Modeling Methodology Research Articles

Global Quality of Service (QoX) Management for Wireless Networks

In the fast-changing technological landscape, novel applications are emerging with the potential to reshape the world. These applications, while promising, impose stringent requirements in terms of quality of service (QoS). The advent of wireless networks like 5G, 6G and Wi-Fi 6 brings about resource management solutions to ensure these requirements while meeting the user expectations within the interconnected environment. Nevertheless, user behaviors are also evolving, highlighting the importance of satisfaction and quality of experience (QoE). Furthermore, changes in user behavior trigger shifts in business models, where the quality of business (QoBiz) takes on a pivotal role. This evolving ecosystem, encompassing QoS, QoE, and QoBiz, demands a comprehensive and adaptable approach that conventional QoS management frameworks fail to perform. This paper introduces an implementation methodology for a global QoS management model named QoXphere. The implementation methodology is grounded in machine learning techniques and addresses the multifaceted aspects of quality of service (QoX) and their interconnections within wireless networks. The objective is to facilitate dynamic resource management that not only elevates user satisfaction but also optimizes provider benefits. Real-world examples illustrate the methodology’s applicability in widely deployed networks, complemented by simulated scenarios of modern network environments that further validate the approach.

BIM-Based Strategies for the Revitalization and Automated Management of Buildings: A Case Study

This study explores the transformative potential of integrating Building Information Modeling (BIM) and Generative Design methodologies in heritage conservation and building management. By utilizing BIM, detailed architectural, structural, and MEP models were created, facilitating precise design and effective stakeholder collaboration. Generative Design enabled the exploration of multiple design solutions, optimizing spatial layouts and structural integrity. The project also integrated automated management systems and IoT sensors to enhance real-time monitoring, energy efficiency, and user comfort through the development of a digital twin. Despite encountering challenges such as technical complexities and budget constraints, the project successfully preserved the cinema’s historical essence while incorporating modern functionalities. The findings highlight the contributions of BIM and Generative Design to the AEC industry, emphasizing their role in improving design accuracy, operational efficiency, and sustainability. This research provides valuable insights for future projects in heritage conservation, offering a blueprint for balancing historical preservation with contemporary needs. The revitalization of the “Ex Cinema Santa Barbara” in Paternò exemplifies these advancements, demonstrating how these technologies can restore and modernize culturally significant historical buildings effectively.

Applications of neural networking in Eyring-Powell nanofluid dynamics on a rotating surface in a porous medium

One of the fundamental aspects of solving difficult and nonlinear mathematical ideas is the use of Artificial Neural Networks due to their exceptional efficiency in handling such problems. In many complex fields such as computational fluid system, biological computation, and biotechnology, a distinct computing structure is provided by Artificial Neural Networks, which is extremely valuable. The main purpose of this article is to dig out the abilities of the Levenberg-Marquardt technique using back-propagation artificial neural networks regarding the fluid mechanics and the heat transport assessment in nanoparticles. This interdisciplinary field explores heat and mass transfer through objects and fluids, and their impacts on temperature as well as concentration distributions. With the help of mathematical modelling and numerical solution methodologies, researchers can simulate and analyze these processes. The present analysis communicates the Eyring-Powell fluid flow caused by a rotating disk placed in the horizontal direction. The fluid flow over a rotating disk through non-linear partial differential equations is modeled. After converting the partial differential equations to ordinary ones, they are tackled numerically through shooting technique. The Levenberg-Marquardt algorithm using back-propagation artificial neural networks technique is used with reference datasets, having 70 % training, 15 % testing, and 15 % to validation. The method is validated with the help of mean squared error, error histogram and comprehensive regression analysis. These figures show the accuracy of the proposed method for solving nonlinear problems. Flow features such as velocity, temperature as well as the concentration profiles are exemplified quantitatively and have been graphically discussed. Velocity of the fluid decreases for porosity and increases for fluid parameter while temperature increases for thermophoresis and Brownian motion parameters. Consistency is shown by getting a minimum absolute error approaching zero, showing the strength of the proposed approach.

