Modelling

The Birnbaum–Saunders (BS) distribution, defined only for non-negative values, is asymmetrical. However, it can be transformed into a normal distribution, which is symmetric. The BS distribution is particularly useful for analyzing data consisting of values greater than zero. This study aims to introduce six approaches for constructing confidence intervals for the difference and ratio of percentiles in Birnbaum–Saunders distributions containing zero values. The proposed approaches include the generalized confidence interval (GCI) approach, the bootstrap approach, the highest posterior density (HPD) approach based on the bootstrap method, the Bayesian approach, the HPD approach based on the Bayesian method, and the method of variance estimates recovery (MOVER) approach. To assess their performance, a Monte Carlo simulation study is conducted, focusing on coverage probability and average length. The results indicate that the MOVER approach and the HPD approach based on the Bayesian method perform better than other approaches for constructing confidence intervals for the difference between percentiles. Moreover, the GCI and Bayesian approaches outperform others when constructing confidence intervals for the ratio of percentiles. Finally, daily wind speed data from the Rayong and Prachin Buri provinces are used to demonstrate the efficacy of the proposed approaches. Full article

Analyzing wildfire behavior is crucial due to its significant environmental repercussions. Among the various influencing factors, terrain slope and wind velocity are pivotal in governing fire spread characteristics. In the present study, we investigate the influence of negative terrain slopes (up to −45°), backward wind velocities (up to 2 m/s), and their combined effects on the surface fire spread rate using the Wildland-Urban Fire Dynamics Simulator (WFDS). Wind velocity in backward flows reduces the rate of spread by 40% at 30° angles, primarily due to the suppression of radiative heat transfer leading to reduced preheating unburnt areas. However, this effect reduces on lower slopes. The key findings reveal a significant increase in fire intensity and the rate of spread when the terrain slope exceeds 20°. The fire front shape evolves from a relatively flat rounded U-shape to a V-shape; it is shown that a downward slope slightly affects the spread rate, and the fire front shape stays flat. Full article

This study explores the performance of a novel curved steel damper system in lessening the seismic response of elevated steel tanks. It specifically examines two types of tanks, broad and tall, by analyzing their behavior during past earthquakes. Non-linear time history analyses were executed using various ground motion records to evaluate the dynamic characteristics and structural responses of the tank systems. The application of the curved steel damper, placed between the tank container and its supporting structure, resulted in a significant reduction in base shear forces and deformations of the tank walls when compared to traditional designs. The findings highlight the effectiveness of the damping system, showing that the curved damper leads to decreased maximum displacement and base shear forces during seismic activities. This research contributes essential knowledge for the seismic design of elevated steel tanks and proposes an innovative, cost-efficient strategy for enhancing their earthquake resilience. Full article

The Bivariate Odd Lindley Half-Logistic (BOLiHL) distribution with progressive Type-II censoring provides a powerful statistical tool for analyzing dependent data effectively. This approach benefits society by enhancing engineering systems, improving healthcare decisions, and supporting effective risk management, all while optimizing resources and minimizing experimental burdens. In this paper, the likelihood function derived under progressive Type-II censoring is generalized for the BOLiHL distribution. The well-known maximum likelihood estimation method and Bayesian estimation are applied to evaluate the parameters of the distribution. A study utilizing simulation techniques is performed to evaluate the performance of the estimators, using statistical analysis metrics for censored observations under a progressive Type-II censoring scheme with varying sample sizes, failure times, and censoring schemes. Additionally, a real dataset is studied to validate the proposed model, delivering impactful analyses for practical applications. Full article

This manuscript presents a theoretical study of a newly developed energy-efficient external wall structure in comparison with a traditional ventilated facade. To conduct numerical studies based on mathematical models of the heat transfer of water vapor filtration through a multilayer filler structure with ventilated and non-ventilated air gaps, a calculation method was developed that additionally considers the presence of heat-reflecting screens and different variations in the geometric parameters of air gaps and thermal insulation layers. The study results demonstrated that the new energy-efficient multilayer wall structure was 6.1–7.2% more efficient in terms of heat transfer resistance than the traditional one, and due to the presence of heat-reflecting screens, the efficiency increased to 15.2–16.3% depending on the geometric parameters of the air and thermal insulation layers of the wall structure. In addition, in all the considered variants of the filler structure geometry (i.e., with closed and ventilated air gaps), there were water vapor condensation zones, but it was established that according to the value of the inadmissibility of moisture accumulation in multilayer wall structures, over the annual period of operation, the structures complied with the standard climatic conditions of Shymkent. The results of this study thus positively complement the existing catalog of energy-efficient wall structures, and the new wall structure can be used while considering the necessary geometric parameters of air and heat-insulating layers when designing buildings in the corresponding climatic conditions. Full article

