Search Results (6,169)

This study presents the quantitative estimation of the effect of temperature on the mechanical properties of 5Cr-0.5Mo steels using an artificial neural network (ANN) model. The developed ANN model predicts yield strength (YS, MPa), ultimate tensile strength (UTS, MPa), elongation (El, %), and reduction in area (RA, %) at different service temperatures. Predictions were validated against experimental data at critical temperatures of 450 °C and 700 °C and found to show high accuracy. Predicted results show minimal errors of 3.84%, 2.3%, 2.2%, and 0.42% for YS, UTS, El, and RA, respectively at 450 °C, and 3.7%, 0.45%, 1.88%, and 0.19%, respectively at 700 °C. Furthermore, ten-fold cross-validation confirmed the generalization capability of the developed model, yielding high coefficients of determination and correlation coefficients together with low normalized prediction errors across all output variables. Despite the absence of explicit metallurgical descriptors, the ANN model successfully quantified the influence of temperature from 25 to 700 °C, demonstrating its effectiveness as a predictive tool for high-temperature Cr–Mo steels. Furthermore, a user-friendly graphical interface was developed to facilitate rapid property estimation, demonstrating the potential of the framework as a supportive tool for the preliminary assessment of high-temperature Cr–Mo steels. Full article

Hot compression experiments were performed to characterize the high-temperature deformation behavior of CoNiV medium-entropy alloy (MEA). Hot compression tests were carried out using a Gleeble-3500 thermomechanical simulator over strain rates of 0.001 s⁻¹ to 1 s⁻¹ and temperatures ranging between 950 °C and 1100 °C. Based on the experimentally determined hot compression data, three models for predicting the flow stress of CoNiV MEA were established: the Zerilli-Armstrong (Z-A) constitutive model, an artificial neural network (ANN) model, and a gated recurrent unit (GRU) model. This study comprehensively evaluated the prediction accuracy of each model using the coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE). The results show that, compared with the other two models, the Z-A model cannot accurately predict the flow behavior of CoNiV MEA in the studied hot-working regime. The R² value of the ANN model is 0.98974, while the GRU model exhibits the highest predictive capability, with an R² value of 0.98981, an MAE of 6.29621, and an RMSE of 13.10832. The proposed model demonstrates superior prediction accuracy compared with other models, enabling high-precision characterization of the high-temperature evolution of the flow stress in the CoNiV MEA. This study provides a theoretical foundation for the design and optimization of hot working parameters for the CoNiV MEA. Full article

Plate–fin heat exchangers (PFHEs) are widely used in petrochemical, energy and electric power, aerospace, and other industries with large heat transfer requirements. The development of performance prediction and optimization methods for PFHEs has become increasingly important in the design and operation of such heat exchangers (HEs). This paper establishes a database of flow and heat transfer characteristics for four types of PFHEs with different structural parameters. Based on this database, the back-propagation artificial neural network (BP-ANN) model was optimized using the particle swarm optimization (PSO) algorithm to form the PSO-BP-ANN model for the performance prediction of these four types of PFHEs. This combination has been found to improve the prediction accuracy and generalization ability of the BP-ANN model. Additionally, the non-dominated sorting genetic algorithm II (NSGA-II) method was used to characterize the relationship between four structural parameters to be optimized (the length, height, spacing, and thickness of the HE fin) and the two objective functions (j and f) of the serrated PFHE in laminar flow. This enables the Pareto optimal solution to be obtained. The results show that, under laminar flow conditions (Re = 800), the serrated fin HE achieves the best heat transfer performance when the fin height, spacing, thickness, and length are 9.29, 1.22, 0.16, and 3.06, respectively. Full article

