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Flow processes onboard ships are common in order to transport fluids like oil, gas, and water. These processes are controlled by PID controllers, acting on the regulation valves as actuators. In case of a malfunction or refitting, a PID controller needs to be re-adjusted for the optimal control of the process. To avoid experimenting on operational real systems, models are convenient alternatives. When real-time information is needed, digital twin (DT) concepts become highly valuable. The aim of this paper is to analyze and determine the optimal NARX model architecture in order to achieve a higher-accuracy model of a ship's flow process. An artificial neural network (ANN) was used to model the process in MATLAB. The experiments were performed using a multi-start approach to prevent overtraining. To prove the thesis, statistical analysis of the experimental results was performed. Models were evaluated for generalization using mean squared error (MSE), best fit, and goodness of fit (GoF) measures on two independent datasets. The results indicate the correlation between the number of input delays and the performance of the model. A permuted k-fold cross-validation analysis was used to determine the optimal number of voltage and flow delays, thus defining the number of model inputs. Permutations of training, test, and validation datasets were applied to examine bias due to the data arrangement during training. [ABSTRACT FROM AUTHOR]

This research paper explores the realm of fault detection in distributed motors through the vision of the Internet of electrical drives. This paper aims at employing artificial neural networks supported by the data collected by the Internet of distributed devices. Cross-verification of results offers reliable diagnosis of industrial motor faults. The proposed methodology involves the development of a cyber–physical system architecture and mathematical modeling framework for efficient fault detection. The mathematical model is designed to capture the intricate relationships within the cyber–physical system, incorporating the dynamic interactions between distributed motors and their edge controllers. Fast Fourier transform is employed for signal processing, enabling the extraction of meaningful frequency features that serve as indicators of potential faults. The artificial neural network based fault detection system is integrated with the solution, utilizing its ability to learn complex patterns and adapt to varying motor conditions. The effectiveness of the proposed framework and model is demonstrated through experimental results. The experimental setup involves diverse fault scenarios, and the system's performance is evaluated in terms of accuracy, sensitivity, and false positive rates. [ABSTRACT FROM AUTHOR]

Detecting instruments in a music signal is often used in database indexing, song annotation, and creating applications for musicians and music producers. Therefore, effective methods that automatically solve this issue need to be created. In this paper, the mentioned task is solved using mel-frequency cepstral coefficients (MFCC) and various architectures of artificial neural networks. The authors' contribution to the development of automatic instrument detection covers the methods used, particularly the neural network architectures and the voting committees created. All these methods were evaluated, and the results are presented and discussed in the paper. The proposed automatic instrument detection methods show that the best classification quality was obtained for an extensive model, which is the so-called committee of voting classifiers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a learning model for tracking the isolines of fluid based on the physical properties of particles in particle-based fluid simulations. Our method involves analyzing which weights, closely related to surface tracking among the various physical properties of fluid particles, are significant. These weights are used as input values for the learning algorithm, enabling relatively accurate isoline tracking. In addition, compared to existing learning models such as linear regression, LSTM (long short-term memory), and learning representation (1-layer) models, our method obtained superior surface tracking results without accumulating errors. By using our proposed network structure to track the fluid surface, it learns and predicts values derived from existing fluid simulation algorithms, eliminating the need for computational processes for level-set values and enabling real-time surface tracking. As the scale of the simulation increases, our method significantly reduces the time and resources consumed compared to traditional methods and can track the fluid surface without additional resource consumption. Furthermore, due to our method's simple network structure, the time consumed in the initial process of loading the model into memory is faster than models such as CNN and LSTM. Our proposed model occupies less than 30 kb of storage space, making it suitable for use in middleware. Lastly, to verify the generality of our method, we conducted tests in a total of five scenes, and in all test scenes, visually natural fluid isolines were represented. [ABSTRACT FROM AUTHOR]

This study presents a numerical approach to the design and optimization of centrifugal impellers used in the pumps of active thermal control systems of spacecraft. Although launch costs have shrunk in the last decade, the performance requirements, such as efficiency and reliability, have increased, as such systems are required to work up to 15 years, depending on the mission. To that effect, our paper deals with the first step in this pump design, namely the hydraulic optimization of the impeller. Constructively, this type of impeller allows for certain balancing systems and labyrinth seals to be applied in a more effective way, as well as allowing for additive manufacturing methods to be used—however, details regarding these aspects are beyond the scope of the current paper. By combining empirical formulas, computational fluid dynamics (CFD) analysis, and artificial neural networks (ANNs), the research focuses on achieving high efficiency and fast manufacturing. A series of geometries have been sized and validated using steady-state RANS (Reynolds Averaged Navier-Stokes) simulations, leading to the identification of the most efficient configuration. Subsequent optimization using an ANN resulted in a refined impeller design with notable improvements in hydraulic performance: a 3.55% increase in efficiency and a 7.9% increase in head. Key parameters influencing impeller performance, including blade number, incidence, and backsweep angles, are identified. This approach offers a comprehensive method to address the evolving requirements of space missions and contributes to the advancement of centrifugal pump technology in the space domain. [ABSTRACT FROM AUTHOR]

Electricity consumption in buildings is one of the major causes of energy usage and knowledge of this can help building owners and users increase energy efficiency and conservation efforts. For Pacific Island countries, building electricity demand data is not readily accessible or available for constructing models to predict electricity demand. This paper starts to fill this gap by studying the case of schools in Fiji. The aim of the paper is to assess the factors affecting electricity demand for grid-connected Fijian schools and use this assessment to build mathematical models (multiple linear regression (MLR) and artificial neural network (ANN)) to predict electricity consumption. The average grid-connected electricity demand in kWh/year was 1411 for early childhood education schools, 5403 for primary schools, and 23,895 for secondary schools. For predicting electricity demand (ED) for all grid-connected schools, the stepwise MLR model shows that taking logarithm transformations on both the dependent variable and independent variables (number of students, lights, and air conditioning systems) yields statistically significant independent variables with an R2 value of 73.3% and RMSE of 0.2248. To improve the predicting performance, ANN models were constructed on both the natural form of variables and transformed variables. The optimum ANN model had an R2 value of 95.3% and an RMSE of 59.4 kWh/year. The findings of this study can assist schools in putting measures in place to reduce their electricity demand, associated costs, and carbon footprint, as well as help government ministries make better-informed policies. [ABSTRACT FROM AUTHOR]

This paper studies an artificial neural network (ANN) for multi-asset European options. Firstly, a simple three-layer ANN-3 is established with undetermined weights and bias. Secondly, the time–space discrete PDE of the multi-asset option is given and the corresponding discrete data are fed into the ANN-3. Then, using least squares error as the objective function, the weights and bias of ANN-3 are trained well. Numerical examples are carried out to confirm the stability, accuracy and efficiency. Experiments show the ANN's relative error is about  0.8 % . This method can be extended into multi-layer ANN- q (q > 3)  and extended into American options. [ABSTRACT FROM AUTHOR]