The importance of using sophisticated interface models to obtain realistic numerical solutions of soil–structure interaction (SSI) problems has been recognised in recent decades. With this aim, various advanced interface models have been developed, which assume that the same advanced constitutive model can describe the soil behaviour inside and outside the shear zone. These models fail to adequately address the experimentally observed stick–slip transition, assuming permanent sticking between the soil and structure. Furthermore, the influence of interface roughness requires model parameter adjustments, e.g., in the critical state of the soil, which are questionable from a physical point of view. To overcome these shortcomings, we propose a general relationship to describe the evolution of the shear strain in the shear zone as a function of the surface roughness, the density, and the normal stress. This relationship, which assumes a stick–slip transition at the interface, can be combined with an advanced constitutive model to describe soil–structure interface behaviour using the same set of model parameters as for the surrounding soil. Depending on the surface roughness of the interface, this transition leads to a localisation within the soil in the shear zone (for rough surfaces) or at the contact surface (for smooth surfaces). The proposed model was validated using interface shear tests from the literature on dry granular soils. A hypoplastic constitutive model was used in the simulations. The comparison of experimental and calculated results demonstrates the ability of the proposed model to realistically reproduce shear stress and relative displacements, including the stick–slip transition observed in the experiments. This instils confidence in the model’s reliability and accuracy, thus providing a reliable numerical tool for SSI analyses. Full article

This paper investigates the flow dynamics of a turbulent fountain with negative buoyancy using a Computational Fluid Dynamics (CFD) model, developed using OpenFOAM^® and calibrated against laboratory experiments. The simulations effectively replicate the geometry and buoyancy fluxes of the fountain, showing a fairly good agreement between the numerical and experimental velocity fields. These simulations are then used to investigate momentum and buoyancy fluxes for various source fluid densities. We find a dominant out-upward momentum transfer in the body of the fountain, while it is mainly out-downward below the inlet section. Furthermore, the vertical flux is almost twice the radial flux, while the tangential components are negligible on the inner side of the fountain. For small density differences between the fountain and the surrounding environment, we find a greater diffusion of the source fluid, while both the vertical and radial salt fluxes increase with increasing density of the fountain. The data generated serve as a significant resource for the development of future CFD models. Full article

Particle swarm optimization (PSO), an important solving method in the field of swarm intelligence, is recognized as one of the most effective metaheuristics for addressing optimization problems. Many adaptive strategies have been developed to improve the performance of PSO. Despite these advances, a key problem lies in defining the configuration criteria of the adaptive algorithm. This study presents an adaptive variant of PSO that relies on fitness landscape analysis, particularly via ruggedness factor estimation. Our approach involves adaptively updating the cognitive and acceleration factors based on the estimation of the ruggedness factor using a machine learning-based method and a deterministic way. We tested them on global optimization functions and the feature selection problem. The proposed method gives encouraging results, outperforming native PSO in almost all instances and remaining competitive with state-of-the-art methods. Full article

This paper presents the development of a computer simulation framework, designed as a cost–effective and technically efficient alternative to experimental studies. The framework focuses on the Bio–Neural Dust System proposed in our previous works, which consists of two components: a light–emitting bio–nanosensor and an opsin–expressing genetically modified neuron. This innovative system holds significant potential for applications in neuroscience and biotechnology research. Programmed in Python, the framework provides researchers with a virtual tool to test and evaluate the Bio–Neural Dust System, enabling the prediction of outcomes for future in vivo experiments. This approach not only conserves resources, but also offers scientists a flexible and accessible means to investigate the complex interactions within the system prior to real–world applications. The framework’s adaptability and potential for diverse research applications highlight its importance in advancing the field of bio–nanotechnology. Full article

Grain storage in silos plays a fundamental role in preserving the quality and safety of agricultural products. This study presents a comparative evaluation of two-dimensional (2D) and three-dimensional (3D) mathematical models to predict the temperature and moisture distribution during unventilated corn storage in cylindrical silos with conical roofs. The models incorporate external temperature fluctuations, solar radiation, grain moisture equilibrium with air humidity via sorption isotherm (water activity), and grain respiration to simulate real-world storage conditions. The 2D model offers computational efficiency and is suitable for preliminary assessments but simplifies natural convection effects and underestimates axial temperature gradients. Conversely, the 3D model provides a detailed representation of heat and moisture transfer phenomena, capturing complex interactions such as buoyancy-driven flow and localized effects of solar radiation. The results reveal that temperature and moisture accumulation are more pronounced in the upper regions of the silo, driven by solar radiation and natural convection, with significant implications for large-scale silos where thermal inertia plays a key role. This dual modeling approach demonstrates that while the 2D model is valuable for quick evaluations, the 3D model is essential for comprehensive insights into thermal and moisture gradients. The findings support informed decision-making in silo design, optimization, and management, enhancing grain storage strategies globally. Full article

