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Superalloys, specifically Inconel 617 (IN617), have distinctive properties, yet it is extremely hard to machine through traditional processes. Electric discharge machining (EDM) has been considered a viable option considering the characteristics and shape intricacy required in the applications of the said alloy. However, EDM's low material removal rate (MRR) impedes its use. Therefore, the machining performance of five different transformer oil (TO) based modified dielectrics and cryogenically treated (CT) tools have been thoroughly investigated herein, which has never been examined so far. A total of 30 experiments were executed employing a full factorial design considering three kinds of electrodes and five different modified dielectrics. CT Cu (CuCT) electrode has given the highest magnitude of MRR (19.65 mm3/min) in the blend of tween 80 and TO, and minimum surface roughness (Rz) 11.0 µm in pure TO. The maximum MRR obtained with the CT electrode is 28% greater in comparison to that recorded for non-treated (NT) electrodes. Among the different electrodes, the CuCT tool performed well in TO-based dielectrics by giving the high MRR compared to the other NT and CT electrodes. CT electrodes yield higher MRR in TO-based dielectrics than kerosene oil (KO) based ones. The highest value of MRR noticed with TO-based dielectric is 20.3% than the maximum MRR noted for the KO-based dielectric. A significant amount of improvement in the MRR (98.56%) and Rz (78.88%) has been observed when the mono-objective optimization is utilized to obtain the optimal set of input variables. Finally, an artificial neural network (ANN) has been modelled to accurately forecast the MRR and Rz value for various input conditions, obviating the necessity for tests, effectively modelling the complicated and nonlinear phenomenon of MRR and Rz. [ABSTRACT FROM AUTHOR]

This paper proposes a solution to enhance and compare different neural network (NN)-based side-slip angle estimators. The feed-forward neural networks (FFNNs), recurrent neural networks, long short-term memory units (LSTMs), and gated recurrent units are investigated. However, there is a lack in the selection criteria of the architectures' hyper-parameters. Therefore, the genetic algorithm is integrated with the NN-based estimators to find the optimal hyper-parameters for the studied architectures. The tuned hyper-parameters in this work include the number of neurons, number of layers, activation function, optimizer type, and learning rate. The objective function of the optimization problem is minimizing the root-mean-square error (RMSE) on multiple testing data. The optimal models are further included in the design of a hybrid NN estimator with Kalman filter. In the hybrid estimators, the optimal NN estimators are used as virtual sensors to correct the prediction of the side-slip angle resulting from the mathematical lateral vehicle model. Eventually, the performance of the best selected model is evaluated in terms of different metrics; mean RMSE, mean error variance, mean training time, and mean estimation time. LSTMs are found to achieve the lowest mean RMSE while being tested on highly generalized data yielding the highest training and estimation time. However, FFNNs achieve the lowest RMSE while being tested on low generalized data and the lowest training and estimation time. Meanwhile, it is observed that the hybrid estimators achieved lower RMSE with great enhancement compared to the non-hybridized ones proving the effectiveness of the proposed approach and increasing the side-slip estimation generalization ability in unknown environments with high uncertainties, which are not covered by the training dataset for the NNs estimators. [ABSTRACT FROM AUTHOR]

In the sintering of advanced ceramics, a digital twin consisted of a finite element model can predict the final shape and microstructure of a sintered ceramic. However, to minimise uncertainties, it is essential to continually update the mechanical properties in the digital twin using data collected from the manufacturing process. One promising approach for achieving this is through artificial neural networks (ANN). This study introduces a machine learning strategy to update the constitutive behaviour of advanced ceramics in finite element analysis of sintering deformation. A major challenge in implementing machine learning in material processing is the huge amount of data required by the training and validation of an ANN, which are often unavailable or incomplete in a real manufacturing process. This study demonstrates that the data requirement can be reduced by employing a two-step training technique. Firstly, the ANN is trained using a nonlinear constitutive law, which describes a general relationship between the strain rates and stresses. Subsequently, the weights and biases of the ANN are transferred for the retraining using limited experimental data for an actual ceramic. It is shown that such approach can successfully capture the nonlinear constitutive behaviour of fine-grained alumina without demanding for large amount of experimental data. A case study is provided, highlighting the feasibility of implementing the ANN in a commercial finite element package, to replace the constitutive law and predict the shrinkage and distortion of a sintering part. In particular, the sintered dumb-bell shape part, simulated using the retrained ANN, showed grain size and relative density that markedly different from those using the nonlinear constitutive law. It is important to note that the proposed methodology is generic and can be used to create ANNs to replace constitutive laws in finite element analysis in digital twins for a wide range of other engineering processes. [ABSTRACT FROM AUTHOR]

In this work, four mainstream machine learning (ML) techniques are used to evaluate the bulk porosity and static elastic modulus of weathered Yungang Grottoes sandstone. Datasets are gathered from the experiments, which includes 432 groups effective experimental data including 8 inputs features. bulk porosity and static elastic modulus were considered as outputs to determine the weathering degrees of Yungang Grottoes sandstone. The 4 performance criteria were used to evaluate the ML models. Results demonstrate that the Artificial Neural Network (ANN) is the best-fitted models for estimating the bulk porosity and static elastic modulus compared to Multiple Linear Regression (MLR), Support Vector Regression (SVR), Gaussian Process Regression (GPR). The accuracy of the trained model for static elastic modulus is slightly higher than that of bulk porosity. The GPR and ANN model can accurately predict the bulk porosity and static elastic modulus in training stages. The ANN with multi-hidden layers developed is competent with high degree of precision and generalization ability for bulk porosity and static elastic modulus compared to other selected regression-based ML models (MLR, SVR, and GPR). The coefficient of determinations of ANN in the range of (0.9537–0.9641) during the testing stages is more stable and higher than that of (0.8883–0.9453) other built ML models. The prediction efficiency of pretrained ANN model was well adjusted for the actual and forecast datasets at the training and testing stages, and the error range was no more than 0.7% and 0.15 GPa at both stages of prediction for bulk porosity and static elastic modulus respectively. And the ANN based static elastic modulus prediction model's error proportions significantly decreased and were confined to a modest range between + 10% and − 10%. The proposed surrogate models are valid for the bulk porosity ranging from 7 to 14% and the static elastic modulus ranging from 0.7 to 1.4 Gpa, which can be utilized for the accurate and fast prediction of the weathering degrees of Yungang Grottoes sandstone. [ABSTRACT FROM AUTHOR]

Different forms of close-packed yarns can be produced by varying the number of monofilaments in the core region, ranging from one to five. Numerous efforts have been made to model or simulate the mechanical response of close-packed yarns; however, previous studies have predominantly focused on one or two monofilaments in the core. In this study, we propose an analytical approach that combines a geometrical model with an artificial neural network (ANN) to predict the tensile behavior of close-packed yarns containing 2 to 5 monofilaments in the core region. The novelty of this hybrid model lies not only in accounting for more than two monofilaments in the core but also in extending the prediction range from elastic to viscoelastic-plastic behavior. Validation of the proposed method showed excellent agreement between experimental and theoretical results. Numerical simulations further confirmed that the results align with theoretical predictions, demonstrating the model's accuracy in predicting the tensile behavior of close-packed yarns. This modeling approach has the potential to significantly improve the understanding and modeling of textile structures. [ABSTRACT FROM AUTHOR]

Memristors‐based neuromorphic devices represent emerging computing architectures to perform complex tasks by outpacing the traditional Von‐Neumann architectures in terms of speed, and energy efficiency. In this work, the resistive switching (RS) behavior of sol‐gel grown and ion‐irradiated BFO films is investigated under electrical stimulus. The Ag/BFO/FTO memristors emulate a combination of digital and analog RS behavior within a single device. The possible mechanism of analog digital hybridity is addressed by considering the formation of the conducting filament by oxygen vacancies, Ag+ ions and Schottky barrier height modulation. The ion‐irradiated BFO samples are analyzed using the Raman, XRD, and XPS studies. To uphold bioinspired synaptic actions, crucial synaptic functionalities like pair‐pulse facilitation and long‐term potentiation/depression are effectively achieved. More intricate synaptic behaviors are also demonstrated such as spike‐time‐dependent plasticity and Pavlovian classical conditioning, which represent the prominent attributes of both learning and forgetting behavior. Additionally, high pattern recognition accuracy (96.1%) is achieved in an artificial neural network simulation by using the synaptic weights of the memristors. This synergistic effect of digital and analog RS in ion‐irradiated BFO can be beneficial for the emulation of complex learning behavior as well as its incorporation into low‐power neuromorphic computing. [ABSTRACT FROM AUTHOR]

