Currently used methods of simulation of doubly fed induction machines (DFIM), especially in real-time simulators (where a relatively large calculation step is used and high adequacy is required), do not provide the required adequacy, especially in ro...
This paper examines the impact of reducing ship turnaround time on the performance of container terminals, with a focus on leveraging artificial intelligence (AI) to enhance operational efficiency. It presents a novel framework combining machine lear...
Hysteresis is a non−unique phenomenon known as a multi−valued mapping in different fields of science and engineering. Accurate identification of the hysteretic systems is a crucial step in hysteresis compensation and control. This study proposes a no...
With the continuous development of the construction technology of the main structure of high-rise buildings, traditional construction techniques have been widely used in high-rise building projects. However, these technologies have problems such as l...
Machine learning (ML) is increasingly used in diverse fields, including animal behavior research. However, its application to ambiguous data requires careful consideration to avoid uncritical interpretations. This paper extends prior research on ring...
The objective of this paper was to create a mathematical model of vacuum drops in a form that enables the testing of the impact of design parameters of a milking cluster on the values of vacuum drops in the claw. Simulation tests of the milking clust...
Seed cotton compression molding solves the inconvenience of seed cotton transportation and storage after mechanical harvesting. Stress relaxation is closely related to the performance of the compressed seed cotton. In this study, an electronic univer...
The outdoor thermal environment can be regarded as a significant factor influencing indoor thermal conditions. The application of phase change materials (PCMs) to the building envelope has the potential to improve the heat storage performance of buil...
In recent years, machine learning (ML) has become a buzzword in the petroleum industry with numerous applications that guide engineers toward better decision making. The most powerful tool that most production development decisions rely on is reservo...
According to the roadmap toward clean energy, natural gas has been pronounced as the perfect transition fuel. Unlike usual dry gas reservoirs, gas condensates yield liquid which remains trapped in reservoir pores due to high capillarity, leading to t...
Tea (Camellia sinensis L.) is one of the most highly consumed beverages globally after water. Several countries import large quantities of tea from other countries to meet domestic needs. Therefore, accurate and timely prediction of tea yield is crit...
In recent years, Machine Learning (ML) has become a buzzword in the petroleum industry, with numerous applications which guide engineers in better decision making. The most powerful tool that most production development decisions rely on is reservoir...
In this paper, a machine-learning-assisted simulation approach for dynamic flow-shop production scheduling is proposed. This is achieved by introducing a novel framework to include predictive maintenance constraints in the scheduling process while a...
The accurate prediction of aerodynamic properties is an essential requirement for the design of applications that involve fluid flows, especially in the aerospace industry. The aerodynamic characteristics of fluid flows around a wing or an airfoil ar...
For the iterative development of the chip, ensuring that the simulation is completed in the shortest time is critical. To meet this demand, the common practice is to reduce simulation time by providing more computing resources. However, this accelera...
The growing global demand for mineral resources is challenging mining operations to maintain productivity while processing lower-grade ores and increasingly complex deposits. This study presents an integrated framework that leverages machine learning...
In aircraft assembly, riveting is a critical joining method that directly determines structural integrity, fatigue life, and overall airframe reliability. With the increasing adoption of human–machine collaborative systems for complex assembly...
Runoff from the high-cold mountains area (HCMA) is the most important water resource in the arid zone, and its accurate forecasting is key to the scientific management of water resources downstream of the basin. Constrained by the scarcity of meteoro...
The efficient scheduling of resources within emergency departments (EDs) is crucial to minimizing patient length of stay (LoS) times and maximizing the utilization of limited resources. Reducing patient wait times can enhance the operation of emergen...
The forecasting of presidential election results (PERs) is a very complex problem due to the diversity of electoral factors and the uncertainty involved. The use of a hybrid approach composed of techniques such as machine learning (ML) and Simulation...
Machine learning techniques have garnered significant attention in various engineering disciplines due to their potential and benefits. Specifically, in reservoir numerical simulations, the core process revolves around solving the partial differentia...
Numerical simulation of ground-penetrating radar (GPR) has been widely used to enhance the interpretation of GPR data and serves as a key component in Full Waveform Inversion (FWI). In response to the time-consuming numerical computation of layered m...
Given the climate change in recent decades and the ever-increasing energy consumption in the building sector, research is widely focused on the green revolution and ecological transition of buildings. In this regard, artificial intelligence can be a...
Hydrostatic thrust bearings are the core part of the hydrostatic spindle, which is widely used in high precision grinding machines. In this paper, the viscosity-temperature (v-t) characteristics of hydrostatic oil are systematically investigated, whi...
As an important part of industrial production, the optimization of circulating water systems is of great significance for improving energy efficiency and reducing operating costs. However, traditional optimization methods lack real-time and dynamic a...