Green logistics has gained significant attention in recent years due to increasing pollution levels and their negative effects. This area of research is crucial as governments and countries worldwide recognize the severity of pollution and its detrimental effects. Despite progress, significant gaps remain due to the lack of advanced models that consider additional factors and the influence of speed on their outcomes. This paper presents a case study on the Volume-based Pollution-Routing Problem with Time Windows (VPRPTW). The objective is to minimize CO${}_2$ emissions and improve customer satisfaction using a fleet of delivery vehicles. We propose a mathematical model and a probabilistic Tabu Search (TS) algorithm to solve the studied VPRPTW. The study revealed a decrease in daily fleet size from 16 to 12, indicating improved operational efficiency. In our study, we evaluate the impact of vehicle speed on fuel consumption and compare the results with a constant route speed to those obtained at varying speeds. Computational experiments reveal a significant difference of over 20% between fixed and variable speed assumptions. Additionally, we confirm that distance alone does not always correlate with energy consumption and CO${}_2$ emissions. This highlights the importance of considering variable speeds in routing problems to assist logistics companies, urban planners, and policymakers achieve more accurate and environmentally friendly transportation solutions. Full article

Fibre-reinforced polymers based on natural fibers, such as flax fibers, exhibit pronounced nonlinear orthotropic material behavior. This presents a significant challenge in finite element analysis (FEA) simulations, as the nonlinear constitutive models available in commercial FEA tools are difficult to apply and fail to capture all the material’s specific characteristics. Relying on initial or reduced secant moduli in linear quasi-static analyses of deformations or stress states can result in inaccurate outcomes and overly optimistic strength predictions, particularly in compression-dominated cases. However, with appropriate modifications, classical laminate theory (CLT) can be adapted for nonlinear analysis. This involves iteratively updating the components of the stiffness matrix for the unidirectional (UD) ply during the calculation process based on the current strain state and stress interactions. This study presents and discusses a computational algorithm for the FEA software ABAQUS/CAE 2019, which incorporates material-related orthotropic nonlinearities and stress-dependent interactions within the CLT framework. The algorithm represents a single-scale material model at the meso level (UD ply) and is based on the concept of orthotropic elasto-damage within the framework of continuum damage mechanics (CDM) theory. Numerical implementation is achieved through a user-defined field (USDFLD) subroutine, accompanied by a pre-processing Python script for managing experimental data, computing data fields, and calculating parameters. As shown below, this type of implementation appears justified compared to a user material subroutine (UMAT) subroutine in terms of computational efficiency and practicality. Full article

This paper discusses the process of high-velocity oxygen fuel (HVOF) spraying with modeling of the gas flow parameters and behavior of WC-Co-Cr powder particles of different fractions (up to 20 µm, 21–35 μm and 36–45 μm). It was found that the temperature of the gas stream reaches a maximum of about 2700 °C, after which it gradually decreases, and the pressure in the combustion chamber (before the exit of gases through the nozzle) reaches maximum values, exceeding 400,000 Pa, and the pressure at the exit of the nozzle stabilizes at about 100,000 Pa, which corresponds to the standard atmospheric pressure. The gas velocity increases to 1300–1400 m/s and then decreases to 400 m/s at a distance of about 150 mm. It was determined that powder particles of the 21–35 µm fraction provide more stable parameters of velocity and temperature. Small particles (up to 20 µm) lose velocity and temperature faster as they advance, which deteriorates the coating quality, which was also experimentally confirmed. All results obtained from the HVOF process modeling fully align with the data from experimental studies. Full article

This study presents an experimental and numerical investigation into the stress field in cylinders manufactured from photosensitive resin using the Masked Stereolithography (MSLA) technique. For material characterization, tensile and bending test data from resin specimens were utilized. The stress field in resin disks was experimentally analyzed using photoelasticity and Digital Image Correlation (DIC) methods, subjected to compressive loads, according to the cylinder–plane contact model. Images were captured during the experiments using polarizing film and a low-cost CPL lens, coupled to a smartphone. The experimental results were compared with numerical and analytical simulations, where the formation of fringes and regions indicating the direction and magnitude of normal and shear stresses were observed, with variations ranging from 0.6% to 8.2%. The convergence of the results demonstrates the feasibility of using parts produced with commercially available photosensitive resin on non-professional printers for studying contact theory and stress fields. In the future, this methodology is intended to be applied to studies on stress in gears. Full article