The introduction of hybrid alternating current (AC)/direct current (DC) distribution networks led to several developments in smart grid and decentralized power system technology. The paper concentrates on several topics related to the operation of hybrid AC/DC networks. Such as optimization methods, control strategies, energy management, protection issues, and proposed solutions. The implementation of neural network optimization methods has great importance for the successful integration of multiple energy sources, dynamic energy management, establishment of system stability and reliability, power distribution optimization, management of energy storage, and online fault detection and diagnosis in hybrid networks like the hybrid AC–DC microgrids (MG). Taking advantage of renewable energy generation and cost-cutting through the neural network optimization technique holds the key to these progressions. Besides identifying the challenges in the operation of a hybrid system, the paper also compares this system to conventional MGs and shows the benefits of this type of system over different MG structures. This review compares the different topologies, particularly looking at the AC–DC coupled hybrid MGs, and shows the important role of the interlinking of converters that are used for efficient transmission between AC and DC MGs and generally used to implement the different control and optimization techniques. Overall, this review paper can be regarded as a reference, pointing out the pros and cons of integrating hybrid AC/DC distribution networks for future study and improvement paths in this developing area. [ABSTRACT FROM AUTHOR]

Multi-ply yarns have been used as the main structure to form strands, braids, and fabrics. Thus, various strategies including experimental, numerical, and analytical models have been utilized to predict their tensile behavior. In analytical models, all are limited to a specific number of fibers in an elastic region. The time-consuming experimental-based studies suffer from the accuracy of subjective results, associated errors of the experiment, ignoring the physical aspect of the problem, and practical issues during the controlling of variables. Concerning the numerical approaches, no insight into generalization is provided and they are bug-prone methods. In this study, a generalized method is developed to predict the whole tensile behavior of polyamide-6 (PA-6) multi-ply yarns with an open-packing structure. This approach is a combination of two geometrical models and an artificial neural network that enable the calculation of the deformation of fibers in different layers, the number of fibers in each layer by taking the gap into account, and an artificial neural network to map from parameters including strain and rate of strain to the reacting force on monofilament. The model has been verified by numerical analysis and examination of several factors including the number of layers, twist level, and rate of strain. After having trained ANN modeling with R 2 ≈ 1 , the proof-of-concept results demonstrated that this established model is capable of predicting the tensile behavior of PA-6 multi-ply yarns with high precision and accuracy (average value of R 2 = 0.97 and MAPE = 4.65 % ). It is envisioned that the presented model can be extensively applied to all yarns. [ABSTRACT FROM AUTHOR]

AbstractThis article investigates the free vibration and damping characteristics of cylindrical sandwich shells with cutouts using both experimental and numerical approaches. The effects of cutout shape, size, and position on the dynamic response of cylindrical sandwich shells are explored. Moreover, Artificial Neural Network (ANN) models are constructed to predict the dynamic behavior of the sandwich shell with a cutout. A user-friendly application, Sandwich Shell Vibration Analysis (SSVibA), is developed, incorporating the ANN models to predict the non-dimensionalized natural frequencies and modal loss factors of the sandwich shell under different shell geometries, boundary conditions and cutout configurations. The objective of the present research is to advance the understanding of the dynamic behavior of sandwich structures by integrating experimental investigations, numerical analyses, and ANN modeling. [ABSTRACT FROM AUTHOR]

The aim of this study is to evaluate the utility of an artificial neural network (ANN) model in diagnosing idiopathic generalized epilepsy (IGE) and to compare the results of the diagnostic model constructed by combining the expression levels of miR‐146a, miR‐155, and miR‐132 genes using ANN, random forest (RF), and discriminant analysis (DA). qRT‐PCR is employed to determine the expression levels of the three miRNA genes. Forty‐six IGE patients and 51 healthy controls were included in the study. Three genetic biomarkers were employed to assess the discriminative power of the disease, and they were combined using ANN. Additionally, the performance of ANN was compared with RF and DA. Compared to healthy controls, the miR‐132 gene was significantly higher (p < 0.001) and the miR‐155 and miR‐146a genes were significantly lower in IGE patients (p < 0.001). The area under the curve (AUC) for predictions made by the ANN, RF, and DA were 0.96, 0.87, and 0.75, respectively, with accuracy rates of 0.96, 0.88, and 0.76, respectively. We demonstrate that ANN exhibits the highest accuracy, AUC, sensitivity, and specificity values among the three methods. The obtained results indicate that the combination of the three genes used as markers in IGE plays a significant role in the diagnosis of the disease. Instead of assessing biomarkers individually for the disease, combining them using machine learning methods leads to improved model performance. Additionally, not relying on a single genetic biomarker for the disease enables discrimination based on the collective impact of all biomarkers. [ABSTRACT FROM AUTHOR]

Tea is one of the most important parts of agricultural production in China. A large amount of waste tea residues can be produced every year with the continuous growth of the tea industry. Waste tea residue shares significant economic and medicinal value, due to its bioactive substances, especially caffeine. However, most waste tea residues cannot be fully utilized in a resourceful way, resulting in the waste of resources and pressure on the environment. In this study, an efficient caffeine extraction was proposed using hydrothermal extraction. A systematic investigation was implemented to explore the effects of different factors on extraction efficiency. Response surface ology (RSM) and artificial neural network (ANN) were also combined to optimize and predict the extraction conditions. Firstly, the efficient extraction of caffeine from tea residue was achieved by hydrothermal extraction. The special properties of water were exploited under high temperature and pressure. As such, the lignocellulose structure in tea residues was effectively destroyed to obtain the dissolution of active substances, such as caffeine. The high efficiency and environmental advantages of hydrothermal approaches were emphasized to treat the wet biomass under mild conditions, compared with the conventional one. The better performance was achieved in the high efficiency, low energy consumption, and reduced use of hazardous chemical solvents. Secondly, the key parameters of extraction (hydrothermal temperature, time, liquid-solid ratio, and pH) were systematically analyzed using RSM. The influencing level of each factor on the caffeine extraction was determined to derive the optimal extraction conditions. The experimental results showed that the hydrothermal temperature shared the most significant effect on the caffeine extraction. The extraction efficiency was significantly improved to appropriately increase the hydrothermal temperature. An ANN model was also established to further validate and optimize the extraction conditions. A large amount of experimental data was trained and then validated to learn and simulate complex nonlinear processes. The ANN model was also used to accurately predict caffeine extraction under different conditions. The prediction of the ANN model showed that the optimal extraction conditions were determined as the hydrothermal temperature of 220 °C, hydrothermal time of 3.5 h, liquid-solid ratio of 50 mL/g, and pH 9.0. A caffeine extraction of 66.19 mg/g was expected to be obtained. There was high agreement with the actual experiment (65.81 ± 0.47 mg/g), indicating a high prediction accuracy. The ANN model was verified to optimize and predict the caffeine extraction from waste tea residue, in terms of the high feasibility and accuracy. In conclusion, the organic solvents were avoided to be environmentally friendly and realize green chemistry, indicating the promising potential for the extraction of natural products. Meanwhile, the efficient extraction of caffeine from waste tea residue can provide a useful reference for the high-value recycling of waste biomass resources using RSM and ANN models. The environmental pressure can be alleviated to reduce the waste of resources for the considerable economic benefits in the tea industries. [ABSTRACT FROM AUTHOR]

