This study presents research on wind-induced vibration energy harvesting (WIVEH) systems, focusing on those consisting of a flexible beam with piezoelectric transducers (PZT) coupled with a bluff-body geometries in a low-speed open wind tunnel. Three configurations were examined: a single cylinder, two cylinders connected by a flat wall, and a cylinder-cuboid hybrid. Continuous wavelet transform (CWT) analysis and statistical evaluation of beam deflections were employed to assess vibration characteristics, frequency content, and voltage output across air velocities from 3.5 to 15 m/s. Physical experiments are performed to compare the performance of systems with different configurations of bluff-body shape and to check the agreement of mathematical model results with empirical data. Investigation demonstrates the role of nonlinear aerodynamic excitation mechanisms of the vortex-induced vibrations (VIV), galloping, and fluttering effects. The influence of design parameters and discuss the challenges in modelling complex fluid-structure interactions with piezoelectric. Results show that bluff body geometry strongly influences beam vibration amplitude, frequency content, and energy harvesting efficiency. The two cylinders configuration exhibited the highest vibration amplitudes, with an 89%, and maximum energy output (~140 μW/cm³), increase in variability relative to the single cylinder, but at the cost of excessive oscillations beyond 10 m/s, compromising structural stability. The single-cylinder configuration showed moderate and predictable behavior with exponential voltage growth (~75 μW/cm³), while the cylinder–cuboid hybrid achieved the most robust performance, with 15% lower variability growth compared to the single cylinder and stable operation above 15 m/s. These findings confirm that geometry-driven flow–structure interactions determine the balance between harvested power and operational safety. The results indicate that while two-cylinder configurations may be effective for controlled low-speed applications such as heating, ventilation, and air conditioning (HVAC) ducts, the cylinder-cuboid hybrid is better suited for broadband, variable-speed environments powering internet of things (IoT) devices and wireless sensors.
This study presents a comprehensive comparison of five analytical-numerical algorithms designed to solve boundary value problems involving bodies coated with functionally graded materials (FGM). The investigation focuses on an axisymmetric heat conduction problem about local heating of the surface of a coated half-space. All proposed algorithms leverage the Hankel integral transformation to handle the problem's radial symmetry efficiently. Three of the methods approximate the continuously varying properties of the FGM coating by discretizing it into a finite number of homogeneous or inhomogeneous layers, for which analytical solutions are derived individually. The change in the thermal conductivity coefficient of the inhomogeneous layer along its thickness is described by a linear or exponential function. The fourth approach transforms the original boundary value problem into the Hankel transform domain, where derivatives are approximated using established finite-difference schemes, enabling a numerical solution. The fifth algorithm uses the approximation of the solution in the functionally graded coating using modeling functions, providing an alternative to layer discretization. The performance, accuracy, and computational efficiency of these five algorithms are assessed and discussed to identify their suitability for practical engineering applications involving FGMs.
This study proposes a vision-based line-following robot solution to overcome the limitations of infrared sensors in industrial environments with variable lighting conditions. The system integrates an automatic inverse gamma correction algorithm with Otsu’s thresholding to optimize contrast, ensuring accurate path extraction under diverse light intensities. Leveraging geometric parameters derived from principal component analysis, a Mamdani-type fuzzy logic controller is designed to coordinate movement, maintaining trajectory stability and mitigating mechanical oscillations. A central contribution of this research is the successful implementation of an intelligent control model on a low-cost hardware platform via an off-board processing architecture. Experimental results demonstrate that the system exhibits flexible responsiveness and reliable tracking, effectively overcoming wireless communication latency challenges to ensure operational performance in intralogistics automation tasks.
