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An instrument-free, user-friendly, and cost-effective PAD colorimetric sensor that can quantitative detection of H 2 O 2 has been realized by using bimetallic nickel–cobalt selenides as highly active peroxidase mimic and smartphone integrated with dark cavity as detector. Integrated with an ANN model and a self-compiled easy-to-use smartphone APP, the intelligent and on-site detection of H 2 O 2 was constructed, which exhibited an ultra-wide dynamic range. [Display omitted] • An effective paper-based colorimetric sensor was prepared based on nanozyme. • The optimized Ni 0.75 Co 0.25 Se exhibited excellent peoxidase-mimetic activities. • The on-site detection of H 2 O 2 was constructed by a self-compiled smartphone APP. • The machine learning-assisted sensor can detect H 2 O 2 over a wide dynamic range. Paper-based analytical devices (PADs) integrated with smartphones have shown great potential in various fields, but they also face challenges such as single signal reading, complex data processing and significant environmental impacting. In this study, a colorimetric PAD platform has been proposed using bimetallic nickel–cobalt selenides as highly active peroxidase mimic, smartphone with 3D-printing dark-cavity as a portable detector and an artificial neural network (ANN) model as multi-signal processing tool. Notably, the optimized nickel–cobalt selenides (Ni 0.75 Co 0.25 Se with Ni to Co ratio of 3/1) exhibit excellent peoxidase-mimetic activities and are capable of catalyzing the oxidation of four chromogenic reagents in the presence of H 2 O 2. Using a smartphone with image capture function as a friendly signal readout tool, the Ni 0.75 Co 0.25 Se based four channel colorimetric sensing paper is used for multi-signal quantitative analysis of H 2 O 2 by determining the Grey, red (R), green (G) and blue (B) channel values of the captured pictures. An intelligent on-site detection method for H 2 O 2 has been constructed by combining an ANN model and a self-programmed easy-to-use smartphone APP with a dynamic range of 5 μM to 2 M. Noteworthy, machine learning-assisted smartphone sensing devices based on nanozyme and 3D printing technology provide new insights and universal strategies for visual ultrasensitive detection in a variety of fields, including environments monitoring, biomedical diagnosis and safety screening. [ABSTRACT FROM AUTHOR]

[Display omitted] • Large datasets of U and UP are constructed with 1526 and 743 fuel samples. • The effect of C, H, and O atomic ratios on HHV is investigated by ternary plots. • HHV of any type of waste and fuel is estimated by artificial neural networks. • Hyperparameter optimization is carried out to obtain best performing ANN models. • Ultimate analysis data is observed to be sufficient for HHV estimation of fuels. Higher heating value (HHV) is one of the most important parameters in determining the quality of the fuels. In this study, comparatively large datasets of ultimate and proximate analysis are constructed to be used in HHV estimation of several classes of fuels, including char & fossil fuels, agricultural wastes, manure (chicken, cow, horse, sheep, llama, and pig), sludge (like paper, paper-mil, sewage, and pulp), micro/macro-algae's, wastes (RDF and MSW), treated woods, untreated woods, and others (non-fossil pyrolysis oils) between the HHV range of 4.22–55.55 MJ/kg. The relationships of carbon, hydrogen, and oxygen atomic ratios for fuel classes are illustrated by using ternary plots, and the effects of elemental composition on HHV was analyzed with the extensive dataset. Then, the ultimate (U) and ultimate & proximate (UP) datasets were utilized separately to estimate the HHV by using artificial neural networks (ANN). Hyperparameter optimization was carried out and the best performing ANNs were determined for each dataset, which yielded R2 values of 0.9719 and 0.9715, respectively. The results indicated that while ANNs trained by both datasets perform remarkably well, utilization of U dataset is sufficient for HHV estimation. Finally, the best performing ANN models for both U and UP datasets are given in a directly utilizable format enabling the accurate estimation of HHV of any fuel for optimization of fuel processing and waste management operations. [ABSTRACT FROM AUTHOR]

• A model to explore the multiphysics characteristic of semiconductors is presented. • The model determines the thermal and mechanical properties of complex patterns. • Results show a strong correlation between thermal and mechanical properties. • Dielectric bands asymmetrically affect thermal and mechanical behavior. • Machine learning can predict one property from another. As semiconductor performance improves through advanced package designs, it becomes important to consider both thermal and mechanical properties. Better understanding their relationship enhances system design and optimization. This study has introduced a numerical method that takes into account both thermal and mechanical fluxes to construct thermal and mechanical property maps for practical application in semiconductor engineering. Furthermore, this paper investigated the relationship between the two properties using the constructed property maps in commercially available semiconductor packages. Owing to the complexity of packaging design patterns, a coupled isoparametric mapping and machine learning (ML) method was introduced to investigate their effects. The model then determines and investigates the anisotropic equivalent thermal and mechanical properties of the commercialized semiconductor packages with complex pattern designs, according to the package materials, volume fractions of each material, and design patterns. This approach pursues more sophistication and practicality compared to simplified composite structure property evaluation models. Additionally, all processes in the algorithm are automated based on ML methods, making them practically applicable in the semiconductor industry. The study shows that there is a strong correlation between the thermal and mechanical characteristics in the complex package patterns. This relationship was able to form a fairly clear category based on the shape of the dielectric band because the dielectric band had significantly different effects on the thermal and mechanical fluxes. The study discussed the physical implications of the similarities and differences between the two behaviors and validated the results of the equivalent properties through finite element (FE) analysis. Finally, the study cross-validates the relationship by showing that information from one flux behavior can be used to predict the other based on the ML method. Based on the results, this paper can provide a better understanding of thermal and mechanical fluxes in semiconductor package patterns for package design and optimization. [ABSTRACT FROM AUTHOR]

With the rapidly evolving geo-political landscape and unceasing advancements in technology, sustainable energy security is a very important topic. Research indicates that electrochemical energy systems are quite promising to solve many of energy conversion, storage, and conservation challenges while offering high efficiencies and low pollution. The paper provides an overview of electrochemical energy devices and the various optimization techniques used to evaluate them. The optimization techniques include linear regression, factorial design, the Taguchi method, artificial neural networks, filters, and a combination of such methods to improve these systems. To support the growing interest in research, the bulk of this study focuses on a review of the most promising and highly researched electrochemical energy devices, such as fuel cells, batteries, and supercapacitors. The paper also provides modest commentary on hydrogen production technologies, electrochemical reactors, and membrane separation technologies, amongst other technologies. Building on a previous paper by the authors of this paper on artificial intelligence in hybrid renewable energy systems with fuel cells, this work provides a comparative review of optimization techniques for supercapacitors by highlighting key findings based on model accuracy. A summary of the advantages and disadvantages of the different major optimization techniques is presented. The paper concludes that a combination of optimization techniques is used to overcome the drawbacks of individual techniques, with adaptive filters being the most widely studied. This paper presents studies on the Design of Experiments (DoE) with the goal of building a better understanding of the relationships that exist between different operating variables in various electrochemical devices. • The electrochemical systems can be optimized for better performance with high accuracy. • The optimization techniques used are either model-based or data-driven. • Linear regression and artificial neural networks are the most common techniques studied. • Hybrid models could be applied to enhance systems' accuracy and minimize errors. [ABSTRACT FROM AUTHOR]