In the current study, the integration of finite element simulation and machine learning is used to find the optimal combination of processing parameters in the directed energy deposition of SS316L. To achieve this, the FE simulation was validated aga...
In this study, we employ seven well-established machine learning algorithms for the stochastic simulation of tropical cyclones in the Northwest Pacific, namely Support Vector Machine (SVM), Random Forest (RF), Bayesian Network (BN), Backpropagation N...
This study presents a comprehensive investigation into the application of reservoir simulation and machine learning techniques to improve the understanding and prediction of reservoir behavior, focusing on the Sarir C-Main field. The research uses va...
With the advent of Industry 5.0, the electrical sector has been endowed with intelligent devices that are propelling high penetration of distributed energy microgeneration, VPP, smart buildings, and smart plants and imposing new challenges on the sec...
Interdisciplinary barriers separating data scientists and geometallurgists have complicated systematic attempts to incorporate machine-learning into mine production management; however, experiences in excavating a vein-hosted gold deposit within the...
The need for effective freight and human transportation systems has consistently increased during the last decades, mainly due to factors such as globalization, e-commerce activities, and mobility requirements. Traditionally, transportation systems h...
G protein-coupled receptor kinase 5 (GRK5) is an important therapeutic target involving cardiovascular diseases, cancer, and inflammatory disorders. However, the features of its activation as an essential function regulation process have been poorly...
In modern processor development, extensive simulation is required before manufacturing to ensure that Central Processing Unit (CPU) designs function correctly and efficiently. This pre-silicon validation process involves running a wide range of softw...
Although heavy oil remains a crucial energy source, its high viscosity makes its utilization challenging. We have performed an interpretable analysis of the relationship between the molecular structure of digital oil and its viscosity using molecular...
Effective household and individual disaster preparedness can minimize physical harm and property damage during catastrophic events. To assess the risk and vulnerability of affected areas, it is crucial for relief agencies to understand the level of p...
Urban transportation systems, particularly underground interchanges, present significant challenges for sustainable and resilient urban design due to their complex road geometries and dense traffic signage. These challenges are further compounded by...
Being the necessary data of the city-scale seismic damage simulations, structural types of buildings of a city need to be collected. To this end, a prediction method of structural types of buildings based on machine learning (ML) is proposed herein....
Owing to the non-uniform distribution of chemical composition and temperature during the heat treatment process, the residual stress and deformation of the workpiece emerge as crucial factors requiring consideration in managing the service performanc...
Leaf–device interaction can effectively be modeled with a finite element model when proper finite element model parameters are applied. In order to investigate the contact mechanism of picking up sugarcane leaf during the operation of a sugarca...
The inherent uncertainties in traditional hydrological models present significant challenges for accurately simulating runoff. Combining machine learning models with traditional hydrological models is an essential approach to enhancing the runoff mod...
The real-time, full-field simulation of the tube hydroforming process is crucial for deformation monitoring and the timely prediction of defects. However, this is rather difficult for finite-element simulation due to its time-consuming nature. To ove...
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Educational planning in Ecuador relies on deterministic projections that fail to quantify demographic uncertainty. This study aimed to develop and validate a hybrid model integrating Machine Learning (XGBoost) and Monte Carlo simulation for probabili...
Hamstrings–quadriceps (H–Q) imbalance represents a biomechanical marker of knee instability and injury risk in running. This in silico (simulation-based) study introduces a digital machine learning framework designed to estimate H–Q...
The optimal machine settings in polymer processing are usually the result of time-consuming and expensive trials. We present a workflow that allows the basic machine settings for the plasticizing process in injection molding to be determined with the...
Many planning and decision activities in logistics and supply chain management are based on forecasts of multiple time dependent factors. Therefore, the quality of planning depends on the quality of the forecasts. We compare different state-of-the-ar...
By introducing a data-driven human–machine collaboration performance evaluation Model (HMCPEM) that utilizes multiple regression and Monte Carlo simulation, the effectiveness and dependability of collaborative decision-making in smart agricultu...
This review examines 80 research studies on electric vehicle (EV) range prediction published between 2013 and 2024. We categorized all studies into three methodological groups such as machine learning (ML), mathematical modeling (MM), and simulation...
Classification of faults in mechanical components using machine learning is a hot topic in the field of science and engineering. Generally, every real-world running mechanical system exhibits personalized vibration behaviors that can be measured with...
A fire egress system is one of the most critical aspects of fire emergency evacuation, which is the cornerstone technology of building fire safety. The high-rise teaching buildings on campus, where vast crowds of people gather, need to be qualified f...
Background: Pediatric and congenital heart surgery (PCHS) is highly risky. Complications associated with this surgical procedure are mainly caused by the severity of the disease or the unnecessary, late, or premature execution of the procedure, which...
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