Review of Fourth-Order Predictive Modeling and Illustrative Application to a Nuclear Reactor Benchmark. I. Typical High-Order Sensitivity and Uncertainty Analysis

This work (in two parts) will review the recently developed predictive modeling methodology called “4th-BERRU-PM” and its applicability to nuclear energy systems as exemplified by an illustrative application to the Polyethylene-Reflected Plutonium (acronym: PERP) OECD/NEA reactor physics benchmark. The acronym 4th-BERRU-PM designates the “Fourth-Order Best-Estimate Results with Reduced Uncertainties Predictive Modeling” methodology, which uses the Maximum Entropy (MaxEnt) principle to incorporate fourth-order experimental and computational information, including fourth (and higher) order sensitivities of computed model responses to model parameters, while yielding best-estimate results with reduced uncertainties for the first fourth-order moments (mean values, covariance, skewness, and kurtosis) of the optimally predicted posterior distribution of model results and calibrated model parameters. The 4th-BERRU-PM methodology encompasses the scopes of high-order sensitivity analysis (SA), uncertainty quantification (UQ), data assimilation (DA) and model calibration (MC), as will be illustrated in this work by means of the above-mentioned OECD/NEA reactor physics benchmark. This benchmark is modeled using the neutron transport Boltzmann equation involving 21,976 imprecisely known parameters, the solution of which is representative of “large-scale computations”. The model result (“response”) of interest is the leakage of neutrons through the outer surface of this spherical benchmark, which can be computed numerically and measured experimentally. Part 1 of this work illustrates the impact of high-order sensitivities, in conjunction with parameter standard deviations of various magnitudes, on the determination of the expected value and variance of the computed response in terms of the first four moments of the distribution of the uncertain model parameters. Part 2 of this work will illustrate the capabilities of the 4th-BERRU-PM methodology for combining computational and experimental information, up to and including forth-order sensitivities and distributional moments, for producing best-estimate values for the predicted responses and model parameters while reducing their accompanying uncertainties.

Hierarchical Optimization Framework for Layout Design of Star–Tree Gas-Gathering Pipeline Network in Discrete Spaces

The gas-gathering pipeline network is a critical infrastructure for collecting and conveying natural gas from the extraction site to the processing facility. This paper introduces a design optimization model for a star–tree gas-gathering pipeline network within a discrete space, aimed at determining the optimal configuration of this infrastructure. The objective is to reduce the investment required to build the network. Key decision variables include the locations of stations, the plant location, the connections between wells and stations, and the interconnections between stations. Several equality and inequality constraints are formulated, primarily addressing the affiliation between wells and stations, the transmission radius, and the capacity of the stations. The design of a star–tree pipeline network represents a complex, non-deterministic polynomial (NP) hard combinatorial optimization problem. To tackle this challenge, a hierarchical optimization framework coupled with an improved genetic algorithm (IGA) is proposed. The efficacy of the genetic algorithm is validated through testing and comparison with other traditional algorithms. Subsequently, the optimization model and solution methodology are applied to the layout design of a pipeline network. The findings reveal that the optimized network configuration reduces investment costs by 16% compared to the original design. Furthermore, when comparing the optimal layout under a star–star topology, it is observed that the investment needed for the star–star topology is 4% higher than that needed for the star–tree topology.

Investigation of Machine Learning Regression Techniques to Predict Critical Heat Flux Over a Large Parameter Space

