Modeling Process Research Articles

Pandemic‐induced uncertainty and tourism demand: Evidence on the moderator effect of democratic institutions

AbstractThis paper explores the mitigating effect of democratic institutions in the nexus of tourism and pandemic‐induced uncertainty in 24 Organisation for Economic Co‐operation and Development (OECD) countries. Utilizing dynamic panel data analysis in the modeling process, the findings reveal that increased pandemic‐related uncertainty in the target market negatively affects the tourism demand. However, the presence of democratic institutions in the destination country positively affects the tourism demand. Finally, democratic institutions help mitigate the negative effects of pandemic uncertainty on tourism demand. The research highlights that a democratic destination can provide a more favorable environment for tourism development amid pandemic uncertainty.

ConGCNet: Convex geometric constructive neural network for Industrial Internet of Things

The intersection of the Industrial Internet of Things (IIoT) and artificial intelligence (AI) has garnered ever-increasing attention and research interest. Nevertheless, the dilemma between the strict resource-constrained nature of IIoT devices and the extensive resource demands of AI has not yet been fully addressed with a comprehensive solution. Taking advantage of the lightweight constructive neural network (LightGCNet) in developing fast learner models for IIoT, a convex geometric constructive neural network with a low-complexity control strategy, namely, ConGCNet is proposed in this article via convex optimization and matrix theory, which enhances the convergence rate and reduces the computational consumption in comparison with LightGCNet. First, a low-complexity control strategy is proposed to reduce the computational consumption during the hidden parameters training process. Second a novel output weights evaluated method based on convex optimization is proposed to guarantee the convergence rate. Finally, The universal approximation property of ConGCNet is proved by the low-complexity control strategy and convex output weights evaluated method. Simulation results, encompassing four benchmark datasets and the real ore grinding process, demonstrate that ConGCNet effectively reduces computational consumption in the modelling process and improves the model’s convergence rate.

Denoising diffusion post-processing for low-light image enhancement

Low-light image enhancement (LLIE) techniques attempt to increase the visibility of images captured in low-light scenarios. However, as a result of enhancement, a variety of image degradations such as noise and color bias are revealed. Furthermore, each particular LLIE approach may introduce a different form of flaw within its enhanced results. To combat these image degradations, post-processing denoisers have widely been used, which often yield oversmoothed results lacking detail. We propose using a diffusion model as a post-processing approach, and we introduce Low-light Post-processing Diffusion Model (LPDM) in order to model the conditional distribution between under-exposed and normally-exposed images. We apply LPDM in a manner which avoids the computationally expensive generative reverse process of typical diffusion models, and post-process images in one pass through LPDM. Extensive experiments demonstrate that our approach outperforms competing post-processing denoisers by increasing the perceptual quality of enhanced low-light images on a variety of challenging low-light datasets. Source code is available at https://github.com/savvaki/LPDM.

Transistor modeling based on LM‐BPNN and CG‐BPNN for the GaAs pHEMT

AbstractIn order to address the challenges of complex process and low precision in traditional device modeling, double hidden layer back propagation neural network (BPNN) are trained using the conjugate gradient (CG) algorithm and the Levenberg–Marquardt (LM) algorithm, the CG‐BPNN and LM‐BPNN models of small signal for gallium arsenide (GaAs) pseudomorphic high electron mobility transistor (pHEMT) are obtained and analyzed here. At first, the scattering parameters (S‐parameters) of GaAs pHEMT are divided into training set and test set randomly. Experimental results show that the CG‐BPNN model is better than another S‐parameters when predicting ImS12 with mean square error (MSE) of 7.6632e‐06, while LM‐BPNN model predicts ImS12 with MSE of 2.4672e‐06. Meanwhile, the MSE of CG‐BPNN model is higher than LM‐BPNN model when predicting all the S‐parameters. In addition, it shows a smaller fluctuation range for the error curve of LM‐BPNN model, which is more stable than the CG‐BPNN model. Therefore, the double hidden layer LM‐BPNN model is the better choice to characterize the small signal of GaAs pHEMT.