Hellenic Open University has developed Onlabs, a virtual biology laboratory for its students to be trained before they use its on-site lab. The evaluation of the user’s performance in the virtual lab with respect to a particular experimental procedure is done with a scoring algorithm specifically designed for this purpose. For the calculation of the user’s overall progress score, an Artificial Neural Network (ANN) is used. The ANN, trained with data from random plays evaluated by biology experts, achieves significant convergence. Yet, when the trained ANN is used for the real-time evaluation of the user’s performance, it produces unrealistic scores, that is, incompatible with human experience, such as unscaled score values as well as a high increase in score with the execution of secondary actions. To overcome this problem, the ANN’s weights are fine-tuned with the use of a Genetic Algorithm (GA) and two algorithms of Swarm Intelligence (SI), Whale Optimization Algorithm (WOA) and Firefly Algorithm (FA). Among those, GA achieves successful optimization of the ANN’s weights, resulting in a more realistic score mechanism. Full article

Maritime accident analysis increasingly uses machine learning to support safety management, but many existing studies focus on single-output prediction, such as accident-occurrence probability, severity class, near-miss frequency, or one specific consequence. This study proposes a data-driven decision-support framework based on a Multi-Input Multi-Output Artificial Neural Network (MIMO-ANN) for the simultaneous prediction of multiple maritime accident consequences. A dataset of 582 recorded accident cases is constructed by integrating SafePASS project records with consequence, severity, and structural-damage information from the literature. The dataset includes 15 input variables covering ship characteristics, operational context, environmental conditions, accident type, and geographical zone and 15 consequence outputs covering structural damage, casualties, emergency-response indicators, total loss, and secondary consequence/escalation mechanisms. The ANN is trained using the Scaled Conjugate Gradient (SCG) algorithm and evaluated under different network configurations and data-partitioning strategies. The best-performing model uses 30 hidden neurons with a 60/20/20 split, achieving a correlation coefficient (R) equal to 0.9249 and a mean squared error (MSE) equal to 0.0240 for testing, and a R equal to 0.9278 and a MSE equal to 0.0231 for validation. Ten-fold cross-validation further confirms internal predictive stability, with mean testing R equal to 0.8803 ± 0.0827 and MSE equal to 0.0445 ± 0.0478. Permutation-based sensitivity analysis shows that accident type, zone, flag, natural light, environment, and visibility are key drivers of predicted consequences, whereas vessel-specific parameters have a secondary, context-dependent influence. The framework should be interpreted as predicting the relative likelihood, severity, or magnitude of accident consequences in recorded or scenario-defined accident cases, not the probability of accident occurrence. Future work should address dataset imbalance, include near-miss and nonserious records, incorporate richer AIS and metocean data, integrate exposure data, and validate the framework using independent accident datasets. Full article

Predicting pain in physical rehabilitation is challenging due to subjectivity, patient variability, and self-report bias, especially in telerehabilitation. This study aims to determine whether machine-learning models based on heart rate (HR), heart rate variability (HRV), and peripheral oxygen saturation (SpO₂) can reliably detect clinically meaningful pain during real rehabilitation sessions, including home-based settings where self-report is least reliable; we hypothesized that these low-cost, non-invasive markers carry sufficient information to flag low-to-moderate pain episodes without relying on self-report. We combined these markers with machine-learning models. These markers were selected for their association with autonomic pain responses and ease of measurement with only two low-cost, non-invasive sensors (a wearable band providing HR and HRV, and a fingertip oximeter providing SpO₂) suitable for clinical and home-based rehabilitation. We evaluated linear regression (LR), random forest (RF), and artificial neural networks (ANNs) using data from 25 participants (aged 20–50) undergoing lower-limb rehabilitation. Signals acquired at 1 Hz were processed via temporal filtering, quality screening, and three missing-value strategies (interpolation, zero imputation, deletion) before normalization and training. LR showed limited predictive power. RF achieved 97.77% accuracy in detecting low-pain episodes, and balanced per-class performance under deletion (76.64%). ANN models contributed a more balanced three-class profile on interpolated data but remained sensitive to class imbalance. Given high-pain scarcity in supervised therapy and underreporting at home, reliable detection of low-to-moderate pain enables timely therapy adjustments. Unlike prior studies using experimentally induced pain, this work captured naturally occurring pain during real rehabilitation, making findings applicable to clinical and telerehabilitation contexts. Physiology-based models with low-cost sensors show promise for personalized rehabilitation, improving adherence and enabling proactive adjustments without added complexity. Full article