Artificial neural networks (ANNs) can be used for accurate heat load forecasting in district heating systems (DHSs). This paper presents an application of a shallow ANN with two hidden layers in the case of a local DHS. The developed model was used to write a simple application in Python 3.10 that can be used in the operation of a district heating plant to carry out a preliminary analysis of heat demand, taking into account the ambient temperature on a given day. The model was trained using the real data from the period 2019–2022. The training was sufficient for the number of 150 epochs. The prediction effectiveness indicator was proposed. In the considered case, the effectiveness of the trained network was 85% and was better in comparison to five different regression models. The developed tool was based on an open-source programming environment and proved its ability to predict heating load. [ABSTRACT FROM AUTHOR]

The primary objective of the research presented in this article is to introduce an artificial neural network that demands less computational power than a conventional deep neural network. The development of this ANN was achieved through the application of Ordered Fuzzy Numbers (OFNs). In the context of Industry 4.0, there are numerous applications where this solution could be utilized for data processing. It allows the deployment of Artificial Intelligence at the network edge on small devices, eliminating the need to transfer large amounts of data to a cloud server for analysis. Such networks will be easier to implement in small-scale solutions, like those for the Internet of Things, in the future. This paper presents test results where a real system was monitored, and anomalies were detected and predicted. [ABSTRACT FROM AUTHOR]

Saltwater intrusion is an essential problem in estuaries that can threaten the ecological environment, especially in high-salinity situations. Therefore in this paper, traditional multiple linear regression (MLR) and artificial neural network (ANN) modeling are applied to forecast overall and high salinity in the Lower Scheldt Estuary, Belgium. Mutual information (MI) and conditional mutual information (CMI) are used to select optimal driving forces (DFs), with the daily discharge (Q), daily water temperature (WT), and daily sea level (SL) selected as the main DFs. Next, we analyze whether applying a discrete wavelet transform (DWT) to remove the noise from the original time series improves the results. Here, the DWT is applied in Signal-hybrid (SH) and Within-hybrid (WH) frameworks. Both the MLR and ANN models demonstrate satisfactory performance in daily overall salinity simulation over the Scheldt Estuary. The relatively complex ANN models outperform MLR because of their capabilities of capturing complex interactions. Because the nonlinear relationship between salinity and DFs is variable at different locations, the performance of the MLR models in the midstream region is far inferior to that in the downstream region during spring and winter. The results reveal that the application of DWT enhances simulation of both overall and high salinity in this region, especially for the ANN model with the WH framework. With the effect of Q decline or SL rise, the salinity in the middle Scheldt Estuary increases more significantly, and the ANN models are more sensitive to these perturbations. [ABSTRACT FROM AUTHOR]

Along with seaports automation, there is growing interest in the automation of Grab-Type Ship Unloader (GTSU) that unloads coal and iron ore from bulk carriers. Autonomous unloading operations of GTSU offer the potential for significant productivity improvement and cost savings. In this paper, an AI model trained with manual operation data was designed for GTSU automation operation, and the AI model was verified through the equation-of-motion-based GTSU operation simulator. The operation data of hoist, grab, and trolley were predicted by training the designed AI model with the manual operation data of GTSU. Before applying the predicted data to the actual equipment, the predicted driving data was verified using the equation-of-motion-based GTSU operation simulator. The AI prediction model was designed using the Multi-Layer Perception network, a type of artificial neural network. The AI prediction model was evaluated with the Mean-Squared Error indicator, and the validation loss was found to be less than 0.02. In addition, verification of the prediction data was performed using the GTSU dynamics-based simulator. The Mean Relative Error was up to 0.50, and the R2 score value exceeded 0.92, indicating that the model is effective in predicting operation data. The proposed AI prediction model will be effectively utilized to implement a fully automated unloading system. [ABSTRACT FROM AUTHOR]

The energy transition towards a low-emission matrix has motivated efforts to reduce the use of fossil fuels in the transportation sector. The growth of the electric mobility market has been consistent in recent years. In Brazil, there has been an accelerated growth in the sales rate of new electric (and hybrid) vehicles (EVs). Fiscal incentives provided by governments, along with the reduction in vehicle costs, are factors contributing to the exponential growth of the EV fleet—creating a favorable environment for the dissemination of new technologies and enabling the participation of players from sectors such as battery manufacturing and charging stations. Considering the international context, the E-Lounge R&D joint initiative aims to evaluate different strategies to economically enable the electric mobility market, exploring EV charging service sales by energy distribution utility companies in Brazil. This work describes the step-by-step development of an ideal model of a charging hub and discusses its operation based on a real deployment, as well as its associated technical and economic feasibility. Using EV charging data based on the E-Lounge's operational behavior, an artificial neural network (ANN) is applied to forecast future energy consumption to each EV charging station. This paper also presents an economic analysis of the E-Lounge case study, which can contribute to proposals for electric vehicle charging ecosystems in the context of smart energy systems. Based on the operational results collected, as well as considering equipment usage projections, it is possible to make EV charging enterprises feasible, even when high investments in infrastructure and equipment (charging stations and battery storage systems) are necessary, since the net present value is positive and the payback period is 4 years. This work contributes by presenting real operational data from a charging hub, a projection model aimed at evaluating future operations, and a realistic economic evaluation model based on a case study implemented in São Paulo, Brazil. [ABSTRACT FROM AUTHOR]

The potential of unconventional hydrocarbon resources has been unlocked since the hydraulic fracturing technique in combination with long horizontal wells was applied to develop this type of reservoir economically. The design and optimization of the fracturing treatment and the stimulated reservoir volume and the forecasting of production performance are crucial for the development and management of such resources. However, the production performance of tight gas reservoirs is a complicated nonlinear problem, described by many parameters loaded with uncertainty. The complexity of the problem influences and inspires the sophistication of the solution to be used. This paper proposed an artificial network model that allows for fast, extended, and accurate analyses of the production performance of multiple fractured unconventional tight gas wells. In the comprehensive approach developed, the reservoir rock parameters, the drainage area, and the hydraulic fracture parameters are treated as a variable input to the model. The analysis is no longer constrained by fixed "shoes box" geometry, and the values of the parameters defining the reservoir and stimulated volume are not limited to a few discrete values. The numerical experiment used to construct a database for model development was designed using a genetically optimized Latin hypercube sampling technique. A special approach was used in the preparation of "blind data", which are crucial for truly reliable model verification. In the result, a developed tool offers an extended rock-fluid description, flexible model, and stimulated reservoir volume dimensioning and parameterization, as well as a high degree of applicability in sensitivity analysis and/or optimization. [ABSTRACT FROM AUTHOR]

