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Automatic determination of manufacturing process sequences for the physical production of given part designs is key to facilitate on-demand cyber manufacturing. In this work, we propose an integrated framework that (i) identifies manufacturing features from 3D part designs using a Graph Neural Network (GNN), (ii) identifies the manufacturing processes necessary to produce all features in the part using a Convolutional Neural Network (CNN) that considers shape, material properties, and quality information, and (iii) outputs an ordered manufacturing sequence that can produce the designed part with the help of sequence mining. Using these methods, the knowledge required to enable automated manufacturing process selection is easily scalable and updatable without requiring manual population of ad-hoc or rule-based descriptions. We present exemplar implementations of the proposed framework by suggesting manufacturing sequences for discrete parts with multiple features. The suggested manufacturing sequences demonstrate the potential of the proposed framework for use in future on-demand cyber manufacturing applications. [ABSTRACT FROM AUTHOR]

Objective.Artificial neural networks (ANNs) are state-of-the-art tools for modeling and decoding neural activity, but deploying them in closed-loop experiments with tight timing constraints is challenging due to their limited support in existing real-time frameworks. Researchers need a platform that fully supports high-level languages for running ANNs (e.g. Python and Julia) while maintaining support for languages that are critical for low-latency data acquisition and processing (e.g. C and C++).Approach.To address these needs, we introduce the Backend for Realtime Asynchronous Neural Decoding (BRAND). BRAND comprises Linux processes, termednodes, which communicate with each other in agraphvia streams of data. Its asynchronous design allows for acquisition, control, and analysis to be executed in parallel on streams of data that may operate at different timescales. BRAND uses Redis, an in-memory database, to send data between nodes, which enables fast inter-process communication and supports 54 different programming languages. Thus, developers can easily deploy existing ANN models in BRAND with minimal implementation changes.Main results.In our tests, BRAND achieved

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 research aims to see how well back-propagation neural network (BPNN) approaches perform in estimating peak ground acceleration in the Himalayan region using various forms of input data (i.e., magnitude, focal depth, hypocentre and S-wave avg. velocity). These criteria were derived from the Himalayan Earthquake Catalogue, which includes all minor and significant earthquakes and their aftershock sequences in prior years. The analysis of high-quality seismological recorded data is important for earthquake prediction. The BPNN is employed for predicting the strong ground motion parameters such as peak ground acceleration, peak ground velocity and peak ground displacement using different activation functions at different layers. This research paper focuses on the model's training to analyze the dataset, including a few parameters to estimate the acceleration of the ground. Translated data into seismicity indicators, which are mathematically derived parameters. These seismicity indicators were utilized for the training of the BPNN to improve decision-making and for peak ground acceleration estimation. Artificial neural network models were used to predict the ground motion intensity measures for future events. The predictive parameters' contributions to the prediction of the considered intensity measurements are finally explored via sensitivity analysis. This study uses the multi-layer BPNN model to discover components in the Himalayan region, using the actual incidence of earthquake seismicity indicators as input and goal vectors. Finally, the BPNN model produces a good estimate for the peak ground acceleration of earthquakes. [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]

The study aimed to compare the development of an artificial neural network (ANN) and multilinear regression (MLR) model used to predict the performance of biogas in a batch-mode underground fixed dome biogas digester. In this study, 50 experimental datasets were used to assess the rate of biogas production with developed ANN and MLR models. The six variables, including solar irradiance, relative humidity, slurry temperature, biogas temperature, pH, and ambient temperature, were selected as the input parameters or predictors of the model. Therefore, the developed ANN and MLR models were used to describe the rate of biogas yield. The study found that the determination coefficient (R2) and root mean square error (RMSE) for ANN and MLR were 0.999/0.968 and 8.33 × 10−6/1.84 × 10−4, respectively. Both models were significant because of their high correlation between measured and predicted values of the biogas yield. However, the ANN performs better because of the smaller RMSE and higher R2 derived compared to the corresponding values of the MLR. The study proved that both the ANN and MLR can accurately predict the rate of biogas production but with better predictions obtained from ANN. [ABSTRACT FROM AUTHOR]