Artificial intelligence (AI) is increasingly used to help consumers detect greenwashing in online food markets, yet how AI-use motivations relate to rational green food consumption intention (RCI) remains unclear. Integrating information processing theory, the consumer decision-making process model, and the theory of planned behavior, this study examines how risk avoidance (RA), performance expectations (PE), and health benefits (HB) are associated with RCI through subjective norm (SN) and perceived behavioral control (PBC). Based on 619 valid responses from a cross-sectional online survey, the data were analyzed using partial least squares structural equation modeling (PLS-SEM) and artificial neural network (ANN). The PLS-SEM results show that PE, RA, SN, and PBC are significantly associated with RCI, whereas HB has no significant direct association. SN is strongly associated with PBC, and the SN–PBC sequential mediation path is supported for RA, PE, and HB. The RA–PBC–RCI path is not supported, indicating that risk awareness does not automatically translate into perceived control. The ANN results identify PE as the strongest nonlinear predictor, followed by RA, while HB shows the weakest predictive importance. The findings advance AI-mediated sustainable consumption research and provide intention-level evidence for responsible online green food purchasing. Full article

Accurate calculation of gamma dose rates in medical and industrial facilities is a critical component of comprehensive dosimetry assessment. Usually, two complementary approaches are employed to this end: experimental measurements and Monte Carlo (MC) simulations, both of which have established themselves as powerful and reliable tools in radiation protection and dosimetry practice. Given the high computational cost of Monte Carlo simulations, artificial intelligence can offer a compelling and efficient alternative for predicting dose rate distributions. This study evaluates the capability of machine learning models to predict MC-calculated dose rates and to identify the optimal ⁶⁰Co source arrangement for the upcoming replenishment. The replenishment scenario involves inserting six new ⁶⁰Co pencil sources. Dose rate prediction was performed using FLUKA MC simulations, complemented by an Artificial Neural Network (ANN)-based predictive model. The ANN model demonstrated strong concordance with FLUKA MC results, with deviations consistently below 1%, and exhibited reliable predictive performance on previously unseen configurations. Based on the dose uniformity ratio and the coefficient of determination, configuration 3 was identified as the optimal arrangement (R² = 0.986). The integration of machine learning with MC simulation proves highly effective, enabling rapid and accurate dose rate prediction around the ⁶⁰Co source while substantially reducing computational time and CPU resource demands. Full article

In this paper, we propose the Fractal Architecture Neural Network (FANN), a recursive neural framework inspired by self-similar fractal geometry. The architecture is governed by a fractal dimension parameter $\alpha$, which controls the branching structure and connectivity density of the network, enabling multiscale feature representation through parameter sharing across recursive paths. We evaluate FANN on synthetic nonlinear regression tasks and compare it with a standard artificial neural network (ANN) and FractalNet in terms of accuracy, training behavior, and model complexity. Experimental results show that FANN achieves competitive or improved predictive performance under comparable computational budgets, demonstrating effective accuracy-to-parameter efficiency. These results suggest that fractal-inspired recursive connectivity can provide a compact mechanism for hierarchical representation learning in neural networks. Full article

