Modeling Techniques Research Articles

Application of structural equation modelling (SEM) to evaluate reworks in sustainable buildings

PurposeSustainable buildings are designed to minimise the adverse impacts of buildings on users, occupants, communities and the environment while enhancing client investment, contractors’ productivity and profit margins. However, sustainable buildings often experience significant rework. This research aims to evaluate the complex interrelationships among the causes of rework in sustainable buildings using structural equation modeling (SEM) techniques. By testing the theory and validating a framework addressing the causes of rework in sustainable buildings, the construction sector can make a meaningful contribution towards a sustainable future.Design/methodology/approachThe study developed a questionnaire comprising 24 identified causes of rework in sustainable buildings, derived from an extensive literature review and field observations. The causes were evaluated using a 4-point Likert scale, ranging from less occurrence to very high occurrence. The survey was administered to construction professionals via online platforms and direct hand delivery.FindingsThe identified causes of rework were grouped into four components through exploratory factor analysis (EFA) and subsequently validated using SEM. These components are competency, information, framework and plan. While the measurement model demonstrated robustness, the structural model indicated the need for further refinement. The study provides actionable strategies to mitigate rework, supporting the advancement of sustainable practices within the construction sector.Originality/valueThe findings of this research carry substantial theoretical and practical significance for advancing knowledge and practices in the sustainable buildings market. Theoretically, the study enriches the understanding of rework causes and their interrelationships, providing a foundation for future research. Practically, the results serve as a vital resource for stakeholders in the construction sector and offer actionable insights to enhance decision-making, improve project outcomes and foster sustainable building practices.

Recent Advancements in Anomaly Detection for Manufacturing Systems

Recent advancements in anomaly detection and root cause analysis have revolutionized manufacturing processes, enabling improved efficiency, quality control, and cost reduction. This review explores the latest developments in these fields, focusing on supervised and unsupervised anomaly detection methods, statistical techniques for root cause analysis, and predictive modeling approaches. Supervised anomaly detection methods, such as classification-based approaches, support vector machines, and decision trees, leverage labeled data to identify anomalies. Unsupervised techniques, including clustering-based methods (e.g., k-means and DBSCAN), density-based approaches (e.g., local outlier factor), and dimensionality reduction (e.g., PCA and autoencoders), detect anomalies without prior knowledge of anomalous instances. Statistical methods for root cause analysis, such as statistical process control, hypothesis testing, correlation analysis, and multivariate techniques (e.g., factor analysis), enable the identification of the underlying causes of manufacturing issues. Predictive modeling techniques, including time-series forecasting, machine learning for predictive maintenance, and optimization methods for process improvement, further enhance manufacturing efficiency. The integration of these techniques has led to significant improvements in quality control, equipment failure prediction, and supply chain optimization. However, challenges remain in handling complex, high-dimensional data and integrating domain knowledge with data-driven approaches. Future research directions include the development of explainable AI, edge computing, and federated learning for anomaly detection in manufacturing. This analysis offers a thorough examination of cutting-edge techniques for detecting anomalies and analyzing root causes, emphasizing their capacity to transform manufacturing operations and stimulate future advancements in these research areas. Keywords—anomaly detection, manufacturing, supervised and unsupervised anomaly detection

A modelling technique to determine the high frequency transformer leakage inductance using the winding structure

The operation and efficiency of isolated DC-DC converters, critical components in solid-state transformers, are significantly impacted by leakage inductance in high-frequency transformers (HFTs). Different converters have varying demands regarding leakage inductance, ranging from precise control to minimal inductance. For instance, the performance of bidirectional isolated converters (BIDCs) and resonant converters is dependent on leakage inductance for the delivery of power. This study proposed a method for the control of leakage inductance by altering the winding configuration. The technique involved positioning the primary and secondary windings at predetermined heights to increase the separation between some winding turns, and thus, enhance leakage inductance. By not varying the average length of turns in the winding configuration, the proposed method was able to maintain a consistent copper loss. A modified mathematical model has been put forward to determine the leakage inductance effectively and precisely. The viability and accuracy of this method were validated through simulations and experiments using a three-phase HFT (3P-HFT) in a 3P-BIDC. The results showed that the leakage inductance that was calculated using the theoretical model was closely correlated to that of the simulation model, with only a variance of 3.9% and an error of 4.53% from the experimental results.

