Search Results (626)

The cross-sectional dimensions of structural elements in a structure are design elements that need to be carefully designed and are related to the stiffness of the structure. Various optimization processes are applied to determine the optimum cross-sectional dimensions of beams or columns in structures. By repeating the optimization processes for multiple load scenarios, it is possible to create a data set that shows the optimum design section properties. However, this step means repeating the same processes to produce the optimum cross-sectional dimensions. Artificial intelligence technology offers a short-cut solution to this by providing the opportunity to train itself with previously generated optimum cross-sectional dimensions and infer new cross-sectional dimensions. By processing the data, the artificial neural network can generate models that predict the cross-section for a new structural element. In this study, an optimization process is applied to a simple tubular column and an I-section beam, and the results are compiled to create a data set that presents the optimum section dimensions as a class. The harmony search (HS) algorithm, which is a metaheuristic method, was used in optimization. An artificial neural network (ANN) was created to predict the cross-sectional dimensions of the sample structural elements. The neural architecture search (NAS) method, which incorporates many metaheuristic algorithms designed to search for the best artificial neural network architecture, was applied. In this method, the best values of various parameters of the neural network, such as activation function, number of layers, and neurons, are searched for in the model with a tool called HyperNetExplorer. Model metrics were calculated to evaluate the prediction success of the developed model. An effective neural network architecture for column and beam elements is obtained. Full article

Monitoring pH and acidity during yoghurt fermentation is essential for product quality and process efficiency. Conventional measurement methods, however, are invasive and labour-intensive. This study developed artificial neural network (ANN) models to predict pH and titratable acidity during yoghurt fermentation using CIELAB colour parameters (L, a*, b*). Reconstituted milk powder with 12% total solids was prepared with varying protein levels (4.2–4.8%), inoculum concentrations (1–3%), and fermentation temperatures (36–44 °C). Data were collected every 10 min until pH 4.6 was reached. Forty models were trained for each output variable, using 90% of the data for training and 10% for validation. The first two phases of the fermentation process were clearly distinguishable, lasting between 4.5 and 7 h and exceeding 0.6% lactic acid in all treatments evaluated. The best pH model used two hidden layers with 28 neurons (R² = 0.969; RMSE = 0.007), while the optimal acidity model had four hidden layers with 32 neurons (R² = 0.868; RMSE = 0.002). The strong correlation between colour and physicochemical changes confirms the feasibility of this non-destructive approach. Integrating ANN models and colourimetry offers a practical solution for real-time monitoring, helping improve process control in industrial yoghurt production. Full article

The process of geothermal energy development may cause induced seismic activities, posing a potential threat to the sustainable utilization and safety of geothermal energy. To effectively evaluate the danger of induced seismic activities, this paper establishes an artificial neural network model and selects nine influencing factors as the input parameters of the neurons. Based on the results of induced seismic activity under different parameter conditions, a sensitivity analysis is conducted for each parameter, and the influence degree of each parameter on the magnitude of induced seismic activity is ranked from largest to smallest as follows: in situ stress state, fault presence or absence, depth, degree of fracture aggregation, maximum in situ stress, distance to fault, injection volume, fracture dip angle, angle between fracture, and fault. Then, the weights of each parameter in the model are modified to improve the accuracy of the model. Finally, through data collection and the literature review, the Pohang EGS project in South Korea is analyzed, and the induced seismic activity influencing factors of the Pohang EGS site are analyzed and evaluated using the induced seismic activity evaluation model. The results show that the induced seismicity are all located below 3.7 km (drilling depth). As the depth increases, the seismicity magnitude also shows a gradually increasing trend. An increase in injection volume and a shortening of the distance from faults will also lead to an increase in the seismicity magnitude. When the injection volume approaches 10,000 cubic meters, the intensity of the seismic activity sharply increases, and the maximum magnitude reaches 5.34, which is consistent with the actual situation. This model can be used for the induced seismic evaluation of future EGS projects and provide a reference for project site selection and induced seismic risk warning. Full article

Investigating brain area functions requires advanced technologies, but meaningful insights depend on correlating neural signals with behavior. Traditional mice behavior annotation methods, including manual and semi-automated approaches, are limited by subjectivity and time constraints. To overcome these limitations, our study employs the YOLO neural network for precise mice tracking and composite RGB frames for behavioral scoring. Our model, trained on over 10,000 frames, accurately classifies sitting, running, and grooming behaviors. Additionally, we provide statistical metrics and data visualization tools. We further combined AI-powered behavior labeling to examine hippocampal neuronal activity using fluorescence microscopy. To analyze neuronal circuit dynamics, we utilized a manifold analysis approach, revealing distinct functional patterns corresponding to transgenic 5xFAD Alzheimer’s model mice. This open-source software enhances the accuracy and efficiency of behavioral and neural data interpretation, advancing neuroscience research. Full article

