Modeling Process Research Articles

Regionalization of GRACE data in shorelines by ensemble of artificial intelligence methods

Groundwater (GW) plays a crucial role in coastal aquifers and arid regions, serving as a lifeline for communities by providing a reliable and resilient water source, making its monitoring essential for sustainable water management. This study aimed at modeling GW via regionalization of the Gravity Recovery and Climate Experiment (GRACE) data based on two methods. The first method directly regionalized the GRACE data for modeling GW via in situ measurements, including the lake level, precipitation, temperature, observed GW and Penman-Monteith-Leuning (PML) evapotranspiration data. The second method included two stages, in the first stage, the GRACE data were downscaled via the Famine Early Warning Systems Network Land Data Assimilation System (FLDAS) data which contains satellite based precipitation, temperature, soil moisture, and snow water equivalent data. In the second stage, the downscaled GRACE was bias corrected to provide regionalized data. Artificial intelligence models consist of shallow networks (Feed Forward Neural Network (FFNN), Adaptive neuro fuzzy (ANFIS), Support Vector Machine (SVR)), the ensemble of shallow networks and Long-Short Term Memory (LSTM) deep learning method were employed in the modeling process and the observed GW level data were targeted for the regionalization. The Link CluE clustering ensemble method was implemented to cluster the piezometers of the aquifer to separate different GW patterns in the area. The proposed methodology was examined over the Miandoab plain, one of the sub-basins of the Lake Urmia, located in Northwest Iran. The modeling results demonstrated that the first method could exhibit superior performance with the Nash-Sutcliffe Efficiency (NSE) of up to 17% higher than the second method. Thus, using in situ observed data for downscaling proved to be more accurate than relying on the data based on the satellite imagery. The results indicated that the ensemble of shallow networks could lead to more precise results than using the deep and shallow learning models, individually, where the NSE for the ensemble of shallow networks was up to 50% higher compared to the LSTM model.

A novel method of detecting malware on Android mobile devices with explainable artificial intelligence

The increasing prevalence of malware targeting android mobile devices has raised significant concerns regarding user privacy and security. In response, effective methods for malware classification and detection are crucial to protect users from malicious applications. This paper presents an approach that leverages deep learning techniques and explainable artificial intelligence (XAI) for android mobile malware classification and detection. Convolutional neural networks (CNNs) are deep learning model that has shown impressive performance in several application areas, including image and text classification. In the context of android mobile malware, CNNs have shown promising results in capturing intricate patterns and features inherent in malware samples. By training these models on large datasets of benign and malicious applications, accurate classification can be achieved. To enhance transparency and interpretability, XAI techniques are integrated into the classification process. These techniques provide insights into the decision-making process of the deep learning models, enabling the identification of critical features and characteristics that contribute to the classification results. This research, by combining deep learning and XAI methods, presents a fresh strategy for identifying and categorizing Android malware. This research paper will focus on a fascinating CNN-based malware categorization technique.

ARIMA Model Time Series Forecasting

Abstract: Time series forecasting is a critical component in various fields such as finance, economics, meteorology, and engineering. Among the multitude of methods available for time series forecasting, the Autoregressive Integrated Moving Average (ARIMA) model stands out for its simplicity and effectiveness. This paper provides a comprehensive review of ARIMA models, focusing on their application in forecasting time series data. We begin with an overview of time series analysis and the theoretical foundations of ARIMA models. Subsequently, we delve into the process of building and fitting ARIMA models, discussing the steps involved and the considerations for model selection. Furthermore, we explore advanced topics such as seasonal ARIMA (SARIMA) models and discuss their relevance in handling seasonal data patterns. Additionally, we review recent advancements and extensions of ARIMA models, including hybrid models and machine learning-based approaches. Finally, we discuss the challenges and limitations associated with ARIMA modeling and provide recommendations for future research directions.