Assessment of climate change impact on irrigation water demand for food security in rice-fallow cropland areas using an integrated modeling technique

Assessment of climate change impact on irrigation water demand for food security in rice-fallow cropland areas using an integrated modeling technique

The Effect of Accessibility and Facility Location Logistics on the Decision Intention of Senior Citizens to use Elderly Care Centers in Thailand: the Mediating Roles of Perceived Service Quality and Perceived Hygiene

Objective: This research aimed to study the impact of accessibility and facility location logistics on the decision-making intention to use an elderly care center. Theoretical Framework: The focus was placed on studying the effect of accessibility and facility location logistics on the decision intention with two mediating variables: perceived service quality and perceived hygiene. Method: The research is based on quantitative research. The sample group consisted of 400 elderly people aged 60 years and over who were interested in or considering using an elderly care center in Bangkok and its vicinity. They were selected by convenience sampling. Data collection was conducted through a questionnaire. The data analysis used the structural equation modeling (SEM) technique. Results and Discussion: The results of the analysis revealed that accessibility and facility location logistics had a direct impact on decision intention. In addition, perceived service quality and perceived hygiene acted as significant mediating variables between accessibility and facility location logistics and decision intention. Research Implications: The results of this study have practical implications for helping elderly care centers improve accessibility and locations to increase the satisfaction of the elderly and their families contributing to achieving SDG-related health and well-being goals, as well as theoretical implications for expanding the understanding of the role of logistics and service quality factors in the decision to use elderly care center services. Originality/Value: The value of this research lies in analyzing the role of accessibility and location of elderly care centers, with an emphasis on their relationship with service quality and perceived cleanliness by users.

A Review of AI Applications in Unconventional Oil and Gas Exploration and Development

The development of unconventional oil and gas resources is becoming increasingly challenging, with artificial intelligence (AI) emerging as a key technology driving technological advancement and industrial upgrading in this field. This paper systematically reviews the current applications and development trends of AI in unconventional oil and gas exploration and development, covering major research achievements in geological exploration; reservoir engineering; production forecasting; hydraulic fracturing; enhanced oil recovery; and health, safety, and environment management. This paper reviews how deep learning helps predict gas distribution and classify rock types. It also explains how machine learning improves reservoir simulation and history matching. Additionally, we discuss the use of LSTM and DNN models in production forecasting, showing how AI has progressed from early experiments to fully integrated solutions. However, challenges such as data quality, model generalization, and interpretability remain significant. Based on existing work, this paper proposes the following future research directions: establishing standardized data sharing and labeling systems; integrating domain knowledge with engineering mechanisms; and advancing interpretable modeling and transfer learning techniques. With next-generation intelligent systems, AI will further improve efficiency and sustainability in unconventional oil and gas development.

Hybridizing evolutionary algorithms and multiple non-linear regression technique for stream temperature modeling