Malignant melanoma, a highly aggressive form of skin cancer, poses a significant global health challenge due to its rapid progression and high mortality rate if not detected on time. Early diagnosis is crucial for improving patient outcomes. The effectiveness of skin cancer detec-tion still faces serious challenges, like visual inspection that is less accurate and time-consuming. However, deep learning-based models provide early and accurate diagnosis, serving as a supporting tool for dermatologists. Thus, this study focuses on indicating the most suit-able model for skin diseases identification. Three prominent, pre-trained deep learning models, ResNet152, DenseNet201 and EfficientNet-B4, were involved in order to detect benign and malignant melanoma skin lesions. The study was performed utilizing a combined ISIC da-tasets gathered between 2018 and 2020 that consist of dermoscopic images. The above-mentioned deep learning algorithms were verified using accuracy, precision, recall, and F1-score metrics. Moreover, in this study the performance of skin cancer detection was enhanced uti-lizing soft, hard voting, and XGBoost ensemble learning methods. Combining two and three models were verified. The single models ob-tained accuracy at the level of 89.20%, 88.20%, and 90.40% for ResNet152, DenseNet201 and EfficientNet-B4, respectively. The soft vot-ing ensemble, merging ResNet-152 with EfficientNet-B4 or all three models, achieved the highest absolute accuracy of 91.30%, demonstrat-ing superior performance in melanoma diagnosis compared to individual models. Hard voting and XGBoost stated to be less effective in melanoma diagnosis. To confirm that the models were making decisions based on the significant image regions representing skin lesions, a visual explainable technique was applied. Gradient-weighted Class Activation Mapping proved the models to focus their attention to the relevant disease features. These findings underscore the potential of combining individual model strengths through ensemble learning to achieve superior diagnostic performance in melanoma detection, supporting clinicians in making more accurate and timely diagnosis.
The governing system of equations for the model of a locally inhomogeneous elastic body is based on constitutive equations generalized to account for local inhomogeneity of the binding energy and includes an equation for the mass density in the form of an inhomogeneous Helmholtz equation. This paper shows that, by selecting appropriate mass sources (the inhomogeneous term in the mass density equation), it is possible to obtain a mass density distribution in the near-surface region that reflects the characteristics of the Abbott–Firestone curve, which is widely used in engineering practice to describe surface roughness. Using a flat surface as an example, the influence of the model parameters on the core, peak, and valley zones of the material ratio curve is investigated. When modeling the influence of the surface roughness parameters of a real body on the effective elastic moduli of thin films, it is assumed that the local Young’s modulus and Poisson’s ratio are functions of the mass density. The solution to the problem for a stretched layer is expressed in quadratures, and its analysis is performed using numerical methods. In particular, it is shown that the characteristic length scales of the size effects of the effective elastic moduli depend on the structural heterogeneity of the material and on the sizes of the core, peak, and valley zones of the roughness profile.
Numerous creative kinds of materials with specific aeronautical uses have been developed over the last few decades. Engine oil serves several functions in an aviation engine, which involve lubrication, cooling purposes, maintenance, rust prevention, and noise lessening. The most important is lubrication. Naturally, without lubrication, all moving components would rapidly run away. This project's effort focuses on reducing expenses by increasing the useful lives of aeroplane parts, in addition to enhancing fuel economy, carrying capacity, and flying range. The significance of the heat transfer analysis on magnetohydrodynamic dusty hybrid nanofluid across a porous stretching sheet with Darcy-Forchheimer flow and thermal radiation are examined in this present study. Appropriate self-similarity conversion is used to change the PDEs into ODEs. After applying transformations, we employed the Bvp4c in the MATLAB solver for graphical purposes. This was done af-ter doing the modifications. Graphs and tables illustrate the effect of active parameters on the fluid's ability to convey significance. The same pattern emerges with increasing values of the I parameter: the energy outline and velocity outline both show a decline. The study we conducted led us to conclude that dusty nanofluids are not as effective in transferring heat as hybrid nanofluids.
The objective of this paper is to structure the role of the human in metal machining by organising operator participation into three functional roles and analysing their evolution across increasing levels of automation. Using drilling operations as a representative case study, the research integrates the Human-centric Manufacturing Model with the ISA-95 architecture to compare human involvement in classical, automated and autonomous production environments. The results indicate a systematic shift of human contribution from direct physical execution toward supervisory, cognitive and organisational functions. Advances in machine learning, digital twins and multi-sensor monitoring - together with increasing material complexity such as composite stacks and additively manufactured components - transform machining into a data-driven process requiring human validation and interpretation rather than manual intervention. Consequently, boundary physical roles diminish, while cognitive-augmented and analytical-organisational roles become central to planning, monitoring and governance of autonomous systems. The findings show that increasing autonomy does not eliminate the human from manufacturing but redefines the operator as a supervisor, interpreter and orchestrator of cyber-physical production systems, supporting safety, reliability and continuous improvement in Industry 5.0 environments.