A Hybrid Experimental Modelling Approach to Solar Photovoltaic Cell Temperature Prediction

Solar cell temperature is critical in the determination of solar energy generated by a solar photovoltaic power plant. High temperatures are associated with a reduction in the energy generated and hence prediction of photovoltaic cell temperature is essential in temperature mitigation and solar energy forecasting especially in commercial power plants. The present study focused on the development of a machine learning based predictive model for solar photovoltaic cell temperature prediction in commercial solar photovoltaic power plants. A physical experimental set up was developed to measure solar cell temperature under different weather and other related parameters. Satellite data were also collated for those parameters difficult to measure experimentally and were used to compliment experimental data used in this study. Satellite data used in the study were statistically transformed for each parameter used to mimic experimentally measured data. Statistical approaches were adopted to analyse the influence of both the dependent and independent variables on solar cell temperature. The analysis included multicollinearity and correlation analysis with the aid of heat maps meant to establish the relationships among the independent and dependent variables. Feature selection and dimensionality reduction was also performed to reduce the input variables and maintain relevant data in the modelling process. Parameters with a strong correlation to each other had some of them eliminated in the modelling process. A solar cell temperature predictive model based on selected weather parameters was developed using a machine learning approach (Random Forests), and parameters used were selected from the statistical analysis. The prediction accuracy of the developed model was analysed using the coefficient of determination (R2) and the mean absolute percentage error (MAPE). The results indicated a higher model performance compared to generic models used in cell temperature prediction. The prediction MAPE for the developed model was 0.83 °C while an R2 value of 0.93 was obtained which was indicative of a good model. The developed model was also comparable to other contemporary models developed to predict solar photovoltaic cell temperature. Simulations were also done to determine the annual energy generated with the incorporation of the solar cell temperature prediction model. The results revealed a 3.4% difference in the annual energy generated between a simulation which considered solar cell temperature and that which ignored the solar cell temperature. The study also revealed that this difference is even larger in monetary terms when lifetime energy generation is considered. The present study demonstrated that Random Forests, a machine learning approach to predictive modelling, can handle complex models and can provide models with a higher accuracy compared to statistical modelling approaches. The study recommends the use of solar cell predictive models to improve the accuracy of energy prediction on solar photovoltaic power plants which in turn assists in energy planning and deployment.

Enhancing interpretability in neural networks for nuclear power plant fault diagnosis: A comprehensive analysis and improvement approach

Deep neural networks, as applied in the field of nuclear power fault diagnosis, have garnered significant attention alongside advancements in artificial intelligence technology. However, the “black box” nature of deep learning models has raised concerns regarding their deployment in scenarios demanding high safety standards, such as nuclear power plants. In this paper, we innovatively propose the utilization of an explainable artificial intelligence method, grounded in game theory, to conduct a detailed analysis of the diagnostic behavior of neural network models applied to nuclear power plants. By leveraging SHAP, the decision-making processes of these opaque models are demystified, offering insights into how and why they arrive at their predictions. In this study, two of the most widely utilized neural network frameworks are applied across six representative fault diagnosis cases for a comprehensive analysis. Based on its analysis results, a novel SHAP-Enhanced Feature Selection for Efficient Neural Network Fault Diagnosis strategy is proposed, significantly reducing model complexity without sacrificing diagnostic performance. This research breaks through the limitations of data-driven models used in the field of nuclear power fault diagnosis, conducts an interpretable analysis of their behavior, and proposes an improved strategy based on the analysis results, contributing to the practical application of data-driven models in nuclear power.

Частная полумарковская модель как инструмент снижения сложности задачи оценивания устойчивости функционирования элементов информационной инфраструктуры, подверженной воздействию угроз

Decision-making on information infrastructure (II) security for its sustainable functioning in the face of threats requires a tool to assess the sustainability of its individual elements. The application of the semi-Markov model to assess the stability of the functioning of elements of II exposed to threats in a direct setting is associated with the increasing complexity of the description of the object of delineation (parametric space) in step progression from the number of the considered impacts, which reduces its practical significance. However, no studies have been found in the scientific literature to reduce the complexity of the semi-Markov model. The article presents an approach to reducing the complexity of modeling by adopting correct assumptions when forming the initial data. Given the conditions under which it is possible to take a series. It was a cost to limit the applicability of the model by significantly reducing the complexity of the modelling. The problem statement and the modified transition graph are given. The novelty of the problem statement is to take into account the limitations on the available resources for the restoration of functionality of the element. To explain the physical essence of the modeling process, a thought experiment with a model is introduced. To solve the problem, the following methods were used: a) expert methods for extraction of initial data; b) mathematical models of private semi-Markov processes; c) methods of transformation of Laplace; r) methods of planning of experiment. Illustrative examples and graphs accompany the task sequence demonstration. As a result of the experiment, the regularities of the studied process, the existence of which was proved formally. As a result of the experiment, the regularities of the studied process were revealed, the existence of which was officially proved. The results of the study broaden knowledge about the application of methods of Markov processes to assess the stability of the functioning of AI elements in relation to the conditions of the impact of threats.