• Thermomechanical problem of FGMs sphere with material uncertainties is analyzed. • The interval random uncertainty model is recommended for material parameters. • A data-driven ANN fast calculation for FGMs sphere uncertainties analysis is established. • The volume fraction distribution is optimized for the safety design of the FGMs sphere. Mechanical analysis and optimal design for functionally graded materials (FGMs) structures are significant. The present paper analyzes the parameter uncertainties problem for the thermomechanical response of the FGMs spherical shells with volume fraction power distribution. Here, the interval random uncertainty model is recommended to describe the uncertainties of each component of material parameters in FGMs, and a data-driven artificial neural network (ANN) fast calculation for FGMs spherical shells thermomechanical problems with uncertainties analysis is established and trained. The thermomechanical coupling response analysis and volume fraction optimization analysis of the FGMs spherical shell with material uncertainties are realized for zirconia-titanium FGMs, with the maximum normalized Von Mises stress as the structural safety index and the ANN as the fast calculation method. In addition, the Von Mises stress of the FGMs spherical shell and a ZrO 2 -Ti alloy double-layer spherical shell under different loads is compared. The results show that material parameter uncertainties, especially those in Young's modulus, thermal expansion coefficient, and uniaxial yield limit significantly affect structural safety. The structural safety index λ calculated based on the volume fraction optimization design of FGMs spherical shell is significantly lower than that of the ZrO 2 -Ti alloy double-layer. The ANN method greatly improves the computational efficiency for uncertain problems and maintains high accuracy, compared with the statistical analysis after many calculations by randomly selecting material parameters. This study provides a path for the safety design of FGMs structures with uncertain parameters. [ABSTRACT FROM AUTHOR]

This paper introduces a novel direct torque control approach based on the decision tree (T-DTC), employing artificial neural networks that are effectively trained to enhance accuracy and robustness. The main objective of T-DTC is the substantial reduction of flux and torque ripples inherent in the conventional DTC, ensuring effective control of the induction motor. The conventional hysteresis controllers for stator flux and electromagnetic torque are replaced by two advanced controllers named M5 Prime model trees. Additionally, the traditional switching table is substituted with a novel decision tree table utilizing the classifier algorithm 4.5. The effectiveness of the proposed T-DTC strategy is demonstrated through simulation in MATLAB/Simulink and validated in real-time using an HIL platform based on OPAL-RT OP 5600 and Virtex 6 FPGA ML605. The results obtained demonstrate a notable improvement compared to existing techniques in the literature. [Display omitted] • An enhanced DTC strategy for induction motor based on decision tree is proposed. • A neural learning process is applied for the designed controllers. • Easy real-time implementation of the Tree-DTC strategy via the HIL tests is realized. • Considerable minimization of stator flux and electromagnetic torque ripples is achieved. • Robust Tree-DTC strategy in terms of speed monitoring and disturbance rejection is achieved. [ABSTRACT FROM AUTHOR]

Researchers are putting in a lot of time and energy into developing methods to use machine learning algorithms, a subfield of AI, to ‎diagnose disease in an individual patient. Extensive studies have been conducted on the potential benefits of using machine learning ‎techniques in the treatment of cardiovascular disease. For this preliminary study, the study have zeroed down on heart illness to get ‎as specific as possible about our methodology. In this paper, the authors investigate the differences in accuracy between different ‎machine learning approaches specifically, (SVM), (KNN), and a(ANN) when applied to the categorization of cardiovascular ‎illness. Our research makes use of approximately seventy thousand patient records from the Kaggle dataset, which focuses on ‎cardiovascular disease The Kaggle dataset comprises a limited number of variables per patient record, encompassing serum ‎cholesterol, diastolic and systolic blood pressure, relative blood glucose levels, and the presence or absence of angina.‎ The present study examines the Kaggle dataset and employs the (KNN), (SVM), and (ANN) methodologies. The results indicate ‎that the K-nearest neighbors (KNN) algorithm, specifically with a value of 9 for the number of neighbors, achieved an accuracy of ‎‎0.997. The SVM model using default hyperparameters achieved an accuracy of 0.9997. The Support Vector Machine (SVM) model ‎utilizing a Radial Basis Function (RBF) kernel and a C value of 100.0 exhibited an accuracy of 0.9998. Conversely, the SVM model ‎employing a linear kernel and a C value of 1000.0 achieved a perfect accuracy of 1.000. The Feedforward neural network achieved a ‎perfect accuracy of 1.000 using the Adam optimization algorithm after 10 epochs and 50 batches..In future research, the authors ‎intend to employ a deep neural network hypermodel in order to enhance the accuracy of their findings. [ABSTRACT FROM AUTHOR]

Hydrogen purification from steam methane reforming (SMR) by pressure swing adsorption (PSA) technology is a common method to obtain high-purity hydrogen. The breakthrough time can well reflect the adsorption dynamics and help PSA cycle design. In order to avoid time-consuming and labor-intensive breakthrough curve experiments, it is very necessary to develop a fast and accurate surrogate model. In this study, a genetic algorithm (GA) and artificial neural network (ANN) are combined to predict and optimize breakthrough times of adsorbates in activated carbon/zeolite layered beds. Using the Latin hypercube sampling strategy, training data sets of GA-optimized ANN (GA-ANN) obtains from the physical model of adsorption, heat and mass transfer model. The genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. The ANN topology has 4 input variables (superficial velocity, activated carbon height, adsorption pressure and feed temperature) and 3 output variables (breakthrough times of CH 4 , CO and CO 2). The number of neurons in the hidden layer for the GA-ANN model was optimized as 7 to predict and optimize the maximum breakthrough time of SMR with a four-component (H 2 /CH 4 /CO/CO 2) system. The sensitivity analysis displays that relative importance to the breakthrough time is in the order of superficial velocity (40.88%) > adsorption pressure (24.55%) > activated carbon height (21.04%) > feed temperature (13.53%). To obtain high-purity hydrogen, the GA-ANN model combined with multi-objective GA optimization is used to maximize the breakthrough times of CH 4 and CO. The GA-ANN surrogate model proposed in this paper can not only accurately and quickly achieve the purpose of predicting the system breakthrough time with a high correlation coefficient (R = 0.9937) but also obtains the optimal operating conditions of PSA hydrogen purification. • A rigorous heat and mass transfer model of multi-component adsorption is established. • Artificial neural network (ANN) is well-trained by Latin hypercube sampling method. • Genetic algorithm (GA) optimizes the weights and biases of ANN for better performance. • Superficial velocity has the highest relative importance value on breakthrough time. • GA-optimized ANN model and GA combined for maximal breakthrough time of impurities. [ABSTRACT FROM AUTHOR]

Multiphase flow has many applications, such as oil and gas industries. Flow meter devices must be calibrated with field or laboratory data. One of the best methods to calibrate the devices and determine flow parameters is artificial neural networks. In addition, due to development of multiphase flow sensors and computer systems, artificial neural networks are employed to determine the flow regime and phase volume fractions along the pipeline. Up until now, different methods of measurement such as flow pressure signal, radioactive, ultrasonic, impedance, and their combination with neural networks have been presented. In this paper, a review of these works is performed. The type of neural network, measurement method, neural network inputs and development of each method are investigated. Studies show that the use of the gamma-ray method is the most prevalent among the papers presented. Also, examining the flow regime detection and flow rate measurement over time has moved to the use of flow parameters, including pressure, temperature, velocity, viscosity, etc. This simple method (using flow parameters) has the potential to gradually replace more advanced techniques like ultrasonic and electrical, along with expensive techniques like gamma ray. The results of this review indicate that employing intricate neural networks with many layers and neurons slightly increases the accuracy of the results. It can also be said that the use of a combination of several techniques has led to improved results. • An extensive investigation has been conducted on using neural networks for the measurement of multiphase flow parameters. • This paper explores the utilization of 20 distinct neural network types within the realm of multiphase flow analysis. • Research has been conducted on the measurement of flow rates, volume fractions, and the identification of flow regimes. • The measurement methods are categorized into radioactive, ultrasonic, electrical, pressure signals, and flow parameters. [ABSTRACT FROM AUTHOR]