Using multiple-point geostatistics for geomodeling of a vein-type gold deposit

Geostatistical cascade modeling of Mineral Resources is challenging in vein-type gold deposits. The narrow shape and long-range features of these auriferous veins, coupled with the paucity of drill-hole data, can complicate the modeling process and make the use of two-point geostatistical algorithms impractical. Instead, multiple-point geostatistics techniques can be a suitable alternative. However, the most challenging part in implementing the MPS is to use a suitable training data set or training image (TI). In this paper, we suggest using the radial basis function algorithm to build a training image and the DeeSse algorithm, one of the multiple-point statistics (MPS) methods, to model two long-range veins in a gold deposit. It is demonstrated that DeeSse can replicate long-range vein features better than plurigaussian simulation techniques when there is a lack of conditioning data. This is shown by several validation processes, such as comparing simulation results with an interpretive geological block model and replicating geological proportions.

Performance Analysis of a Blockchain Process Modeling: Application of Distributed Ledger Technology in Trading, Clearing and Settlement Processes

This study aims to assess the extent to which blockchain technology (BCT) may constitute an alternative to the conventional stock trading system and emphasize the changing roles of the key parties. It is expected that BCT would enhance the performance of the process across the three stages (i.e. trading, clearing and settlement within the stock exchange environment). A thorough literature review is conducted to understand the BCT performance modeling techniques and approaches (empirical and analytical) and to examine the theoretical potentials and capabilities of BCT in the financial markets. The case study and simulation methods are used to evaluate the impact of BCT implementation in optimizing the process of trading, clearing and settlement in Abu Dhabi Securities Exchange (ADX) stock-trading activities. This paper presents a simulation analysis comparing a blockchain system with a traditional trading system in the context of stock market. The simulation procedures involve modeling processes over different durations and transaction volumes, using metrics such as process time and cycle time to evaluate performance. The performance index combines these metrics with weights to ensure accurate and consistent measurements. Simulation results reveal that the blockchain system significantly outperforms the current trading system, especially at higher transaction volumes, highlighting its scalability and efficiency. A threshold of 30,000 transactions is identified as the point where blockchain’s benefits become apparent. The analysis shows that blockchain significantly elevate the process efficiency. It reduces both cycle time and process time across varying transaction volumes, maintaining consistency and reliability. Additionally, a simple simulation using the Hyperledger Fabric platform demonstrates the practical implementation of a permissioned blockchain for clearing transactions, emphasizing the system's capability to manage high transaction volumes efficiently and securely. The use of blockchain network for handling seamless transactions using pre-defined smart contracts significantly improves the performance of the stock trading processes, specifically in the clearing phase. Interestingly, the BCT system drops the need for a “third party” (i.e. stock custodian) across the three stages. At the end of the paper, we propose a thereat mitigating model for stock trading with a new blockchain system.

ANN-based framework for modeling process induced variation using BSIM-CMG unified model

In this work, we present a machine-learning augmented compact modeling framework for modeling process induced variations in advanced semiconductor devices. The framework employs BSIM-CMG unified compact model at the core and can be used for any advanced devices like GAA nanosheets and nanowires, FinFETs etc. We have validated the model with extensive numerical simulations and experimental data such as 14nm technology FinFET and 24nm technology Nanowire. Our results show excellent accuracy in modeling variability in key electrical parameters of the device including off-current (Ioff), on-current (Ion), threshold voltage (Vth), subthreshold swing (SS) etc. We observe that the overall accuracy of the ML-based framework strongly depends on the nature and physical behavior of the core model used for modeling the nominal device.

Integrated Model for Predicting Demand of Supersonic Transports Under Low-Boom Constraint

Commercial supersonic aircraft could return between 2035 and 2040 due to the desire for fast travel and advancements in technology that could reduce the cost of supersonic transport. Nevertheless, several challenges remain to make supersonic travel a reality. This paper presents an integrated model that ties the aircraft preliminary design process, the economics of aircraft development and operations, and passenger preferences to estimate worldwide demand for subsonic overland, Mach cutoff, and low-boom aircraft design concepts. This comprehensive and iterative methodology takes into account the uncertainty of market demand for supersonic travel and the complexities of supersonic network operations. Based on the study of four conceptual supersonic designs, we found that low-boom aircraft could attract 28% more demand worldwide compared to an optimized Mach cutoff design because of the additional time-saving benefits on overland routes. The results of this work offer valuable guidance to aircraft designers by explicitly tying the technical parameters of a design and the potential market demand. In addition, the integrated model developed may streamline the initial design development cycle by quantifying fleet size as part of an integrated modeling process.