A unifying and accurate model to predict critical heat flux (CHF) over a wide range of conditions has been elusive since wall boiling research emerged. With the release of the experimental data utilized in the development of the 2006 Groeneveld CHF lookup table (LUT), by far the most extensive public CHF database available to date (nearly 25 000 data points), the development of data-driven prediction models over a large parameter space in simple geometry (vertical, uniformly heated round tubes) can be achieved. Furthermore, the popularization of machine learning techniques to solve regression problems has led to more advanced tools for analyzing large and complex databases. This work compares three machine learning algorithms to predict the CHF database. For each selected regression algorithm (ν-Support vector, Gaussian process, and neural network), a set of optimized hyperparameters is applied. Among the investigated algorithms, the neural network emerges as the most effective, achieving a CHF predicted/measured factor standard deviation of 12.3%, three times less than that of the LUT. In comparison, the Gaussian process regression and the ν-support vector regression achieve a standard deviation of 17.7%, about two times lower than the LUT. Hence, all considered algorithms significantly outperform the LUT prediction performance. The neural network model and training methodology are designed to prevent overfitting, which is confirmed by data analyses of the CHF predictions. Finally, future development directions (including data coverage, transfer learning, and uncertainty quantification) are discussed.

Reflective coatings: Enhancing building performance and sustainability

Reflective coatings stand at the forefront of sustainable construction, offering a powerful solution to industry challenges posed by climate change, resource scarcity, and rising energy costs. These innovative coatings significantly enhance energy efficiency, improve thermal comfort, and contribute to urban heat management by reducing buildings' absorption of solar heat. This leads to a decreased reliance on air-conditioning systems, resulting in substantial energy savings and a lower carbon footprint for building operations. Our comprehensive study examines these coatings' application on roofs, walls, and windows, confirming their effectiveness in maintaining cooler building interiors and reducing the demand on mechanical cooling systems. By meticulously analyzing materials, application techniques, and modeling methodologies, the paper elucidates the coatings' pivotal role in promoting environmentally responsible building practices. The findings reveal that reflective coatings have the potential to reduce solar heat gain by about 40 %. This reduction can lead to a corresponding indoor temperature drop of 2–4°C in naturally ventilated buildings or a decrease in cooling energy use in air-conditioned buildings, provided the air conditioning system is not undersized. These energy savings are particularly significant in urban environments, where widespread adoption could potentially lower ambient temperatures by approximately 1°C, thus addressing the urban heat island (UHI) effect more effectively. In conclusion, our research offers a quantified assessment of reflective coatings, emphasizing their value within the broader discourse of environmentally responsible construction. Their substantial potential to support urban sustainability objectives is evident, marking them as a promising avenue for future development and implementation.

Fuzzy Logic Prediction of Hypertensive Disorders in Pregnancy Using the Takagi–Sugeno and C-Means Algorithms

Hypertensive disorders in pregnancy, which include preeclampsia, eclampsia, and chronic hypertension, complicate approximately 10% of all pregnancies in the world, constituting one of the most serious causes of mortality and morbidity in gestation. To help predict the occurrence of hypertensive disorders, a study based on algorithms that help model this health problem using mathematical tools is proposed. This study proposes a fuzzy c-means (FCM) model based on the Takagi–Sugeno (T-S) type of fuzzy rule to predict hypertensive disorders in pregnancy. To test different modeling methodologies, cross-validation comparisons were made between random forest, decision tree, support vector machine, and T-S and FCM methods, which achieved 80.00%, 66.25%, 70.00%, and 90.00%, respectively. The evaluation consisted of calculating the true positive rate (TPR) over the true negative rate (TNR), with equal error rate (EER) curves achieving a percentage of 20%. The learning dataset consisted of a total of 371 pregnant women, of which 13.2% were diagnosed with a condition related to gestational hypertension. The dataset for this study was obtained from the Secretaría de Salud del Estado de Hidalgo (SSEH), México. A random sub-sampling technique was used to adjust the class distribution of the data set, and to eliminate the problem of unbalanced classes. The models were trained using a total of 98 samples. The modeling results indicate that the T-S and FCM method has a higher predictive ability than the other three models in this research.

Topic Clusters of Successful Aging Studies: Results of a Topic Modeling Approach.