Solid propellant grain reverse design aims to discover optimal grain geometries by shape optimization methods to match the desired solid motor performance curves. To maximize the performance matching degree and the propellant loading fraction simultaneously, this study develops a multi-objective evolutionary neural network for the grain reverse design, where the burning surface regression calculation is efficiently employed using the fast-sweeping method. Then, grain shape feature extraction and pattern analysis are achieved through image singular value decomposition and self-organizing mapping, respectively. Finally, the design case of a dual-thrust motor and a Mars ascent vehicle show that the method can well balance the performance-matching degree and propellant loading fraction. Moreover, without any training data set, it can generate dozens of grain shape patterns, highlighting their diversity and providing new ideas for solid rocket motor designers. Our method can offer a new pathway for the research field of solid rocket motor design. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) has a wide and increasing range of applications across various sectors. In medicine, AI has already made an impact in numerous fields, rapidly transforming healthcare delivery through its growing applications in diagnosis, treatment and overall patient care. Equally, AI is swiftly and essentially transforming the landscape of kidney transplantation (KT), offering innovative solutions for longstanding problems that have eluded resolution through traditional approaches outside its spectrum. The purpose of this review is to explore the present and future applications of artificial intelligence in KT, with a focus on pre-transplant evaluation, surgical assistance, outcomes and post-transplant care. We discuss its great potential and the inevitable limitations that accompany these technologies. We conclude that by fostering collaboration between AI technologies and medical practitioners, we can pave the way for a future where advanced, personalised care becomes the standard in KT and beyond. [ABSTRACT FROM AUTHOR]

Background: The averaged cortical thickness of meta-ROI is currently being used for the diagnosis and prognosis of Alzheimer's disease (AD) using structural MRI brain images. The purpose of this work is to present a hybrid meta-ROI for the detection of AD. Methods: The AD detectability of selected cortical and volumetric regions of the brain was examined using signal detection theory. The top performing cortical and volumetric ROIs were taken as input nodes to the artificial neural network (ANN) for AD classification. Results: An AD diagnostic accuracy of 91.9% was achieved by using a hybrid meta-ROI consisting of thicknesses of entorhinal and middle temporal cortices, and the volumes of the hippocampus and inferior lateral ventricles. Pairing inferior lateral ventricle dilation with hippocampal volume reduction improves AD detectability by 5.1%. Conclusions: Hybrid meta-ROI, including the dilation of inferior lateral ventricles, outperformed both cortical thickness- and volumetric-based meta-ROIs in the detection of Alzheimer's disease. [ABSTRACT FROM AUTHOR]

In this study, the transverse shear strength (TSS) retention of two types of new-generation glass fiber-reinforced polymer (GFRP) bars, namely ribbed (RB) and sand-coated (SC) bars, was investigated under alkaline, acidic, and marine conditions in both high-temperature and laboratory environments for up to one year. The ribbed GFRP bars exhibited no notable reduction in strength under ambient conditions after 12 months, but under high-temperature conditions (60 °C), they showed TSS reductions of 10.6%, 9.7%, 11.1%, and 10.9% for exposure solutions E1, E2, E3, and E4, respectively. The sand-coated GFRP bars showed slight strength reductions under ambient conditions and moderate reductions under high-temperature conditions (60 °C), with TSS reductions of 22.5%, 29.0%, 13.0%, and 13.7% for the same solutions, highlighting the detrimental effect of high temperatures on the degradation of the resin matrix. Comparative analyses of older-generation ribbed (RB-O1 and RB-O2) and sand-coated (SC-O) GFRP bars exposed to similar conditioning solutions for the same duration were also performed. In addition, linear regression and artificial neural network (ANN) models were developed to predict strength retention. Models developed using linear regression and ANNs achieved coefficients of determination (R2) of 0.69 and 0.94, respectively, indicating that the ANN model is a more robust tool for predicting the TSS of GFRP bars than is the conventional linear regression model. [ABSTRACT FROM AUTHOR]

The article focusses on detecting the unbalance of a mechanical component in the electric drive system of a two-mass servomechanism with a permanent magnet synchronous motor (PMSM), which is connected to the load via a long, flexible shaft. In the example analysed, the degree of unbalance was determined using the reference current signal from the speed controller of the field-orientated control (FOC) system. The authors presented a two-mass model with an unbalanced mechanical system. The short-time Fourier transform (STFT) transform was used to analyse the symptoms of unbalance, and an artificial neural network multi-layer perceptron (MLP) was used for system state inference. The effectiveness of the presented analysis, based on the reference current signal from the sensor embedded in the control system, was experimentally confirmed. [ABSTRACT FROM AUTHOR]

Although the double-double (DD) laminates proposed by Tsai provide a promising option for achieving better structural performance with lower manufacturing and maintenance costs, the buckling performance of perforated DD laminates still remains clear. In this study, optimal ply angles, rotation angles, and the corresponding maximum buckling loads are determined for DD laminates with various cutouts, which are used for comparisons to evaluate the effects of cutout size and shape on the buckling behaviour of perforated DD laminates. Apart from conventional circular and elliptical cutouts, the use of a combined-shape cutout for DD laminates is also investigated. As a large number of optimisations are required to obtain the maximum buckling loads for different cases in this study, an efficient optimisation method for perforated DD laminates is proposed based on an artificial neural network (ANN) and a genetic algorithm (GA). Unlike conventional quadaxial (QUAD) laminates, the repetition of a four-ply sublaminate in DD laminates makes their layup to be represented by only two ply angles; hence, the application of ANN models for predicting the buckling behaviour of various perforated DD laminates is studied in this paper. The superior performance of the ANN models is demonstrated by comparisons with other machine learning models. Instead of using the time-consuming FEA, the developed ANN model is utilised within a GA to obtain the maximum buckling load of perforated DD laminates. Compared to the circular cutout, the use of elliptical and combined-shape cutouts leads to more noticeable changes in the optimal ply angles as the cutout size increases. Based on the obtained results, the use of the combined-shape cutout is recommended for DD laminates. [ABSTRACT FROM AUTHOR]

This study explores machine learning for estimating soil moisture at multiple depths (0–5 cm, 0–10 cm, 0–20 cm, 0–50 cm, and 0–100 cm) across the coterminous United States. A framework is developed that integrates soil moisture from Soil Moisture Active Passive (SMAP), precipitation from the Global Precipitation Measurement (GPM), evapotranspiration from the Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS), vegetation data from the Moderate Resolution Imaging Spectroradiometer (MODIS), soil properties from gridded National Soil Survey Geographic (gNATSGO), and land cover information from the National Land Cover Database (NLCD). Five machine learning algorithms are evaluated including the feed-forward artificial neural network, random forest, extreme gradient boosting (XGBoost), Categorical Boosting, and Light Gradient Boosting Machine. The methods are tested by comparing to in situ soil moisture observations from several national and regional networks. XGBoost exhibits the best performance for estimating soil moisture, achieving higher correlation coefficients (ranging from 0.76 at 0–5 cm depth to 0.86 at 0–100 cm depth), lower root mean squared errors (from 0.024 cm3/cm3 at 0–100 cm depth to 0.039 cm3/cm3 at 0–5 cm depth), higher Nash–Sutcliffe Efficiencies (from 0.551 at 0–5 cm depth to 0.694 at 0–100 cm depth), and higher Kling–Gupta Efficiencies (0.511 at 0–5 cm depth to 0.696 at 0–100 cm depth). Additionally, XGBoost outperforms the SMAP Level 4 product in representing the time series of soil moisture for the networks. Key factors influencing the soil moisture estimation are elevation, clay content, aridity index, and antecedent soil moisture derived from SMAP. [ABSTRACT FROM AUTHOR]

A fast and accurate radiative transfer model is the prerequisite in the field of atmospheric remote sensing for limb atmospheric inversion to tackle the drawback of slow calculation speed of traditional atmospheric radiative transfer models. This paper established a fast computing model (ANN-HASFCM) for high-spectral-resolution absorption spectra by using artificial neural networks and PCA (principal component analysis) spectral reconstruction technology. This paper chose the line-by-line radiative transfer model (LBLRTM) as the comparative model and simulated training spectral data in the oxygen A-band (12,900–13,200 cm−1). Subsequently, ANN-HASFCM was applied to the retrieval of the atmospheric density profile with the data of the Global Ozone Monitoring by an Occultation of Stars (GOMOS) instrument. The results show that the relative error between the optical depth spectra calculated by LBLRTM and ANN-HASFCM is within 0.03–0.65%. In the process of using the global-fitting algorithm to invert GOMOS-measured atmospheric samples, the inversion results using Fast-LBLRTM and ANN-HASFCM as forward models are consistent, and the retrieval speed of ANN-HASFCM is more than 200 times faster than that of Fast-LBLRTM (reduced from 226.7 s to 0.834 s). The analysis shows the brilliant application prospects of ANN-HASFCM in limb remote sensing. [ABSTRACT FROM AUTHOR]