The paper introduces a deep learning (DL) model as an alternative approach to predict the supercontinuum generation in waveguides. The traditional technique of using the numerical method for this task is computationally laborious and time-consuming. The Artificial Neural Network (ANN) model described in the paper is trained from scratch and demonstrates high precision in predicting the spectral bandwidth of supercontinuum phenomena. It achieves a mean squared error of 0.0032, which indicates its ability to accurately map the optical characteristics of the waveguide. Furthermore, the deep learning model significantly reduces the computational time required compared to simulative processes conducted in COMSOL Multiphysics and MATLAB software, approximately taking only 50% of the time. By leveraging deep learning techniques, the model offers a promising alternative to the numerical method for predicting supercontinuum generation in planar waveguides. It overcomes the limitations associated with computational resources and complexity, making it an attractive substitute for traditional numerical techniques. • Investigated chalcogenide planar waveguide, generating versatile supercontinuum spectra dataset. • Preprocessed dataset for Tanh activation function compatibility. • Developed multi-layered ANN model with decaying learning rate for predicting supercontinuum spectra. • Efficiently predicts spectral broadening at waveguide's end and different lengths. • Developed ANN model handles both linear and nonlinear waveguide dynamics effectively. [ABSTRACT FROM AUTHOR]

Breast cancer is one of the serious diseases that threaten the life of many women worldwide. The seriousness of this disease is that it is often discovered in late stages after a period of its occurrence. This causes a wide spread of the disease and difficulty in its treatment. Another important characteristic of this disease is that it can return again after a period of its treatment. Therefore, predicting the occurrence or recurrence of such disease early is the best solution to have a high cure rate. The main objective of this paper is to improve the prediction performance of the breast cancer recurrence. Many methods have been proposed to predict breast cancer recurrence. However, these methods did not achieve the desired results on one of the most famous datasets in the field of breast cancer recurrence's prediction (i.e., Wisconsin Prognosis Breast Cancer (WPBC) dataset). The highest accuracy achieved using the previous methods is 89.89%. Therefore, this paper provides a framework for improving the prediction of breast cancer recurrence. The proposed framework has the ability to overcome many of the challenges in the existing dataset such as: (a) the problem of imbalance between classes using a data over-sampling technique; and (b) the large number of data dimensions using Principal Component Analysis (PCA), and a wrapper dimensionality reduction technique based on Genetic Algorithm (GA). It also uses the neural network algorithm to fuse the results of two individual classifiers (i.e., Random Forest (RF) and Support Vector Machine (SVM)). Our proposed framework evaluation showed a significant improvement in the predication performance. It achieved an accuracy of 98.3%, area under the curve of 99%, and precision, recall, and f1-measure of 98%. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative decision support system for improving ship energy efficiency. It combines an engine optimization model and an artificial neural network (ANN). Real-time data regarding the ship and engine performance is obtained along a specific ship navigation, and an engine optimization model is built using Ricardo Wave software. This model is validated with high accuracy. Ship-specific parameters are derived, and fuel consumption is estimated using ANN models with different structures. The study identifies the most suitable ANN model with 1 hidden layer and 5 neurons based on error metric evaluation where the 0.99697 R2, 0.00035 RMSE, and 0.06470 MAPE scores are calculated. This approach offers a cost-effective solution for shipping companies to monitor critical engine parameters in real-time without investing in sensors or data collection systems. Thus, it contributes to dealing with the problem of the scarcity of analyzable data in the maritime literature, which is one of the significant issues in the papers related to energy efficiency, machine learning, and deep learning. It is an innovative approach since the parameters that are related to real-time operations rather than considering only instruction book information are aggregated, and the produced data is analyzed by an intelligent tool. • A system is proposed to control ship energy efficiency level by combining an optimization model and a neural network • A simulation model is created in the Ricardo Wave tool and the engine model is validated by actual data. • The ship fuel consumption is estimated by an artificial neural network based on the derived input parameters. • The prediction process is evaluated by error metrics to determine its success. [ABSTRACT FROM AUTHOR]

Corrosion presents significant challenges in the marine, offshore, and oil and gas industries, resulting in annual economic losses amounting to billions of dollars. To address these losses and ensure the structural integrity of marine infrastructure, it is essential to implement effective corrosion monitoring techniques. In recent years, machine learning algorithms have gained prominence across various fields, offering innovative solutions to corrosion-related concerns. This paper provides a comprehensive critical review, primarily focusing on the two most prevalent machine learning algorithms: artificial neural networks and random forests. The review critically analyzes their applications, methodologies, and effectiveness in the realm of marine and offshore steel structures, oil and gas pipelines, as well as construction materials like Al alloys and Mg alloys and the analysis of corrosion coating behavior. Furthermore, this study explores the key findings and inherent limitations of these machine learning techniques, emphasizing their potential in corrosion prediction, detection, and the mitigation of corrosion issues in the marine, offshore, and oil and gas industries. By identifying existing research gaps and offering recommendations for future investigations, this paper emerges as an invaluable resource for researchers, engineers, and practitioners aiming to advance corrosion prevention and management strategies within these pivotal domains. • In-depth analysis of ANN and RF in maritime, offshore, and oil and gas corrosion research (2018-2023). • Critical evaluation of ML algorithms in maritime steel structures, pipelines, and construction materials. • Valuable review for cost-effective maritime corrosion maintenance strategies. • Provides concise corrosion management expertise to empower researchers and industry experts. [ABSTRACT FROM AUTHOR]

• Trapping indices stabilization is lagged as injection period is active. • Greater CO 2 cumulative volume occurs as reservoir pressure is far from caprock fracture pressure. • Larger closures will require pressure management-based brine production to reach theoretical storage capacity. • ML-based CO 2 storage modelling preliminary de-risked saline aquifers lacking numerical models. This paper presents a machine learning (ML) model designed to speed up the appraisal of geologic CO 2 storage sites by predicting the effectiveness in trapping and accommodating CO 2 in saline aquifers. Considering the urgency of de-risking as much geologic CO 2 storage resources as possible to help with CO 2 emission reduction Paris' goal, ML-based reservoir modelling has been documented as proper tool when a faster, good approximate, and less expensive approach is needed to surrogate multiple assessments of storage sites traditionally performed by long-timeframe and multi-stage geologic CO 2 storage numerical modelling approach. In this paper, a case study is presented. It consisted of a dataset comprised of six geologic aquifer parameters (CO 2 residual saturation, horizontal permeability, vertical to horizontal permeability ratio, porosity, brine salinity, and CO 2 flow rate) and elapsed time as input data, and as output data the CO 2 trapping mechanism indices (Solubility Trapping Index, Residual Trapping Index, and Structural Trapping Index) along with the dynamic storage capacity (CO 2 injected volume). Such dataset was used to train and test the artificial neural network (ANN) model. The dataset was generated from thousands of post-processed numerical realizations at several injection periods by applying design of experiment using a synthetic aquifer model derived from the Bunter Sandstone Closure 36 aquifer numerical model, from the Southern North Sea. The ANN architecture designed in Python consisted of 3 hidden layers and 40 nodes and its performance was assessed using the coefficient of determination (R2) and root mean squared error (RMSE). The ANN performance showed accuracies (R2) for training and testing with 96% and 95% of precision respectively. Practical application of the ANN model was successfully implemented to CO 2 storage aquifer sites selected from CO2Stored® database which lacking numerical models (Bunter Closure 3, 9, 35, and 40), obtaining at the end of 100-years injection case a Structural, Residual, and Solubility Trapping Index averaging 83%, 11%, and 6% respectively, with low variation coefficient indicating that trapping indices were predicted properly because aquifers selected for ANN model application have similar structures (dome-like shape) and reservoir properties. In addition, CO 2 injected volume predictions for 100-years injection case were ranging from 397 to 456 million ton (Mt) totalling 2.1 giga ton (Gt) of potential storage capacity which represents 70% of total theoretical volumetric capacity. These results show the significant impact to accelerate geologic CO 2 storage sites assessment by implementing ML-based modelling to preliminary de-risking groups of saline aquifers and reasonably consider them technically feasible CO 2 storage sites in UK. [ABSTRACT FROM AUTHOR]