Literature regarding successful aging reflects a wide variety of fields and perspectives. Given the range of definitions and approaches found in published literature, it is important to investigate clusters of topics studied over time. This study aimed to show the change of topic clusters within successful aging studies. The study used topic modeling methodology to analyze vast amounts of abstract data. Among publications collected from Scopus (4,458) and Web of Science (5,187), 5,610 publications were analyzed. Topic clusters were analyzed in two ways: by a) division of time (1960s - 1990s, 2000s, 2010s, 2020s) and b) all years combined. In the 1960s - 1990s, 11 topic clusters ranging from health to emotional well-being emerged without any dominant domain. In the 2000s, two clusters related to social support and health appeared as major clusters. In the 2010s, one topic cluster that included words related to health and social participation was the biggest. In the 2020s, emotional health and social participation appeared again as one of the major clusters and health-related topics started to diverge into subgroups like physical health and mental health. In all years of publications combined, the major cluster involved words that are related to either health or social domains. Results revealed that successful aging has been studied in many fields using multidimensional perspectives. The dominant categories were health and social domains. These findings suggest interprofessional practice, an interdisciplinary approach in research, and multi-sector involvement in policy.

A Comprehensive Heritage BIM Methodology for Digital Modelling and Conservation of Built Heritage: Application to Ghiqa Historical Market, Saudi Arabia

A Comprehensive Heritage BIM Methodology for Digital Modelling and Conservation of Built Heritage: Application to Ghiqa Historical Market, Saudi Arabia

PET digitization chain for Monte Carlo simulation in GATE

Objective. We introduce a versatile methodology for the accurate modelling of PET imaging systems via Monte Carlo simulations, using the Geant4 application for tomographic emission (GATE) platform. Accurate Monte Carlo modelling involves the incorporation of a complete analytical signal processing chain, called the digitizer in GATE, to emulate the different count rates encountered in actual positron emission tomography (PET) systems. Approach. The proposed approach consists of two steps: (1) modelling the digitizer to replicate the detection chain of real systems, covering all available parameters, whether publicly accessible or supplied by manufacturers; (2) estimating the remaining parameters, i.e. background noise level, detection efficiency, and pile-up, using optimisation techniques based on experimental single and prompt event rates. We show that this two-step optimisation reproduces the other experimental count rates (true, scatter, and random), without the need for additional adjustments. This method has been applied and validated with experimental data derived from the NEMA count losses test for three state-of-the-art SiPM-based time-of-flight (TOF)-PET systems: Philips Vereos, Siemens Biograph Vision 600 and GE Discovery MI 4-ring. Main results. The results show good agreement between experiments and simulations for the three PET systems, with absolute relative discrepancies below 3%, 6%, 6%, 7% and 12% for prompt, random, true, scatter and noise equivalent count rates, respectively, within the 0–10 kBq·ml−1 activity concentration range typically observed in whole-body 18F scans. Significance. Overall, the proposed digitizer optimisation method was shown to be effective in reproducing count rates and NECR for three of the latest generation SiPM-based TOF-PET imaging systems. The proposed methodology could be applied to other PET scanners.

Thermoelectric properties enhancement and optimization of SnTe-based material with single doping: RSM-ANN approach

Tin Telluride (SnTe) stands out among semiconductor materials, drawing attention as an environmentally friendly alternative to conventional Lead Telluride (PbTe). This study explores the novel incorporation of titanium (Ti) and zirconium (Zr) as dopant elements in SnTe-based materials, an approach that has never been previously investigated. Leveraging mathematical modeling and machine learning methodologies, we use Response Surface Methodology (RSM) and Artificial Neural Network-Genetic Algorithm (ANN-GA) to predict thermoelectric properties of SnTe-based materials and find the optimum dopant element addition and operating temperature. Eleven samples featuring varying Ti and Zr concentrations are employed in experimental investigations, with dopant amounts ranging from 0 to 0.05 M. Characterization of each sample is conducted across a temperature spectrum from 50 to 450 °C, yielding a comprehensive dataset of 99 entries for neural network model construction. The generated RSM and ANN models demonstrate remarkable predictive accuracy for the thermoelectric properties of SnTe-based materials, showcasing coefficient of determination (R2) values spanning from 0.986 to 0.998. This study uses RSM and ANN-GA to predict an optimized zT value of 0.558. This value surpasses the highest zT value from the experiment by 3.33 %. In conclusion, this investigation underscores the potency of RSM and ANN as valuable tools for advancing research on SnTe-based thermoelectric materials, exemplifying their capacity to hasten the exhausting process of finding the optimum dopant addition and operating temperature.