Abstract The present study hybridizes the new-generation evolutionary algorithms and the nonlinear regression technique for stream temperature modeling and compares this approach with conventional gray and black box approaches under natural flow conditions, providing a comprehensive assessment. The nonlinear equation for water temperature modeling was optimized using biogeography-based optimization (BBO) and invasive weed optimization (IWO), simulated annealing algorithm (SA) and particle swarm optimization (PSO). Two black box approaches, a feedforward neural network (FNN) and a long short-term memory (LSTM) network, were also employed for comparison. Additionally, an adaptive neuro-fuzzy inference system (ANFIS) served as a gray box model for river thermal regimes. The models were evaluated based on accuracy, complexity, generality and interpretability. Performance metrics, such as the Nash–Sutcliffe efficiency (NSE), showed that the LSTM model achieved the highest accuracy (NSE = 0.96) but required significant computational resources. In contrast, evolutionary algorithm-based models offered acceptable performance while reducing the computational complexities of LSTM, with all models achieving NSE values above 0.5. Considering interpretability, accuracy and complexity, evolutionary-based nonlinear models are recommended for general applications, such as assessing thermal river habitats. For tasks requiring very high accuracy, the LSTM model is preferred, while ANFIS provides a balanced trade-off between accuracy and interpretability, making it suitable for engineers and ecologists. While all models demonstrate similar generality, this model is developed for a specific location. For other locations, independent models with a similar architecture would need to be developed. Ultimately, the choice of model depends on specific objectives and available resources.

MultiProduct Optimization of Cedrelinga cateniformis (Ducke) Ducke in Different Plantation Systems in the Peruvian Amazon

This study addressed multi-product optimization in Cedrelinga cateniformis plantations in the Peruvian Amazon, aiming to maximize volumetric yields of logs and sawn lumber. Data from seven plantations of different ages and types, established on degraded land, were analyzed by using ten stem profile models to predict taper and optimize wood use. In addition, the structure of each plantation was evaluated using diameter distributions and height–diameter ratios; log and sawn timber production was optimized using SigmaE 2.0 software. The Garay model proved most effective, providing high predictive accuracy (adjusted R2 values up to 0.963) and biological realism. Marked differences in volumetric yield were observed between plantations: older and more widely spaced plantations produced higher timber volumes. Logs of optimal length (1.83–3.05 m) and larger dimension wood (e.g., 25.40 × 5.08 cm) were identified as key contributors to maximizing volumetric yields. The highest yields were observed in mature plantations, in which the total log volume reached 508.1 m3ha−1 and the sawn lumber volume 333.6 m3ha−1. The findings demonstrate the power of data-driven decision-making in the timber industry. By combining precise modeling and optimization techniques, we developed a framework that enables sawmill operators to maximize log and lumber yields. The insights gained from this research can be used to improve operational efficiency and reduce waste, ultimately leading to increased profitability. These practices promote support for smallholders and the forestry industry while contributing to the long-term development of the Peruvian Amazon.

The role of visuals in the perceptions of risk and of self-efficacy and the behaviors towards the pandemic of Covid-19

The Covid-19 has spread rapidly, becoming a global health crisis. In crises, especially in public health problems such as a pandemic, it is important to reach the target audience effectively. In this process, the use of visuals helps to convey risk messages effectively. To control the pandemic and prevent its spread, individuals’ risk perception, self-efficacy and behaviors towards the pandemic are of great importance. The aim of this study is to measure how the visuals those individuals are exposed to about the Covid-19 pandemic affect their perceived risk level and self-efficacy, and how these relationships direct their behavior towards the pandemic. For the implementation of the study, data were collected from 410 people working in public institutions, in Türkiye. Structural equation modeling, t-test and Anova techniques analyzed the data collected in the survey conducted in the survey model. According to the research results, gender and educational status significantly affect employees’ attention to visuals and risk perception. According to the results of the research model, the research factors affect each other positively. Individuals’ interest in visuals increases their perceived risk and self-efficacy perceptions. Perceived risk and self efficacy for the epidemic, on the other hand, affect their behavior towards the epidemic.