• This paper suggests a grating Moiré fringe-based error correction approach that uses an artificial neural network (ANN) and the quartile method (QM). • The approach uses QM for data cleaning operations to eliminate outliers and creates the ANN model with a structure of fully connected layer-normalization layer-fully connected layer for regression training in order to achieve lightweight and stability. • The proposed QM-ANN error correction model's mean absolute error (MAE) can reach 3.3 nm once the QM data has been cleaned and the ANN model has been trained. Many interference factors of the complex and changeable working environment of the grating sensor are the main factors affecting the accuracy; a grating Moiré fringe error correction model based on the quartile method (QM) and artificial neural network (ANN) is proposed in this paper. Firstly, the grating sensor's signals in the working process are collected, and three kinds of interference signals (temperature, humidity and vibration) from the grating sensor working environment are collected simultaneously. Secondly, the QM is used to detect outliers from the collected data and delete data outliers directly. Subsequently, the processed data are input into the ANN model for training. The model is lightweight and stable, because it adopts the cascaded structure of full connection layer-normalization layer-full connection layer, which does not need deploy the complex convolution layer module etc. Finally, the corrected data are output after being processed by the QM-ANN error correction model. The experimental results show that the mean absolute error (MAE) of the proposed QM-ANN error correction model is better than QM-convolutional neural network (CNN), QM-long short-term memory (LSTM) and ANN, CNN, LSTM models without QM. It shows that the proposed model has better robustness and practicability. [ABSTRACT FROM AUTHOR]

Genetic algorithms (GA) and artificial neural networks (ANN) as intelligent algorithms have been widely applied in engineering fields such as structural design. The GFRP-concrete-steel composite column (GCS), known for its corrosion resistance, has garnered significant attention in its design. However, the application of GA and ANN methods in the context of GCS has primarily been confined to enhancing the predictive accuracy of ANN. This paper introduces a novel approach for the structural optimization of GCS using GA and ANN. A total of 1050 finite element models were generated and analyzed using ANSYS for the purpose of training ANN. The validity of these finite element models was confirmed through comparative analysis with experimental data. The trained ANN was then utilized to predict the loading capacity of GCS for each generation in the GA. By collecting pricing data for GFRP tubes, steel tubes, and concrete under various parameters, pricing fitting formulas for the three materials were derived. Subsequently, the cost of GCS under different parameter sets was calculated. The maximization of the ratio of GCS loading capacity to its cost (LP) was established as the optimization objective. Through the iterative optimization process of the GA-ANN algorithm, the structural optimization was achieved to maximize the economic efficiency of GCS. Comparing the optimization results with numerical simulation results showed that errors were mostly contained within 10 % or even as low as 5 %, thereby validating the accuracy of the GA-ANN algorithm. The applicability of the GA-ANN algorithm to GCS structural optimization was demonstrated by altering a single concrete variable. The method proposed in this paper can be effectively applied to the structural design and optimization of composite columns with a sandwich structure, such as GCS. [ABSTRACT FROM AUTHOR]

Microgrids are extensively integrated into electrical systems due to their many technical, economic, and environmental advantages. However, they encounter a challenge as they experience high-frequency fluctuations caused by the stochastic nature of renewable energy generation, electric loads, and the presence of Electric Vehicles (EVs). Therefore, various techniques, algorithms, and controllers have been introduced to ensure effective Load Frequency Control (LFC) and maintain a stable power system in microgrids. These methods aim to ensure that the system's frequency remains stable and within an acceptable range, especially when faced with changing load demands and other factors. This paper presents a novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN), to optimize the load frequency model of a microgrid with vehicle-to-grid integration. The results are then compared under various scenarios, including renewable energy integration, EV charging and discharging dynamics, and varying load demands. The comparative analysis involves assessing the performance of the conventional Proportional–Integral–Derivative (PID) controller, the PSO-PID controller, and the newly proposed controlling technique. The suggested controller attains 99.904% efficiency with a negligible mean squared error of 1.1112 × 10−7, decreasing the integrated time absolute error to 1.0 × 10−4. It shows rapid response, precise targeting, and quick peak output ability, with marginal overshoot and undershoot, and a transient time of 28.5626 s, efficiently controlling microgrid frequency. Stability analysis validates the effectiveness of the proposed PSO-TANN controller in ensuring stability within the microgrid's LFC system during uncertainties and disturbances. This establishes resilience, diminishes settling time, and maintains reliable performance while controlling frequency. • An investigation into an islanded microgrid's Load Frequency Control (LFC) model framework has been conducted, comprising diesel generator, solar PV system, wind turbine, and EVs operating in vehicle-to-grid mode to supply power to electric loads, consequently impacting the frequency dynamics of the microgrid system. • A novel enhanced control approach, Particle Swarm Optimization-Trained Artificial Neural Network (PSO-TANN) has been implemented to optimize the load frequency model of a microgrid. • An extensive comparative analysis with other controllers, including PID and PSO-PID, is conducted across diverse scenarios involving renewable energy integration and EV charging and discharging dynamics under various load disturbances to reduce settling time, control system frequency, and ensure reliable performance. • The stability analysis validates the efficiency of the proposed PSO-TANN controller in ensuring reliability during uncertainties and disturbances, maintaining overall robustness and stability of the LFC system within the microgrid. [ABSTRACT FROM AUTHOR]

This paper proposes a novel hardware architecture for a Feed-Forward Neural Network (FFNN) with the objective of minimizing the number of execution clock cycles needed for the network's computation. The proposed architecture depends mainly on using two physical layers that are multiplexed and reused during the computation of the FFNN to achieve an efficient parallel design. Two physical layers are designed to handle the computation of different sizes of Neural Networks (NN). The proposed FFNN architecture hardware resources are independent of the NN's number of layers, instead, they depend only on the number of neurons in the largest layer. This versatile architecture serves as an accelerator in Deep Neural Network (DNN) computations as it exploits parallelism by making the two physical layers work in parallel through the computations. The proposed implementation was implemented with 18-bit fixed point representation reaching 200 MHz clock speed on a Spartan7 FPGA. Furthermore, the proposed architecture achieves a lower neuron computation factor compared to previous works in the literature. [Display omitted] [ABSTRACT FROM AUTHOR]

