Understanding ion electrosorption under strong electric fields is essential for advancing electrochemical desalination technologies and high-performance energy storage devices. In this work, we apply a symmetric modified Stern–Poisson–Boltzmann framework that incorporates steric constraints arising from the finite size of ions in aqueous electrolytes confined within an electrified channel. Unlike classical Gouy–Chapman theory, the proposed model accounts for excluded-volume effects, enabling the analysis of concentrated solutions and high surface potentials. We employ analytical expressions for the electric potential distribution, surface charge density, differential capacitance, and diffuse layer thickness for monovalent and divalent electrolytes at the equilibrium state that are solved numerically at the equilibrium state. Increasing electrode potential enhances surface charge density by orders of magnitude, while higher ionic valence significantly compresses the diffuse layer at low potentials but introduces repulsive crowding at strong polarization. The model further predicts that ion-removal efficiency in the channel center depends critically on electrode spacing, bulk concentration, and applied potential, with removal rates exceeding 90% under optimized conditions. These findings provide theoretical insight into steric-limited electrokinetic processes and offer design guidelines for electrochemical water purification and electrostatic energy-storage systems.

The incorporation of first principles computational methods in Thermoluminescence (TL) enables the comprehensive investigation of the luminescent characteristics exhibited by solid materials. These calculations offer valuable insights into the electronic structures and thermal behavior, providing a deeper understanding of the underlying mechanisms. Density Functional Theory (DFT) is a powerful tool for predicting the electronic structure properties, as the Density of States (DOS) of materials. By theoretically depicting the electronic states arising from electron transitions, the present study focuses on the DFT study of undoped BeO and of BeO with substitutional Si4+, Mg2+ and Cr3+, Mg2+ doping, and the connection of TL emission caused by recombination of electrons in centers, as outlined in the One Trap - One Recombination (OTOR) center model. This approach offers essential details regarding the origins of electron traps, facilitating a deeper exploration of the intrinsic contribution of DFT in Stimulated Luminescence (SL) analysis.

A machine learning approach, namely symbolic regression (SR), is applied in the stellarator Wendelstein 7-X (W7-X), to investigate the effect of six plasma parameters (line integrated electron density, heating power, toroidal plasma current, fraction of radiated power, core and edge ion temperatures) on the sub-divertor neutral gas pressure. Based on the data from the OP1.2b experimental campaign, closed-form expressions of the neutral gas pressure in terms of the plasma parameters are deduced for the standard, high iota and high mirror magnetic configurations at three different ports of the exhaust system. While common regression schemes assume a predetermined functional form, SR autonomously discovers, via genetic programming, the functional structure of the model, purely from data. In all cases, the optimized data driven SR framework clearly points out that, in estimating the neutral gas pressure, the most dominant parameters are the electron density and the heating power, while the remaining plasma parameters have minor impact, at least from the statistical point of view and may not be included in the correlations. Balancing model generality, complexity(COMP) and accuracy for all considered magnetic configurations and ports, the proposed closed form expressions contain only the product of electron density and heating power raised at some powers, times a constant. The proposed two-parameter symbolic expressions, exhibiting low COMP and excellent accuracy metrics, provide a practical and analytical tool for the acceleration of the neutral gas pressure calculations, that are otherwise computationally very expensive and for the overall performance assessment of the W7-X exhaust system. They may also contribute to more efficient experimental design and operation. performance assessment of the W7-X exhaust system. They may also contribute to more efficient experimental design and operation.

This research investigates high school students’ understanding of mathematical, physical, and biological systems exhibiting Fractal structures, with the aim of enhancing their comprehension of nature’s intrinsic complexity. The research explores the pedagogical integration of Fractal Geometry, emphasizing key concepts such as scaling, self-similarity, and Fractal dimension, and examines their applications in both mathematical and natural systems. The research employs an instructional intervention designed to evaluate students’ conceptual understanding, the characterization of Fractal sets and systems, and the impact on their motivation for learning Natural Sciences. Both qualitative and quantitative methods were used, including pre- and post-test questionnaires, worksheets, and statistical analyses. Nonparametric tests (Wilcoxon Signed-Rank and Mann–Whitney U) were used due to nonnormal data distributions, and the reliability and validity of the instruments were verified. The results reveal that Students’ initial alternative conceptions shifted toward scientifically accepted ideas, and their motivation for the Natural Sciences increased substantially. Field-based activities involving authentic Fractal structures proved particularly effective in engaging learners, fostering active participation, retention, and meaningful knowledge construction. The research demonstrates that the pedagogical use of Fractal Geometry provides an innovative and effective approach to teaching complex scientific phenomena. It bridges theoretical concepts and practical experience, enhances critical and creative thinking, and contributes to scientific literacy, offering new tools for interdisciplinary education and for teaching the natural world.