Maximizing the recombinant protein yield necessitates optimizing the production medium. This can be done using a variety of methods, including the conventional "one‐factor‐at‐a‐time" approach and more recent statistical and mathematical methods such as artificial neural network (ANN), genetic algorithm, etc. Every approach has advantages and disadvantages of its own, yet even when a technique has flaws, it is nevertheless used to get the best results. Here, one categorical variable and four numerical parameters, including post‐induction time, inducer concentration, post‐induction temperature, and pre‐induction cell density, were optimized using the 232 experimental assays of the central composite design. The direct and indirect effects of factors on the yield of anti‐epithelial cell adhesion molecule extracellular domain fragment antibody were examined using statistical methods. The analysis of variance results indicate that the response surface methodology (RSM) model is effective in predicting the amount of produced single‐chain fragment variable (p‐value = 0.0001 and R2 = 0.905). For ANN modeling, the evaluation using normalized root mean square error (NRMSE) and R2 values shows a good fit (R2 = 0.942) and accurate predictions (NRMSE = 0.145). The analysis of error parameters and R2 of a dataset, which contained 30 data points randomly selected from the complete dataset, showed that the ANN model had a higher R2 value (0.968) compared to the RSM model (0.932). Furthermore, the ANN model demonstrated stronger predictive ability with a lower NRMSE (0.048 vs. 0.064). Induction at the cell density of 0.7 and an isopropyl β‐D‐1‐thiogalactopyranoside concentration of 0.6 mM for 32 h at 30°C in BW25113 was the ideal culture condition leading to the protein yield of 259.51 mg/L. Under the optimum conditions, the output values predicted by the ANN model (259.83 mg/L) were more in line with the experimental data (259.51 mg/L) than the RSM (276.13 mg/L) expected value. This outcome demonstrated that the ANN model outperforms the RSM in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

In order to use the high ability of the artificial neural network (ANN) in data fitting, this paper introduces an ANN in stochastic process to describe the mean function for degradation modeling. Due to the fact that the existing method cannot handle the bivariate dependent degradation conditions, a bivariate dependent degradation model based on Copula function and ANN‐supported stochastic processes is proposed. Considering the random effects caused by individual difference, it is assumed that the unknown parameters in the stochastic processes and Copula functions are randomly distributed. Based on the maximum likelihood and moment estimation methods, a related statistical inference method for ANN training and parameter estimation is developed to use the bivariate dependent degradation model. An actual fatigue crack dataset is used to demonstrate the validity of the proposed method. The obtained results show that the dependent relationship between two degradation indicators should not be neglected, and it can be efficiently handled by the proposed method. Furthermore, the proposed degradation model can provide reliability and degradation intervals with enough precision due to the fact that it considers the random effects caused by individual difference. [ABSTRACT FROM AUTHOR]

Background: This study aims to develop an artificial neural network (ANN) prediction model incorporating random forest (RF) screening ability for predicting the risk of depression in adolescents and identifies key risk factors to provide a new approach for primary care screening of depression among adolescents. Methods: The data were from a large cross‐sectional study conducted in China from July to September 2021, enrolling 8635 adolescents aged 10–17 with their parents. We used the Patient health questionnaire (PHQ‐9) to rate adolescent depression symptoms, using scales and single‐item questions to collect demographic information and other variables. Initial model variables screening used the RF importance assessment, followed by building prediction model using the screened variables through the ANN. Results: The rate of depression symptoms in adolescents was 24.6%, and the depression risk prediction model was built based on 70% of the training set and 30% of the test set. Ten variables were included in the final prediction model with a model accuracy of 85.03%, AUC of 0.892, specificity of 89.79%, and sensitivity of 70.81%. The top 10 significant factors of depression risk were adolescent rumination, adolescent self‐esteem, adolescent mobile phone addiction, peer victimization, care in parenting styles, overprotection in parenting styles, academic pressure, conflict in parent–child relationship, parental rumination, and relationship between parents. Conclusions: The ANN model based on the RF effectively identifies depression risk in adolescents and provides a methodological reference for large‐scale primary screening. Cross‐sectional studies and single‐item scales limit further improvements in model accuracy. [ABSTRACT FROM AUTHOR]

The current study investigates removing tetracycline from water using batch, column, and tank experiments with statistical modelling using ANN for continuous tests. An artificial neural network (ANN) using the Levenberg-Marquardt back-propagation (LMA) training algorithm is constructed to compare the effectiveness of Tetracycline removal from aqueous solution using the sorption technique with prepared adsorbent. Several characterization analyses XRD, FT-IR, and SEM are employed for prepared Brownmillerite (Ca2Fe2O5)–Na alginate beads. The operating conditions of batch tests involved, contact time (0.1–3 h), initial of tetracycline (Co) of (100–250 mg/L), pH (3–12), agitation speed (50–250) rpm and dosage of adsorbent (0.2–1.2 g/50 mL). The outcomes of experiments have demonstrated that the optimum conditions for the batch test to achieve the maximum adsorbent capacity (qmax =7.845 mg/g) are achieved at pH 7, contact time 1.5 h, adsorbent dose 1.2 g/50 mL, agitation speed of 200 rpm, and initial concentration of TC 100 mg/L. Minimum mean square error (MSE) values of 7.09E-04 for 30 hidden neurons and 0.0029 for 59 hidden neurons in the 1D and 2D systems are accomplished, respectively. The artificial neural network model has exhibited excellent performance with correlation coefficients exceeding 0.980 for the operating variables, demonstrating its accuracy and effectiveness in predicting the experimental outcomes. According to sensitivity analysis, the influential parameter in the column test (1D) is the flow rate (mL/min), with a relative importance of 32.769%. However, in the tank test (2D), time (day) is signified as an influential parameter with a relative importance of 31.207%. [ABSTRACT FROM AUTHOR]

A crucial factor in determining coal's quality is its gross calorific value (GCV). GCV is generally determined experimentally using an adiabatic bomb calorimeter. But the laboratory measurement process is complicated, tedious, expensive, and time-consuming. To address these problems, this study has developed five machine learning techniques, such as multilayer perceptron artificial neural network (MLP-ANN), multiple variable regression (MVR), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM). ANN is further regularized using early stopping to avoid overfitting, whereas the MVR is regularized by employing L1, L2, and elastic networks. An inter-item correlation matrix, scatterplots, and sensitivity analysis are used to rank the parameters of the ultimate and proximate analyses according to their significance. A total of 6582 datapoints from the US. Geological Survey Coal Quality (COALQUAL) database are utilized for this study. The models' performance is measured using coefficient of determinant (${{\rm{R}}^2}$ R 2 ), mean squared error (MSE), explained error (EV), maximum error (max_error), minimum error (min_error), mean absolute error (MAE), and mean absolute percentage error (MAPE), respectively. The study's findings demonstrate that MVR, MLP-ANN, SVM, and ANFIS are highly efficient concerning both prediction and computation effectiveness. The prediction performances of these models are somewhat similar, with a coefficient of determinant of over 0.99. Hence, simple MVR is sufficient to predict GCV accurately instead of complex modeling. Parameters ranking indicates that oxygen (O), carbon (C), and moisture (M) are the most significant input variables for GCV prediction. [ABSTRACT FROM AUTHOR]

Studies on aerodynamic controls of central-slotted box decks primarily focused on mitigating vortex-induced vibrations (VIV), as this type of deck typically performs well against flutter instability. However, as the span length increases, the critical wind speed of aerodynamic static instability (U cr ) might be lower than flutter critical wind speed. Thus, U cr will determine the overall aerodynamic performance of such bridges. Investigating this instability through wind tunnel testing methods and numerical simulation can be expensive and time-consuming. In this paper, surrogate models using machine learning approaches, specifically artificial neural network (ANN) and extreme gradient boosting (XGBoost), were developed and optimized for fast and reliable prediction for U cr based on wind tunnel tests and simulation data. The results demonstrated that the built surrogate models can predict U cr accurately. The parametric study results showed that the height ratio of wind fairing apex (a/b), wind angle of attack (α), and length of the main span (L) have the most influence on the U cr compared with other parameters. Finally, based on the developed ANN surrogate model and the artificial bee colony (ABC) optimization algorithm, an optimized section was proposed to enhance the section's performance against aerodynamic static instability. [ABSTRACT FROM AUTHOR]