Laser-induced breakdown spectroscopy (LIBS) has played a critical role in Mars exploration missions, substantially contributing to the geochemical analysis of Martian surface substances. However, the complex nonlinearity of LIBS processes can considerably limit the quantification accuracy of conventional LIBS chemometric methods. Hence chemometrics based on artificial neural network (ANN) algorithms have become increasingly popular in LIBS analysis due to their extraordinary ability in nonlinear feature modeling. The hidden layer activation functions are key to ANN model performance, yet common activation functions usually suffer from problems such as gradient vanishing (e.g., Sigmoid and Tanh) and dying neurons (e.g., ReLU). In this study, we propose a novel LIBS quantification method, named the Bayesian optimization-based tunable Softplus backpropagation neural network (BOTS-BPNN). Based on a dataset comprising 1800 LIBS spectra collected by a laboratory duplicate of the MarSCoDe instrument onboard the Zhurong Mars rover, we have revealed that a BPNN model adopting a tunable Softplus activation function can achieve higher prediction accuracy than BPNN models adopting other common activation functions if the tunable Softplus parameter β is properly selected. Moreover, the way to find the proper β value has also been investigated. We demonstrate that the Bayesian optimization method surpasses the traditional grid search method regarding both performance and efficiency. The BOTS-BPNN model also shows superior performance over other common machine learning models like random forest (RF). This work indicates the potential of BOTS-BPNN as an effective chemometric method for analyzing Mars in situ LIBS data and sheds light on the use of chemometrics for data analysis in future planetary explorations. Full article

The accurate estimation of the higher heating value (HHV) of wood biomass is essential to evaluating the latter’s energy potential as a renewable energy material. This study proposes an Artificial Neural Network (ANN) model to predict the HHV by using proximate analysis parameters—moisture, volatile matter, ash, and fixed carbon. A dataset of 252 samples (177 for training and 75 for testing), sourced from the Phyllis database, which compiles the physicochemical properties of lignocellulosic biomass and related feedstocks, was used for model development. Various ANN architectures were explored, including one to three hidden layers with 1 to 20 neurons per layer. The best performance was achieved with the 4–11–11–11–1 architecture trained using the backpropagation algorithm, yielding an adjusted R² of 0.967 with low mean absolute error (MAE) and root mean squared error (RMSE) values. A graphical user interface (GUI) was developed for real-time HHV prediction across diverse wood types. Furthermore, the model’s performance was benchmarked against 26 existing empirical and statistical models, and it outperformed them in terms of accuracy and generalization. This ANN-based tool offers a robust and accessible solution for carbon utilization strategies and the development of new energy storage material. Full article

Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has proven to be an efficient way of solving time series problems. This study employs a multilayer perceptron network with backpropagation training and Bayesian regularisation to enhance generalisation and minimise overfitting errors. The research aggregates real consumption data from 200 households and 348 electric vehicles. The developed method was validated using MAPE, which resulted in errors below 6%. Short-term forecasts were made across the four seasons, predicting the total aggregate demand of households and vehicles for the next 24 h. The methodology produced significant and relevant results for this problem using hybrid training, a few-neuron architecture, deep learning, fast convergence, and low computational cost, with potential for real-world application. The results support the electrical power system by optimising these loads, reducing costs and energy generation, and preparing a new scenario for EV penetration rates. Full article

The study of neuronal electrical activity and spatial organization is essential for uncovering the mechanisms that regulate neuronal electrophysiology and function. Mathematical models have been utilized to analyze the structural properties of neuronal networks, predict connectivity patterns, and examine how morphological changes impact neural network function. In this study, we aimed to explore the role of the actin cytoskeleton in neuronal signaling via primary cilia and to elucidate the role of the actin network in conjunction with neuronal electrical activity in shaping spatial neuronal formation and organization, as demonstrated by relevant mathematical models. Our proposed model is based on the polygamma function, a mathematical application of ramification, and a geometrical definition of the actin cytoskeleton via complex numbers, ring polynomials, homogeneous polynomials, characteristic polynomials, gradients, the Dirac delta function, the vector Laplacian, the Goldman equation, and the Lie bracket of vector fields. We were able to reflect the effects of neuronal electrical activity, as modeled by the Van der Pol equation in combination with the actin cytoskeleton, on neuronal morphology in a 2D model. In the next step, we converted the 2D model into a 3D model of neuronal electrical activity, known as a core-shell model, in which our generated membrane potential is compatible with the neuronal membrane potential (in millivolts, mV). The generated neurons can grow and develop like an organoid brain based on the developed mathematical equations. Furthermore, we mathematically introduced the signal transduction of primary cilia in neurons. Additionally, we proposed a geometrical model of the neuronal branching pattern, which we described as ramification, that could serve as an alternative mathematical explanation for the branching pattern emanating from the neuronal soma. In conclusion, we highlighted the relationship between the actin cytoskeleton and the signaling processes of primary cilia. We also developed a 3D model that integrates the geometric organization unique to neurons, which contains soma and branches, such that the mathematical model represents the interaction between the actin cytoskeleton and neuronal electrical activity in generating action potentials. Next, we could generalize the model into a cluster of neurons, similar to an organoid brain model. This mathematical framework offers promising applications in artificial intelligence and advancements in neural networks. Full article