Accurate prediction of biomass pyrolysis products remains challenging due to the inherent complexity of thermochemical kinetics and the lack of mechanistic interpretability in modern Machine Learning (ML) models. This study addresses the black-box problem by comparing a standard Artificial Neural Network (ANN) against a novel Hybrid Physics-Informed Neural Network (PINN) and a Transparent Model (Rough Set ML, RSML) for biochar yield prediction. The standard ANN demonstrated poor generalization performance ($R^2$ = −2.4109) and exhibited physical inconsistency, quantified by a low Physical Consistency Degree ($PCD=0.6429$) and non-monotonic behavior in Partial Dependence Analysis. The PINN was implemented using the Independent Parallel Reactions Scheme (IPRS) to enforce kinetic constraints via a Partial Differential Equation loss ($L_{PDE}$). The results show a critical trade-off: the PINN under standard balancing failed, yielding a PCD value of 0.0714, yet an Extended Kinetic Fitting mode successfully achieved perfect physical coherence ($PCD=1$), demonstrating that enforcing physics acts as a powerful regularizer, leading to a significant improvement in precision ($R^2$ = 0.82). Furthermore, this coherent PINN autonomously discovered a valid Activation Energy ($E_a=150$ kJ/mol), offering direct mechanistic insights by establishing a thermodynamically consistent global activation energy barrier for the primary thermal decomposition stage. This is complemented by the RSML model, which generated highly certain ($cer\ge 95\%$) IF–THEN rules, translating kinetic principles into actionable operational guidelines (e.g., specific thresholds for operating temperature and feedstock Ash content). The study suggests that PIML is a promising pathway for achieving reliable, robust, and mechanistically interpretable modeling in chemical engineering. Full article

This study reports cellulase production by Bacillus paralicheniformis using Plumeria alba leaf powder under submerged fermentation with a focus on systematic bioprocess optimization. Physical parameters were first optimized using a one-factor-at-a-time (OFAT) approach, followed by optimization of yeast extract, MgSO₄ and (NH₄)₂SO₄ via a central composite design (CCD) and response surface methodology (RSM). An artificial neural network (ANN) with a 5:3:1 network trained by the Levenberg–Marquardt algorithm further improved prediction of carboxylmethylcellulase (CMCase) and filter paper cellulase (FPase) activities. This study is the first to exploit Plumeria alba leaf powder as an untapped, low-cost lignocellulosic substrate for cellulase production by B. paralicheniformis and uniquely benchmarks RSM against ANN-based modeling to identify superior predictive frameworks for bioprocess optimization. Under optimized conditions (24 h, 4% w/v substrate, 1% v/v inoculum), the maximum FPase and CMCase activities reached 60.53 IU/mL/min and 332.10 IU/mL/min respectively. Partial characterization showed optimum FPase and CMCase activities at 50 °C and 70 °C, respectively, at pH 7.5. Enzymes also showed activation by NaCl and some select solvents while tolerating a broad range of metal ions. The enzymatic hydrolysis of P. alba biomass released 59.42 mg/mL total reducing sugars after 8hr, confirming efficient saccharification from a low-cost feedstock. The ANN model (R² = 97.59% for CMCase; 85.95% for FPase) outperformed RSM (R² = 85.95% and 78.25%, respectively), while radial basis function optimization reached 99.99%. These findings highlight B. paralicheniforms cellulase as a promising biocatalyst for biorefinery applications and demonstrate the value of integrating RSM and ANN for process optimization. Full article

The solar energy generation in Low Earth Orbit is experiencing rapid periodic fluctuations in irradiance and temperature due to orbital motion, eclipse transitions, and thermal cycling. All these conditions significantly affect the efficiency of conventional system algorithms. The hybrid MPPT strategies that combine classical methods and intelligent controllers have promised a good solution for improving tracking speeds, reducing steady-state oscillations, and increasing energy extraction under high variation conditions. This paper presents a structured review of hybrid MPPT algorithms suitable for LEO applications under these control strategies, Incremental-Artificial Neural Network (INC-ANN) and Perturb & Observe-Fuzzy Logic Control (P&O-FLC). The review highlights their advantages, limitations, computational specifications, and suitability for LEO solar power subsystems. This work forms an ongoing study since the simulation of this subsystem is not yet available. This paper aims to establish the technical foundation and methodology direction for the future implementation and evaluation of hybrid MPPT techniques under simulated LEO conditions. Full article