• A flat plate heat pipe with dimensions of 500 × 200 × 3 mm3 and containing 19 independent microchannels inside is developed. • CFD modeling based on the VOF model and improved Lee model was performed to reproduce the heat transfer behaviors. • The ANN model agrees well with the simulated data with MSE and R of 1.30 × 10-5 and 0.9990, respectively. • The model constructed can provide an effective tool for the adaptation and optimization of heat pipe in 5G base station. To address the significantly increased heat dissipation requirements of 5G base stations, a flat plate heat pipe (FPHP) of 500 × 200 × 3 mm3 comprising of 19 independent channels was developed. To solve the problem of simulation distortion due to the high superheat of the initial boiling of the working fluid in the ultra-long channel of 490 × 5 × 1.5 mm3, this paper adopts the Lee model considering the superheat, and simulates based on the Volume of fluid method, and the simulation results are compared with the experiments, and the average relative error is less than 5 %. The FPHP was explored at 100–500 W heat power and filling ratio of 30 %-70 %, depending on the application scenarios of the base station. By partitioning the 19 channels and calculating the thermal resistance of the FPHP based, an Artificial neural network (ANN) model with 10 hidden layer nodes is established based on 675 data sets. The results showed that the mean square error and correlation coefficient were 1.30 × 10-5 and 0.9990. The ANN prediction results are validated, the mean relative error is 5.4 %, demonstrating that the ANN model can be an effective tool to optimize the FPHP for the telecommunication equipment. [ABSTRACT FROM AUTHOR]

• Developing optimized neural network-based models for trapdoor stability prediction. • New FELA solutions for 3D rectangular trapdoors in anisotropic clays. • Developing hybrid machine learning models based on ANNs with various optimization algorithms. • Examining several performance metrics, regression plots, and rank analysis. The problem of trapdoor stability is a crucial problem in geotechnical engineering. This study is the first to introduce novel neural network-based metaheuristic models for the stability prediction of 3D rectangular trapdoors in anisotropic and nonhomogeneous clays. However, no researcher has considered such trapdoor problems in the past. In this study, the dataset is obtained by using finite element limit analysis (FELA). The proposed hybrid machine learning models based on artificial neural networks (ANNs) and various types of optimization algorithms (OAs), namely, ant colony optimization (ACO), artificial lion optimization (ALO), the imperialist competition algorithm (ICA), and shuffled complex evolution (SCE), are also proposed in this paper by undergoing rigorous optimization to ensure accuracy and efficiency in capturing the intricate dynamics of the stability investigations from the models. The performance of the proposed ANN-based models is assessed using several performance metrics, regression plots, and rank analysis. The proposed ANN-SCE model outperforms the other proposed models in predicting trapdoor stability, where the ANN-SCE model achieved the highest rank, with a score of 58, followed by the ANN-ALO (47), ANN-ICA (33), and ANN-ACO (22) models. The proposed neural network-based metaheuristic models deliver precise and effective forecasts of trapdoor stability to make informed decisions concerning road design and mitigation tactics, ultimately improving the robustness of infrastructure facing geotechnical challenges. [ABSTRACT FROM AUTHOR]

Date fruit (Phoenix dactylifera L.) is known for its diversity in terms of genotypes that differ in shape, color, texture, taste, and other physical and sensory attributes, in addition to biochemical compounds. This richness generates complex, long, and tedious work at the scale of farm as well as in handling, processing, and storage facilities to prepare standardized, and homogenous final products that deal with consumer preferences and needs. To alleviate this, an increased importance and particular trend were observed recently for the use of artificial intelligence (AI) techniques and computer vision technology since they are considered as accurate, objective, non-destructive, fast, and efficient emerging technologies. In this context, this manuscript provides comprehensive insights into key applications of AI techniques and computer vision technology, using different classification models and algorithms developed on the basis (for example) of neural network classifiers, such as convolutional and artificial neural network, on harvest and postharvest assessment of date fruit. Thus, the importance of date fruit dataset used in the AI domain was discussed with link to all accessible datasets in the field of date palm. In addition, available and relevant AI tools and approaches applied to the postharvest of date fruit from maturity distinguishing until storage through other key handling operations, sorting, grading, etc., were detailed in the five sections of this paper. In terms of conclusion, this review showed that these smart technologies can play a substantial role and offer new opportunities to improve the whole value chain of date palm sector. • Harvesting and post-harvest handling impact quality and storability of dates. • These operations are tedious and subjective leading to quantitative/qualitative losses. • Artificial intelligence (AI) techniques are emergent technologies that can reduce these losses. • AI techniques are more accurate, fast, non-destructive and objective. [ABSTRACT FROM AUTHOR]

Cold-Formed Steel Lipped (CFSL) channels are susceptible to a localized failure mechanism known as web crippling, triggered by concentrated loads or reactions applied to the web of the section. These loads induce buckling and distortion in the web, ultimately leading to the member's collapse. It is a challenging task to accurately determine the web crippling capacity of a CFSL channel due to its complexity and various influencing factors. This paper presents hybrid soft computing techniques for accurately predicting the web crippling capacity of CFSL channels subjected to two flange load cases. The developed soft computing techniques combine Artificial Neural Networks (ANN) with either Genetic Algorithms (GA) or Particle Swarm Optimization (PSO) to improve computational efficiency and accuracy. The finite element models of CFSL channels are developed and validated by experimental results and then employed to generate a database, which is used to train machine learning models, including ANN, GA-ANN, and PSO-ANN. Analysis is undertaken on the reliability of existing design formulas for determining the web crippling capacity of CFSL channels. It is shown that the PSO-ANN model outperforms the other models in terms of prediction accuracy. The existing design codes and formulas are not reliable in estimating the web crippling capacity of CFSL channels. However, the proposed model yields good correlation with finite element analysis results. A user- friendly graphical interface tool is developed for the practical design of cold-formed steel lipped channels. • FE model is developed that simulates the web crippling behavior of CFSL channels. • Hybrid ML models are developed for predicting web crippling capacity of CFSL channels. • Reliability of existing formulas for estimating web crippling capacity is investigated. • A user-friendly graphical interface tool is developed for design purpose. [ABSTRACT FROM AUTHOR]

Stainless steel has many advantages when used in structures, however, the initial cost is high. Hence, it is essential to develop reliable and accurate design methods that can optimize the material. As novel, reliable soft computation methods, machine learning provided more accurate predictions than analytical formulae and solved highly complex problems. The present study aims to develop machine learning models to predict the cross-section resistance of circular hollow section stainless steel stub column. A parametric study is conducted by varying the diameter, thickness, length, and mechanical properties of the column. This database is used to train, validate, and test machine learning models, Artificial Neural Network (ANN), Decision Trees for Regression (DTR), Gene Expression Programming (GEP) and Support Vector Machine Regression (SVMR). Thereafter, results are compared with finite element models and Eurocode 3 (EC3) to assess their accuracy. It was concluded that the EC3 models provided conservative predictions with an average Predicted-to-Actual ratio of 0.698 and Root Mean Square Error (RMSE) of 437.3. The machine learning models presented the highest level of accuracy. However, the SVMR model based on RBF kernel presented a better performance than the ANN, GEP and DTR machine learning models, and RMSE value for SVMR, ANN, GEP and DTR is 22.6, 31.6, 152.84 and 29.07, respectively. The GEP leads to the lowest level of accuracy among the other three machine learning models, yet, it is more accurate than EC3. The machine learning models were implemented in a user-friendly tool, which can be used for design purposes. • The paper studies behaviour of stainless steel CHS stub columns. • ANN, SVR, GEP and DTR machine learning algorithms are developed and validated. • Machine learning-based design approach for predicting the capacity is proposed. • A comparison with EC3 is presented to assess the performance of the models. [ABSTRACT FROM AUTHOR]