Sustainable Fishing Livelihoods: Exploring the Potential of Sail Technology Using Computational Fluid Dynamic in Traditional Fishing Boats

The potential of using sails and Computational Fluid Dynamics (CFD) to improve the sustainability of traditional fishing boats in the Maluku region. This study highlights the importance of cost-effectiveness and environmental sustainability in the fishing industry. This study shows that Computational Fluid Dynamics (CFD) is effective in simulating the thrust force generated by sails. Sails were used to generate thrust forces on conventional fishing vessels by implementing CFD methodology with three different models. Various sail shapes were incorporated into the modelling process to assess their impact on the thrust force. Subsequently, a comprehensive analysis was conducted to evaluate the sail thrust force under typical operating conditions, focusing specifically on the average speed of the vessel at 15 knots in the waters surrounding the Maluku Islands. The results reveal that sail model 1 has the highest thrust force, amounting to 240.2 N. Sail models 3 and 2 generate thrust forces of 238.9 N and 227.9 N, respectively. The fluid flow conditions around sail model 1 and screen model 2 provide valuable insights into how the sail interacts with the fluid environment to produce thrust. By understanding and optimizing these fluid dynamics, designers and engineers can enhance the performance of sail models and improve their efficiency in harnessing wind power for propulsion. This study highlights the importance of ship science and technology in the design of appropriate propulsion systems for traditional fishing vessels.

Nonlinear dynamical modeling and response analysis of complex structures based on assumed mode weighting

The assumed mode method (AMM) is widely used for dynamical modeling but faces increasing challenges in developing low-dimensional and high-precision models of complex structures for investigations of nonlinear dynamical responses and active vibration control. This is mainly because the current complex structure has an increasing number of components, each of which needs to be discretized by multiple assumed modes in dynamical modeling, leading to increasing degrees of freedom for the system. To address this issue, this paper proposes a dimension-reduced method based on the assumed mode weighting method, referred to as Mode Weighting Method (MWM). The modeling process of a multi-beam structure is fully presented as an example to show the strategy of the MWM. During this process, the assumed mode (AM) model (the dynamic model derived in terms of a set of assumed modes) is developed first where the joints of the structure between its components are considered as artificial springs and each component is discretized by its vibration modes without constraints. The components of the eigenvector of the characteristic equation derived from the AM model are taken as the weighting coefficients to get the approximate analytical global modes of the structure by multiplying the corresponding assumed modes. Consequently, a nonlinear dynamic model with a few degree of freedom is obtained by discretizing the structure with the weighted modes. Moreover, a technique for simply obtaining the weighted mode (WM) models by directly processing the AM model through a transformation matrix is presented. To validate the proposed method, the simulations on the natural characteristics and the dynamical responses for both linear and nonlinear models, are performed respectively based on the WM and the finite element models, where both results are well matched each other. This work provides a new way of developing advanced dynamical modeling methods for structural dynamics design, optimization, and active vibration control.

Accessing Horizon Picking Uncertainty using Seismic Complex Trace Attributes

Many uncertainty sources are linked to the reservoir modeling processes, significantly affecting on all phases of the exploration, development, and production phases of an oil & gas field. Structural uncertainty due to velocity models are commonly considered by taking a range of equiprobable domain conversion velocity models and evaluating the related impact on GRV distributions. In contrast, the seismic interpretation inaccuracy is commonly neglected, because the difficulty in quantifying it. This means that seismic mapping is frequently treated as having no variation or having the intrinsically imprecisions assigned arbitrarily. In this work, we propose an objective approach to evaluate the horizon picking uncertainties, which was based on the complex seismic trace attributes extraction considering a previously mapped reference reflector. In real seismic data, phase distortions may arise from a vast bunch of reasons, such as dispersion, attenuation, bandwidth limitations. Thus, the instantaneous phase attribute can be an indicator of uncertainties associated with seismic reflectors interpretation.