In contrast to cumbersome benchtop spectrometers, integrated on-chip spectrometers are well-suited for portable applications in health monitoring and environmental sensing. In this paper, we have developed an on-chip spectrometer with a programmable silicon photonic filter by simply using parallel cascaded micro-ring resonators (MRs). By altering the transmission spectrum of the filter, multiple and diverse sampling of the input spectrum is achieved. Then, combined with an artificial neural network (ANN) model, the incident spectrum is reconstructed from the sampled signals. Each MR is coupled to adjacent ones, and the phase shifts within each MR can be independently tuned. Through dynamic programming of the phases of these MRs, sampling functions featuring diverse characteristics are obtained based on a single programmable filter with an adjustable number of sampling channels. This eliminates the need for a filter array, significantly reducing the area of the on-chip reconstructive spectrometer. The simulation results demonstrate that the proposed design can achieve the reconstruction of continuous and sparse spectra within the wavelength range of 1450 nm to 1650 nm, with a tunable resolution ranging from 2 nm to 0.2 nm, depending on the number of sampling states employed. This benefit arises from the programmable nature of the device. The device holds tremendous potential for applications in wearable optical sensing, portable spectrometry, and other related scenarios. [ABSTRACT FROM AUTHOR]

Obtaining mathematical models of nonlinear cyber–physical systems for use in controller design is both difficult and time consuming. In this paper, an ANN-based method is proposed to design a controller for a nonlinear system that does not require a mathematical model. The developed ANN-based control algorithm is implemented directly on a real-time field controller, and its performance is evaluated without the use of auxiliary devices, such as PCs or workstations. By executing machine learning algorithms on local devices or embedded systems, edge artificial intelligence (Edge AI) with transfer learning gives priority to processing data at the source, minimizing the necessity for continuous connectivity to remote servers. The control algorithm was developed using the Matlab Simulink environment. The first and second ANNs were cascaded, wherein the first ANN computes the appropriate pressure signal for the given displacement, while the second predicts the force based on the pressure value from the first ANN. Subsequently, the ANN-based control algorithm was converted to SCL code using the Simulink PLC Coder and deployed on the PLC for operation. The algorithm was tested using two different scenarios. The conducted tests demonstrated the successful prediction of pressure signals corresponding to the targeted displacement values and accurate estimation of force values. Experimental work was carried out on PAM manipulators as a nonlinear model application, and the obtained results were discussed. [ABSTRACT FROM AUTHOR]

Patient-specific 3D models of the human mandible are finding increasing utility in medical fields such as oral and maxillofacial surgery, orthodontics, dentistry, and forensic sciences. The efficient creation of personalized 3D bone models poses a key challenge in these applications. Existing solutions often rely on 3D statistical models of human bone, offering advantages in rapid bone geometry adaptation and flexibility by capturing a range of anatomical variations, but also a disadvantage in terms of reduced precision in representing specific shapes. Considering this, the proposed parametric model allows for precise manipulation using morphometric parameters acquired from medical images. This paper highlights the significance of employing the parametric model in the creation of a personalized bone model, exemplified through a case study targeting mandibular prognathism reconstruction. A personalized model is described as 3D point cloud determined through the utilization of series of parametric functions, determined by the application of geometrical morphometrics, morphology properties, and artificial neural networks in the input dataset of human mandible samples. With 95.05% of the personalized model's surface area displaying deviations within −1.00–1.00 mm relative to the input polygonal model, and a maximum deviation of 2.52 mm, this research accentuates the benefits of the parametric approach, particularly in the preoperative planning of mandibular deformity surgeries. [ABSTRACT FROM AUTHOR]

Unsteady aerodynamic prediction at high angles of attack is of great importance to the design and development of advanced fighters. In this paper, a weighted feature fusion model (WFFM) that combines the state-space model and neural networks is proposed to build an unsteady aerodynamic model for the precise simulation and control of post-stall maneuvers. In the proposed model, the influences of the physical model on neural networks are considered and adjusted by introducing a standardization layer and a new weighting method. A long short-term memory (LSTM) network is used to fuse two mappings: one from flight states to aerodynamic loads, and the other from low-fidelity data to high-fidelity data. Data from wind tunnel oscillation experiments at high angles of attack using a new kind of wire-driven parallel robot and the traditional tail support are used for verifying the proposed aerodynamic model. The output of the WFFM is also compared with predictions from other models, such as the state-space model, single LSTM model, and feature fusion model not including a feature weighting layer. Results demonstrate improved accuracy of the proposed model in the interpolation and extrapolation tests. Furthermore, the WFFM is applied to the flight simulation of F-16 with different control inputs. Compared with conventional models, the WFFM shows improved accuracy and better generalization capability. [ABSTRACT FROM AUTHOR]

This paper presents a new approach for the online estimation of stator and rotor resistance of induction motors for speed sensorless vector-controlled drives, using feed-forward artificial neural networks with advanced adaptive learning rates. For the rotor resistance estimation, a neural network model based on rotor speed and stator currents is developed. The rotor flux linkages acquired from the voltage model are compared with the neural network model. The feed-forward neural network employs an adaptive learning rate as the function of the obtained error during training for quick convergence with minimal estimation error. A two-layered neural network model based on the stator voltage and current equations is developed for the stator resistance estimation. The d-q axes stator currents obtained from the developed model are compared with the acquired d-q axes stator currents. For the fast convergence with minimal estimation error, an adaptive learning rate as the function of error is adopted during training. Furthermore, the neural network estimates the induction motor's speed. The simulation and experimental results justify that the developed algorithms track variation in the resistances quickly and precisely along with the speed as compared with the conventional constant learning rate algorithm, leading to reliable operation of the drive. [ABSTRACT FROM AUTHOR]

Quantum neural networks (QNNs) leverage the strengths of both quantum computing and neural networks, offering solutions to challenges that are often beyond the reach of traditional neural networks. QNNs are being used in areas such as computer games, function approximation, and big data processing. Moreover, quantum neural network algorithms are finding utility in social network modeling, associative memory systems, and automatic control mechanisms. Nevertheless, ensuring the reliability of quantum neural networks is crucial as it directly influences network performance and stability. To investigate the reliability of quantum neural networks, this paper proposes a methodology wherein operator measurements are performed on the final states of the output quantum states of a quantum neural network. The proximity of these measurements to the target value is compared, and the fidelity value, combined with a quantum gate operation, is utilized to assess the reliability of the quantum neural network. Through network training, the results demonstrate that, under optimal parameters, both the fidelity of the final state measurement value and the target value of the model approach are approximately equal to 1. It indicates that training mitigates the errors stemming from encoding into the initial quantum state, thereby resulting in enhanced system reliability and accuracy. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach to estimating short-term production of wind farms, which are made up of numerous turbine generators. It harnesses the power of big data through a blend of data-driven and model-based methods. Specifically, it combines an Artificial Neural Network (ANN) for immediate future predictions of wind turbine power output with a stochastic model for dependability, using Hybrid Reliability Block Diagrams. A thorough state-of-the-art review has been conducted in order to demonstrate the applicability of an ANN for non-linear stochastic problems of energy or power forecast estimation. The study leverages an innovative cluster analysis to group wind turbines and reduce the computational effort of the ANN, with a dependability model that improves the accuracy of the data-driven output estimation. Therefore, the main novelty is the employment of a hybrid model that combines an ANN with a dependability stochastic model that accounts for the realistic operational scenarios of wind turbines, including their susceptibility to random shutdowns This approach marks a significant advancement in the field, introducing a methodology which can aid the design and the power production forecast. The research has been applied to a case study of a 24 MW wind farm located in the south of Italy, characterized by 28 turbines. The findings demonstrate that the integrated model significantly enhances short-term wind-energy production estimation, achieving a 480% improvement in accuracy over the solo-clustering approach. [ABSTRACT FROM AUTHOR]