• Micromechanics-based ANN establishes structure-property linkage under humid ageing. • Framework to generate artificial RVE with random fibre distribution is developed. • Effective approach is developed to process physical descriptors from microstructure images. • ANN established relation between transverse modulus and geometry, property, and environment. • Contribution of microstructure morphology and humid condition on FRP degradation is investigated. This paper proposes an innovative micromechanics-based artificial neural network (ANN) method to efficiently investigate the transverse modulus of unidirectional fibre/epoxy composites under humid conditions. In this research, a novel approach is developed to establish relations between the geometrical, mechanical, and environmental properties of the microstructure and the material's performance under transverse tension. A framework is developed to artificially generate periodic representative volume elements (RVEs) while taking into account the interphase region between fibre and matrix. The RVEs are analysed by the finite element method to obtain the transverse moduli of composites. Two-point correlation functions and principal component analysis techniques are applied to extract and compress statistical information from microstructure images. A database establishment framework is developed to create three batches of data with 1000, 2000, and 3000 sizes. An ANN-based prediction framework is developed by integrating 10-fold cross-validation and Bayesian optimisation to optimise the neural network architecture and establish an efficient structure-property linkage under the influence of humid conditions. The prediction results demonstrate the efficiency of ANN in mapping microstructural data to an effective transverse modulus. A parametric study by ANN reveals the role of microstructure geometrical features and humid environmental parameters on the transverse performance of the composite. [ABSTRACT FROM AUTHOR]

Electricity market liberalization and the absence of cost-efficient energy storage technologies have led to the transformation of state-owned electricity companies into complex electricity market entities, each having a different time horizon. Deregulation has intensified competition, giving rise to increased uncertainty caused by a multitude of interrelated exogenous factors, resulting in unexpected fluctuations in electricity prices. As a consequence, market participants encounter elevated risks and seek effective mitigation strategies. In this paper, the challenges described in the literature are addressed by studying price distribution histograms in the German and Finnish electricity markets. The objective is to identify normal price intervals that can serve as a foundation for an integrated Day-Ahead price forecasting methodology. A novel approach utilizing the Extreme Learning Machine in combination with Bootstrap intervals is proposed and applied to both markets. The findings demonstrate that Bootstrap intervals effectively capture normal prices, whereas extremely high prices typically align with the upper limits of Bootstrap intervals. Conversely, negative prices tend to fall outside the lower boundaries of the intervals. In order to assess the performance of the proposed methodology, a comparative analysis of its forecasting accuracy against the well-established Generalized AutoRegressive Conditional Heteroskedasticity and AutoRegressive Fractionally Integrated Moving Average models is conducted. In addition, both the computational efficiency and forecasting accuracy of the Extreme Learning Machine in comparison to the Artificial Neural Network are assessed. The results reveal the superior efficiency of the Extreme Learning Machine. The developed forecasting model could potentially assist market participants in making well-informed decisions and executing optimal bidding strategies in response to various scenarios before the Day-Ahead market closes. Notably, the proposed methodology transcends the limitations of fixed price thresholds and effectively addresses market nuances, including the occurrence of negative prices, thus offering a more comprehensive approach for electricity price forecasting. • Analysis of German & Finnish of Day-Ahead prices reveals new insights. • Proposed a new method of classifying market prices into unique classes. • ELM & Bootstrap forecast methodology with very good performance. • ELM & Bootstrap methodology outperforms traditional ones in accuracy & efficiency. [ABSTRACT FROM AUTHOR]

• The influence law between IPCF and tubular parameters is clarified. • A universal multi-cell cross-section characterization scheme is proposed. • The mapping mechanism between IPCF and multi-cell configuration is revealed. • The generalization ability of the prediction model is validated. Accurately predicting the initial peak crushing force (IPCF) of the CFRP/metal hybrid structure is of great significance for the high-efficiency energy absorption of energy-absorbing elements and ordered deformation of combined energy-absorbing devices. Due to the significant differences in material properties between CFRP and metal and the high complexity of cross-sectional shape, a combined method of machine learning and mechanism analysis is proposed for predicting the IPCF of CFRP/Al hybrid energy-absorbing tube. First, a high-precision finite element model validated by the axial and oblique experiments is conducted to generate the dataset for predictive analysis. Second, a sample space through the design of experiment (DOE) is established for data fitting between structural parameters and IPCF based on traditional response surface model (RSM). It is found that traditional RSM method can successfully predict the IPCF of the single-cell hybrid tube, but cannot effectively predict the IPCF of the multi-cell tube due to the strong coupling effect between inner and outer plates. To address the issue of the lack of accurate correspondence between structural parameters and IPCFs in multi-cell hybrid tube, the indicator of relative density is proposed to characterize complex cross-sectional features. Surprisingly, there is a strong correlation between relative density and IPCF where multi-cell tubes with the same relative density have almost the same IPCF. Third, different artificial neural networks (ANN) are trained by a pre-simulated IPCF database of CFRP/Al hybrid energy-absorbing tube depending on the parameter characterization scheme based on mechanism analysis. In order to enhance the network's performance, the optimal configuration parameters of the neural network, including the number of layers and nodes, are ascertained through a combination of K-fold cross-validation and the Grid Search method. As a result, compared with the traditional RSM and Kriging model, the IPCF of CFRP/Al hybrid energy-absorbing tube can be predicted in small samples with high accuracy using the optimal ANN model. Finally, Through the comparative analysis of the IPCF of hybrid tubes with reported multi-cell configurations, it is found that the prediction model proposed in this paper has good generalization ability, which can be used to predict more complex types of multi-cell tube configurations. The present study offers important suggestions for the overall control of multi-materials hybrid energy-absorbing tube and the ordered deformation of combined energy-absorbing devices. [Display omitted] [ABSTRACT FROM AUTHOR]

[Display omitted] • A novel high-resolution and rapid (50 ms) at-line UV imaging system was developed. • Colourless high- and non-fluorescent APIs were investigated in white tablets. • UV images correlated with the fluorescent spectra of the APIs. • Artificial neural networks were trained and optimized for API content determination. • The prediction error was 4.41% and 3.98% for amlodipine and valsartan respectively. This paper presents a novel high-resolution and rapid (50 ms) UV imaging system, which was used for at-line, non-destructive API content determination of tablets. For the experiments, amlodipine and valsartan were selected as two colourless APIs with different UV induced fluorescent properties according to the measured solid fluorescent spectra. Images were captured with a LED-based UV illumination (385–395 nm) of tablets containing amlodipine or valsartan and common tableting excipients. Blue or green colour components from the RGB colour space were extracted from the images and used as an input dataset to execute API content prediction with artificial neural networks. The traditional destructive, solution-based transmission UV measurement was applied as reference method. After the optimization of the number of hidden layer neurons it was found that the relative error of the content prediction was 4.41 % and 3.98 % in the case of amlodipine and valsartan containing tablets respectively. The results open the possibility to use the proposed UV imaging-based system as a rapid, in-line tool for 100 % API content screening in order to greatly improve pharmaceutical quality control and process understanding. [ABSTRACT FROM AUTHOR]