Discrete choice modelling of hypertension patients’ preferences for attributes of a public medical facility in Ibadan, Nigeria

BackgroundNot much is known about hypertension patients’ preferences for attributes of public medical facilities in Nigeria and how these preferences influence their choices of medical facilities for treatment. An understanding of what these patients want especially in terms of service delivery could contribute to improved hypertension control.ObjectiveThis study aimed to determine hypertension patients’ preferences for attributes of a public medical facility in Ibadan, Nigeria.MethodsA Discrete Choice Experiment (DCE) that utilized three hypothetical medical facilities was used for the study. A simple random sample (SRS) of 150 eligible hypertension patients was selected from a secondary medical facility in Ibadan, Nigeria. An efficient D-optimal choice design was adopted and used in generating nine hypothetical choice sets for the experiment. Each patient was expected to study the nine sets carefully and choose an option from each of the sets. The first choice set was repeated as the tenth set to examine respondents’ consistency. The DCE questionnaires were administered using a one-to-one interview method. A mixed logit regression modeling technique was used to obtain parameter and Willingness to Pay (WTP) estimates.ResultsThe patients preferred medical facilities with waiting time before consultation with medical doctors to be between thirty minutes and one hour. The attribute level ‘‘a lot of information’’ was the most preferred. The patients were unwilling to pay for the “little or no drugs and diagnostic equipment” attribute level. A negative and significant coefficient for cost indicated that higher out-of-pocket costs negatively affected hypertension patients’ choice of a public medical facility.ConclusionHypertension patients attending a public medical facility in Ibadan, Nigeria preferred a facility with access to comprehensive information about their health in addition to reasonable waiting times, availability of a lot of drugs and diagnostic equipment, and reduced out-of-pocket costs. Provision of healthcare services that align with these preferences could enhance patient satisfaction thus contributing to improved hypertension control.

Estudio del rendimiento académico mediante la comparación de modelos de regresión y árboles de clasificación

This article aims to identify the factors that affect academic performance by comparing regression models and decision trees to determine the factors involved. The methodology adopted is quantitative in nature, focused on the collection of numerical data and its statistical analysis, in order to evaluate the relationships between different variables and determine those factors that influence academic performance. The population studied includes remedial students in the statistics career, who underwent an exploratory and descriptive analysis, using two statistical methods. Two modeling techniques were used: multinomial logistic regression and classification trees. The variables evaluated included sociodemographic factors, previous academic performance, and characteristics of the educational environment. The results showed that the logistic regression model achieved 100% accuracy with an AUC of 1, indicating perfect classification ability. In comparison, the classification tree model had an accuracy of 70.83% with an AUC of 0.7042, reflecting moderate classification ability. From these results, key factors that affect academic performance were identified, such as study habits, interest in the career and psychological aspects. In conclusion, multinomial logistic regression was more effective and accurate in analyzing the quantitative relationships between the variables that affect academic performance, outperforming the classification tree method.

Performance of heat transfer in Al2O3/MWCNT hybrid nanofluid flow using machine learning models

Abstract This investigation diverges from traditional studies concentrating on single-component nanofluids, instead examining the thermophysical benefits of hybrid nanofluids, like Al₂O₃/MWCNT, aimed at improving thermal conductivity and heat transfer efficiency in passive systems. Machine learning is a promising solution for designing efficient heat exchangers by understanding intricate relationships and utilizing suitable modelling techniques. Numerical simulations were conducted to validate the benchmark results; later, passive techniques were incorporated into the numerical model to predict the heat transfer characteristics. The dataset derived from numerical simulation results is employed to train contemporary machine learning methodologies, including support vector regression (SVR), decision tree (DT), and random forest (RF). Data from experiments and CFD analysis were gathered for preprocessing and machine learning (ML) analysis. The preprocessing phase involved the application of a standard scaler operation to enhance accuracy levels. The models underwent validation using ten experimental data samples to assess their performance against statistical tool metrics. A higher thermal performance factor (ThPF) is observed with the divergent nozzle insert in the plain tube at 0.028 vol% of MWCNT/Al2O3 (HNF3) at Reynolds number 3093. R2 values of SVR, DT and RF are predicted as 0.95, 0.98 and 0.99, respectively, for the case of HNF3 fluid flowing through the divergent nozzle insert. The investigation broadens its conclusions to include improvements in passive heat transfer, encompassing extended surfaces and geometric alterations, offering practical guidance for developing advanced heat exchanger designs.