The AM60B magnesium alloy remains a pivotal player in the pursuit of lighter, biodegradable and more sustainable solutions for various biomedical implants because of its excellent machinability, exceptional strength and resistance to degradation. However, AM60B magnesium alloys exhibit poor machinability when machined by conventional methods, since they are susceptible to deformation and degradation at elevated temperatures. The current work focused on multiresponse optimization for AM60B magnesium alloys for wire electrical discharge machining (WEDM), where multiple output variables including the machining rate, surface irregularity, and microindentation hardness, were considered simultaneously. Response surface methodology (RSM) along with artificial neural network (ANN) were utilized to investigate the effect of these parameters on the control of these output characteristics. The findings demonstrated the best combination of input specifications, with a discharge duration of 112.743 µs, a spark gap time of 57.6532 µs, a discharge voltage of 6.63 V and a wire advance rate of 5.39357 mm/min which yielded the best surface irregularity, microindentation hardness, and machining rate from the RSM. ANN and RSM models were effective in simulating the experiments, with predicted values closely aligning with the experimental results. An optimally trained network model exhibited good agreement with a mean error less than 5%. Additionally, the condition of the machined surface, including any cracks, voids, or other flaws, was examined using scanning electron microscope (SEM). [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]

The grading of products is important in many ways. One of the important activities after harvesting agricultural products is product grading based on shape and color dimensions. This activity in the agricultural transformation industries, Bas Controller, improves various processes on fruits and vegetables with the same dimensions, which improves the storage conditions of the product, creates added value for the farmer, and gives the consumer the power to choose. The main focus of this study is the application of image processing in the field of identification and classification of fruits. It is an application that has received much less attention than other applications of image processing. The proposed systems presented in this article, are software solutions based on image processing techniques, including histogram matching techniques, for detection, Sable edge detection algorithms, Private edge and Kenny edge, Otsu threshold limit, and clustering method It is an automatic mean and classification of different degrees of fruit. In addition, it has been mentioned more about the examination and description of product grading and clustering methods, that by using the proposed application hardware and its connection with the software, a big step can be taken in product quality grading. This method can be used in product classification and packaging. The accuracy rate for peaches, lemons, apples, and tomatoes is 94.58%, 88.23%, 70%, and 93.33%, respectively. The best accuracy for all 20 sample levels is for peach fruit. [ABSTRACT FROM AUTHOR]

Photovoltaic (PV) systems offer cost-effective power solutions for outlying islands but often compromise system stability due to reduced inertia. This study introduces a Virtual Synchronous Generator (VSG) control strategy, integrated with Energy Storage Systems (ESS) and PV, to enhance system inertia. By optimizing coordination between these energy sources, the proposed method mitigates oscillations and improves grid stability. However, PV-VSG systems are generally not favored by energy providers due to the requirement for pre-curtailment of power output. To address this, the paper proposes a parameter design method for VSG control of ESS and PV, utilizing multi-objective genetic algorithm (MOGA) optimization to simultaneously increase the frequency nadir and minimize the settling time after disturbances. Additionally, an adaptive curtailment decision and parameter design method based on artificial neural networks is introduced to enhance the feasibility of PV-VSG systems by reducing PV pre-curtailment and prioritizing PV power release and ESS charging during frequency oscillations. Real data from the Penghu Archipelago in Taiwan are used to build a dynamic model in DIgSILENT, enabling interaction with MOGA. The Value at Risk (VaR) method with dual stochastic variables is employed to assess the allowable PV installed capacity. The results show that when VaR is set at 1%, the proposed PV-VSG method can increase PV penetration by 57.5% compared to scenarios without VSG. Furthermore, compared to traditional PV-VSG methods, the proposed approach achieves a 16.8% increase in PV penetration and reduces annual PV curtailment by 25 MWh. This study also evaluates the economic impact of planners choosing different risk levels, offering valuable insights for grid development in remote or island regions. [ABSTRACT FROM AUTHOR]

This article explores the potential uses of artificial intelligence (AI) in managing lung cancer. AI algorithms, such as machine learning and deep learning, have shown promise in detecting and characterizing lung nodules, improving screening and diagnosis. AI models can also predict treatment responses and guide therapy selection, optimize radiation therapy, and assist in bronchoscopy and thoracic surgery. However, the introduction of AI in healthcare raises ethical and legal concerns, such as patient autonomy, bias, and liability. More research is needed to evaluate the effectiveness of AI-assisted techniques and their impact on patient outcomes. Overall, integrating AI into clinical practice has the potential to improve patient outcomes and redefine modern medicine. [Extracted from the article]

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]