The main goal of this work is to combine the usage of the numerical homogenization technique for determining the effective properties of representative volume elements with artificial neural networks. The effective properties are defined according to the classical laminate theory. The purpose is to create and train a rapid surrogate model for the quick calculation of the mechanical properties of hollow concrete slabs. First, the homogenization algorithm was implemented, which determines membrane, bending and transverse shearing properties of a given parametrized hollow-core precast slab reinforced with steel bars. The algorithm uses the finite element mesh but does not require a formal solution of the finite element method problem. Second, the learning and training artificial intelligence framework was created and fed with a dataset obtained by optimal Latin hypercube sampling. In the study, a multilayer perceptron type of artificial neural network was used. This allows for obtaining rapid calculations of the effective properties of a particular hollow-core precast slab by using a surrogate model. In the paper, it has been proven that such a model, obtained via complex numerical calculations, gives a very accurate estimation of the properties and can be used in many practical tasks, such as optimization problems or computer-aided design decisions. Above all, the efficient setup of the artificial neural network has been sought and presented. [ABSTRACT FROM AUTHOR]

Pore pressure prediction is a critical parameter in petroleum engineering and is essential for safe drilling operations and wellbore stability. However, traditional methods for pore pressure prediction, such as empirical correlations, require selecting appropriate input parameters and may not capture the complex relationships between these parameters and the pore pressure. In contrast, artificial neural networks (ANNs) can learn complex relationships between inputs and outputs from data. This paper presents a new empirical correlation for predicting pore pressure using ANNs. The proposed method uses 42 datasets of well log data, including temperature, porosity, and water saturation, to train ANNs for pore pressure prediction. The trained model, with the Bayesian regularization backpropagation function, predicts the pore pressure with an average absolute percentage error (AAPE) and correlation coefficient (R) of 4.22% and 0.875, respectively. The trained ANN is then used to develop a new empirical correlation that relates pore pressure to the input parameters considering the weights and biases of the optimized ANN model. To validate the proposed correlation, it is applied to a blind dataset, where the model successfully predicts the pore pressure with an AAPE of 5.44% and R of 0.957. The results show that the proposed correlation provides accurate and reliable predictions of pore pressure. The proposed method provides a robust and accurate approach for predicting pore pressure in petroleum engineering applications, which can be used to improve drilling safety and wellbore stability. [ABSTRACT FROM AUTHOR]

Optimal power flow (OPF) is a crucial tool in the operation and planning of modern power systems. However, as power system optimization shifts towards larger-scale frameworks, and with the growing integration of distributed generations, the computational time and memory requirements of solving the alternating current (AC) OPF problems can increase exponentially with system size, posing computational challenges. In recent years, machine learning (ML) has demonstrated notable advantages in efficient computation and has been extensively applied to tackle OPF challenges. This paper presents five commonly employed OPF transformation techniques that leverage ML, offering a critical overview of the latest applications of advanced ML in solving OPF problems. The future directions in the application of machine learning to AC OPF are also discussed. [ABSTRACT FROM AUTHOR]

In the epoch characterized by the anticipation of autonomous vehicles, the quality of the embedded system software, its reliability, safety, and security is significant. The testing of embedded software is an increasingly significant element of the development process. The application of artificial intelligence (AI) algorithms in the process of testing embedded software in vehicles constitutes a significant area of both research and practical consideration, arising from the escalating complexity of these systems. This paper presents the preliminary development of the AVESYS framework which facilitates the application of open-source artificial intelligence algorithms in the embedded system testing process. The aim of this work is to evaluate its effectiveness in identifying anomalies in the test environment that could potentially affect testing results. The raw data from the test environment, mainly communication signals and readings from temperature, as well as current and voltage sensors are pre-processed and used to train machine learning models. A verification study is carried out, proving the high practical potential of the application of AI algorithms in embedded software testing. [ABSTRACT FROM AUTHOR]

Manufacturing defects, such as porosity and inclusions, can significantly compromise the structural integrity and performance of additively manufactured parts by acting as stress concentrators and potential initiation sites for failure. This paper investigates the effects of pore system morphology (number of pores, total volume, volume fraction, and standard deviation of size of pores) on the material response of additively manufactured Ti6Al4V specimens under a shear–compression stress state. An automatic approach for finite element simulations, using the J2 plasticity model, was utilized on a shear–compression specimen with artificial pores of varying characteristics to generate the dataset. An artificial neural network (ANN) surrogate model was developed to predict peak force and failure displacement of specimens with different pore attributes. The ANN demonstrated effective prediction capabilities, offering insights into the importance of individual input variables on mechanical performance of additively manufactured parts. Additionally, a sensitivity analysis using the Garson equation was performed to identify the most influential parameters affecting the material's behaviour. It was observed that materials with more uniform pore sizes exhibit better mechanical properties than those with a wider size distribution. Overall, the study contributes to a better understanding of the interplay between pore characteristics and material response, providing better defect-aware design and property–porosity linkage in additive manufacturing processes. [ABSTRACT FROM AUTHOR]

Batteries have been considered a key element in several applications, ranging from grid-scale storage systems through electric vehicles to daily-use small-scale electronic devices. However, excessive charging and discharging will impair their capabilities and could cause their applications to fail catastrophically. Among several diagnostic indices, state-of-charge estimation is essential for evaluating a battery's capabilities. Various approaches have been introduced to reach this target, including white, gray, and black box or data-driven battery models. The main objective of this work is to provide an extensive comparison of currently highly utilized machine learning-based estimation techniques. The paper thoroughly investigates these models' architectures, computational burdens, advantages, drawbacks, and robustness validation. The evaluation's main criteria were based on measurements recorded under various operating conditions at the Energy Systems Research Laboratory (ESRL) at FIU for the eFlex 52.8 V/5.4 kWh lithium iron phosphate battery pack. The primary outcome of this research is that, while the random forest regression (RFR) model emerges as the most effective tool for SoC estimation in lithium-ion batteries, there is potential to enhance the performance of simpler models through strategic adjustments and optimizations. Additionally, the choice of model ultimately depends on the specific requirements of the task at hand, balancing the need for accuracy with the complexity and computational resources available and how it can be merged with other SoC estimation approaches to achieve high precision. [ABSTRACT FROM AUTHOR]