Machine Learning methods are exploited to extract a universal approach for self-diffusion coefficient calculation in molecular fluids. Analytical expressions are derived through symbolic regression for fluids both in bulk and confined nanochannels. The symbolic regression framework is trained on simulation data from molecular dynamics and correlates the values of the self-diffusion coefficients with macroscopic properties, such as density, temperature, and the width of confinement. New expressions are derived for nine different molecular fluids, while an all-fluid universal equation is extracted to capture molecular behavior as well. In such a way, a highly computationally demanding property is predicted by easy-to-define macroscopic parameters, bypassing traditional numerical methods based on mean squared displacement and autocorrelation functions at the atomistic level. To achieve generalizability and interpretability, simple symbolic expressions are selected from a pool of genetic programming-derived equations. The obtained expressions present physical consistency, and they are discussed in terms of explainability. The accurate prediction of the self-diffusion coefficient both in bulk and confined systems is important for advancing the fundamental understanding of fluid behavior and leading the design of nanoscale confinement devices containing real molecular fluids.

This study uses a conformable derivative of order β to investigate a fractional Whitham–Broer–Kaup (FWBK) model. This model has significant uses in several scientific domains, such as plasma physics and nonlinear optics. The enhanced modified Sardar sub-equation EMSSE approach is applied to achieve precise analytical solutions, demonstrating its effectiveness in resolving complex wave photons. Bright, solitary, trigonometric, dark, and plane waves are among the various wave dynamics that may be effectively and precisely determined using the FWBK model. Furthermore, the study explores the chaotic behaviour of both perturbed and unperturbed systems, revealing illumination on their dynamic characteristics. By demonstrating its validity in examining wave propagation in nonlinear fractional systems, the effectiveness and reliability of the suggested method in fractional modelling are confirmed through thorough investigation.

Artificial intelligence (AI) methods have significantly impacted various areas of technology, particularly in fields where large datasets are available. Screw designs are proprietary, and there is very limited information available in the open literature. In this study, we generated a dataset of 232 designs using computer simulation software for screw extrusion, involving solids transport, melting, and melt pumping. The parameters (features) and the outputs (targets) were introduced into four powerful machine learning (ML) algorithms. The capabilities of the four algorithms were assessed by comparing the predictions of each of the algorithms to the corresponding results of the simulations. Three of the algorithms demonstrated satisfactory performance, with the best-performing one being further tested on an "unseen" dataset, which involved a screw of 75 mm and another of 127 mm in diameter. A machine-learning technique called Permutation Feature Importance (PFI) was used to identify the features (parameters) with the greatest impact on the predictions. It is suggested that the same ML methodologies could be applied to datasets of existing real screw designs.Highlights Dataset obtained from simulation software. Four machine learning algorithms were employed. Assessment of algorithms based on training and testing data. Identification of parameters having greatest impact. Satisfactory predictions of mass flow rate, exit temperature, melting length, and more.

Kinetic theory and modeling have been proven extremely suitable in computing the flow rates in rarefied gas pipe flows, but they are computationally expensive and more importantly not practical in design and optimization of micro- and vacuum systems. In an effort to reduce the computational cost and improve accessibility when dealing with such systems, two efficient methods are employed by leveraging machine learning (ML). More specifically, random forest regression (RFR) and symbolic regression (SR) have been adopted, suggesting a framework capable of extracting numerical predictions and analytical equations, respectively, exclusively derived from data. The database of the reduced flow rates W used in the current ML framework has been obtained using kinetic modeling and it refers to nonlinear flows through circular tubes (tube length over radius l ∈ [0,5] and downstream over upstream pressure p ∈ [0,0.9] ) in a very wide range of the gas rarefaction parameter δ∈ [0,10^3] . The accuracy of both RFR and SR models is assessed using statistical metrics, as well as the relative error between the ML predictions and the kinetic database. The predictions obtained by RFR show very good fit on the simulation data, having a maximum absolute relative error of less than 12.5% . Various expressions of the form of W=W(p,l,δ ) with different accuracy and complexity are acquired from SR. The proposed equation, valid in the whole range of the relevant parameters, exhibits a maximum absolute relative error less than 17% . To further improve the accuracy, the dataset is divided into three subsets in terms of δ and one SR-based closed-form expression of each subset is proposed, achieving a maximum absolute relative error smaller than 9% . Very good performance of all proposed equations is observed, as indicated by the obtained accuracy measures. Overall, the present ML-predicted data may be very useful in gaseous microfluidics and vacuum technology for engineering purposes.