Machine Learning for Sparse Nonlinear Modeling and Control

Machine learning is rapidly advancing nearly every field of science and engineering, and control theory is no exception. In particular, it has shown incredible promise for handling several of the main challenges facing modern dynamics and control, including complexity, unmodeled dynamics, strong nonlinearity, and hidden variables. However, machine learning models are often expensive to train and deploy, fail to generalize beyond the training data, and suffer from a lack of explainability, interpretability, and guarantees, all of which limit their use in real-world and safety-critical control applications. Sparse nonlinear modeling and control techniques are a powerful class of machine learning that promote parsimony through sparse optimization, providing data-efficient models that are more interpretable and generalizable and have proven effective for control. In this review, we explore the use of sparse optimization in the context of machine learning to develop compact models and controllers that are easy to train, require significantly less data, and make low-latency predictions. In particular, we focus on applications in model predictive control and reinforcement learning, two of the foundational algorithms in control theory.

Fuzzy Control of Multivariable Nonlinear Systems Using T–S Fuzzy Model and Principal Component Analysis Technique

In this work, a new nonlinear control method is proposed, which integrates the Takagi–Sugeno (T–S) fuzzy model with the Principal Component Analysis (PCA) technique. The approach uses PCA to reduce the system’s dimensionality, minimizing the number of fuzzy rules required in the T–S fuzzy model. This reduction not only simplifies the system variables but also decreases the computational complexity, resulting in a more efficient control with smooth transient responses and zero steady-state error. To validate the performance of this PCA-based approach for both system identification and control, an interconnected double-tank system was employed. The results demonstrate the method’s capacity to maintain control accuracy while reducing computational load, making it a promising solution for applications in industrial and engineering systems that require robust, efficient control mechanisms.

Structural insights into the recognition of RALF peptides by FERONIA receptor kinase during Brassicaceae pollination.

Ensuring species integrity and successful reproduction is pivotal for the survival of angiosperms. Members of Brassicaceae family employ a "lock and key" mechanism involving stigmatic (sRALFs) and pollen RALFs (pRALFs) binding to FERONIA, a Catharanthus roseus receptor-like kinase 1-like (CrRLK1L) receptor, to establish a prezygotic hybridization barrier. In the absence of compatible pRALFs, sRALFs bind to FERONIA, inducing a lock state for pollen tube penetration. Conversely, compatible pRALFs act as a key, facilitating successful fertilization. Competing pRALFs reduce the sRALFs binding to FERONIA in a dose-dependent manner, enabling pollen tube penetration. Despite its crucial role in Brassicaceae hybridization, the structural basis of this binding remains elusive owing to the highly flexible nature of RALF peptides. Using advanced structural modeling techniques and flexible peptide molecular docking, this study reveals that pRALFs and sRALFs bind to negatively charged pockets in FERONIA with varying binding affinities. Our study unveils the structural basis of this binding, shedding light on the molecular mechanism underlying hybridization barriers in Brassicaceae.

Advances and Challenges in Thermoacoustic Network Modeling for Hydrogen and Ammonia Combustors

The transition to low-carbon energy systems has heightened interest in hydrogen and ammonia as sustainable alternatives to traditional hydrocarbon fuels. However, the development and operation of combustors utilizing these fuels, like other combustion systems, are challenged by thermoacoustic instabilities arising from the interaction between unsteady heat release and acoustic wave oscillations. Among many different methods for studying thermoacoustic instabilities, thermoacoustic network models have played an important role in analyzing the essential dynamics of these instabilities in combustors operating with low-carbon fuels. This paper provides a comprehensive review of thermoacoustic network modeling techniques, focusing specifically on their application to hydrogen- and ammonia-based combustion systems. We outline the key mathematical frameworks derived from fundamental equations of motion, along with experimental validations and practical applications documented in existing studies. Furthermore, current research gaps are identified, and future directions are proposed to improve the reliability and effectiveness of thermoacoustic network models, contributing to the advancement of efficient and stable low-carbon combustors.