Organizations managing high-performance computing systems face a multitude of challenges, including overarching concerns such as overall energy consumption, microprocessor clock frequency limitations, and the escalating costs associated with chip production. Evidently, processor speeds have plateaued over the last decade, persisting within the range of 2 GHz to 5 GHz. Scholars assert that brain-inspired computing holds substantial promise for mitigating these challenges. The spiking neural network (SNN) particularly stands out for its commendable power efficiency when juxtaposed with conventional design paradigms. Nevertheless, our scrutiny has brought to light several pivotal challenges impeding the seamless implementation of large-scale neural networks (NNs) on silicon. These challenges encompass the absence of automated tools, the need for multifaceted domain expertise, and the inadequacy of existing algorithms to efficiently partition and place extensive SNN computations onto hardware infrastructure. In this paper, we posit the development of an automated tool flow capable of transmuting any NN into an SNN. This undertaking involves the creation of a novel graph-partitioning algorithm designed to strategically place SNNs on a network-on-chip (NoC), thereby paving the way for future energy-efficient and high-performance computing paradigms. The presented methodology showcases its effectiveness by successfully transforming ANN architectures into SNNs with a marginal average error penalty of merely 2.65%. The proposed graph-partitioning algorithm enables a 14.22% decrease in inter-synaptic communication and an 87.58% reduction in intra-synaptic communication, on average, underscoring the effectiveness of the proposed algorithm in optimizing NN communication pathways. Compared to a baseline graph-partitioning algorithm, the proposed approach exhibits an average decrease of 79.74% in latency and a 14.67% reduction in energy consumption. Using existing NoC tools, the energy-latency product of SNN architectures is, on average, 82.71% lower than that of the baseline architectures. [ABSTRACT FROM AUTHOR]

The manufacturing processes and design of metal and alloy products can be performed over a wide range of strain rates and temperatures. To design and optimize these processes using computational mechanics tools, the selection and calibration of the constitutive models is critical. In the case of hazardous and explosive impact loads, it is not always possible to test material properties. For this purpose, this paper assesses the efficiency and the accuracy of different architectures of ANNs for the identification of the Johnson–Cook material model parameters. The implemented computational tool of an ANN-based parameter identification strategy provides adequate results in a range of strain rates required for general manufacturing and product design applications. Four ANN architectures are studied to find the most suitable configuration for a reduced amount of experimental data, particularly for cases where high-impact testing is constrained. The different ANN structures are evaluated based on the model's predictive capability, revealing that the perceptron-based network of 66 inputs and one hidden layer of 30 neurons provides the highest prediction accuracy of the effective flow stress–strain behavior of Ti64 alloy and three virtual materials. [ABSTRACT FROM AUTHOR]

Atmospheric drag stands out as the predominant non-gravitational force acting on satellites in Low Earth Orbit (LEO), with altitudes below 2000 km. This drag exhibits a strong dependence on the thermospheric mass density (TMD), a parameter of vital significance in the realms of orbit determination, prediction, collision avoidance, and re-entry forecasting. A multitude of empirical TMD models have been developed, incorporating contemporary data sources, including TMD measurements obtained through onboard accelerometers on LEO satellites. This paper delves into three different TMD modelling techniques, specifically, Fourier series, spherical harmonics, and artificial neural networks (ANNs), during periods of geomagnetic quiescence. The TMD data utilised for modelling and evaluation are derived from three distinct LEO satellites: GOCE (at an altitude of approximately 250 km), CHAMP (around 400 km), and GRACE (around 500 km), spanning the years 2002 to 2013. The consistent utilisation of these TMD data sets allows for a clear performance assessment of the different modelling approaches. Subsequent research will shift its focus to TMD modelling during geomagnetic disturbances, while the present work can serve as a foundation for disentangling TMD variations stemming from geomagnetic activity. Furthermore, this study undertakes precise TMD modelling during geomagnetic quiescence using data obtained from the GRACE (at an altitude of approximately 500 km), CHAMP (around 400 km), and GOCE (roughly 250 km) satellites, covering the period from 2002 to 2013. It employs three distinct methods, namely Fourier analysis, spherical harmonics (SH) analysis, and the artificial neural network (ANN) technique, which are subsequently compared to identify the most suitable methodology for TMD modelling. Additionally, various combinations of time and coordinate representations are scrutinised within the context of TMD modelling. Our results show that the precision of low-order Fourier-based models can be enhanced by up to 10 % through the utilisation of geocentric solar magnetic coordinates. Both the Fourier- and SH-based models exhibit limitations in approximating the vertical gradient of TMD. Conversely, the ANN-based model possesses the capacity to capture vertical TMD variability without manifesting sensitivity to variations in time and coordinate inputs. [ABSTRACT FROM AUTHOR]

Structures made of heterogeneous materials, such as composites, often require a multiscale approach when their behavior is simulated using the finite element method. By solving the boundary value problem of the macroscale model, for previously homogenized material properties, the resulting stress maps can be obtained. However, such stress results do not describe the actual behavior of the material and are often significantly different from the actual stresses in the heterogeneous microstructure. Finding high-accuracy stress results for such materials leads to time-consuming analyses in both scales. This paper focuses on the application of machine learning to multiscale analysis of structures made of composite materials, to substantially decrease the time of computations of such localization problems. The presented methodology was validated by a numerical example where a structure made of resin epoxy with randomly distributed short glass fibers was analyzed using a computational multiscale approach. Carefully prepared training data allowed artificial neural networks to learn relationships between two scales and significantly increased the efficiency of the multiscale approach. [ABSTRACT FROM AUTHOR]

A methodology to predict key aspects of the structural response of masonry walls under blast loading using artificial neural networks (ANN) is presented in this paper. The failure patterns of masonry walls due to in and out-of-plane loading are complex due to the potential opening and sliding of the mortar joint interfaces between the masonry stones. To capture this response, advanced computational models can be developed requiring a significant amount of resources and computational effort. The article uses an advanced non-linear finite element model to capture the failure response of masonry walls under blast loads, introducing unilateral contact-friction laws between stones and damage mechanics laws for the stones. Parametric finite simulations are automatically conducted using commercial finite element software linked with MATLAB R2019a and Python. A dataset is then created and used to train an artificial neural network. The trained neural network is able to predict the out-of-plane response of the masonry wall for random properties of the blast load (standoff distance and weight). The results indicate that the accuracy of the proposed framework is satisfactory. A comparison of the computational time needed for a single finite element simulation and for a prediction of the out-of-plane response of the wall by the trained neural network highlights the benefits of the proposed machine learning approach in terms of computational time and resources. Therefore, the proposed approach can be used to substitute time consuming explicit dynamic finite element simulations and used as a reliable tool in the fast prediction of the masonry response under blast actions. [ABSTRACT FROM AUTHOR]

To address the issues of workpiece distortion and excessive material melting caused by heat accumulation during laser cutting of thin-walled sheet metal components, this paper proposes a segmented optimization method for process parameters in sheet metal laser cutting considering thermal effects. The method focuses on predetermined perforation points and machining paths. Firstly, an innovative temperature prediction model T p r , t is established for the nth perforation point during the cutting process, with a prediction error of less than 10%. Secondly, using the PSO-BP-constructed prediction model for laser cutting quality features and an empirical model for processing efficiency features, a multi-objective model for quality and efficiency is generated. The NSGA II algorithm is employed to solve the objective optimization model and obtain the Pareto front. Next, based on the predicted temperature at the perforation point using the model T p r , t , the TOPSIS decision-making method is applied. Different weights for quality and efficiency are set during the cutting stages where the temperature is below the lower threshold and above the upper threshold. Various combinations of machining parameters are selected, and by switching the parameters during the cutting process, the thermal accumulation (i.e., temperature) during processing is controlled within a given range. Finally, the effectiveness of the proposed approach is verified through actual machining experiments. [ABSTRACT FROM AUTHOR]

