Modeling Methodology Research Articles

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 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.

Application of Artificial Neural Network (ANN) in Ultrasound‐Assisted Extraction of Bioactive Compounds

ABSTRACTArtificial neural network (ANN) is regarded as a promising tool among the recent trends of modeling methodologies for the nonlinear mapping of multiple variables. In the case of the ultrasound‐assisted extraction (UAE) process, the majority of the response‐based predictive models are administered with nonlinear modeling approaches. Numerical models provide restricted extension and abilities to show such nonlinear prescient conditions. Due to this, ANN modeling gets considerable scope for effectively mapping the variables. In addition, the essential of no prior specific, in the event that there ought to emerge an event of fitting the data or information, make ANN the most popular and compelling one for modeling the extraction process. This paper provides the basics of principles and applications of ANNs in process modeling and optimization of different UAE‐based extraction perspectives. In addition, the prevalence of the UAE process in diverging from other extraction methods is highlighted. Further, the impact of various process factors like ultrasound intensity, exposure time, and temperature during the UAE interaction and their prescient modeling approaches for efficient controlling of the responses are detailed. Hence, this review would be useful for modeling and optimization of thermal and non‐thermal‐based extraction processes, which would assist the food processing industries to improve food quality.

Unlocking the Nexus: Tourism, Clean Energy, Innovation, and Environmental Sustainability in the Top 20 Tourist Nations.

This study investigates the interplay between tourism activities, renewable energy utilization, and environmental impacts by analyzing a panel of the top 20 tourism nations over the period from 2005 to 2020. This research is motivated by an increasing interest in the intersection of tourism, a significant economic driver, and the objectives of environmental sustainability. The discourse surrounding the impact of tourism on environmental health is substantial, particularly in relation to the adoption of renewable energy and the carbon footprint associated with the sector. The research posits that the advancement of tourism has a beneficial impact on the consumption of renewable energy, subsequently aiding in the promotion of environmental sustainability. This analysis delves into the dual impact of tourism, suggesting that while it has the potential to enhance the utilization of renewable energy, it may concurrently present challenges to the quality of the environment. The investigation utilizes sophisticated econometric modelling methodologies, namely the Cross-Sectional Autoregressive Distributed Lag (CS-ARDL) and Nonlinear Autoregressive Distributed Lag (NARDL) models, to analyze these relationships. The methodologies employed facilitate a comprehensive examination of both short-term and long-term impacts within the chosen nations throughout the designated timeframe. The results indicate a statistically significant and positive correlation between the development of tourism and the consumption of renewable energy, thereby reinforcing the hypothesis that an increase in tourism activity is associated with a rise in the utilization of clean energy sources. The observed relationship is consistent across both short-term and long-term analyses, indicating that tourism contributes positively to the adoption of renewable energy. The study reveals that tourism not only facilitates the integration of renewable energy but is also linked to environmental degradation, underscoring the imperative for sustainable practices. The research findings indicate that tourism has the potential to catalyze the adoption of renewable energy, presenting significant advantages for environmental sustainability.

DEVELOPMENT AND OPTIMIZATION OF MECHANISTICALLY SOUND DEMENTIA CARE INTERVENTIONS

Abstract Optimizing care for persons living with dementia (PLWD) and their caregivers (CGs) is an urgent priority. A recent AHRQ report highlights the poor quality of existent dementia interventions emphasizing the need to develop methodologically sound interventions that test theory-based mechanisms hypothesized to underlie intervention effects. This symposium will describe methodology and applications of the NIH Stage Model and the Science Of Behavior Change (SOBC) experimental medicine approach and how they inform interventions for PLWD and their CGs. Dr. Cornelius will introduce SOBC and discuss complexities of specifying conceptual models, measuring mechanisms, and study methodology in a dyadic context. Dr. Vranceanu will introduce the NIH stage model and discuss the development and refinement of the Mindfulness and Self-Compassionate Care Program for stressed CGs of PLWD who endorse behavioral symptoms (NIH Stage 1A), showcasing mechanism and measure selection, feasibility testing, and first test of mechanistic target engagement. Dr. Mroz will describe a Stage 0 Appreciative Inquiry approach, in partnership with PLWD (mild-moderate), to define promising pathways for measuring and intervening to bolster purpose in the wake of a dementia diagnosis. Dr. Plys will present feasibility, mechanism engagement, and clinical outcome results of an NIH Stage 1B trial comparing a mindfulness mobile app to a podcast control for stressed CGs of PLWD. Dr. Berridge will describe an intervention’s non-linear pathway to Stage 1B mechanistic research to support empowered shared decision making about technology use for dementia care. The discussant, Dr. Rice will contextualize these findings within the larger literature and NIA funding opportunities.