Accurate material description is crucial to achieve high-quality results in computational analysis software. Phenomenological constitutive laws generalize the material behaviour observed in simple mechanical tests. The resulting empirical expressions contain parameters that need to be calibrated through an inverse optimization process. Advancements in Digital Image Correlation (DIC) techniques have enabled the extraction of non-uniform multi-axial displacement fields, facilitating the development of heterogeneous mechanical specimens and unlocking access to richer material behaviour data. Despite these advancements, constitutive models are still limited by mathematical formulations and biases due to simplifying assumptions. Artificial Neural Networks (ANNs), as universal function approximators, could potentially drive a paradigm shift in the field. ANNs do not require explicit pre-formulations, hence avoiding the need for identifying unknown parameters. Moreover, ANNs are able to implicitly learn patterns purely from data, allowing to circumvent the biases induced by the simplifying assumptions of first-order principles. However, the training of ANNs for implicit constitutive modelling is not straightforward, given the impossibility of obtaining direct measurements of stress. This paper explores the integration of Recurrent Neural Networks (RNNs) with the Virtual Fields Method (VFM) for material modelling. This approach uses global force and displacement data to indirectly train the neural network, with the equilibrium being evaluated globally through the VFM loss function. The proposed method is (i) computationally efficient by not requiring Finite Element Analysis (FEA), (ii) compatible with both full-field measurements and numerically generated data, and (iii) able to handle experimental boundary conditions. • A novel method integrates RNNs with the VFM for implicit material modelling. • The RNN is trained without labels, using only displacement/strain and global force. • The approach is real-test oriented, compatible with both DIC and numerical data. • The methodology is independent of the boundary conditions. • The method is computationally more efficient than FEA indirect training. [ABSTRACT FROM AUTHOR]

• Nozzle turbulator is used to improve the paddy drying in a column fluidized bed dryer. • The regression to predict trends in paddy drying behavior are reported. • Artificial neural network model is used to predict the drying behavior. After rice is harvested, it must be dried before its products can be stored. Therefore, this paper presented a novel and simple method for improving paddy drying process in a column fluidized bed dryer. Additionally, artificial neural network methods were applied to predict the drying behavior. The experiments were conducted under two different drying chamber characteristics (a conventional chamber and a chamber fitted with nozzle) and four different air flow velocities (3.03, 3.52, 4.12 and 4.85 m/s) at a drying air temperature of 60 °C. The results showed that the chamber fitted with the nozzle reduced the drying time by approximately 67, 52 and 38 % at the air velocity of 3.52, 4.12 and 4.85 m/s, respectively. The specified experimental conditions and the calculated moisture content of the paddy in this work were used as input and output data for the artificial neural network, respectively, to predict drying characteristics. The artificial neural networks were developed with various parameters, including activation function, sampling type, split ratio, number of neurons and epoch number. The optimal model provided the root mean squared error of 0.111 and the coefficient of determination of 0.999. The best prediction was observed in the model using rectifier activation function with a split ratio of 0.85, epoch number of 1800, and 50 and 90 neurons in the first and second hidden layers, respectively. [ABSTRACT FROM AUTHOR]

Acquiring a new customer is far more expensive than retaining a customer. Hence, customer retention is a key aspect of business for a firm to maintain and improve on its market share and profit. The paper analyses customer retention strategies by employing an artificial neural network-based decision model to a real-life dataset collected from 311 mobile service users in India. Seven linear and non-linear adaptive models are developed using features related to customer dissatisfaction (DSF), customer disloyalty (DLF) and customer churn (CF). Findings of this study suggest that non-linear models are most efficient in predicting customer churn, and both DSF and DLF variables significantly affect the retention strategy. Three groups of customers are discussed in this study in the order of least likelihood of churning to most likelihood. Finally, a priority matrix based on key performance indicators is proposed to help service providers target potential customers to retain. • This study investigates the customer retention problem in the telecommunication sector in India. • Neural networks based decision models are deployed to analyse customer retention strategies of mobile service providers. • The effectiveness of proposed model has been tested on real-life data collected from 311 mobile service users in India. • The study provides valuable insights for service providers to develop customer retention strategy in this sector. [ABSTRACT FROM AUTHOR]

[Display omitted] • Innovative approach in dissolution prediction utilizing UV and VIS images. • VIS images enable a non-destructive prediction of compression force. • Particle size evaluation utilizing UV and VIS images for dissolution prediction. • Utilizing machine vision to gather information on API content. • Machine vision-based comprehensive quality assessment of tablets. Machine vision systems have emerged for quality assessment of solid dosage forms in the pharmaceutical industry. These can offer a versatile tool for continuous manufacturing while supporting the framework of process analytical technology, quality-by-design, and real-time release testing. The aim of this work is to develop a digital UV/VIS imaging-based system for predicting the in vitro dissolution of meloxicam-containing tablets. The alteration of the dissolution profiles of the samples required different levels of the critical process parameters, including compression force, particle size and content of the API. These process parameters were predicted non-destructively by multivariate analysis of UV/VIS images taken from the tablets. The dissolution profile prediction was also executed using solely the image data and applying artificial neural networks. The prediction error (RMSE) of the dissolution profile points was less than 5%. The alteration of the API content directly affected the maximum concentrations observed at the end of the dissolution tests. This parameter was predicted with a relative error of less than 10% by PLS models that are based on the color components of UV and VIS images. In conclusion, this paper presents a modern, non-destructive PAT solution for real-time testing of the dissolution of tablets. [ABSTRACT FROM AUTHOR]

Porous fin (PF) has obvious advantages in heat dissipation, so it is necessary to analyze and optimize its performance. In this paper, a multi-scale cylindrical porous fin (PF) model is built. Firstly, the fin length and height ratio are chosen as the optimization variables, constructal design is conducted with the objective of complex function (CF) composed of maximum temperature difference (MTD) and pumping power consumption (PPC). Then, an artificial neural network (ANN) model is established by using data samples, and multi-objective optimization (MOO) is performed using the NSGA-II method. The findings indicated that the optimal length and height ratio of the fin are 4.763 mm and 1.017 under the conditions that the total PF envelope volume and PF-material volume are fixed. The CF is reduced by 51.0% compared with that before optimization. The multi-scale cylindrical porous fin with different heights is superior to the single-scale one with equal height. For MOO, the lowest deviation index obtained by TOPSIS or LINMAP decision-making strategy (DMS) is 0.258. For this case, the MTD is reduced by 81.9%, and the PPC is reduced by 18.7%. Therefore, the relevant optimal structure herein can be used as the optimal design solution for the multi-scale cylindrical porous fin. • Multi-scale cylindrical porous fin heat dissipation model is built based on constructal theory. • Linear weighted sum of maximum temperature difference and power consumption is minimized. • Multi-objective optimization based on artificial neural network and NSGA-II is also conducted. • Optimal structure of for multiple requirements is gained by three decision-making strategies. • Hot point temperature and pumping power consumption are reduced by 81.9% and 18.7%. [ABSTRACT FROM AUTHOR]

• Artificial Intelligence to predict two-phase frictional pressure gradient. • Collection of a pressure drop database for a wide range of operating conditions. • Effect of the output and input type and numerosity on prediction accuracy. • Good accuracy with non-dimensional output and mixed input values. • Conventional methods always preserve physical trend with respect to neural networks. This paper presents a discussion on several possibilities to predict the frictional pressure gradient during two-phase flow, with both the application of artificial intelligence and the implementation of conventional correlations and predictive methods. To this purpose, a huge database of approximately 8000 data points has been collected from 49 sources available in scientific literature, including 23 working fluids and the following ranges of parameters: mass fluxes from 32.7 to 2000 kg/m2s, saturation temperatures from -190°C to +120°C (reduced pressures from 0.021 to 0.780), tube diameters from 0.5 to 14.0 mm. This consolidated database has been used to train several artificial neural networks (ANNs), by using only two hidden layers (shallow neural networks) and evaluating the effect of: training and testing datasets choice (either test data included or outside the training domain), the number of neurons for each hidden layer (from 1 to 50), the type of output (either dimensional or non-dimensional), the type and number (from 1 to 22) of input parameters. The best results (MAPE of 16.8% and 88% of data within ±30%) have been obtained by using the liquid-only two-phase multiplier Φ L O 2 as non-dimensional output and 12 mixed input parameters. Compared to the statistics of well-established literature correlations for frictional pressure drop (best MAPE of 22% and 73% of data points predicted within a ±30% error range, provided by Mauro et al. mechanistic method), the ANN demonstrates therefore a higher general accuracy. However, the use of Artificial Neural Networks does not guarantee a physical trend, which is instead preserved with conventional prediction methods. [ABSTRACT FROM AUTHOR]