Many dynamic random-access memory (DRAM) manufacturing companies encounter significant resistance value deviations during the PVD sputtering process for manufacturing Ti thin films. These resistance values are influenced by the thickness of the thin films. Current mitigation strategies focus on adjusting film thickness to reduce resistance deviations, but this approach affects product structure profile and performance. Additionally, varying Ti thin film thicknesses across different product structures increase manufacturing complexity. This study aims to minimize resistance value deviations across multiple film thicknesses with minimal resource utilization. To achieve this goal, we propose the TSDTM-ANN-GA framework, which integrates the two-stage dynamic Taguchi method (TSDTM), artificial neural networks (ANN), and genetic algorithms (GA). The proposed framework requires significantly fewer experimental resources than traditional full factorial design and grid search method, making it suitable for resource-constrained and low-power computing environments. Our TSDTM-ANN-GA framework successfully identified an optimal production condition configuration for five different Ti thin film thicknesses: Degas temperature = 245 °C, Ar flow = 55 sccm, DC power = 5911 W, and DC power ramp rate = 4009 W/s. The results indicate that the deviation between the resistance values and their design values for the five Ti thin film thicknesses decreased by 86.8%, 94.1%, 95.9%, 98.2%, and 98.8%, respectively. The proposed method effectively reduced resistance deviations for the five Ti thin film thicknesses and simplified manufacturing management, allowing the required design values to be achieved under the same manufacturing conditions. This framework can efficiently operate on resource-limited and low-power computers, achieving the goal of real-time dynamic production parameter adjustments and enabling DRAM manufacturing companies to improve product quality promptly. [ABSTRACT FROM AUTHOR]

Quantum coherence is a crucial resource in numerous quantum processing tasks. The robustness of coherence provides an operational measure of quantum coherence, which can be calculated for various states using semidefinite programming. However, this method depends on convex optimization and can be time-intensive, especially as the dimensionality of the space increases. In this study, we employ machine learning techniques to quantify quantum coherence, focusing on the robustness of coherence. By leveraging artificial neural networks, we developed and trained models for systems with different dimensionalities. Testing on data samples shows that our approach substantially reduces computation time while maintaining strong generalizability. [ABSTRACT FROM AUTHOR]

Monitoring the integrity of aeronautical structures is fundamental for safety. Structural Health Monitoring Systems (SHMSs) perform real-time monitoring functions, but their performance must be carefully assessed. This is typically done by introducing artificial damages to the components; however, such a procedure requires the production and testing of a large number of structural elements. In this work, the damage detection performance of a strain-based SHMS was evaluated on a composite helicopter rotor blade root, exploiting a Finite Element (FE) model of the component. The SHMS monitored the bonding between the central core and the surrounding antitorsional layer. A damage detection algorithm was trained through FE analyses. The effects of the load's variability and of the damage were decoupled by including a load recognition step in the algorithm, which was accomplished either with an Artificial Neural Network (ANN) or a calibration matrix. Anomaly detection, damage assessment, and localization were performed by using an ANN. The results showed a higher load identification and anomaly detection accuracy using an ANN for the load recognition, and the load set was recognized with a satisfactory accuracy, even in damaged blades. This case study was focused on a real-world subcomponent with complex geometrical features and realistic load conditions, which was not investigated in the literature and provided a promising approach to estimate the performance of a strain-based SHMS. [ABSTRACT FROM AUTHOR]

A map of the environment is the basis for the robot's navigation. Multi-robot collaborative autonomous exploration allows for rapidly constructing maps of unknown environments, essential for application areas such as search and rescue missions. Traditional autonomous exploration methods are inefficient due to the repetitive exploration problem. For this reason, we propose a multi-robot autonomous exploration method based on the Transformer model. Our multi-agent deep reinforcement learning method includes a multi-agent learning method to effectively improve exploration efficiency. We conducted experiments comparing our proposed method with existing methods in a simulation environment, and the experimental results showed that our proposed method had a good performance and a specific generalization ability. [ABSTRACT FROM AUTHOR]

In recent years, the increasing demand and rising prices of rare earth elements (REEs), along with their attendant supply risk (about 95% of these elements are supplied by China), have led the European Commission to consider REEs as critical raw materials. Developing and optimizing processes for recovering REEs from secondary sources such as NdFeB magnets is fundamental in this context. A novel method to recover REEs by leaching with citric acid and subsequently separating these elements using the solvent extraction method has been introduced. Therefore, this research investigates the leaching efficiency of REEs, Fe, and B from NdFeB magnets. A full factorial design, with 18 experimental setups, was conducted to optimize the citric acid concentration (1–3 mol/L), leaching time (1–3 h), and solid–liquid ratio (5–10%wt./vol.). All tests were carried out at room temperature and 150 rpm. Different optimizations (response surface methodology (RSM) and artificial neural network (ANN) analysis) are used to maximize the REEs' leaching efficiency. RSM resulted in a maximum extraction yield of total rare earth elements (TREEs) of about 89% in the investigated experimental plan. This result is similar to that for ANN analysis (about 86%), but more accurate than that for RSM. In fact, for the ANN, an overall R-value higher than 0.99 was obtained. This result indicates that the developed ANN can be used as an accurate model for estimating the leaching efficiencies of REEs from NdFeB magnets. [ABSTRACT FROM AUTHOR]

In this work we construct a surrogate model using artificial neural networks (ANN) to predict the steady-state behavior of an unmanned combat aircraft. We employ various strategies to improve the model's accuracy, including the consideration of design tolerances, creating independent surrogate models for the different flow regimes and encoding non-numeric input features. We also explore alternative machine learning models, albeit they demonstrated a lower reliability than ANNs. Two scenarios are considered for the target variable: one focusing solely on predicting the pitching moment coefficient, and the other incorporating the roll moment coefficient as well. We investigate different methods for handling multiple targets, finding that constructing a single model with multiple outputs consistently outperforms developing separate models for each target variable. Overall, the ANN provides predictions that show excellent agreement with the experimental data, demonstrating its effectiveness and reliability in aerodynamic modeling. [ABSTRACT FROM AUTHOR]