Enhancing climate resilience in northern Ghana: A stochastic dominance analysis of risk-efficient climate-smart technologies for smallholder farmers

Northern Ghana is a semi-arid region characterised by a unimodal rainfall pattern, and hot and dry weather conditions. This, coupled with the reliance on rain-fed agriculture and the lack of resources to employ irrigation methods, makes smallholder farmers in the region increasingly vulnerable to climate-related crop failure. In recent years, climate-smart technologies (CSTs) such as changing planting dates (PD), compartmental bunding (CB), mulching (M), and transplanting (TP) have been recommended to minimise yield losses. However, there is limited information on the most risk-efficient CSTs for crops cultivated in the region. This study used a stochastic dominance approach to identify the most risk-efficient CSTs for maize, rice, and sorghum. The stochastic modelling process used the Aqua-crop model to simulate climate-related yield variability using Ghana climate data, and gross margin variability with crop budgets from literature sources. The study’s findings revealed that changing planting date from April to May was the most risk-efficient choice for maize and sorghum under farmers’ practice and recommended practice. In contrast, transplanting was the most risk-efficient technology for rice farming in the study area. The study also highlights the importance of considering the risk-averse nature of smallholder farmers when selecting CSTs. By identifying the most risk-efficient CSTs, the study can help improve the resilience of smallholder farmers. These findings have important implications for the development and adoption of CSTs in northern Ghana.

Electrofacies-driven 3D-static reservoir modeling of the Late-Cenomanian AbuRoash’G lithounit (Abu-Gharadig Basin, Egypt): Sequence stratigraphic and geomodel constraints for a gas-bearing estuarine system

Estuarine-systems, developed upon transgressive-phases, feature high-quality reservoir-facies, e.g. tidal-bars, that are important stratigraphic-plays critical for hydrocarbon exploration-development. However, capturing their intricate architectural elements (heterogeneity and quality) is still challenging due to the complex stacking-nature and limited-examples. Moreover, defining reservoir-boundaries upon static-modeling of reservoirs cannot be efficient unless it is controlled by stratal-geometries and established depositional-models. To this end, in this study, we performed 3D-static geocellular reservoir-modeling process for the Late-Cenomanian AbuRoash“G” Member (Abu-Gharadig Basin, Egypt) with sequence-stratigraphic and geomodel, relative-geological-time (RGT) model and horizon-stacks, constraints. In this investigation, as an effective-workflow, not only facies-analysis, integrating seismic-stratigraphy and GR-log motifs, was applied for paleo-environment reconstruction, but also machine learning-based electrofacies were applied, through self-organized-maps (SOM), to accurately recognize complex facies-assemblages present. Object-based and pixel-based stochastic-simulation processes were applied upon geocellularly modeling rock and fluid properties, utilizing key-information scales of seismic and well-log data. The results show that three third-order depositional sequences dominate the succession, resting on the Late-Cretaceous unconformity, of which sequence-1 encloses the lowstand and transgressive systems-tracts of the fluvio-estuarine Bahariya and Abu Roash“G” units, respectively. The transgressive phase built AbuRoash“G” lithounit features an estuarine depositional-system encompassing four facies-associations, of which tidal-sand-bars represent significant gas-bearing reservoir-quality facies. The tidal-bar facies’ efficient reservoir quality calls for attention and testing in future development plans and investigation in similar settings. Furthermore, the facies-constrained workflow established in this study, for reservoir modeling, can worldwide help identify the ultimate reservoir-configuration, as long as the 3D-static modeling process is controlled by the stratal and geomodel restraints.