This study presents a continuous analytical model for the dynamic analysis of thin-walled steel box beams filled with polymer concrete. The model incorporates transverse, longitudinal, and torsional vibrations, includes damping effects, and enables the calculation of frequency response functions. Its formulation is based on a coupled system of partial differential equations derived from Timoshenko beam theory and de Saint Venant’s torsion model, providing a closed-form solution for modal parameters and vibrational response. Experimental validation performed on a steel-polymer concrete beam showed close agreement between predicted and measured natural frequencies, mode shapes, and frequency response functions. Additional comparisons with one-dimensional, three-dimensional finite element models as well as rigid finite element models from the literature confirmed that the proposed approach can serve as an attractive alternative to other computational methods. The model offers a favorable balance between accuracy and simplicity, making it well suited for preliminary design and vibration analysis of steel-polymer composite structures.
This study examines the vibration behavior of fiber-metal laminate (FML) composite cantilever beams reinforced with multi-walled carbon nanotube (MWCNT) nanoparticles, employing both experimental and numerical approaches. Vibration analysis of FML beams is essential to ensure structural stability, fatigue resistance, and reliability in critical applications such as aircraft fuselage panels, ship hulls, and other lightweight load-bearing components. Experimental analyses were performed using a Fast Fourier Transform (FFT) analyzer, while numerical simulations were conducted in ANSYS under cantilevered boundary conditions. The natural frequencies and vibration responses of 2/1 FML composite beam configurations were evaluated to determine the influence of varying MWCNT content (3–5% by weight). The results reveal that an increase in nanoparticle concentration leads to higher bending natural frequencies and lower vibration amplitude ratios. Among the tested specimens, the 5% MWCNT-reinforced FML beam demonstrated superior dynamic stability compared to the 3% and 4% specimens, whereas the 3% MWCNT composites exhibited a more pronounced damping effect. Theoretical and numerical predictions of vibration amplitude ratios and natural frequencies showed strong agreement with experimental results. Furthermore, it was observed that increasing the beam length reduced the natural frequencies of the composite beams. Overall, the study confirms that MWCNT reinforcement significantly enhances the dynamic performance of FML composites within the investigated range. These results offer valuable insights for structural health monitoring and integrity assessment of advanced laminated composites. Future research should explore MWCNT concentrations beyond 5%, as agglomeration effects may influence the observed frequency trends. It is also found that the vibration amplitude gradually decreases from the free end to the fixed end of the composite FML cantilever beam when the beam vibrates at the first bending natural frequency.
Agricultural automation is an increasingly common trend. These activities involve the use of solutions well-known from other high-tech industries. Such solutions are robots replacing human work in places where work is monotonous, in a forced body position or must be highly efficient. As an example, this publication cites the analysis of needs and the construction of a robot for mushroom picking. Poland is one of the world's largest producers of these mushrooms. Poor working conditions and growing wage pressure cause producers to look for other solutions. There are many challenges in the automation of mushroom picking, starting from unfavourable environmental conditions, the construction of mushroom growing racks and quality issues. The introduction to the paper includes an overview of solutions for automatic mushroom harvesting. In the further part of the paper, the Authors focus on presenting and describing their own platform for detecting and for picking Agaricus bisporus mushrooms. Due to the specific nature of mushroom cultivation, it was necessary to analyse environment and mechanical limitations. The authors chose SCARA kinematics, therefore the paper presents an analysis of kinematics, a description of the structure, accuracy tests and proposals for further improvements in the robot design. Because entire arm moves along the Z axis, it was experimentally checked how such a large moving mass affects the positioning accuracy.