This scientific paper explores the use of correlation coefficients of vibration signals and machine learning algorithms for structural damage assessment in beams under moving loads. The paper discusses the challenges of maintaining structural integrity and the importance of automated, nondestructive monitoring techniques. Preprocessing techniques, such as the random decrement technique (RDT), are highlighted for improving data analysis. Machine learning algorithms are identified as valuable tools for structural damage assessment. The paper concludes by emphasizing the potential of machine learning in safeguarding critical infrastructures. The text also discusses trigger points and the vibration response of a slender beam under a moving load. An artificial neural network (ANN) is proposed as a computational model for identifying non-linear features. Experimental testing on a simulated bridge girder using accelerometers collected data to identify and locate damage in the beam. The ANN achieved high accuracy in detecting damage appearance and location, but further research is needed to improve accuracy in real-world situations. [Extracted from the article]

Recent artificial neural networks that process natural language achieve unprecedented performance in tasks requiring sentence-level understanding. As such, they could be interesting models of the integration of linguistic information in the human brain. We review works that compare these artificial language models with human brain activity and we assess the extent to which this approach has improved our understanding of the neural processes involved in natural language comprehension. Two main results emerge. First, the neural representation of word meaning aligns with the context-dependent, dense word vectors used by the artificial neural networks. Second, the processing hierarchy that emerges within artificial neural networks broadly matches the brain, but is surprisingly inconsistent across studies. We discuss current challenges in establishing artificial neural networks as process models of natural language comprehension. We suggest exploiting the highly structured representational geometry of artificial neural networks when mapping representations to brain data. [ABSTRACT FROM AUTHOR]

The article focuses on predicting rolling force (RF) in the hot rolling process by incorporating nano-lubrication conditions through strategies available in finite element method (FEM) software. Additional work includes RF prediction by artificial neural network (ANN) modelling. The experimental RF were collected from an experimental rolling mill (ERM) for hot rolling IS 2062 E250 steel. The simulations were performed using SIMUFACT and the results were validated by experimental data. The prediction was made accurately by adjusting the lubrication panel available in the software. Then, FEM and ANN were performed to predict RF at various combinations of process parameters. FEA simulated hot rolling of IS 2062 E250 with varying process parameters. It incorporates changing 5 process parameters for FEA as roll speed, reduction%, nano-lubrication condition, billet size (thickness), and rolling temperature. Then, the accumulation of data for further application in ANN modelling. The validation of FEM software was successful with an error of less than 5%. According to the validation, the lubrication corresponding to the nano-lubrication setup was fixed in the software. The mesh was optimised and detected the least RF deviation at 1.8 mm mesh. The ANN was performed from the simulation data. The ANN performed better for a single hidden layer model for 8 neurons. The training, validating, and testing of the model resulted in an R-square value of 0.991, 0.987, and 0.994, respectively. The root mean square error (RMSE) value obtained was 0.985. Therefore, the ANN model developed was performing better. The article uses the combined strategy of FEM and ANN, with available lubrication panels in FEM software for accurate RF predictions. [ABSTRACT FROM AUTHOR]

Submerged arc welding (SAW), renowned for its high deposition rate and superior weld quality, is the go-to method for joining thick metals in heavy structures. However, industry beams and columns welded with SAW can exhibit detrimental defects like undercut, porosity, and burn-through, significantly impacting weld properties. This study addresses this challenge by presenting a multi-objective optimization approach for SAW parameters on AISI 1020 mild steel. Aiming to optimize tensile strength, hardness, and bead width, the study employs Taguchi's design of experiments and couples the multi-objective Jaya algorithm with an artificial neural network (ANN). This synergistic combination yielded optimal process parameters: 417 A welding current, 20.7 mm electrode stick-out, 33.7 V voltage, and 505.8 mm/min transverse speed. These settings translated into exceptional weld characteristics, with ultimate tensile strength reaching 427 MPa, hardness of 73.9 HRB, and bead width of 14.29 mm. Confirmation tests further validated these findings, demonstrating minimal error and solidifying the effectiveness of the optimization approach. This research paves the way for enhanced weld quality and process control in heavy structural applications. [ABSTRACT FROM AUTHOR]

This study investigated the mechanical properties of a low-plasticity clay soil reinforced with polypropylene (PP) fiber in various contents (0.05%, 0.10%, 0.15%, and 0.20%) and lengths (6, 12, and 19 mm). The reinforced specimens were subjected to unconsolidated-undrained (UU) triaxial compression tests under three different confining pressures (50, 100, and 200 kPa). The optimum fiber contents in specimens reinforced with 6-, 12-, and 19-mm PP fiber were determined as 0.15%, 0.15%, and 0.20%, respectively. As a result, the highest values regarding deviator stress at failure (σdev), energy absorption capacity (EAC), and shear strength parameters occurred in specimens containing 0.20% PP (19 mm). As a result of the reinforcement process, the most remarkable improvements in the σdev, cohesion, internal friction angle, and EAC values of the natural soil are 59.95%, 21.80%, 63%, and 34.70%, respectively. Linear and nonlinear relationships between σdev and fiber length, fiber content, and confining pressure were investigated by multiple linear regression and artificial neural network methods. Equations were generated to predict σdev of a low-plasticity clay soil reinforced with PP fiber and were made available to geotechnical researchers. [ABSTRACT FROM AUTHOR]

The significance of soil in agriculture and human survival cannot be overstated. Given its limited supply, improving soil properties is imperative. This study addresses this challenge by predicting five major soil properties—organic carbon, pH values, Mehlich-3 extractable calcium, Mehlich-3 extractable phosphorus, and sand content—utilizing mid-infrared absorbance measurements from the African Soil Information Service (AfSIS) dataset, covering non-desert regions of Africa. With a pressing need for a reliable and efficient model to predict African soil properties using spectral measurements, this study fills a crucial gap, addressing the scarcity of functional soil property databases in Africa. The developed model eliminates costly soil sample preparation and lengthy chemical analysis, applicable in both onsite and laboratory settings for determining soil functional properties. By employing stacked autoencoders for feature dimensionality reduction and combining discrete wavelet transform with two feature selection methods (stepwise regression and random forest) to build robust multi-layer perceptron (MLP) models, the study offers a comprehensive approach. Evaluation metrics including root mean square error (RMSE), scatter index (SI), Variance Accounted For (VAF), Nash-Sutcliffe Model Efficiency (NSE), and Correlation Coefficient (R) demonstrate the superior performance of the MLP model informed by stacked autoencoder-selected features, outperforming models informed by wavelet-transformed features and partial least square regression. This best-performing autoencoder-based model presents a valuable tool for soil scientists tasked with modeling African soil properties. [ABSTRACT FROM AUTHOR]

Increasing consumers demand for natural and additive -free foods and high volumes of food industry wastes, are stimulating the use of these resources in other food industries. Tomato pomace is one of the food factory wastes is the resulting by -product of tomato paste and sauce factories. The aim of this study was to evaluate the effect extraction method and microwave pretreatment of tomato seeds on the physicochemical characteristics of their extracted oil. The seeds were treated with microwaves using various power levels (0, 200 and 500 W) and different process times (0, 1, 3 and 5 min) and their oil was extracted by Soxhlet and press methods. Fatty acids composition of oils was determined by gas chromatography. Some physicochemical characteristics of extracted seed oil including oil yield, viscosity, acid value, peroxide value, and color index (L, b, a values) were evaluated. Data was analyzed with factorial treatment structure in a Completely Randomized Design in three replications. The experimental data was modeled by artificial neural network with 3 inputs (extraction method, microwave power and pretreatment time) and 7 outputs (oil yield, acid value, peroxide value, viscosity, L value, b value and a value). The results of artificial neural network modeling showed that the network with a 3 - 8 -7 structure and using the Hyperbolic tangent activation function can predict the oil yield, acid value, peroxide value, viscosity, L value, b value and a value of tomato seed oil with high correlation coefficient and low error. Based on the results of the sensitivity analysis, the extraction method compared to the power and time of microwave assisted pretreatment of seeds was determined as the main factor. [ABSTRACT FROM AUTHOR]