The Reduced-Order Modeling Approach for a Double-Damper Concept: A Comparison with a Single Damper for Comfort Analysis

This paper explores the modeling and simulation of an innovative double-damper suspension system, evaluating its effectiveness through different test scenarios. The double damper integrates two individual dampers into a unified assembly. The modeling process involves representing the damper as two distinct dampers and a body block, accounting for the additional degree of freedom introduced by combining the two dampers. Simulink/MATLAB is employed for modeling the pressure, discharge, and force equations of the damper. A simplified quarter-car model is designed to conduct simulations for different road profiles, evaluating the efficacy of this double-damper model. The reduced-order modeling approach, suitable for complex systems like dampers, is utilized. Dedicated mathematical models are utilized to examine both single- and double-damper configurations, with the resulting non-linear equations solved using Newton’s iterative method. The equations derived for the single damper provide the basis for modeling the double-damper system. In this model, two separate dampers, each possessing similar properties, are simulated and considered to be rigidly linked at their connection point. Consequently, it is assumed that a portion of the force and velocity experienced by the lower damper is transmitted to the upper damper, and vice versa. Simulation results demonstrate that the innovative double-damper design outperforms a single passive damper in attenuating the oscillations of both the sprung and unsprung masses. Moreover, this innovative concept offers increased adaptability to balance between ride comfort and road holding, a feature previously limited to passive suspension systems.

Non-linear three-mode coupling of gravity modes in rotating slowly pulsating B stars

Context. Slowly pulsating B (SPB) stars display multi-periodic variability in the gravito-inertial mode regime with indications of non-linear resonances between modes. Several have undergone asteroseismic modeling in the past few years to infer their internal properties, but only in a linear setting. These stars rotate fast, so that rotation is typically included in the modeling by means of the traditional approximation of rotation (TAR). Aims. We aim to extend the set of tools available for asteroseismology, by describing time-independent (stationary) resonant non-linear coupling among three gravito-inertial modes within the TAR. Such coupling offers the opportunity to use mode amplitude ratios in the asteroseismic modeling process, instead of only relying on frequencies of linear eigenmodes, as has been done so far. Methods. Following observational detections, we derive expressions for the resonant stationary non-linear coupling between three gravito-inertial modes in rotating stars. We assess selection rules and stability domains for stationary solutions. We also predict non-linear frequencies and amplitude ratio observables that can be compared with their observed counterparts. Results. The non-linear frequency shifts of stationary couplings are negligible compared to typical frequency errors derived from observations. The theoretically predicted amplitude ratios of combination frequencies match with some of their observational counterparts in the SPB targets. Other, unexplained observed ratios could be linked to other saturation mechanisms, to interactions between different modes, or to different opacity gradients in the driving zone. Conclusions. For the purpose of asteroseismic modeling, our non-linear mode coupling formalism can explain some of the stationary amplitude ratios of observed resonant mode couplings in single SPB stars monitored during 4 years by the Kepler space telescope.

STRIDE-Based Cybersecurity Threat Modeling, Risk Assessment and Treatment of an In-Vehicle Infotainment System

In modern automobiles, the infotainment system is crucial for enhancing driver and passenger capabilities, offering advanced features such as music, navigation, communication, and entertainment. Leveraging Wi-Fi, cellular networks, NFC, and Bluetooth, the system ensures continuous internet connectivity, providing seamless access to information. However, the increasing complexity of IT connectivity in vehicles raises significant cybersecurity concerns, including potential data breaches and exposure of sensitive information. To enhance security in infotainment systems, this study applied component-level threat modeling to a proposed infotainment system using the Microsoft STRIDE model. This approach illustrates potential component-level security issues impacting privacy and security concerns. The study also assessed these impacts using SAHARA and DREAD risk assessment methodologies. The threat modeling process identified 34 potential security threats, each accompanied by detailed information. Moreover, a comparative analysis is performed to compute risk values for prioritizing treatment, followed by recommending mitigation strategies for each identified threat. These identified threats and associated risks require careful consideration to prevent potential cyberattacks before deploying the infotainment system in automotive vehicles.