Analysis of the Accuracy of CAD Modeling in Engineering and Medical Industries Based on Measurement Data Using Reverse Engineering Methods

The reverse engineering (RE) process is often necessary in today’s engineering and medical industries. Expertise in measurement technology, data processing, and CAD modeling is required to ensure accurate reconstruction of an object’s geometry. However, errors are generated at every stage of geometric reconstruction, affecting the dimensional and geometric accuracy of the final 3D-CAD model. In this article, the geometry of reconstructed models was measured using contact and optical methods. The measurement data representing 2D profiles, 3D point clouds, and 2D images acquired in the reconstruction process were saved to a stereolithography (STL) model. The reconstructed models were then subjected to a CAD modeling process, and the accuracy of the parametric modeling was evaluated by comparing the 3D-CAD model to the 3D-STL model. Based on the results, the model used for clamping and positioning parts to perform the machining process and the connecting rod provided the most accurate mapping errors. These models represented deviations within ±0.02 mm and ±0.05 mm. The accuracy of CAD modeling for the turbine blade model and the pelvis part was comparable, presenting deviations within ±0.1 mm. However, the helical gear and the femur models showed the highest deviations of about ±0.2 mm. The procedures presented in the article specify the methods and resolution of the measurement systems and suggest CAD modeling strategies to minimize reconstruction errors. These results can be used as a starting point for further tests to optimize CAD modeling procedures based on the obtained measurement data.

System optimization study of hybrid K-edge/XRF densitometer for uranium–plutonium solution measurement

In this paper, a comprehensive hybrid K-edge/XRF densitometer (HKED) device model is constructed using MCNP simulation. After the modeling process, a systematic simulation study is conducted to analyze the physical parameters and material selection of KED and XRF. The simulation results reveal that the optimal parameters for the X-ray tube are an X-ray source voltage of 160 kV and a 1 mm Fe filter. The sample should be placed in a vial with an inner diameter of 1.4 cm and an outer diameter of 2 cm. For the KED technique, the determined main parameters are a 1.9 cm Fe filter rod and an inner diameter of 0.08 cm for the collimator. For the XRF technique, the determined main parameters are a 0.01 cm Gd filter and an inner diameter of 0.3 cm for the collimator, with a detector angle of 150°. After selecting appropriate parameters, the average calibration factor Δμ of the KED technique was found to be 3.301 cm2 g−1, with a relative standard deviation (RSD) of 3.36%. Additionally, the comparison between the simulated and calculated values of uranium concentration revealed a minimum measurement error of 0.4%. The minimum detection concentration of KED for uranium solutions is approximately 1 g/L. For plutonium solutions ranging from 0.5 to 20 g/L, linear fitting of the Ka1 net peak area and plutonium concentration showed a coefficient of determination (R2) of 0.999. The detection limit of XRF for plutonium measurement was 2.33✕10−4 g/L. The linear fitting coefficients (R2) of uranium concentration versus K-edge transmission rate and plutonium concentration versus Ka1 net peak area for the hybrid technique in measuring uranium-plutonium mixed solutions are determined as 0.999 and 0.996, respectively, demonstrating the response relationship of the HKED device to uranium and plutonium under different concentrations.

Analysis of Traffic Injury Crash Proportions Using Geographically Weighted Beta Regression

The classical linear regression model allows for a continuous quantitative variable to be modeled simply from other variables. However, this model assumes independence between observations, which, if ignored, can lead to methodological issues. Additionally, not all data follow a normal distribution, prompting the need for alternative modeling methods. In this context, geographically weighted beta regression (GWBR) incorporates spatial dependence into the modeling process and analyzes rates or proportions using the beta distribution. In this study, GWBR was applied to the traffic injury (fatal and non-fatal) crash proportions in Fortaleza, Ceará, Brazil, from 2009 to 2011. The results demonstrated that the local approach using the beta distribution is a viable model for explaining the traffic injury crash proportions, due to its flexibility in handling both symmetric and skewed distributions. Therefore, when analyzing rates or proportions, the use of the GWBR model is recommended.