Conventional concrete causes significant environmental problems, including resource depletion, high CO2 emissions, and high energy consumption. Steel slag aggregate (SSA), a by-product of the steelmaking industry, offers a sustainable alternative due to its environmental benefits and improved mechanical properties. This study examined the predictive power of four modeling techniques—Gene Expression Programming (GEP), an Artificial Neural Network (ANN), Random Forest Regression (RFR), and Gradient Boosting (GB)—to predict the compressive strength (CS) of SSA concrete. Using 367 datasets from the literature, six input variables (cement, water, granulated furnace slag, superplasticizer, coarse aggregate, fine aggregate, and age) were utilized to predict compressive strength. The models' performance was evaluated using statistical measures such as the mean absolute error (MAE), root mean squared error (RMSE), mean values, and coefficient of determination (R2). Results indicated that the GB model consistently outperformed RFR, GEP, and the ANN, achieving the highest R2 values of 0.99 and 0.96 for the training and testing dataset, respectively, followed by RFR with R2 values of 0.97 (training) and 0.93 (testing), GEP with R2 values of 0.85 (training) and 0.87 (testing), and ANN with R2 values of 0.61 (training) and 0.82 (testing). Additionally, the GB model had the lowest MAE values of 0.79 MPa (training) and 2.61 MPa (testing) and RMSE values of 1.90 MPa (training) and 3.95 MPa (testing). This research aims to advance predictive modeling in sustainable construction through analysis and well-defined conclusions. [ABSTRACT FROM AUTHOR]

Over the past decade, there has been a dramatic increase in the use of various technologies in the Architecture, Engineering, and Construction sector. Artificial intelligence has played a significant role throughout the different phases of the design and construction process. A growing body of literature recognizes the importance of artificial neural network applications in numerous areas of the construction industry and the built environment, presenting a need to explore the main research themes, attributes, benefits, and challenges. A three-step extensive research method was utilized by conducting a bibliometric search of English language articles and conducting quantitative and qualitative analyses. The bibliometric analysis aimed to identify the current research directions and gaps forming future research areas. The scientometric analysis of keywords revealed diverse areas within the construction industry linked to ANNs. The qualitative analysis of the selected literature revealed that energy management in buildings and construction cost predictions were the leading research topics in the study area. These findings recommend directions for further research in the field, for example, broadening the application ranges of ANNs in the current Construction 4.0 technologies, such as robotics, 3D printing, digital twins, and VR applications. [ABSTRACT FROM AUTHOR]

In mountainous areas, high-filled subgrade often experiences significant post-construction settlement. Prematurely paving the cement-stabilized gravel layer on an unstable subgrade can easily lead to subsequent cracking. To accurately predict the settlement of high-filled subgrade and determine the appropriate timing for paving the cement-stabilized layer, this study proposes a subgrade settlement prediction method combining an Artificial Neural Network (ANN) with a Genetic Algorithm (GA). Using monitoring data from a high-filled subgrade on a highway in Hunan Province, China, a GA–ANN model was established to predict settlement curves. The predicted data from the GA–ANN model were compared with measured data and ANN predictions to validate the advantages of using GA–ANN for subgrade settlement prediction. The results indicate that the GA–ANN model significantly outperforms the ANN model due to GA's ability to provide more reasonable weight biases for ANN through global search optimization. Predictions of settlement data beyond 50 days using both ANN and GA–ANN showed that the GA–ANN prediction curve closely matched the measured curve, with a basic deviation within ±3 mm. In contrast, ANN's prediction error gradually increased to over 5 mm as the observation time increased, with predicted values lower than measured values, leading to an overly optimistic estimation of early settlement convergence. Based on the predicted data and settlement standards, the estimated timing for laying the stabilized layer was determined. During the laying process, no cracking was observed in the stabilized layer. The project has been in operation for six months, with the road surface in good condition. This study provides a valuable reference for the laying of stabilized layers on similar high-filled and ultra-high-filled subgrades. [ABSTRACT FROM AUTHOR]

With the progressive reduction in virgin material availability and the growing global concern for sustainability, civil engineering researchers worldwide are shifting their attention toward exploring alternative and mechanically sound technological solutions. The feasibility of preparing both cold and hot asphalt mixtures (AMs) for road pavement binder layers with construction and demolition wastes (C&DWs) and reclaimed asphalt pavement (RAP) partially replacing virgin materials like limestone aggregates and filler has already been proven. The technical suitability and compliance with technical specifications for road paving materials involved the evaluation of mechanical and volumetric aspects by means of indirect tensile strength tests and saturated surface dry voids, respectively. Thus, the main goal of the present study is to train, validate, and test selected machine learning algorithms based on data obtained from the previous experimental campaign with the aim of predicting the volumetric properties and the mechanical performance of the investigated mixtures. A comparison between the predictions made by ridge and lasso regression techniques and both shallow (SNN) and deep neural network (DNN) models showed that the latter achieved better predictive capabilities, highlighted by fully satisfactory performance metrics. DNN performance can be summarized by R2 values equal to 0.8990 in terms of saturated surface dry void predictions, as well as 0.9954 in terms of indirect tensile strength predictions. Predicted observations can be thus implemented within the traditional mix design software. This would reduce the need to carry out additional expensive and time-consuming experimental campaigns. [ABSTRACT FROM AUTHOR]

On schematic metro maps, high-quality label placement is helpful to passengers performing route planning and orientation tasks. It has been reported that the artificial neural network (ANN) has the potential to place labels with learned labeling knowledge. However, the previous ANN-based method only considered the effects of station points and their connected edges. Indeed, unconnected but surrounding features (points, edges, and labels) also significantly affect the quality of label placement. To address this, we have proposed an improved method. The relations between label positions and both connected and surrounding features are first modeled based on labeling natural intelligence (i.e., the experience, knowledge, and rules of labeling established by cartographers). Then, ANN is employed to learn such relations. Quantitative evaluations show that our method reaches lower percentages of label–point overlap (0.00%), label–edge overlap (4.12%), and label–label overlap (20.58%) compared to the benchmark (4.17%, 14.29%, and 35.11%, respectively). On the other hand, our method effectively avoids ambiguous labels and ensures labels from the same line are placed on the same side. Qualitative evaluations show that approximately 75% of users prefer our results. This novel method has the potential to advance the automated generation of schematic metro maps. [ABSTRACT FROM AUTHOR]

Conveyor belts are vital for material transportation in coal mines due to their cost-effectiveness and versatility. These belts endure significant wear from harsh operating conditions, risking substantial financial losses if they fail. This study develops five artificial neural network (ANN) models to predict conveyor belt damage using 11 parameters from the Belchatow brown coal mine in Poland. The models target five outputs: number of repairs and cable cuts, cumulative number of repairs and cable cuts, and their ages. Various optimizers (Adam, Nadam, RMSprop, Adamax, and stochastic gradient descent or SGD) and activation functions (ReLU, Swish, sigmoid, tanh, Leaky ReLU, and softmax) were tested to find the optimal configurations. The predictive performance was evaluated using three error indicators against actual mine data. Superior models can forecast belt behavior under specific conditions, aiding proactive maintenance. The study also advocates for the Diagbelt+ system over human inspections for failure detection. This modeling approach enhances proactive maintenance, preventing total system breakdowns due to belt wear. [ABSTRACT FROM AUTHOR]