Building a sustainable institutional model for ornamental fish farming export villages in Indonesia

ABSTRACT Despite its great fisheries biodiversity, Indonesia is not the world's largest ornamental fish exporter. To improve export competitiveness, it is also necessary to strengthen the cluster competitiveness through institutional development. Indonesia contains several ornamental fish-producing regions, and the government has designated several aquaculture farming villages, including four regions for ornamental fish farming. This research aims to build a sustainable institutional model of an ornamental fish farming export village (OFFEV) that is expected to increase Indonesia's ornamental fish exports. The study utilized the Interpretive Structural Modeling (ISM) methodology. This approach aims to ascertain the hierarchical prioritization of stakeholders and the requisite programme stages for achieving the success of the OFFEV initiative. Through identification, judgment, and validation from 18 experts who participated in the study, the results reveal that the fisheries department, Ministry of Marine Affairs and Fisheries, trade attachés, customs, and research institutions can drive the OFFEV. Short-term programmes are the main priorities, including sustainable cultivation and quarantine assistance, cooperative establishment, facilities and infrastructure support, broodstock aid, funding, product marketing training, and partnership programmes. Finally, based on the ISM results and expert opinion, we built an OFFEV model centred on export-oriented Village-Owned Enterprises or multi-party cooperatives.

Threat Modelling and Security Enhancements in Wireless Body Area Networks for Smart Healthcare

Wireless Body Area Networks (WBANs) are critical components of emerging smart healthcare solutions. Due to the intrinsic nature of wireless communication and the potentially sensitive medical data they carry, such networks are often very vulnerable to many types of security threats. As a result, the security of WBANs becomes a prime concern when they are considered for use in healthcare or medical applications. This text enumerates common security threats that a typical WBAN should prepare for. It also provides a set of enhancement mechanisms and techniques that could be implemented to augment the security of WBANs. Furthermore, a vital precautionary activity that should be taken in the initial steps towards developing a secure WBAN system— threat modeling—is also described. This text presents two elaborated example cases for the threat modeling technique for WBANs: patrolling and insulin injection WBANs. The cases demonstrate that threats in WBANs are further sources of vulnerabilities, beyond the definition of threats from common knowledge, that should be evaluated and addressed before deployment. Results show that there are plenty of possible problems that threaten the security and privacy of WBAN systems, which should be taken into strong consideration. It is believed that the proposed threat modeling methodology here will significantly contribute to secure designs of WBANs and can also potentially be generalized to other embedded systems as well.

Precise hexagonal pixel modeling and an easy-sharing storage scheme for remote sensing images based on discrete global grid system