Towards a Circular Solar Power Sector: Experience with a Support Framework for Business Model Innovation

AbstractThe rapid expansion of the global solar photovoltaic (PV) market as part of the transition to a low-carbon energy future will increase both demand for raw materials used in PV product manufacturing as well as future PV panel waste volumes. There is an urgent need for solar industry businesses to adopt circular business models, and to support this process through targeted tools and methods that can facilitate the innovation process of such models. This paper employs an action-oriented case study methodology, drawing on insights garnered from five demonstrator cases that have applied and validated a Circular Business Model Innovation (CBMI) framework within the PV sector. Results show how this framework was applied in practice during the ideation, design, experimentation, and scaling phases of the innovation process. Specifically, results show how an experimental and iterative approach was found critical to assess viability, desirability, and feasibility of circular business strategies. The study’s empirical testing of tools with the context of a Circular Economy complements earlier theoretical conceptualizations of business model innovation processes. It illustrates how using multiple tools can provide firms with complementary insights in each stage of ideation, design, testing, and scaling of business models. It is recommended that policy makers and business leaders endorse such co-creative and experimental approaches to foster learning and business model innovation, thereby catalysing the transformation of an entire renewable energy industry sector towards Circular Economy.

A hybrid feature weighted attention based deep learning approach for an intrusion detection system using the random forest algorithm.

Due to the recent advances in the Internet and communication technologies, network systems and data have evolved rapidly. The emergence of new attacks jeopardizes network security and make it really challenging to detect intrusions. Multiple network attacks by an intruder are unavoidable. Our research targets the critical issue of class imbalance in intrusion detection, a reflection of the real-world scenario where legitimate network activities significantly out number malicious ones. This imbalance can adversely affect the learning process of predictive models, often resulting in high false-negative rates, a major concern in Intrusion Detection Systems (IDS). By focusing on datasets with this imbalance, we aim to develop and refine advanced algorithms and techniques, such as anomaly detection, cost-sensitive learning, and oversampling methods, to effectively handle such disparities. The primary goal is to create models that are highly sensitive to intrusions while minimizing false alarms, an essential aspect of effective IDS. This approach is not only practical for real-world applications but also enhances the theoretical understanding of managing class imbalance in machine learning. Our research, by addressing these significant challenges, is positioned to make substantial contributions to cybersecurity, providing valuable insights and applicable solutions in the fight against digital threats and ensuring robustness and relevance in IDS development. An intrusion detection system (IDS) checks network traffic for security, availability, and being non-shared. Despite the efforts of many researchers, contemporary IDSs still need to further improve detection accuracy, reduce false alarms, and detect new intrusions. The mean convolutional layer (MCL), feature-weighted attention (FWA) learning, a bidirectional long short-term memory (BILSTM) network, and the random forest algorithm are all parts of our unique hybrid model called MCL-FWA-BILSTM. The CNN-MCL layer for feature extraction receives data after preprocessing. After convolution, pooling, and flattening phases, feature vectors are obtained. The BI-LSTM and self-attention feature weights are used in the suggested method to mitigate the effects of class imbalance. The attention layer and the BI-LSTM features are concatenated to create mapped features before feeding them to the random forest algorithm for classification. Our methodology and model performance were validated using NSL-KDD and UNSW-NB-15, two widely available IDS datasets. The suggested model's accuracies on binary and multi-class classification tasks using the NSL-KDD dataset are 99.67% and 99.88%, respectively. The model's binary and multi-class classification accuracies on the UNSW-NB15 dataset are 99.56% and 99.45%, respectively. Further, we compared the suggested approach with other previous machine learning and deep learning models and found it to outperform them in detection rate, FPR, and F-score. For both binary and multiclass classifications, the proposed method reduces false positives while increasing the number of true positives. The model proficiently identifies diverse network intrusions on computer networks and accomplishes its intended purpose. The suggested model will be helpful in a variety of network security research fields and applications.