Transfer Learning for Thickener Control

Thickener control is a key area of focus in the minerals processing industry, particularly due to its crucial role in water recovery, which is essential for sustainable resource management. The highly nonlinear nature of thickener dynamics presents significant challenges in modeling and optimization, making it a strong candidate for advanced surrogate modeling techniques. However, traditional data-driven approaches often require extensive datasets, which are frequently unavailable, especially in new plants or unexplored operational domains. Developing data-driven models without enough data representative of the dynamics of the system could result in incorrect predictions and consequently, unstable response of the controller. This paper proposes the application of a methodology that leverages transfer learning to address these data limitations to enhance surrogate modeling and model predictive control (MPC) of thickeners. The performance of three approaches—a base model, a transfer learning model, and a physics-informed neural network (PINN)—are compared to demonstrate the effectiveness of transfer learning in improving control strategies under limited data conditions.

Deep generative modeling of annotated bacterial biofilm images

Biofilms are critical for understanding environmental processes, developing biotechnology applications, and progressing in medical treatments of various infections. Nowadays, a key limiting factor for biofilm analysis is the difficulty in obtaining large datasets with fully annotated images. This study introduces a versatile approach for creating synthetic datasets of annotated biofilm images with employing deep generative modeling techniques, including VAEs, GANs, diffusion models, and CycleGAN. Synthetic datasets can significantly improve the training of computer vision models for automated biofilm analysis, as demonstrated with the application of Mask R-CNN detection model. The approach represents a key advance in the field of biofilm research, offering a scalable solution for generating high-quality training data and working with different strains of microorganisms at different stages of formation. Terabyte-scale datasets can be easily generated on personal computers. A web application is provided for the on-demand generation of biofilm images.

Research on the construction of intelligent-aided design platform for green buildings

While socio-economic development and technological advancement bring great convenience to our life, energy consumption and environmental pollution have also become the core issues affecting human survival and development. This study focuses on the development of an intelligent design platform for green buildings, aimed at promoting sustainability through low energy consumption and reduced environmental impact. The study of existing national standards and requirements for the intelligent design of green buildings, combined with the unique characteristics of green buildings and user needs, has led to the development of an optimized auxiliary design platform system. By incorporating algorithm optimization and modeling techniques, this platform enhances the design process. Experimental evaluations of the system reveal that the intelligent auxiliary design offers more accurate predictions, thereby improving user service and experience. Additionally, the system contributes to energy efficiency and environmental sustainability by reducing the building’s ecological footprint. This research highlights the potential of intelligent systems to facilitate more efficient and environmentally friendly green building designs.

Gaussian Process-Based Robust Optimization with Symmetry Modeling and Variable Selection

Gaussian process (GP)-based robust optimization is an effective tool in product quality improvement. However, most existing variable selection methods are designed for parametric models and are unsuitable for nonparametric GP models. Additionally, improving the prediction accuracy of GP models with limited design points remains a significant challenge in robust optimization. To address these issues, this article proposes a GP-based multi-stage robust parameter optimization method that integrates symmetry modeling, sensitivity analysis (SA), and Markov Chain Monte Carlo (MCMC) techniques. First, a modified expected improvement (EI) criterion is introduced to enhance the utilization efficiency of design points. Second, a nonparametric variable selection technique based on SA is developed for GP models to identify significant variables. This method considers both independent variables and their interactions, improving the interpretability of GP models. Finally, the selected variables are used to construct the robust optimization model, and the genetic algorithm (GA) is employed to search for the optimal solution within the feasible domain. Numerical simulations and real-world experiments demonstrate the effectiveness of the proposed method. Comparative results indicate that the proposed method obtains more robust optimal input parameter settings compared to existing approaches.