Modeling the successive failure of complex dams systems: A necessity in the light of climatic shifts in extreme storms

This study investigates the effect of extreme storms on the safety of flood protection dams within complex schemes. A modeling methodology is developed using HEC-RAS 2D and presented on an illustrative case, where nine scenarios are simulated. The impact of climatic shifts on extreme storms and dams’ safety is highlighted. The results showed that, despite multi-reservoir systems can be efficient in flood protection against their design storms, they can cause catastrophic damage when subjected to extreme events that have become evident nowadays due to climatic shifts. This is attributed to the successive failure of the water storage structures in a progressive (“domino-like”) manner. The consequences of such breaches can significantly multiply the hazards. These findings can be linked to storm Daniel that occurred at Wadi Derna in Libya and caused two successive dam breaches, leaving a tragedy. The proposed modeling methodology can be useful for designers in evaluating complex scenarios.

Hydrogen in the Deep Earth

Hydrogen is one of the most difficult elements to characterize in geological materials. Even at trace levels, hydrogen has a major impact on the properties of minerals, silicate melts, and fluids, and thus on the physical state of the mantle and crust. The investigation of H-bearing species in deep minerals, melts, and fluids is challenging because these phases can be strongly modified during transport to Earth’s surface. Furthermore, interpretation of experimental studies can be clouded by kinetic inhibitions and other artifacts. Nevertheless, recent improvements in analytical, experimental, and modeling methodologies have enabled advances in our understanding of how hydrogen is incorporated in the deep Earth, which is essential for constraining hydrogen cycling and storage through geologic time.

A framework for fully automated reconstruction of semantic building model at urban-scale using textured LoD2 data

The CityGML Level of Detail 3 (LoD3), a widely adopted standard for three-dimensional (3D) city modeling, has been accessible for an extended period. However, its comprehensive implementation remains limited due to challenges such as insufficient automation and inconsistent data quality. This research introduces an innovative and fully automated framework aimed at urban-scale semantic building model reconstruction. The proposed framework addresses three critical challenges: (1) proposing facade layout graph model to formalize the geometry and topological relationships of semantic entities on building facades, thereby promoting the deduction of structural completeness and the reconstruction of semantic facade models; (2) establishing a mapping relationship between texture images, semantic entities, and building shells guided by the facade layout graph to ensure consistent correlations among the geometry, semantics, and topology of building models; (3) developing an efficient representation methodology for semantic building models utilizing a parameter set derived from the facade layout graph. The proposed framework has been successfully validated by reconstructing 8,681 buildings from three different locations in Berlin. The results demonstrate an outstanding reconstruction accuracy of 91%, with a time efficiency of only 3.42 s per building. Visual analysis further confirms that the framework effectively fulfills the application prerequisites of 3D GIS. The code of the proposed framework is available in the repository: https://github.com/wangyuefeng2017/LoD3Framework-.

Increasing the Sustainability of the Strategic Development of Oil Producing Companies in Mexico

In the oil industry, there is a gap between the goals of sustainable development, the implementation of oil projects and its specific consequences. Oil projects are implemented in isolation from other variables, have an insufficiently targeted impact on the territory and often have a negative impact on the environment. The purpose of the study is to improve the efficiency of oil producing companies and increase their contribution to the development of the country’s economy as a whole. The methodology used in this article is based on the concept of sustainable development, systemic and integrated approaches, methodology of sub-potentials and modeling of business processes of a circular economy. The results of the study include a methodological approach to the formation of an effective business model for oil companies. We propose this methodological approach to select the projects of oil companies, taking into account economic, environmental and other factors, and the most promising prospects for Mexico. The significance of the study is that the proposed methodology makes it possible to increase the sustainability of the development of oil companies and integrate their business processes into the task of increasing the efficiency of operation and development of the territory. The novelty of the study lies in the application of the concept of sub-potentials and the calculation of critical indicator values for oil producing companies in Mexico to prevent the transition of sub-potentials of functioning and development into sub-potentials of threat and containment during project implementation.