Beyond analytics: Using computer‐aided methods in educational research to extend qualitative data analysis

AbstractThis study proposes and demonstrates how computer‐aided methods can be used to extend qualitative data analysis by quantifying qualitative data, and then through exploration, categorization, grouping, and validation. Computer‐aided approaches to inquiry have gained important ground in educational research, mostly through data analytics and large data set processing. We argue that qualitative data analysis methods can also be supported and extended by computer‐aided methods. In particular, we posit that computing capacities rationally applied can expand the innate human ability to recognize patterns and group qualitative information based on similarities. We propose a principled approach to using machine learning in qualitative education research based on the three interrelated elements of the assessment triangle: cognition, observation, and interpretation. Through the lens of the assessment triangle, the study presents three examples of qualitative studies in engineering education that have used computer‐aided methods for visualization and grouping. The first study focuses on characterizing students' written explanations of programming code, using tile plots and hierarchical clustering with binary distances to identify the different approaches that students used to self‐explain. The second study looks into students' modeling and simulation process and elicits the types of knowledge that they used in each step through a think‐aloud protocol. For this purpose, we used a bubble plot and a k‐means clustering algorithm. The third and final study explores engineering faculty's conceptions of teaching, using data from semi‐structured interviews. We grouped these conceptions based on coding similarities, using Jaccard's similarity coefficient, and visualized them using a treemap. We conclude this manuscript by discussing some implications for engineering education qualitative research.

Lipidomics study of Liujunzi decoction in hyperlipidemia rats with spleen deficiency based on UPLC-Q-TOF/MS

Hyperlipidemia refers to the abnormal levels of triglyceride (TG), total cholesterol (TC), low-density lipoprotein (LDL-C) and high-density lipoprotein (HDL-C) in peripheral blood circulation. It is a predominant risk factor underlying cardiovascular and cerebrovascular diseases, including coronary heart disease and atherosclerosis. Furthermore, it is also one of the most prevalent chronic diseases globally. Liujunzi Decoction is the basic prescription for the treatment of spleen and stomach diseases. It can tonify the spleen and qi, remove dampness, and reduce turbidity. Moreover, it is also clinically used for the treatment of spleen deficiency hyperlipidemia. However, its metabolites and therapeutic effect on spleen deficiency hyperlipidemia have not been comprehensively determined in vitro and in vivo. This study established a rat model of spleen deficiency hyperlipidemia by inducing starvation and satiety disorders, exhaustion swimming, and intragastric administration of the fat emulsion. To identify related metabolite changes and serum lipid composition, UPLC-Q-TOF-MS, PCA, and OPLS-DA lipidological methods were performed. The results demonstrated significant changes in rat's signs during the modeling process, which were consistent with the criteria for the syndrome differentiation of spleen deficiency in traditional Chinese medicine. Furthermore, this study identified 100 potential biomarkers in rat serum, of which 52 were associated with lipid synthesis, such as LPC, PC, PI, PE, PA, Cer, SM, etc. The pathways involved were glycerol phospholipid, sphingomyelin, and glycerol ester metabolisms. After the Liujunzi decoction intervention, 56 potential biomarkers were observed in the high-dose group, alleviating the metabolic spectrum imbalance by reducing metabolite levels. In addition, metabolic pathway disturbances were markedly improved. This study provides references for future studies on Liujunzi decoction and furnishes essential data for assessing the relationships between chemical constituents and pharmacological activities of Liujunzi decoction.