ABSTRACT Discrete Global Grid Systems (DGGSs) are an emerging Earth reference model that support the integration and analysis of remote sensing (RS) data. Grid modeling, sampling, quantization, and storage are the key points and difficulties of DGGS. The Icosahedral Snyder Equal-area Aperture-4 Hexagonal DGGS was introduced as the basic framework to improve the geospace sampling efficiency. We presented an approach for precise hexagonal pixel modeling and an easy-sharing storage scheme compatible with open-standard formats for RS images based on the DGGS. Firstly, the proposed interpolation is computed by overlaps between quadrilateral and hexagonal pixels. The hexagonal grids were then mapped onto the icosahedral surface, and a strict correspondence between hexagonal and rectangular pixels is established. The open-standard formats were used to accurately store the hexagonal attribute values and metadata. Finally, a multiscale hexagonal aggregation algorithm based on the dataset was designed. Experiments showed that the proposed modeling methodology had a higher accuracy and smaller errors. The hexagonal data can be stored as regular rectangles with a fixed pattern in any standard format. This storage scheme was more conducive to data processing and sharing compared to SMOS, which was expected to promote hexagonal DGGS in RS data organization, processing and sharing.

Numerical simulation of carbonaceous raw material combustion in a coal seam channel

Purpose. The research aims to investigate the combustion behavior of carbonaceous raw materials, specifically pulverized coal, in a cylindrical channel within a coal seam. The research focuses on understanding the temperature field dynamics, the distribution of combustion products, and the stabilization of the combustion process over time. Methods. The research uses advanced Computational Fluid Dynamics (CFD) modeling using non-premixed combustion and the k-epsilon turbulence model. A cylindrical channel with a length of 30 m and a diameter of 1 m is selected, reflecting optimal conditions for co-gasification based on previous studies. Heat transfer processes are incorporated by activating the energy equation, accounting for heat generation from chemical reactions and its transfer via convection, conduction, and radiation. These considerations accurately represent the thermal and flow dynamics in the confined geometry. Findings. The simulation indicates the temperature field stabilization, with a peak of 1540°C achieved in the combustion zone after 24 hours, gradually decreasing to approximately 520°C further downstream. Oxidation reactions are most active within the first 6 m of the channel, producing CO2 as the primary combustion product. The flow velocity analysis indicates intense turbulence near the inlet, enabling efficient mixing of fuel and oxidizer. As the process progresses, turbulence intensity decreases, maintaining a steady distribution of thermal energy and stable downstream flow behavior. Originality. The research has resulted in the development of a comprehensive methodology for modeling pulverized coal combustion process in geometrically constrained coal seam channels, representing the staged progression of the process (early, mid, and final stages). Patterns of temperature field stabilization and the distribution of chemical species along the channel have been identified. Practical implications. TThe findings provide a foundation for optimizing underground coal combustion and co-gasification processes to improve geo-reactor systems’ efficiency.

Using agent-based modeling method to assess the quality of architectural and planning solutions for open public spaces

This article considers the application of the agent-based modeling method using the AnyLogic program to assess the quality of architectural and planning solutions for open public spaces, using as an example the Embankment in the city of Polevskoy. The methodology of agent-based modeling is an effective tool for studying interactions between various agents in public spaces and evaluating their impact on the living environment, behavior model. In this work, we present the process of creating a model, defining key agents and influencing factors, as well as the analysis results that allow conclusions to be drawn about the quality of the architectural and planning solution for the Embankment in a small city.

Reproduction of Road Scenarios for Simulated Driving Using LiDar Surveying Technique

Nowadays, driving simulation devices represent a continuously evolving and developing area in the world of virtual reality. One of the fundamental elements in the design of driving software is the track model. This work aims to study the use of advanced technologies for the three-dimensional modeling of a racing track in a driving simulator. Specifically, it employs the LiDAR methodology to acquire the three-dimensional coordinates of a 1 km long circuit located on the Fisciano campus of the University of Salerno. The purpose of this work is to explain and present a novel track acquisition and modeling methodology within the realm of simulated driving reality. Following the study, the Virtual Reality Laboratory’s driving simulator at the Department of Industrial Engineering conducted tests to validate the proposed circuit model. The test rides analyzed the realism of the driving experience, thereby validating the proposed track model. This phase was complemented by a series of proposals for possible future developments in the field of three-dimensional modeling applied to driving simulation and beyond. In the end, the 3D model obtained demonstrated the high definition of the acquired result and the speed with which multiple data were obtained simultaneously, thanks to the laser scanner used.