The main reaction parameters examined were the amount of blast furnace slag, the hydration duration, ammonium acetate concentration, and temperature. The Response surface methodology was employed to quantify their impact on the sorbent's surface area. Using a central composite design, the surface area of the resulting sorbent corresponding to Brunauer-Emmett-Teller (BET) was investigated. The sorbents produced range in surface area from 49.89 to 155.33 m2/g. Additionally, the effectiveness and response prediction capacities of the Response Surface Methodology (RSM) and Artificial Neural Network (ANN) modeling methodologies were investigated. The models were assessed using various statistical metrics, including (MSE) mean squared error, (ARE) average relative errors, the (SSE) sum of squared errors, (HYBRID) Hybrid fractional error function, (SAE) Sum of the absolute errors, (R2)coefficient of determination, and Root means square. According to statistical evidence, the ANN approach surpassed the RSM-CCD model approach. The surface area of the sorbent was shown to be significantly influenced by interactions between variables in addition to all the individual variables examined. The sorbent was made from a material with substantial structural porosity based on SEM. The functional groups were identified using FTIR. The XRF determined the elemental composition of BFS-based sorbents. [ABSTRACT FROM AUTHOR]

Estimation of the reservoir parameters such as porosity, lithology and saturation from well logs and seismic data would require the shear wave velocity data. The high cost of shear wave well logging and subsequently the lack of the shear velocity log in most wells are the main reasons to estimate the shear velocity log. The routine methods for estimating the shear velocity are empirical relations, regression analysis, intelligent methods and rock physics models. Most of these methods do not include the impact of geometry of pore spaces and minerals (aspect ratio). Excluding geometry of pore spaces and minerals from the estimation procedure would create considerable estimation error in the oil and gas reservoirs, particularly in the carbonates. In the first step, shear velocity log is estimated using empirical relations, regression analysis and neural network methods. The estimated shear velocity log using neural network is better than empirical and regression methods. Subsequently, a new simple equation utilizing rock physics models is developed to estimate the shear velocity log. The Xu–White (Geophys Prospect 43: 91–118, 1995) and as reported by Gassmann (Elasticity of porous media, Vierteljahrsschrde Naturforchenden, Gesellschaft, 1951) relations were employed to include the impact of lithology, fluid, minerals and pore spaces. The estimated shear velocity log using new equation compares well with the original as reported by Gassmann (Elasticity of porous media, Vierteljahrsschrde Naturforchenden, Gesellschaft, 1951) and Xu–White (Geophys Prospect 43: 91–118, 1995) rock physics methods. The average difference between the measured and estimated velocity logs using new method is 12.6 m/s (or 0.41%). This equation includes the impact of major parameters in the rock physics model in the simple form; thus, it would be applicable in the carbonate and clastic reservoirs. [ABSTRACT FROM AUTHOR]

Aerodynamic modeling and control law design methods are crucial foundational technologies that enable efficient maneuvering flight of aircraft. Hence, this study focuses on how to construct the aerodynamic model and design control law using machine learning, as well as their differences from classical methods. To conduct this, a modified NACA0012 airfoil with a tail functioning as an elevator is employed as the geometry model. Through the utilization of the rigid dynamic grid method and overlapping grid technology, the pitch of the airfoil and the deflection of the elevator are efficient to execute. Firstly, the pitching moment coefficient is sampled through both steady and unsteady computations. Then, multivariate nonlinear regression (MNR) models and artificial neural network (ANN) models are established based on steady and unsteady sampling data, respectively. Additionally, the obtained model is evaluated using open‐loop control laws. Based on the evaluation, the proportional‐integral‐derivative (PID) control algorithm is used to design the airfoil pitching control law using the MNR model. Meanwhile, deep reinforcement learning (DRL) is used to design the pitching control law using the ANN model. Finally, the PID and DRL controllers are implemented in a CFD environment for airfoil pitching control to verify their effectiveness in application scenarios. The results suggest that models based on both steady and unsteady data can reflect dynamic aerodynamic characteristics. However, using unsteady computation for data sampling significantly reduces time consumption compared to steady computation. Furthermore, the model constructed by ANN may have unexpected excellent characteristics. Both the PID and DRL control laws, designed based on the models, perfectly complete the control process in the CFD environment. This study provides valuable insights for the implementation of controllable maneuvering flight in aircraft. [ABSTRACT FROM AUTHOR]

The enormous amount of solar power and its state-of-the-art capturing technology that produces electricity, increases the grid interconnection rate of photovoltaic (PV) plants. Prior knowledge of the aperiodic characteristics of solar energy received by the Earth’s surface is essential for PV plants to ensure reliable operation and stable, secure grid connections. Power generation under diffused sunlight by PV plants with the most widely used first-generation solar cells has significant limitations. A dye-sensitized solar plant (DSSP), utilizes dye-sensitized solar cell (DSSC) technology, remains active in low light conditions. Though the activeness enables such plants to generate electricity throughout the year while receiving diffuse sunlight, unlike conventional solar technologies the plants have a requirement of knowledge of future power generation. This could be the first paper to report on the generated power forecast for such plants. The study has utilized a machine learning approach with a proposed DSSP model for the forecast. Spyder, an open-source integrated development environment, is the test workbench for the experimentation. The results show the robustness and reliability of the method, regardless of the weather conditions in the test area. The DSSP, along with the prediction model, will moderately overcome the shortcomings of power generation under diffuse daylight with intermittent solar radiation and grid connections. [ABSTRACT FROM AUTHOR]

Introduction: Developing somatic embryogenesis is one of the main steps in successful in vitro propagation and gene transformation in the carrot. However, somatic embryogenesis is influenced by different intrinsic (genetics, genotype, and explant) and extrinsic (e.g., plant growth regulators (PGRs), medium composition, and gelling agent) factors which cause challenges in developing the somatic embryogenesis protocol. Therefore, optimizing somatic embryogenesis is a tedious, time-consuming, and costly process. Novel data mining approaches through a hybrid of artificial neural networks (ANNs) and optimization algorithms can facilitate modeling and optimizing in vitro culture processes and thereby reduce large experimental treatments and combinations. Carrot is a model plant in genetic engineering works and recombinant drugs, and therefore it is an important plant in research works. Also, in this research, for the first time, embryogenesis in carrot (Daucus carota L.) using Genetic algorithm (GA) and data mining technology has been reviewed and analyzed. Materials and methods: In the current study, data mining approach through multilayer perceptron (MLP) and radial basis function (RBF) as two well-known ANNs were employed to model and predict embryogenic callus production in carrot based on eight input variables including carrot cultivars, agar, magnesium sulfate (MgSO4), calcium dichloride (CaCl2), manganese (II) sulfate (MnSO4), 2,4-dichlorophenoxyacetic acid (2,4-D), 6-benzylaminopurine (BAP), and kinetin (KIN). To confirm the reliability and accuracy of the developed model, the result obtained from RBF-GA model were tested in the laboratory. Results: The results showed that RBF had better prediction efficiency than MLP. Then, the developed model was linked to a genetic algorithm (GA) to optimize the system. To confirm the reliability and accuracy of the developed model, the result of RBF-GA was experimentally tested in the lab as a validation experiment. The result showed that there was no significant difference between the predicted optimized result and the experimental result. Conclutions: Generally, the results of this study suggest that data mining through RBF-GA can be considered as a robust approach, besides experimental methods, to model and optimize in vitro culture systems. According to the RBF-GA result, the highest somatic embryogenesis rate (62.5%) can be obtained from Nantes improved cultivar cultured on medium containing 195.23 mg/l MgSO4, 330.07 mg/l CaCl2, 18.3 mg/l MnSO4, 0.46 mg/l 2,4- D, 0.03 mg/l BAP, and 0.88 mg/l KIN. These results were also confirmed in the laboratory [ABSTRACT FROM AUTHOR]