A frequency-reconfigurable MIMO antenna with high gain, low mutual coupling and highly suppressed side lobe level (SLL) for applications in 24 GHz ISM band sensing and automotive radar systems was designed, modeled, and simulated. The reconfigurability feature was modeled with the implementation of a varactor diode in the model to alter the frequency in a wide band around 24 GHz. The design features 2- and 4-port MIMO antenna each comprising a 1 × 8 microstrip patch array. At the core of achieving both a high gain of 16 dBi and high isolation of 38.4 dB at a resonance frequency of 24.120 GHz lies the integration of a metamaterial absorber, comprising an optimized split-ring unit cell to effectively mitigate interference among the MIMO elements. Noteworthy impedance bandwidths of the sensor antenna span from 23.8 to 24.3 GHz, catering to diverse frequency requirements. The proposed sensor antenna feature a half-power beamwidth of 74° in the E-plane and 11° in the H-plane and an SLL of −24 dB at 24.120 GHz showing its robust performance characteristics across multiple operational dimensions. Full article

This study investigates the seismic performance of two-, four-, and six-story composite buildings equipped with viscous wall dampers, focusing on structures with concrete-filled steel tubular (CFST) columns and steel beams. Through nonlinear time history analyses using sequential ground motions, the research evaluates the effectiveness of viscous wall dampers in mitigating seismic demands. Results demonstrate significant reductions in both interstory drift ratios and peak floor accelerations across all building heights when dampers are installed. The study particularly highlights the dampers’ effectiveness in controlling drift demands in lower and middle floors while managing acceleration amplification at upper levels. The findings validate the integration of viscous wall dampers into mid-rise composite structures and underscore the importance of considering sequential ground motions in seismic performance evaluations. Full article

Surrogate modeling technology is used to create lightweight analogs of resource- and calculation-intensive software, provided that the problem can be reduced to the regression problem. In this article, we construct a surrogate model for predicting annual energy consumption using the open-source EnergyPlus software and various sampling techniques. A general algorithm for an error-based adaptive sampling technique to build the surrogate model is presented. The best results were shown by the composite Mixed Sampling method with a data refining window the size of 70% and a LightGBM regression model. The best attained metrics values are as follows: MSE = 7.76, RMSE = 1.47, MAE = 0.98 and R² = 0.99. For a small number of iterations, an error-based adaptive sampling technique with hyperparameter tuning is preferable to the static sampling approach. For a large number of iterations, both techniques show approximately good predictive results of the built surrogate model. After hyperparameter tuning was performed, the average value of the MSE metric decreased from 43.43 to 7.76. A gas thickness feature greater than 0.015 had no positive effect on energy-saving optimization. For temperatures on a summer day of 30 degrees and above, there was a sharp increase in energy consumption. The maximum dry bulb temperature on a winter and summer day and the wind speed on a winter day were the most important features of the built surrogate model with 492, 483 and 443 gain values of the feature importance method, respectively. Full article

The relevance of creating specialized computer programs that convert a virtual 3D model of an object into machine code (G-code) for controlling the process of 3D printing products from wire raw materials is substantiated. It is shown that for wire-based additive technologies, a fundamentally important requirement is to ensure the continuity of the surfacing trajectory within one section. A method for determining a continuous surfacing trajectory is proposed, the implementation of which requires two stages: performing a numerical analysis of a two-dimensional region with boundary conditions describing this section; and running a heuristic algorithm for the movement of the surfacing head, in which the direction of movement is selected based on the results of the analysis. The procedure for setting boundary conditions and an algorithm for numerically solving the boundary value problem of determining the field of the “height” function for each section are described. The principles of operation of the heuristic algorithm for selecting the direction of head movement based on the calculated height field and continuous determination of the proximity of adjacent layers and section boundaries are disclosed. An analysis of the algorithm operation is carried out using a section with holes as an example, and the potential of using numerical methods to calculate the change in the temperature field during the surfacing process is shown. Full article

Efficient management of hospital evacuations and pharmaceutical supply chains is a critical challenge in modern healthcare, particularly during emergencies. This study addresses these challenges by proposing a novel bi-objective optimization framework. The model integrates a Mixed-Integer Linear Programming (MILP) approach with advanced machine learning techniques to simultaneously minimize total costs and maximize patient satisfaction. A key contribution is the incorporation of a Gated Recurrent Unit (GRU) neural network for accurate drug demand forecasting, enabling dynamic resource allocation in crisis scenarios. The model also accounts for two distinct patient destinations—receiving hospitals and temporary care centers (TCCs)—and includes a specialized pharmaceutical supply chain to prevent medicine shortages. To enhance system robustness, probabilistic demand patterns and disruption risks are considered, ensuring supply chain reliability. The solution methodology combines the Grasshopper Optimization Algorithm (GOA) and the ɛ-constraint method, efficiently addressing the multi-objective nature of the problem. Results demonstrate significant improvements in cost reduction, resource allocation, and service levels, highlighting the model’s practical applicability in real-world scenarios. This research provides valuable insights for optimizing healthcare logistics during critical events, contributing to both operational efficiency and patient welfare. Full article