Models of data processing and logical access segregation considering the heterogeneity of entities in information systems

The subject of the research is the process of logical access segregation to data in information systems. The aim of the article is to improve the accuracy and reliability of modeling processes for data processing and logical access segregation considering the heterogeneity of entities in information systems. The tasks to be solved include: conducting a comparative analysis of modern data access distribution models, integrating simpler role-based models, synthesizing hierarchical role-based models, developing enforced typing models based on trust relationships, and presenting the main provisions of the security policy integration process. The methods used are: systems analysis, component design, logical and simulation modeling in the form of role-based access segregation models. The results obtained include: development of data processing models and logical access segregation in information systems that take into account the heterogeneity of entities and the multi-level structure of information systems. The models differ from known ones by considering the heterogeneity of entities and the multi-level structure of information systems. This has increased scalability by up to 35% due to a modular approach to defining security policies. Additionally, the developed model demonstrates 25% higher implementation practicality as it easily integrates with existing access control systems and adapts to various platforms and environments. The proposed models are effective for large information systems and distributed environments due to their modularity and ability to adapt to different operational conditions. This ensures reliable access control in systems with numerous subjects and objects. The implementation of multi-level RBAC models has improved the accuracy and reliability of results.

Three-Dimensional Geological Modelling in Earth Science Research: An In-Depth Review and Perspective Analysis

This study examines the development trajectory and current trends of three-dimensional (3D) geological modelling. In recent years, due to the rising global energy demand and the increasing frequency of regional geological disasters, significant progress has been made in this field. The purpose of this study is to clarify the potential complexity of 3D geological modelling, identify persistent challenges, and propose potential avenues for improvement. The main objectives include simplifying the modelling process, improving model accuracy, integrating different data sources, and quantitatively evaluating model parameters. This study integrates global research in this field, focusing on the latest breakthroughs and applications in mineral exploration, engineering geology, geological disaster assessment, and military geosciences. For example, unmanned aerial vehicle (UAV) tilt photography technology, multisource data fusion, 3D geological modelling method based on machine learning, etc. By identifying areas for improvement and making recommendations, this work aims to provide valuable insights to guide the future development of geological modelling toward a more comprehensive and accurate “Transparent Earth”. This review underscores the global applications of 3D geological modelling, highlighting its crucial role across various sectors such as mineral exploration, the oil and gas industry, urban planning, geological hazard assessment, and geoscientific research. The review emphasizes the sector-specific importance of this technology in enhancing modelling accuracy and efficiency, optimizing resource management, driving technological innovation, and improving disaster response capabilities. These insights provide a comprehensive understanding of how 3D geological modelling can significantly impact and benefit multiple industries worldwide.

Ecosystem service trade‐offs and synergies and their drivers in severely affected areas of the Wenchuan earthquake, China

AbstractThe suddenness and destructive power of earthquake disasters pose significant threats to ecosystems. Understanding the changes in post‐earthquake ecosystem services and the complex relationships of their trade‐offs and synergistic effects is crucial for post‐disaster recovery and achieving a holistic and sustainable balance of human well‐being. However, quantifying post‐disaster ecosystem services and these intricate relationships, as well as probing the mechanisms behind their formation from a spatial perspective, remain challenging. This study presents a comprehensive framework that integrates spatial statistical methods, the InVEST model, geodetectors, and the Multiscale Geographically Weighted Regression model (MGWR) to quantify trade‐offs and synergistic effects among ecosystem services before and after earthquakes, and to investigate the spatial heterogeneity of these relationship effects with various driving factors. Results indicate (1) Typical ecosystem services, that is, water yield, soil conservation, carbon storage, and habitat quality, have more weak trade‐off effects and fewer synergistic effects. These relationship intensities vary considerably spatially, demonstrating some agglomerative distribution, suggesting that the ecosystem has not yet recovered to a good state after the quake; (2) The impact of different factors on the trade‐offs and synergies between ecosystem services varies. Interaction effects of driving factors are stronger than singular effects. Relationships between soil conservation and other ecosystem services are primarily influenced by topography and climate. Relationships involving carbon storage are mainly affected by vegetation and climate, while those concerning habitat quality are attributed mostly to human and climatic factors; (3) In the modeling process, considering the different spatial scales of driving factors, MGWR outperforms the Geographically Weighted Regression in explaining the relationship between ecosystem services and driving factors. Moreover, several spatially feasible and targeted ecological measures are proposed to enhance the synergy among ecosystem services. By focusing on the changes in ecosystem services, this study reveals the intrinsic mechanisms of trade‐offs and synergies between ecosystem services, offering insights for related studies in other regions.