The thermal conductivity of steel is high compared to other materials such as concrete or timber. Therefore, fire protection measures are applied to prolong the duration between the onset of fire exposure and the final loss of load-bearing function of a steel structure. The most common passive fire protection measure is the application of intumescent coating (IC), a thin film that expands at elevated temperatures and forms an insulating char layer of lower thermal conductivity. This paper focuses on structural steel beams with IPE open-section profiles protected by a water-based IC and subjected to static and standard fire loading. ANSYS 16.0 is used to simulate heat transfer, with thermal conductivity function described by standard multivariate linear regression analysis, followed by mechanical analysis considering degradation of material mechanical properties at elevated temperatures. Simulations are conducted for all IPE profile sizes, with varying initial degrees of utilisation, beam lengths, and coating thicknesses. Results indicated fire resistance times ranging from 24 to 53.5 min, demonstrating a relatively good level of fire resistance even with the minimal IC thickness. Furthermore, artificial neural networks were developed to predict the fire resistance time of steel members with IC using varying numbers of hidden neurons and subset ratios. The model achieved a predictability level of 99.9% upon evaluation. Full article

Metal-organic frameworks (MOFs) have been extensively studied for hydrogen storage due to their unique properties. This paper aims to develop several regression-based machine learning models to predict the hydrogen storage capacity of MOFs, including artificial neuron network (ANN), support vector regression (SVR), random forest (RF), extreme learning machine (ELM), kernel extreme learning machine (KELM), and generalized regression neural network (GRNN). An improved population-based metaheuristic optimization algorithm, the artificial lemming algorithm (ALA), is employed to select the hyperparameters of these machine learning models, enhancing their performance. All developed models are trained and tested using experimental data from multiple studies. The performance of the models is evaluated using various statistical metrics, complemented by regression plots, error analysis, and Taylor graphs to further identify the most effective predictive model. The results show that the ALA-RF model obtains the best performance in predicting hydrogen storage, with optimal values of coefficient of determination (R²), root mean square error (RMSE), Willmott’s index (WI), and weighted average percentage error (WAPE) in both training and testing phases (0.9845 and 0.9840, 0.2719 and 0.2828, 0.9961 and 0.9959, and 0.0667 and 0.0714, respectively). Additionally, pressure is identified as the most significant feature for predicting hydrogen storage in MOFs. These findings provide an intelligent solution for the selection of MOFs and optimization of operational conditions in hydrogen storage processes. Full article

Reinforced concrete structures often require retrofitting due to damage caused by natural disasters such as earthquakes, floods, or hurricanes; deterioration from aging; or exposure to harsh environmental conditions. Retrofitting strategies may involve adding new structural elements like shear walls, dampers, or base isolators, as well as strengthening the existing components using methods such as reinforced concrete, steel, or fiber-reinforced polymer jacketing. Selecting the most appropriate retrofit method can be complex and is influenced by various factors, including initial cost, long-term maintenance, environmental impact, and overall sustainability. This study proposes utilizing an artificial neural network (ANN) to predict sustainable and cost-effective seismic retrofit solutions. By training the ANN with a comprehensive dataset that includes jacket thickness, material specifications, reinforcement details, and key sustainability indicators (economic and environmental factors), the model was able to recommend optimized retrofit designs. These designs include ideal values for jacket thickness, concrete strength, and the configuration of reinforcement bars, aiming to minimize both costs and environmental footprint. A major focus of this research was identifying the optimal number of neurons in the hidden layers of the ANN. While the number of input and output neurons is defined by the dataset, determining the right configuration for hidden layers is critical for performance. The study found that networks with one or two hidden layers provided more reliable and efficient results compared to more complex architectures, achieving a total regression value of 0.911. These findings demonstrate that a well-tuned ANN can serve as a powerful tool for designing sustainable seismic retrofit strategies, helping engineers make smarter decisions more quickly and efficiently. Full article