Contaminated water has remained an unsolved problem for decades, particularly when the contamination derived from heavy metals. A possible solution is to mix the contaminated water with magnetic nanoparticles so that an adsorption process can take place. In that frame, Tesla valve micromixer and Fe3O4 magnetic nanoparticles were selected to perform simulations for encounter maximum mixing efficiency. These simulations focus on inlet velocities ratios between contaminated water and nanoparticles and inlet rates of nanoparticles. The maximum mixing efficiency was 44% for the inverse double Tesla micromixer found for the combination of Fe3O4 nanoparticles as the inlet rate and with inlet velocity ratios of VpVc=10.

In this paper, we compare the dynamics of the growth rates of the original Divisia monetary aggregates, the credit card-augmented Divisia monetary aggregates, and the credit card-augmented Divisia inside monetary aggregates. This analysis is based on the methods of recurrence plots, recurrence quantification analysis, and visual boundary recurrence plots which are phase space methods designed to depict the underlying dynamics of the system under study. We identify the events that affected Divisia money growth and point out the differences among the different Divisia monetary aggregates based on the recurrence and visual boundary recurrence plots. We argue that the broad Divisia monetary aggregates could be used for monetary policy and business cycle analysis as they are exhibiting less fluctuation compared to the narrow Divisia monetary aggregates. They could positively affect policy decisions regarding environmental choices and sustainability. We also point out the changes in the monetary dynamics locating the 2008 global financial crisis and the Covid-19 pandemic.

As the world shifts towards a low-carbon economy, the strategic deployment of renewable energy sources (RESs) is critical for maximizing energy output and ensuring sustainability. This study introduces GREENIA, a novel artificial intelligence (AI)-powered framework for optimizing RES placement that holistically integrates machine learning (gated recurrent unit neural networks with swish activation functions and attention layers), evolutionary optimization algorithms (Jaya), and Shapley additive explanations (SHAPs). A key innovation of GREENIA is its ability to provide natural language explanations (NLEs), enabling transparent and interpretable insights for both technical and non-technical stakeholders. Applied in Greece, the framework addresses the challenges posed by the interplay of meteorological factors from 10 different meteorological stations across the country. Validation against real-world data demonstrates improved prediction accuracy using metrics like root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). SHAP analysis enhances transparency by identifying key meteorological influences, such as temperature and humidity, while NLE translates these insights into actionable recommendations in natural language, improving accessibility for energy planners and policymakers. The resulting strategic plan offers precise, intelligent, and interpretable recommendations for deploying RES technologies, ensuring maximum efficiency and sustainability. This approach not only advances renewable energy optimization but also equips stakeholders with practical tools for guiding the strategic deployment of RES across diverse regions, contributing to sustainable energy management.

The integration of machine learning (ML) techniques into industrial and manufacturing applications has seen great growth in recent years. Various numerical and analytical models have been proposed, based either on experimental results or simulation results, and have helped to understand phenomena that take place during the life cycle of a material. In this direction, a large experimental data set to determine the compressive strength of FRP (FRP) prestressed concrete specimens has been used as a basis in this work. The obtained measurements are correlated with the mechanical and structural properties of the material and fed into a ML model. The model is trained on the experimental values ​​and can provide predictions for conditions within or outside the value range of the input data. Various MLalgorithms are implemented and studied for their prediction accuracy, and the results show that ML can be an important computational tool, which can act as a complement to expensive experiments or time-consuming simulations in engineering sciences.

The viscosity and thermal conductivity coefficients of the Lennard-Jones fluid are extracted through symbolic regression (SR) techniques from data derived from simulations at the atomic scale. This data-oriented approach provides closed form relations that achieve fine accuracy when compared to well-established theoretical, empirical, or approximate equations, fully transparent, with small complexity and high interpretability. The novelty is further outlined by suggesting analytical expressions for estimating fluid transport properties across the whole phase space, from a dilute gas to a dense liquid, by considering only two macroscopic properties (density and temperature). In such expressions, the underlying physical mechanisms are reflected, while, at the same time, it can be a computationally efficient alternative to costly in time and size first principle and/or molecular dynamics simulations.

Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, argon, krypton, nitrogen, and oxygen, are gathered from experimental and simulation data in the literature and constitute a primary database for further investigation. The data refers to a wide pressure–temperature (P-T) phase space, covering fluid states from gas to liquid and supercritical. The database is enriched with new simulation data extracted from our equilibrium molecular dynamics (MD) simulations. A machine learning (ML) framework with ensemble, classical, kernel-based, and stacked algorithmic techniques is also constructed to function in parallel with the MD model, trained by existing data and predicting the values of new phase space points. In terms of algorithmic performance, it is shown that the stacked and tree-based ML models have given the most accurate results for all elements and can be excellent choices for small to medium-sized datasets. In such a way, a twofold computational scheme is constructed, functioning as a computationally inexpensive route that achieves high accuracy, aiming to replace costly experiments and simulations, when feasible.

Motivated by the significance and complexity of exploring spatiotemporal patterns - regions within an urban environment, particularly in the context of extreme heat events- this research analyzes meteorological time series through complex network analysis. The data collected for the examination area is focused on Athens, Greece, and covers sections of the city’s urban landscape. The data was obtained from the Copernicus observation component of the European Union. Initially, the time series are transformed into networks using correlation network methodology, followed by examination of the discriminative capability of the topological measures of networks degree and modularity as community - region detection methods. Of particular interest is that our findings suggest that the proposed complex network analysis can lead to the extraction of spatial urban regions closely linked to land use and building heights in corresponding areas. These results may help investigate the spatial variability of heat in the urban environment and inform urban planning and management strategies in policy decision-making regarding the intensity of urban heat throughout the city and the planning of climate change adaptation strategies.

Buildings are responsible for around 30% and 42% of the consumed energy at the global and European levels, respectively. Accurate building power consumption estimation is crucial for resource saving. This research investigates the combination of graph convolutional networks (GCNs) and long short-term memory networks (LSTMs) to analyze power building consumption, thereby focusing on predictive modeling. Specifically, by structuring graphs based on Pearson’s correlation and Euclidean distance methods, GCNs are employed to discern intricate spatial dependencies, and LSTM is used for temporal dependencies. The proposed models are applied to data from a multistory, multizone educational building, and they are then compared with baseline machine learning, deep learning, and statistical models. The performance of all models is evaluated using metrics such as the mean absolute error (MAE), mean squared error (MSE), R-squared (R2), and the coefficient of variation of the root mean squared error (CV(RMSE)). Among the proposed computation models, one of the Euclidean-based models consistently achieved the lowest MAE and MSE values, thus indicating superior prediction accuracy. The suggested methods seem promising and highlight the effectiveness of GCNs in improving accuracy and reliability in predicting power consumption. The results could be useful in the planning of building energy policies by engineers, as well as in the evaluation of the energy management of structures.

This study investigates the time-series behavior of vegetable prices in the Central Market of Thessaloniki, Greece, using Recurrence Plot (RP) analysis and Recurrence Quantification Analysis (RQA), which considers non-linearities and does not necessitate stationarity of time series. The period of study was 1999–2016 for practical and research reasons. In the present work, we focus on vegetables available throughout the year, exploring the dynamics and interrelationships between their prices to avoid missing data. The study applies RP visual inspection classification, a clustering based on RQA parameters, and a classification based on the RQA analysis graphs with epochs for the first time. The aim of the paper was to investigate the grouping of products based on their price dynamical behavior. The results show that the formed groups present similarities related to their use as dishes and their way of cultivation, which apparently affect the price dynamics. The results offer insights into market behaviors, helping to inform better management strategies and policymaking and offer a possibility to predict variability of prices. This information can interest government policies in various directions, such as what products to develop for greater stability, identity for fluctuating prices, etc. In future work, a larger dataset including missing data could be included, as well as a machine-learning algorithm to classify the products based on the RQA with epochs graphs.

Convolutional neural networks (CNN) have been widely adopted in fluid dynamics investigations over the past few years due to their ability to extract and process fluid flow field characteristics. Both in sparse-grid simulations and sensor-based experimental data, the establishment of a dense flow field that embeds all spatial and temporal flow information is an open question, especially in the case of turbulent flows. In this paper, a deep learning (DL) method based on computational CNN layers is presented, focusing on reconstructing turbulent open channel flow fields of various resolutions. Starting from couples of images with low/high resolution, we train our DL model to efficiently reconstruct the velocity field of consecutive low-resolution data, which comes from a sparse-grid Direct Numerical Simulation (DNS), and focus on obtaining the accuracy of a respective dense-grid DNS. The reconstruction is assessed on the peak signal-to-noise ratio (PSNR), which is found to be high even in cases where the ground truth input is scaled down to 25 times.