Polymer flooding is a prominent enhanced oil recovery process that is widely recognized for its cost-effectiveness and substantial success in increasing oil production. In this study, the Buckley–Leverett mathematical model for polymer flooding was used to generate more than 163,000 samples that reflect different reservoir conditions using seven input parameters. We introduced artificial noise into the dataset to simulate real-world conditions and mitigate overfitting. Seven classic machine learning models and two neural networks were trained on this dataset to predict the oil recovery factor based on the input parameters. Among these, polynomial regression performed best with a coefficient of determination ( R 2 ) of 0.909, and the dense neural network and cascade-forward neural network achieved R 2 scores of 0.908 and 0.906, respectively. Our analysis included permutation feature importance and metrics analysis, where key features across all models were identified, and the model's performance was evaluated on a range of metrics. Compared with similar studies, this research uses a significantly larger and more realistic synthetic dataset that explores a broader spectrum of machine learning models. Thus, when applied to a real dataset, our methodology can aid in decision-making by identifying key parameters that enhance oil production and predicting the oil recovery factor given specific parameter values. [ABSTRACT FROM AUTHOR]

Three-dimensional printing, or additive manufacturing, represents one of the fastest growing branches of the industry, and fused deposition modeling (FDM) is one of most frequently used technologies. Three-dimensional printing does not provide high-quality surfaces, so finishing is required, and milling is one of the best methods for improving surface quality. The combination of 3D printing and traditional manufacturing technologies is known as hybrid manufacturing. In order to improve quality and determine optimal machining parameters, researchers increasingly use artificial intelligence methods. In the context of manufacturing technologies, both multiple regression analysis (MRA) and artificial neural networks (ANNs) have proven to be highly reliable in predicting and optimizing machining processes. This study focuses on the use of MRA and an ANN to analyze the influence of machining parameters such as feed rate, depth of cut, and spindle speed on the surface roughness of a 3D-printed part in a milling process. The study compares the measured results with the outcomes obtained through MRA and the ANN to assess their effectiveness in predicting and optimizing surface roughness. The results show that higher accuracy was obtained from the ANN method. [ABSTRACT FROM AUTHOR]

Accurately and instantly estimating the hydrodynamic characteristics in two-phase liquid–gas flow is crucial for industries like oil, gas, and other multiphase flow sectors to reduce costs and emissions, boost efficiency, and enhance operational safety. This type of flow involves constant slippage between gas and liquid phases caused by a deformable interface, resulting in changes in gas volumetric fraction and the creation of structures known as flow patterns. Empirical and numerical methods used for prediction often result in significant inaccuracies during scale-up processes. Different methodologies based on artificial intelligence (AI) are currently being applied to predict hydrodynamic characteristics in two-phase liquid–gas flow, which was corroborated with the bibliometric analysis where AI techniques were found to have been applied in flow pattern recognition, volumetric fraction determination for each fluid, and pressure gradient estimation. The results revealed that a total of 178 keywords in 70 articles, 29 of which reached the threshold (machine learning, flow pattern, two-phase flow, artificial intelligence, and neural networks as the high predominance), were published mainly in Flow Measurement and Instrumentation. This journal has the highest number of published articles related to the studied topic, with nine articles. The most relevant author is Efteknari-Zadeh, E, from the Institute of Optics and Quantum Electronics. [ABSTRACT FROM AUTHOR]

Supramolecular solvent-based extraction (SUPRAS) stands out as a promising approach, particularly due to its environmentally friendly and efficient characteristics. This research explores the optimization of SUPRAS extraction for sango radish and kale microgreens, focusing on enhancing the extraction efficiency. The Taguchi experimental design and artificial neural network (ANN) modeling were utilized to systematically optimize extraction parameters (ethanol content, SUPRAS: equilibrium ratio, centrifugation rate, centrifugation time, and solid-liquid ratio). The extraction efficiency was evaluated by measuring the antioxidant activity (DPPH assay) and contents of chlorophylls, carotenoids, phenolics, and anthocyanidins. The obtained results demonstrated variability in phytochemical contents and antioxidant activities across microgreen samples, with the possibility of achieving high extraction yields using the prediction of optimized parameters. The optimal result for sango radish can be achieved at an ethanol content of 35.7%; SUPRAS: equilibrium ratio of 1 v/v, centrifugation rate of 4020 rpm, centrifugation time of 19.84 min, and solid-liquid ratio of 30.2 mg/mL. The following parameters are predicted for maximal extraction efficiency for kale: ethanol content of 35.64%; SUPRAS: equilibrium ratio of 1 v/v; centrifugation rate of 3927 rpm; centrifugation time of 19.83 min; and solid-liquid ratio of 30.4 mg/mL. Additionally, laboratory verification of predicted SUPRAS parameters showed very low divergency degrees for both microgreens (–3.09 to 2.36% for sango radish, and −2.57 to 3.58% for kale). This potential of SUPRAS extraction, coupled with statistical and computational optimization techniques, can enhance the recovery of valuable bioactive compounds from microgreens and contribute to green extraction applications. [ABSTRACT FROM AUTHOR]

We develop a phase-resolved wave field reconstruction method by the learning-based downsampling network for processing large amounts of inhomogeneous data from non-contact wave optical observations. The Waves Acquisition Stereo System (WASS) extracts dense point clouds from ocean wave snapshots. We couple learning-based downsampling networks with the phase-resolved wave reconstruction algorithm, and the training task is to improve the wave reconstruction completeness ratio C R . The algorithm first achieves initial convergence and task-optimized performance on numerical ocean waves built by the linear wave theory model. Results show that the trained sampling network can lead to a more uniform spatial distribution of sampling points and improve C R at the observed edge regions far from the optical camera. Finally, we apply our algorithm to a natural ocean wave dataset. The average completeness ratio is improved over 30% at low sampling ratios ( S R ∈ [ 2 − 9 , 2 − 7 ] ) compared to the traditional FPS method and Random sampling method. Moreover, the relative residual between the final reconstructed wave and the natural wave is less than 15%, which provides an efficient tool for wave reconstruction in ocean engineering. [ABSTRACT FROM AUTHOR]

Mobile robots are autonomous devices capable of self-motion, and are utilized in applications ranging from surveillance and logistics to healthcare services and planetary exploration. Precise trajectory tracking is a crucial component in robotic applications. This study introduces the use of improved sliding surfaces and artificial neural networks in controlling mobile robots. An enhanced sliding surface, combined with exponential and hyperbolic tangent approach laws, is employed to mitigate chattering phenomena in sliding mode control. Nonlinear components of the sliding control law are estimated using artificial neural networks. The weights of the neural networks are updated online using a gradient descent algorithm. The stability of the system is demonstrated using Lyapunov theory. Simulation results in MATLAB/Simulink R2024a validate the effectiveness of the proposed method, with rise times of 0.071 s, an overshoot of 0.004%, and steady-state errors approaching zero meters. Settling times were 0.0978 s for the x-axis and 0.0902 s for the y-axis, and chattering exhibited low amplitude and frequency. [ABSTRACT FROM AUTHOR]