The present research proposes an Artificial Neural Network (ANN) model to predict the critical buckling load of six different types of metallic aerospace grid-stiffened panels: isogrid type I, isogrid type II, bi-grid, X-grid, anisogrid, and waffle, all subjected to compressive loading. Six thousand samples (one thousand per panel type) were generated using the Latin Hypercube Sampling method to ensure a diverse and comprehensive dataset. The ANN model was systematically fine-tuned by testing various batch sizes, learning rates, optimizers, dense layer configurations, and activation functions. The optimized model featured an eight-layer architecture (200/100/50/25/12/6/3/1 neurons), used a selu–relu–linear activation sequence, and was trained using the Nadam optimizer (learning rate = 0.0025, batch size = 8). Using regression metrics, performance was benchmarked against classical machine learning models such as CatBoost, XGBoost, LightGBM, random forest, decision tree, and k-nearest neighbors. The ANN achieved superior results: MSE = 2.9584, MAE = 0.9875, RMSE = 1.72, and R² = 0.9998, significantly outperforming all other models across all metrics. Finally, a Taylor Diagram was plotted to assess the model’s reliability and check for overfitting, further confirming the consistent performance of the ANN model across both training and testing datasets. These findings highlight the model’s potential as a robust and efficient tool for predicting the buckling strength of metallic aerospace grid-stiffened panels. Full article

This study proposes Resource-Adaptive Differential Evolution (RADE), a novel optimization algorithm for training lightweight and interpretable dendritic neuron models (DNMs) in classification tasks. RADE introduces dynamic population partitioning, poor-individual-guided mutation, adaptive parameter control, and lightweight archiving to achieve efficient and robust learning. Inspired by biological and algorithmic symmetry, RADE leverages structural and behavioral balance in both the evolutionary process and the DNM architecture. DNMs inherently exhibit symmetric processing through multiple dendritic branches that independently and equivalently aggregate localized inputs. RADE preserves and enhances this structural symmetry by promoting balanced learning dynamics and pruning redundant dendritic components, leading to compact and interpretable neuron morphologies. Extensive experiments on real-world and synthetic datasets demonstrate that RADE consistently outperforms existing methods in terms of classification accuracy, convergence stability, and model compactness. Furthermore, the resulting neuron structures can be mapped to logical circuits, making the RADE-DNM highly suitable for neuromorphic and edge computing applications. This work highlights the synergistic role of symmetry in achieving resource-efficient and transparent artificial intelligence. Full article

Telemedicine has emerged as a vital tool for expanding healthcare access, particularly in underserved areas, yet its effectiveness is often hindered by inefficient queuing systems, fluctuating patient demand, and limited resources. This study addresses these challenges by developing a hybrid Artificial Neural Network–Particle Swarm Optimization (ANN-PSO) model aimed at improving the performance of telemedicine queuing systems. A simulation-based dataset was generated to represent patient arrivals, service rates, and queuing behaviors. An ANN was trained to predict key performance metrics, including queue intensity, system utilization, and delays. To further enhance the model’s predictive capabilities, PSO was applied to optimize critical ANN parameters, such as neuron count, swarm size, and acceleration factors. The optimized ANN-PSO model achieved high predictive accuracy, with correlation coefficients (R²) consistently exceeding 0.90 and low mean squared errors across most outputs. The findings show that optimal parameter configurations vary depending on the specific performance metric, reinforcing the value of adaptive optimization. The results confirm the ANN-PSO model’s ability to accurately predict queuing behavior and optimize system performance, providing a practical decision-support tool for telemedicine administrators to dynamically manage patient flow, reduce waiting times, and enhance resource utilization under variable demand conditions. Full article

This study investigates the synthesis of biodiesel from three vegetable oils with significantly different chemical compositions. Based on the properties of these biodiesel samples, a method was proposed to estimate the density of petrodiesel–biodiesel blends using an artificial neural network (ANN). The ANN employed in this research consisted of 10 neurons. The experimental data showed a high correlation, indicating effective training and precise estimations in relation to the provided training data. The accuracy of the estimations was evaluated by comparing the blending densities determined through the method presented in this study with the mean of three estimations generated by the neural network. The deviation between the determined and estimated values ranged from 4.1 to 25.2 kg/m³, which is attributable to the limited size of the training database. Most errors fell between −7.1% and 3.8%, with the lowest error being observed for petrodiesel–Brassica carinata biodiesel blends. Excellent correlations for both training and validation data were obtained (R = 0.99 and R = 0.98) for blends incorporating palm and Brassica carinata biodiesel. The estimation method using neural networks proposed in this paper can be effectively adapted for other mixtures and to estimate additional blending properties, accommodating each user’s needs. Full article