Optimization of the FDM Processing Parameters on the Compressive Properties of ABS Objects for the Production of High-Heeled Shoes

The influence of 3D printing parameters on compressive properties is an important factor in the application of additive manufacturing processes for products subjected to compressive loads in use. In this study, the compressive strength and compressive modulus of acrylonitrile/butadiene/styrene (ABS) test specimens fabricated using the fused deposition modeling (FDM) process were investigated with the aim of producing products of high-heeled shoes for women. The experimental part of the study includes a central composite experimental design to optimize the main 3D printing parameters (layer thickness, infill density, and extrusion temperature) and the infill geometry (honeycomb and linear at a 45° angle—L45) to achieve maximum printing properties of the 3D-printed products. The results show that the infill density has the greatest influence on the printing properties, followed by the layer thickness and, finally, the extrusion temperature as the least influential factor. The linear infill at a 45° angle resulted in higher compressive strength and lower compressive modulus values compared to the honeycomb infill. By optimizing the results, the maximum compressive strength (that of L45 is 41 N/mm2 and that of honeycomb 35 N/mm2) and modulus (that of L45 is 918 N/mm2 and that of honeycomb is 868 N/mm2) for both types of infill is obtained at a layer thickness of 0.1 mm and infill density of 40%, while the temperature for L45 can be in the range of 209 °C to 254 °C, but for the honeycomb infill, the processing temperature is 255 °C. Additionally, the study highlights the potential for sustainable manufacturing practices and the integration of advanced 3D printing technologies to enhance the efficiency and eco-friendliness of the production process.

Numerical Validation of Fully Coupled Nonlinear Seismic Soil–Pile–Structure Interaction

While many researchers have broadly studied soil–structure interaction (SSI) problems to comprehend SSI effects on the overall system’s behavior, some critical numerical modeling issues have not been sufficiently investigated to achieve the most accurate results. Furthermore, most scholars have not provided detailed explanations of the validation process for their proposed numerical models. Modeling pile foundations in a three-dimensional (3D) continuum system in a fully coupled manner is often challenging for engineers who do not specialize in structural and geotechnical earthquake engineering, as it can be very time-consuming and complicated. Therefore, this work aims to validate a finite element model of seismic soil–pile–structure interaction (SPSI) problems in a continuum soil body by comparing the results of numerical models with those of a centrifuge test and computed numerical simulations available in the literature. In this regard, several newly developed elements in OpenSees (Version 3.6.0) are tested. The results of this work demonstrate a closer alignment with prior experimental research findings. It is believed that providing detailed numerical modeling and validation processes will assist researchers in better understanding crucial issues in modeling soil–pile–structure interaction problems.

Fuzzy control for networked systems with mixed data missing under quantization based event-triggered scheme

This paper presents an innovative event-triggered fuzzy control approach for networked systems operating over unreliable network channels. Traditional complicated modeling and analysis process of network induced issues is replaced by a bran-new logarithmic quantization based event-triggered scheme method. The joint characterization of mixed data missing, arising from the event-triggered mechanism and unreliable network channels, becomes a challenging issue due to disrupted data sequences and variable relations. Consequently, this paper contributes a formulated auxiliary stochastic sequence approach to overcome the difficulty of dynamics modeling. With an formulated event driven controller, the modeling and implementation of the closed-loop systems are constructed and further simplified. Considering the difference and relation between the membership functions of plant and controller, we present a new fuzzy output feedback controller design method to obtain feasible result via uncertainty based method. Finally, we verify the validity of the designed control policy via a simulation example.

Leveraging Quadratic Polynomials in Python for Advanced Data Analysis

Objectives This study aims to provide a comprehensive overview of the role of quadratic polynomials in data modeling and analysis, particularly in representing the curvature of natural phenomena. Methods We begin with a fundamental explanation of quadratic polynomials and describe their general forms and theoretical significance. We then explored the application of these polynomials in regression analysis, detailing the process of fitting quadratic models to the data using Python libraries NumPy and Matplotlib. The methodology also included calculation of the coefficient of determination (R-squared) to evaluate the polynomial model fit. This study utilizes illustratively generated data to demonstrate the application of quadratic polynomials in Python for robust data analysis. Results Using practical examples accompanied by Python scripts, this study demonstrated the application of quadratic polynomials to analyze data patterns. These examples illustrate the utility of quadratic models in applied analytics. Conclusions This study bridges the gap between theoretical mathematical concepts and practical data analysis, thereby enhancing the understanding and interpretation of the data patterns. Furthermore, its implementation in Python, released under MIT’s license, offers an accessible tool for public use.