This paper describes the development of a framework for deterministic vessel motion prediction in real-time using an Artificial Neural Network (ANN), with the measurements of vessel motion and upstream wave elevation as the inputs. Instead of relying on sensitivity analysis to select the ANN training dataset, the location and duration of upstream wave elevation is estimated based on group velocity of the incident waves, given a user-specified prediction time horizon for vessel motion. This method is a form of physics-informed feature extraction that only uses correlated information to train the ANN in order to reduce computational effort. The developed framework is promising, as the ANN trained using physics-based numerical results can provide motion predictions with an error down to 6% for surge, heave and pitch motions of a turret-moored Floating Production Storage Offloading (FPSO) vessel. By applying the ANN training using numerical results onto experimental results of the FPSO, an error of 22% was obtained for pitch motions. At an instant of time, up to 120 s of vessel motion can be predicted in real-time. This physics-informed feature extraction method can be automated as part of the framework for the ANN training and testing pipeline in the future. • Combining far field wave and history of vessel motion for motion prediction. • Physics-informed feature extraction to select ANN training dataset. • Prediction horizon is deducible from the predictable zone of far field wave. • Improvements in prediction accuracy compared to brute force approach. [ABSTRACT FROM AUTHOR]

Viscoelastic characterization of asphalt binders is critical for selecting suitable binders for a given climatic region and for improving long-term performance prediction using mechanistic-empirical pavement analysis and design methods, such as the Mechanistic-Empirical-Pavement-Design-Guide (MEPDG). A-VTS information (viscosity data) was used in the past and existing databases have this type of information. Nowadays, Dynamic Shear Modulus (|G*|) and Phase Angle (δ) determination using the Dynamic Shear Rheometer (DSR) is common practice. This resulted in the existence of old and new databases in highway agencies. The goal is to find a way to combine these separate data sets and merge them into a unified national database based on |G*| and δ. The work discussed in this paper is critical to make this merger possible and to use this in future binder to mixture predictive model developments and in MEPDG. In this study, |G*| and δ prediction models were developed. A machine learning technique named Artificial Neural Network (ANN) was employed in this process. 8940 data points from 41 binders were utilized in the model development process. After robust training, validation, and testing process, a |G*| prediction model with seven neurons and a δ prediction model with three neurons were reported. The models were compared with previous Bari-Witczak and Onifade-Birgisson models. ANN-based models performed better in every statistical parameter. Two equations were also extracted from the ANN models, which can be used reliably to predict |G*| and δ from A-VTS data. These two models with closed-form equations can be incorporated into the MEPDG for |G*| and δ predictions. • New models for Binder dynamic shear modulus and phase angle using binder viscosity data with temperature and frequency. • Artificial Neural Network (ANN) is utilized in the model development process. • An equation is extracted from each ANN-based model. • The prediction performance of new models is better than existing models in every statistical parameter. [ABSTRACT FROM AUTHOR]

• A new model for heat capacity calculation of pure compounds in liquid phase was developed. • This model is based on artificial neural networks and group contribution theory. • This new model outperformed the group contribution model of Aspen PlusⓇ simulator. Liquid Cp of pure compounds is essential for process modeling, optimization, and design. However, the experimental information reported for the heat capacities of new chemical and emerging compounds is limited, and the predictive models that have been reported for the estimation of this property may show high modeling errors, mainly in specific regions of the phase diagram. This paper reports the development, testing, and comparison of an improved thermodynamic model for the calculation of this property by exploiting the synergy between group contributions and artificial neural networks. An extensive database constituted by 22715 experimental values of liquid heat capacity ranging from 6.71E+04 to 1.11E+06 J/kmol · K over a wide temperature range (85.59–860 K) of 441 compounds was used for the model development. Different input variables and artificial neural networks configurations were evaluated to select the best thermodynamic model for calculating this thermodynamic property. The performance of this improved model was characterized in terms of its capabilities and limitations for Cp calculation of different chemical families where the impact of the molecular weight and reduced temperature was also discussed. The results showed that the thermodynamic model incorporating reduced temperature as an input parameter improved the Cp calculations near to the critical point. The calculation errors of this new model ranged from 0.47 to 7.72% for most of the analyzed chemical families. This new model was compared with the Cp model used by the Aspen PlusⓇ process simulator and the results showed its superior capabilities and estimations. Therefore, it can be easily implemented in commercial process simulators for Cp calculation of different chemical families, over a broad temperature range, as an alternative reliable thermodynamic tool for process systems engineering. [ABSTRACT FROM AUTHOR]

Building energy demand accounts for an important part of global energy consumption and emissions. Load forecasting is a crucial functionality for building management systems (BMSs) in order to optimize the energy supply and use with the goal of achieving economic and environmental benefits. Furthermore, if the building is a sports/event venue (one of the most energy-intensive facility type), load forecasting acquires an even greater importance. This paper presents a new forecasting hybrid technique that combines an ensemble of Artificial Neural Networks (ANNs) with an AutoRegressive Integrated Moving Average with eXternal inputs (ARIMAX) model. The proposed methodology is designed for the 24-hour-ahead electrical consumption forecasting of sports venues. These types of buildings are generally characterized by large fluctuations in their load caused by the hosted events, and therefore they are challenging for forecasting tools. The ANNs ensemble is composed of the average output of several multi-layer perceptrons, while the ARIMAX is used to correct the prediction provided by the ANNs working on the ensemble residual. The proposed approach can be easily implemented within a BMS that can be crucial for an efficient facility management. The hybrid methodology has been tested and validated on the load data of a real football arena. The proposed methodology achieves a MAPE of 9.03 % on an open dataset, and 9.38% on a closed dataset, outperforming standard machine learning techniques, hybridization techniques involving time-series and machine learning methods, as well as a state of the art event venue load forecasting technique. Furthermore, a feature importance study reveals that all the selected inputs are crucial in achieving the result. Finally, the inclusion of results related to an open dataset encourages further studies and comparisons. • An approach to load forecasting specifically tailored to stadiums. • A novel hybrid technique involving an ensemble of ANNs combined with an ARIMAX. • Experiments on open dataset of a stadium in Norway to facilitate reproducibility. • Comprehensive comparison with standard and state-of-the-art benchmarks. • A feature importance study on the selected inputs. [ABSTRACT FROM AUTHOR]

In this paper, the main objective is to estimate the percentage of glycosylated hemoglobin through an easily accessible computational platform to estimate the risk of generating type 2 diabetes mellitus in the Mexican population. The estimation of the computational tool is developed through an artificial neural network model, which was trained and validated according to a population sample of 1120 Mexican people between 18 and 59 years old. The model inputs were gender, age, body mass index, waist circumference, weekly food consumption, family history, and whether the person suffers from any chronic degenerative disease other than T2DM. We used the percentage of glycosylated hemoglobin as output, estimated according to a dynamic glucose model. The estimation results present a coefficient of determination of 99 %, demonstrating an acceptable performance of the neural network model. The developed platform is an aid tool for health personnel, which seeks to generate a first approximation to the glycemic status of those communities with a high marginalization index for generating disease prevention strategies. [ABSTRACT FROM AUTHOR]