced data, shuffling the data randomly prior to a train/test split can help improve estimation metrics. Our methodology can outperform existing machine learning algorithms and complex deep learning models. Applying our proposed methodology is a simple and fast way to predict labels with class imbalance. It does not require additional techniques to balance classes. It does not involve preselecting important variables, which saves time and makes the model easy for analysis. This makes it an effective methodology for initial and further modeling of data with class imbalance. Moreover, our methodologies show how to increase the effectiveness of the machine learning models based on the standard approaches and make them more reliable.

An enhanced predictive modelling framework for highly accurate non-alcoholic fatty liver disease forecasting

Non-alcoholic fatty liver disease (NAFLD) is a chronic medical ailment characterized by accumulation of excessive fat in the liver of non-alcoholic patients. In absence of any early visible indications, application of machine learning based predictive techniques for early prediction of NAFLD are quite beneficial. The objective of this paper is to present a complete framework for guided development of varied predictive machine learning models and predict NAFLD disease with high accuracy. The framework employs step–by-step data quality enhancement to medical data such as cleaning, normalization, data upscaling using SMOTE (for handling class imbalances) and correlation analysis-based feature selection to predict NAFLD with high accuracy using only clinically recorded identifiers. Comprehensive comparative analysis of prediction results of seven machine learning predictive models is done using unprocessed as well as quality enhanced data. As per the observed results, XGBoost, random forest and neural network machine learning models reported significantly higher accuracies with improved ‘AUC’ and ‘ROC’ values using preprocessed data in contrast to unprocessed data. The prediction results are also assessed on various quality metrics such as ‘accuracy’, ‘f1-score’, ‘precision’, and ‘recall’ significantly support the need for presented methodologies for qualitative NAFLD prediction modelling.

Development of face image recognition algorithm using CNN in airport security checkpoints for terrorist early detection

Ensuring airport security is of paramount importance to safeguard the lives of passengers and prevent acts of terrorism. In this context, developing advanced technology for early terrorist detection is crucial. This paper presents a novel approach to enhancing security measures at airport checkpoints by applying Convolutional Neural Network (CNN) and Artificial Neural Network (ANN) algorithms in face image recognition. Our system utilizes state-of-the-art artificial intelligence techniques to analyze facial features. Our research uses VGG architecture and pre-trained with face data as a CNN model. This model is used to extract face embedding features from the dataset. These embedding features are then compressed with Principal Component Analysis (PCA) to obtain the meaningful feature as training data for the ANN algorithm. We trained our system using data from 500 identities data with 60 data for each identity. This training enables our system to recognize known terrorists and individuals on watchlists by comparing the facial features of individuals passing through security checkpoints with those in the database. The proposed CNN-ANN-based face recognition system not only enhances airport security but also significantly reduces the processing time for security checks. It can quickly identify potential threats, allowing security personnel to take appropriate actions in real time ensuring a rapid response to security concerns. We present the architecture, training methodology, and evaluation of the CNN-ANN model, achieving a high accuracy of 91.16% and precision of 91.36%. Through this research, we aim to increase airport security and strengthen efforts to combat terrorism, making air travel safer and more secure for all passengers.

An ontology to describe the damage process to built cultural heritage triggered by earthquakes: The CHEDO model

Information on cultural heritage is critical both to understand its value and to support management. Collecting, managing, visualizing, and sharing this information requires standardization. In the last decades, architecture has advanced in information management with the implementation of Building Information Modelling (BIM) methodology. The application of this methodology to cultural heritage has associated challenges due to the complexity and heterogeneity of the information. The integration of ontologies and BIM allows obtaining semantically enriched digital models, which has led to a growing interest in the field of architectural cultural heritage. In this article, the Cultural Heritage Earthquake Damage Ontology (CHEDO) model is presented. This ontology has been designed to document damage and loss caused by earthquakes in cultural heritage, through its integration into BIM systems interconnected with databases, although this integration is not addressed in the present work. The CHEDO model integrates terms and relationships to document the damage process in relation to the building characteristics (architectural, spatial, constructional, and geometrical) and the physical environment of the same. This model, by providing a comprehensive description of the building and its environment, allows for complete and accurate documentation of damage, which supports the planning of post-earthquake restoration and reconstruction. To facilitate interoperability with parallel ontologies in close domains of architectural cultural heritage, CHEDO was designed taking as reference the high-level ontology Conceptual Reference Model (CRM) developed by the International Committee for Documentation (CIDOC), of the International Council of Museums (ICOM). The proposed ontology is structured in four blocks: 'building and surrounding environment', to describe the building and its environment; 'building damage', to represent the damage and its causes; 'location', to describe the location of the building and the external processes; and 'terminology', to categorise and classify certain classes of the ontology.

Balancing Growth and Preservation: Strategic Pathways for Sustainable Rural Tourism in China’s Environmental Landscape

Sustainable rural tourism development is paramount for the socio-economic advancement of rural regions in China. This study systematically prioritizes the key drivers essential for the successful implementation of sustainable rural tourism development by employing the Interpretive Structural Modeling (ISM) and MICMAC analysis methodologies. Through an extensive literature review and expert consultations, we identify and categorize the critical drivers influencing sustainable rural tourism. Utilizing the ISM approach, we construct a hierarchical structure to elucidate the interrelationships among these drivers. Following this, MICMAC analysis is applied to assess their driving and dependence power. We find that government policy and support is the most powerful driver forming the basis for sustainable rural tourism development. Moreover, our findings uncover a complex network of interdependencies, with several drivers emerging as crucial for effective implementation. This research provides invaluable insights for policymakers, stakeholders, and practitioners, offering a robust framework to inform strategic decision-making in fostering sustainable rural tourism development in China.

Accurate and Broadband Three-Phase Motor Modeling Methodology Based on Vector Fitting

Accurate and Broadband Three-Phase Motor Modeling Methodology Based on Vector Fitting