Features of Digital Tools Utilized in Mathematical Modeling Process

Features of Digital Tools Utilized in Mathematical Modeling Process

3D printing for the reinterpretation of architectural heritage: Proposal of a model

3D printing for the reinterpretation of architectural heritage stands out as one of the most efficient ways of using contemporary printing technologies in terms of architecture. The aim of the study is to present a comprehensive methodology on how to use 3D printing techniques for reinterpretation of architectural heritage. In this context, the scope of the study consists of pioneering information obtained from literature studies focusing on the details of the use of three-dimensional printing techniques in the promotion, preservation and remodeling of cultural heritage and architectural heritage components. The method of the study was determined as identifying leading themes through literature research and presenting a method proposal by grouping conceptually related themes. The unique value of the study is important in that the model to be created offers a potential to affect mass production tools, offices and local governments by incorporating contemporary architectural heritage products into a modeling process that can produce more innovative and faster solutions. The findings of the study show that the different techniques used can respond to different detail scales and different contextual challenges. Each modeling method and strategy creates different feedback mechanisms, mediating the recognition of the architectural heritage element by larger user groups or the more effective protection of the architectural heritage element. In the model presented as a result of the study, which mediates the gathering of the techniques used in different sample projects on a common working ground with a series of successive strategies, a comprehensive basis for which 3D printing solution will be used for which problem is presented and solution suggestions are offered for a case-oriented 3D printing strategy.

Modelling the determinants for sustainable smart city through interpretive structure modelling and analytic hierarchy process

Rapid increasing urbanization and resource scarcity are global phenomena nowadays, leading to the urban transformation of cities into smart cities. This article explores sustainability by using the lens of the spirit of place (SOP) for smart city development by proposing a model for the transformation of the cities into smart cities and attainment of the sustainable development simultaneously based on Interpretive Structure Modelling (ISM) and Analytic Hierarchy Process (AHP). This study followed a systematic approach by utilizing an analytical framework that included an extensive literature review and urban experts' opinions for the identification of a pool of indicators and its evaluation for validity, pilot testing, and administration of a questionnaire to a population sample. The study utilizes a sample of 142 participants who have witnessed the transformation of their city over the years. The research showed that every place has its own identity known to be the ‘spirit of place’ that helps in assessing the sustainable characteristics and utilizing that in the path of planning and development for the attainment of sustainable development. It also showed that urban developers should consider local populations’ views and important aspects in designing and planning development projects to achieve sustainable development with resilient infrastructure. This study will help facilitate sustainability at a local level for urban developers, planners, and decision-makers while crafting strategic plans.

Land cover fraction mapping across global biomes with Landsat data, spatially generalized regression models and spectral-temporal metrics

Mapping land cover in highly heterogeneous landscapes is challenging, and classifications have inherent limitations where the spatial resolution of remotely sensed data exceeds the size of small objects. For example, classifications based on 30-m Landsat data do not capture urban or other heterogeneous environments well. This limitation may be overcome by quantifying the subpixel fractions of different land cover types. However, the selection process and transferability of models designed for subpixel land cover mapping across biomes is yet challenging. We asked to what extent (a) locally trained models can be used for sub-pixel land cover fraction estimates in other biomes, and (b) training data from different regions can be combined into spatially generalized models to quantify fractions across global biomes. We applied machine learning regression-based fraction mapping to quantify land cover fractions of 18 regions in five biomes using Landsat data from 2022. We used spectral-temporal metrics to incorporate intra-annual temporal information and compared the performance of local, spatially transferred, and spatially generalized models. Local models performed best when applied to their respective sites (average mean absolute error, MAE, 9–18%), and also well when transferred to other sites within the same biome, but not consistently so for out-of-biome sites. However, spatially generalized models that combined input data from many sites worked very well when analyzing sites in many different biomes, and their MAE values were only slightly higher than those of the respective local models. A weighted training data selection approach, preferring training data with a lower spectral distance to the image data to be predicted, further enhanced the performance of generalized models. Our results suggest that spatially generalized regression-based fraction models can support multi-class sub-pixel fraction estimates based on medium-resolution satellite images globally. Such products would have great value for environmental monitoring in heterogeneous environments and where land cover varies along spatial or temporal gradients.