The visual quality of beverages is a major factor affecting consumers’ perception, quality evaluation, and market acceptance. Traditional colorimetric analysis is accurate but requires specialized equipment, time-consuming sample preparation, and substantial financial and time investment. The objective of this study was to develop a rapid, inexpensive, and accurate alternative method to predict the main color attributes of a date syrup–buttermilk beverage during processing and storage using an artificial neural network (ANN) approach. A multilayer perceptron ANN was developed using a back propagation algorithm. The ANN included three input variables (concentration of date syrup, storage cooling temperature, and storage time), one hidden layer with twenty neurons, and nine output color attributes (lightness, redness/greenness, yellowness/blueness, hue angle, Chroma, total color difference, browning index, whiteness index, and yellow index). To compare the effectiveness of the ANN model for the prediction of color attributes, the multiple linear regression (MLR) models were developed using the same inputs and the same training dataset. Experimental results indicated that all processing variables and their interactions had a significant effect on the color attributes of the beverage (p < 0.001). The trained ANN model exhibited excellent prediction capacity during the validation phase with high coefficients of determination (R² range was between 0.9974 and 0.9997) with lower root mean squared error than MLR. Moreover, sensitivity analysis indicated date syrup concentration as the most influential factor on the final color profile. The developed ANN model provides an effective approach for the offline prediction of color quality during processing and storage under laboratory conditions. Although the integration of the ANN model with inline sensors may offer opportunities for future intelligent quality-control applications, real-time implementation and industrial deployment were not evaluated in the present study. Full article

The cold V-bending of Grade 4 titanium bone plates at room temperature is a critical forming operation that must be optimized to control strain localization and springback and to reduce the risk of surface cracking. This study proposes a combined experimental, numerical, and artificial intelligence-based approach for the analysis and optimization of this process. Tensile tests were first performed to characterize the mechanical behavior of the material and to calibrate the constitutive law used in the finite element model. The numerical model was then validated through comparison with experimental V-die bending results. A design of experiments was subsequently applied to investigate the effects of sheet thickness, die shoulder distance, punch radius, and punch displacement on two key responses: equivalent plastic strain (PEEQ) and spring back. The results show that sheet thickness and die shoulder distance are the most influential parameters. In addition, artificial neural network models were developed to predict process responses, and Bayesian regularization showed the best overall predictive performance among the tested ANN training algorithms, namely Levenberg–Marquardt, Bayesian regularization, and scaled conjugate gradient. The proposed framework provides a basis for optimizing the forming of titanium orthopedic implants. Full article

Reusing industrial byproducts to prepare recycled aggregate concrete (RAC) is a sustainable approach that can protect the ecological environment. This study tested the possibility of preparing an eco-efficient recycled concrete containing steel slag (SS), ground-granulated blast-furnace slag (GGBS), and polypropylene (PP) fibers to avoid resource waste and depletion and decrease CO₂ emissions. To this end, 12 mix proportions were designed to analyze the effects of SS, GGBS, and PP fibers on the macro- and micro-performances of the developed RAC. The experimental results showed that increasing the SS content decreased the RAC mechanical strength, whereas partially substituting SS with GGBS in the RAC improved the mechanical properties, especially at a later stage. Adding PP fibers to the RAC containing SS and GGBS significantly increased the splitting tensile strength. However, it had little effect on the compressive strength as the PP fiber content was less than 0.6%. The microscopic experiment revealed that adding GGBS promoted the degree of hydration of SS, reduced the Ca (OH)₂ content, made the ITZ structure more compact, and optimized the pore characteristics of the RAC. Furthermore, according to the raw materials and results of mechanical properties, a hybrid Genetic Algorithm/Artificial Neural Network (GA-ANN) technique was proposed to predict the compressive strength of the RAC containing SS, GGBS, and PP fibers. We found that the proposed GA-ANN model effectively predicts the compressive strength. The findings of this study demonstrate that preparing RAC incorporating SS, GGBS, and PP fibers is promising for the reuse of industrial byproducts and construction waste. Full article