Syngas (CO + H 2) production through biomass gasification offers a promising and sustainable alternative to conventional fuels. This study investigates the co-gasification of palm oil decanter cake (PODC) and Alum Sludge (AS), utilizing response surface methodology (RSM) and artificial neural network (ANN) techniques to optimize and predict syngas production. Conducted in a fixed bed horizontal reactor, the experiment investigates temperature, airflow rate, and particle size as input parameters. Results revealed that optimal condition of 900 °C temperature, 10 mL/min airflow rate, and 2 mm particle size yielded the highest syngas production at 39.48 vol%. The RSM showed an R2 value of 0.9896, whereas ANN network revealed an overall R2 value of 0.971. Both models demonstrated strong alignment with experimental data and the modelled equation. This research demonstrates the effective use of statistical modelling to enhance the efficiency and effectiveness of syngas production, thereby fostering advancements in sustainable energy production. [Display omitted] • PODC and AS co-gasification was carried out in fixed bed horizontal tube furnace reactor. • RSM and ANN were applied for optimization and prediction of syngas production. • At optimal condition of 900 °C, 10 mL/min, and 2 mm, 39.48 vol% syngas was produced. • RSM and ANN model prediction accuracy were 98.96% and 97.1%, respectively. • Both RSM and ANN satisfactory validate and predict the response. [ABSTRACT FROM AUTHOR]

AbstractThrough the increasing use of supplementary cementitious materials, the properties of concrete have taken on increased significance in a design code. Using reliable prediction models based on a small data set for the mechanical properties and durability of concrete can reduce the number of trial batches and experiments needed to produce useful design data in the laboratory, reducing time as well as resources. In this study, we investigate how the properties of water penetration, chlorine resistance, and compressive strength can be predicted by polynomial regression (PR), random forest (RF) regression, and artificial neural networks (ANNs) based on the input values of density, workability, and the constituent amount of rice husk ash, cement, and nano SiO2. We vary the training data used and test the coefficient of determination (R2 score) on the remaining data as a test set to measure predictive capability. We show that RFs and ANNs outperform PR in all settings and have unambiguously extrapolating properties when hyperparameter optimization is designed for this purpose. Remarkably, we obtain R2 scores on the test data of 0.858 − 0.990 for RFs and 0.825 − 0.985 for ANNs. [ABSTRACT FROM AUTHOR]

In this study, it is aimed to predict the performance of concretes obtained by using supplementary cementitious materials (SCM) before and after high temperature using artificial neural network. Thus, in addition to contributing to sustainable development and circular economy by using waste materials in concrete production, predicting concrete strength using artificial neural network without the need for experimental studies will provide a great advantage in practice. In addition, it will also contribute to the literature in terms of determining the optimum amount of metakaolin to be used with fly ash in concrete production. Metakaolin, silica fume and fly ash were used as SCM in different proportions in concrete mixes. Accordingly, a total of 22 concrete series were prepared, one of which was the control series. Porosity, ultrasonic pulse velocity, pressure and tensile strength tests were applied to the series at the end of 7th, 28th and 90th curing periods before high temperature. In order to determine the strength losses after elevated temperature, porosity and compressive strength tests were applied at temperatures of 400, 600 and 800 °C. Mineral additive series showed positive mechanical properties up to 20%. However, it has been observed that the use of fly ash after a certain rate causes a decrease in strength. After elevated temperature, strength loss was observed in all series due to the increase in temperature, while it was observed that the rate of being affected by elevated temperature decreased as the percentage of metakaolin increased. Optimum mineral additive usage percentages were determined as 10% fly ash and 15% metakaolin. On the other hand, the use of mineral additives above the optimum level caused the performance of the concrete to decrease. Then, the concrete compression strengths obtained at 7th, 28th, and 90th days and at 400, 600 and 800 °C temperatures are taken as the outputs of the ANN. The artificial neural network provided the closest results to experimental data. Moreover, to prove the predictive performance of ANN, a comparative analysis was made with GPR, SVM and LR and the smallest value of the RMSE value is obtained with the ANN model. Finally, a fivefold cross-validation criteria was used to objectively present the performance of the model. [ABSTRACT FROM AUTHOR]

ABSTRACT This study employs advanced data mining techniques to investigate the DASS‐42 questionnaire, a widely used psychological assessment tool. Administered to 680 students at Necmettin Erbakan University's Ahmet Kelesoglu Faculty of Education, the DASS‐42 comprises three distinct subscales—depression, anxiety and stress—each consisting of 14 items. Departing from traditional statistical methodologies, the study harnesses the power of the WEKA data mining program to analyse the dataset. Employing Naive Bayes (NB), Artificial Neural Network (ANN), Logistic Regression (LR), Support Vector Machine (SVM) and Random Forest (RF) algorithms, the research unveils novel insights. The ANN method emerges as a standout performer, achieving remarkable distinctiveness scores for all subscales: depression (99.26%), anxiety (98.67%) and stress (97.35%). The study highlights the potential of data mining in enhancing psychological assessment and showcases the ANN's prowess in capturing intricate patterns within complex psychological dimensions. By charting a course beyond conventional statistical methods, this research pioneers a new frontier for employing data mining within the realm of social sciences. As a result of the study, it is recommended that teacher candidates in the teacher education process should have knowledge about depression, anxiety and stress, and relevant courses on these topics should be added to the curriculum of teacher education programs. [ABSTRACT FROM AUTHOR]

BACKGROUND RESULTS CONCLUSION Strawberry is a rich source of antioxidants, including ascorbic acid (ASA) and polyphenols, which have numerous health benefits. Antioxidant content and activity are often determined manually using laboratory equipment, which is destructive and time‐consuming. This study constructs a prediction model for antioxidant compounds utilizing machine learning (ML) and multiple linear regression based on environmental, plant growth and agronomic fruit quality‐related parameters as well as antioxidant levels. These were studied in three farms at two‐week intervals during two years of cultivation.During the ML model screening, artificial neural network (ANN)‐boosted models displayed a moderate coefficient of determination (R2) at 0.68–0.78 and relative root mean square error (RRMSE) at 3.8–4.8% in polyphenols and total ASA levels, as well as a high R2 of 0.96 and low RRMSE at <3.0% in antioxidant activity. Additionally, we developed variable selection models regarding the antioxidant activity, and variables two and five (environmental parameters and leaf length, respectively) with high accuracy were selected. The linear regression analysis between the actual and predicted data of antioxidants in the ANN‐boosted models revealed high fitness with all parameters in almost all training, validation and test sets. Furthermore, environmental parameters are essential in developing such reliable models.We conclude that ANN‐boosted, stepwise and double‐Lasso regression models can predict antioxidant compounds with enhanced accuracy, and the relevant parameters can be easily acquired on‐site without the need for any specific equipment. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

A comprehensive model based on CFD modelling as well as AI/ML based modelling for an alkaline water electrolysis (AWE) cell is presented. A single cell 2D multiphase CFD model is solved in COMSOL Multiphysics 6.1® and is successfully validated with the experimental results for different operating conditions. The CFD model accurately computes the concentration and flow profiles of produced oxygen and hydrogen gases, the movement of the bubbles, and turbulence within the cell as well as the impact of current density, electrolyte flow rate and electrode-diaphragm distance. Further, integrating the CFD model with a neural network model enhances its potential for better cell design and performance. Multiple inputs and single output (MISO) artificial neural network (ANN) models are developed to predict the performance of the AWE cell. The ANN models are trained using the Levenberg-Marquardt algorithm, which operates as a feed-forward back-propagation network. The trained ANN models accurately predicts the complex relationships between input parameters (temperature, initial current density, and electrolyte weight concentration) and output parameters (actual current density and cell voltage) with an R2 value of 0.999 for both the outputs. This integrative CFD and ANN approach provides a comprehensive understanding of the AWE cell's behavior, further optimizing its design for efficient hydrogen production, offering a robust process that minimizes both computational resources and time as well as contributes for the scale up of the process. [Display omitted] • A comprehensive model based on CFD and ANN modeling for an alkaline water electrolysis (AWE) cell is presented. • The 2D multiphase CFD model is successfully validated with the experimental results for different operating conditions. • MISO models are developed to predict the performance of the AWE cell. • The trained ANN model was able to predict the input/output relationship with high accuracy. [ABSTRACT FROM AUTHOR]