A Systematic Methodology for Modeling and Attitude Control of Multibody Space Telescopes

A Systematic Methodology for Modeling and Attitude Control of Multibody Space Telescopes

Investigating the Potential of Using Mixdown Altitudes to Forecast Peak Wind Gusts

Abstract Forecast models based on the gust factor, the ratio of peak gust to sustained wind speed, have shown promise in predicting peak wind gusts in recent years. These models assume that turbulent vertical transport forced by wind flowing over upstream terrain mixes high-momentum air aloft down to the surface. A recently constructed database of hourly peak gusts, together with approximately coincident, nearby upper-air wind observations, is used to identify mixdown altitudes, the altitudes from which peak gusts descend, during different weather scenarios at 16 locations across the United States. Median mixdown altitudes generally ranged from 50 to 450 m AGL with occasional exceptions, particularly for convective gusts and at mountainous locations where terrain effects are likely to amplify gusts. A mixdown model in which surface peak gusts are predicted by obtaining forecast upper-air winds within this altitude interval was developed and tested. Our results suggest that a mixdown model methodology for forecasting peak gusts may be feasible at locations and during weather conditions where terrain-forced turbulent mixing is the principal cause of wind gusts.

Strategizing emissions reduction investment for a livestock production farm amid power demand pattern: A path to sustainable growth under the carbon cap environmental regulation

Livestock production companies come under increasing responsibility to reduce their environmental impact, and thereby, the simultaneous decision-making of inventory replenishment and emissions reduction investment has become essential for ensuring sustainable development in the livestock farming business. This study investigates, for the first time, the best investment strategy for a livestock farming business under the carbon cap (CC) environmental legislation, taking into account both the edible and non-edible parts of slaughtering mature growing items (GIs) after procuring and feeding baby GIs. By fusing economic and environmental factors, this study aims to shed light on two crucial issues: (i) figuring out the appropriate level of investment needed for the farm to adhere to the CC environmental regulation; and (ii) evaluating the effect of the investment decision on the farm's expenses and emissions levels. To deal with these insights, a thorough analytical framework integrating mathematical modeling methodology, economic evaluation, and carbon accounting approaches is employed. By analyzing the interaction between the farm's emissions reduction investments and replenishment choices, the cost-effective investment level is determined that enables the farm to satisfy the carbon cap obligation while guaranteeing maximum operational efficiency. The results of this study have important ramifications for livestock farming businesses trying to make their way through the stringent CC emission law. The results indicate that in order to keep the business feasible when the cap of the CC guideline is low, the livestock-producing farm should give priority to investing in minimizing feed emissions and using cutting-edge manure treatment methods.

Brand loyalty for mobile devices from the perspective of Vietnamese culture: antecedents and moderating effects of age

This study examines the correlations between brand personality, identity, perceived quality, and loyalty, as well as the moderating effect of age. The paper uses structural equation modeling and survey methodologies to present the findings. The data were collected from 228 Vietnamese respondents for the primary analysis. The research results show that brand personality, identification, and perceived quality positively affect brand loyalty. Age also negatively moderates the relationship between brand personality and brand loyalty, and positively moderates the relationship between perceived quality and brand loyalty; however, it does not moderate brand identification-loyalty connections. Additionally, brand loyalty is influenced by brand personality, identification, and perceived quality. Age moderates these relationships. The findings on the moderating effects of age are pioneering academically, especially in Vietnam. This study offers practical recommendations to address branding and marketing issues in business management. Based on moderating effects, managers and businesspeople can create marketing and branding campaigns that target the right age groups for each antecedent.