The solar-driven high-temperature steam electrolysis is promising for efficient large-scale H 2 production. In this study, a comprehensive component-to-system model and optimization framework is developed to investigate the performance of a zero-emission H 2 production system based on solar power plant and protonic ceramic electrolysis cell. Compared to previous system studies, the detailed description of cell internal operating characteristics is realized by integrating multi-physics simulation and artificial neural network. After parametric analyses, it is found that the system energy/exergy efficiency and co-generation performance are complicated by each subsystem. And the optimal system performance (η th = 50.63 %, Z = 179.63 $·h−1 and η ex = 33.03 %, Z = 178.94 $·h−1, with LCOE = 0.172 $·kWh−1 and Z H2 = 6.497 $·kg−1) is obtained considering cell operating features and system energy-exergy-economic factors through multi-objective optimizations. Besides, the tradeoff between system maximum H 2 production capacity and cell internal thermal conditions is revealed. This study can facilitate the development of zero-emission green H 2 production driven by renewable energy. • A novel SPT-PCEC based integrated system is proposed for green H 2 production. • Both PCEC state and system energy-exergy-economic performance are considered. • Effects of each subsystem parameters on system performance are analyzed. • Optimal system performance under different constraints is obtained. • The tradeoff between system H 2 capacity and PCEC thermal conditions is revealed. [ABSTRACT FROM AUTHOR]

Salt marshes provide diverse habitats for a wide range of creatures and play a key defensive and buffering role in resisting extreme marine hazards for coastal communities. Accurately obtaining the terrains of salt marshes is crucial for the comprehensive management and conservation of coastal resources and ecology. However, dense vegetation coverage, periodic tide inundation, and pervasive ditch distribution create challenges for measuring or estimating salt marsh terrains. These environmental factors make most existing techniques and methods ineffective in terms of data acquisition resolution, accuracy, and efficiency. Drone multi-line light detection and ranging (LiDAR) has offered a fire-new perspective in the 3D point cloud data acquisition and potentially exhibited great superiority in accurately deriving salt marsh terrains. The prerequisite for terrain characterization from drone multi-line LiDAR data is point cloud filtering, which means that ground points must be discriminated from the non-ground points. Existing filtering methods typically rely on either LiDAR geometric or intensity features. These methods may not perform well in salt marshes with dense, diverse, and complex vegetation. This study proposes a new filtering method for drone multi-line LiDAR point clouds in salt marshes based on the artificial neural network (ANN) machine learning model. First, a series of spatial–spectral features at the individual (e.g., elevation, distance, and intensity) and neighborhood (e.g., eigenvalues, linearity, and sphericity) scales are derived from the original data. Then, the derived spatial–spectral features are selected to remove the related and redundant ones for optimizing the performance of the ANN model. Finally, the reserved features are integrated as input variables in the ANN model to characterize their nonlinear relationships with the point categories (ground or non-ground) at different perspectives. A case study of two typical salt marshes at the mouth of the Yangtze River, using a drone 6-line LiDAR, demonstrates the effectiveness and generalization of the proposed filtering method. The average G-mean and AUC achieved were 0.9441 and 0.9450, respectively, outperforming traditional geometric information-based methods and other advanced machine learning methods, as well as the deep learning model (RandLA-Net). Additionally, the integration of spatial–spectral features at individual–neighborhood scales results in better filtering outcomes than using either single-type or single-scale features. The proposed method offers an innovative strategy for drone LiDAR point cloud filtering and salt marsh terrain derivation under the novel solution of deeply integrating geometric and radiometric data. [ABSTRACT FROM AUTHOR]

Three-phase induction motors play a key role in industrial operations. However, their failure can result in serious operational problems. This study focuses on the early identification of faults through the accurate diagnosis and classification of faults in three-phase induction motors using artificial intelligence techniques by analyzing current, temperature, and vibration signals. Experiments were conducted on a test bench, simulating real operating conditions, including stator phase unbalance, bearing damage, and shaft unbalance. To classify the faults, an Auto-Regressive Neural Network with Exogenous Inputs (NARX) was developed. The parameters of this network were determined through a process of selecting the best network by using the scanning method with multiple training and validation iterations with the introduction of new data. The results of these tests showed that the network exhibited excellent generalization across all evaluated situations, achieving the following accuracy rates: motor without fault = 94.2 %, unbalanced fault = 95%, bearings with fault = 98%, and stator with fault = 95%. [ABSTRACT FROM AUTHOR]

The quality of the refill friction stir spot welding (RFSSW) process is heavily dependent on the selected welding parameters that influence the resultant joint characteristics. Thermomechanical phenomena integral to the process were investigated using finite element (FE) analysis on two dissimilar materials. This FE analysis was subsequently validated through controlled experiments to ensure reliability. An artificial neural network (ANN) was employed to create a neural model based on an experimental setup involving 120 different sets of welding parameters. The parameters adjusted in the experimental plan included pin penetration depth, rotational speed, retention time, and positioning relative to material hardness. To assess the neural model's accuracy, outputs such as maximum temperature and normal stress at the end of the welding process were analyzed and validated by six data sets selected for their uniform distribution across the training domain. [ABSTRACT FROM AUTHOR]

AbstractThe mass transfer performance of isopropanol (IPA) absorption by triethylene glycol (TEG) aqueous solution in a spray-bed absorber was investigated, and the mass transfer coefficients predicted by an artificial neural network (ANN) and response surface methodology were compared in this study. The operating variables affecting the mass transfer performance of a spray-bed absorber include the gas and TEG fluxes, concentration of IPA, concentration of TEG aqueous solution, and operating temperature. The experimental results demonstrated that the mass transfer coefficient of a spray-bed absorber increased with increases in gas and liquid fluxes and decreased with increases in concentration of IPA. The Back-Propagation algorithm in the ANN model was selected to predict the mass transfer coefficients of the spray–bed absorption system. Hidden layers with 2, 4 and 6 neurons respectively were set to analyze the effect of the number of neurons on the target value, and then different transfer functions were used to compare the accuracy in predicting mass transfer coefficient. Finally, the influences of the number of hidden layers and different learning rules on the prediction of mass transfer coefficients were examined. The simulated results showed that the average error of the ANN using the TanhAxon transfer function, the Conjugate Gradient learning rule, two hidden layers, and 6 neurons in each hidden layer was within 1%. The results not only verified the successful establishment of the experimental device but also proved the accurate prediction of mass transfer coefficients by the ANN model. [ABSTRACT FROM AUTHOR]

This study evaluates different input variable selection (IVS) methods for precise daily precipitation estimation using artificial neural network (ANN) models. The effectiveness of the models is measured using the average Nash-Sutcliffe efficiency (NSE) from 50 runs per model. Initially, a multiple linear regression and a base model with all 16 parameters resulted in test NSE scores of 0.390 and 0.656, respectively. Subsequently, the self-organizing map (SOM), analytic hierarchy process, K-nearest neighbour, and K-means methods were used to select input variables of different types. The SOM method achieved its best performance with a test NSE of 0.692 when four variables were selected, while the other methods reached their peak performance with the same NSE of 0.718 when two similar variables were selected. This highlights the importance of IVS methods in using ANN models to overcome overfitting and achieve an efficient model for precise precipitation estimation. [ABSTRACT FROM AUTHOR]

Optical synapses offer a promising solution to the high energy consumption of von Neumann architectures. Despite significant research, existing photoconductivity modulation methods are typically unidirectional, and inhibitory behavior still depends on electrical stimulation. To address this, a two‐terminal planar fully optically modulated synapse device based on a ZnAlSnO/SnS heterostructure, demonstrating bidirectional optical response is presented. This device exhibits an excitatory postsynaptic current (EPSC) when exposed to 370 nm UV light and generates an inhibitory postsynaptic current (IPSC) under 630 nm red light. Continuous potentiation and depression stimuli reveals the stability of fully optically modulated artificial synapses. Leveraging its fully optically modulated conductance, a three‐layer artificial neural network is implemented for handwritten digit and clothing recognition, achieving accuracies of 91.12% and 78.22%, respectively. Additionally, based on its unique electrical response to UV light pulse, the development process of the Polaroid camera is well simulated. This work not only enriches the content of optical synapses, but also contributes to advancements in artificial intelligence, brain‐like computing, and image‐processing technologies. [ABSTRACT FROM AUTHOR]