Magnetohydrodynamic (MHD) flows of hybrid nanofluids offer significant potential in biomedical engineering, particularly for enhancing site-specific drug delivery. Traditional drug transport methods often lack precise control over particle accumulation, limiting therapeutic efficiency. This study develops a novel two-phase MHD model that couples an Fe₂O₃–H₂O nanofluid with a gallium-based liquid metal under interfacial slip conditions to improve targeted drug delivery. The primary objective is to investigate the combined effects of magnetic fields, interfacial slip, and nanoparticle dynamics on fluid transport, heat transfer, and drug accumulation at the nanofluid–metal interface. The model incorporates Brownian motion, thermophoresis, viscous dissipation, thermal radiation, and chemical reaction effects to accurately capture the coupled mass and heat transfer processes. The governing nonlinear equations are transformed using similarity methods and solved numerically with a finite difference approach, incorporating adaptive mesh refinement to ensure stability and convergence. Results indicate that increasing the magnetic field (Hartmann number) enhances interface stability by up to 36%. Brownian motion and thermophoresis increase nanoparticle concentration near the interface by approximately 22% and 18%, respectively, facilitating improved drug transport. The combined electromagnetic and thermophoretic effects further raise the local drug accumulation by nearly 30% compared to non-magnetic flow conditions. In conclusion, the proposed hybrid nanofluid–liquid metal two-phase model provides a new computational framework for magnetically guided, site-specific drug delivery. The findings offer valuable insights for designing advanced biomedical systems where precise control of nanoparticle-mediated drug transport is critical, demonstrating that interfacial slip and electromagnetic effects can significantly enhance therapeutic efficiency.
Heat transfer within the photovoltaic module determines the operating temperature of the cells and, consequently, the amount of energy loss. A widely used material for the photovoltaic (PV) arrays is crystalline silicon. Energy losses occurring during the photovoltaic energy conversion process in a power plant average 26.8% per year, which is due to many factors. It can be stated that the major fraction of losses is related to the temperature increase of the silicon solar cells. In real operating conditions, solar cells and modules operate at different temperatures, either due to changes in ambient temperature (atmospheric conditions changing with the seasons) and cooling rate, depend-ing on wind speed and insolation, rain, snow, etc., or due to changes in the amount of heat (electrical power lost on the internal re-sistance of the cell), emitted during their operation. The possibility of operation of these devices in ground applications in the temperature range from -20 to +70oC should be taken into account. The given range, of course, does not apply to operation in the tropics. The detailed studies of the impact of temperature on the electrical parameters of crystalline silicon solar cells have been presented. The theoretical justification of the temperature influence mechanism on the exploitation parameters of the silicon solar cells has been submitted. The better photovoltaic cells and modules, the lower the values of temperature coefficients, in particular, attention should be paid to the decrease in generated energy with increasing temperature. The experimental results were compared with the theoretical predictions and the results, obtained by other authors and producers. The conclusions emphasize the need to maintain the optimal tempera-ture from the energy efficiency point of view, in the range of 22-25oC, which is most easily achieved by using one of the methods of cool-ing the rear surface of the module.
The features of phase formation in Ni–Cr and Ni–Mo solid solutions during interaction of the components of Ni–20 wt% Cr and Ni–20 wt% Mo powder mixtures by high-temperature vacuum treatment and mechanical alloying were investigated. During high-temperature treatment, FCC nickel-based solid solutions formed due to diffusion of chromium or molybdenum into the nickel lattice. Diffusion interaction started at 1000°C and completed at 1200°C with formation of homogeneous Ni–Cr or Ni–Mo solid solutions. Mechanical alloying behavior strongly depended on the alloying element. Homogeneous FCC Ni–20 wt% Cr solid solution powder with a particle size of 50–100 μm was obtained after 15 h of low-energy milling in a Pulverisette 6 planetary mill. In contrast, complete homogenization of the Ni–20 wt% Mo system was not achieved even after 60 h of milling. The obtained results demonstrate that mechanical alloying is more efficient for Ni–Cr powders, whereas high-temperature vacuum synthesis is more suitable for obtaining homogeneous Ni–Mo solid solutions.
In this paper, we introduce an innovative improvement to the traditional Extended Kalman Filter-based Simultaneous Localization and Mapping framework, called Entropy-Gated Feature-Aware EKF-SLAM. Our approach presents a flexible information-theoretic mechanism that adjusts the impact of each landmark observation in real-time, depending on the historical entropy of its measurement distribution. Traditional EKF-SLAM assumes that all observations are equally trustworthy based on noise models. In contrast, our approach assesses long-term measurement consistency using rolling entropy profiles, which allows for per-landmark trust gating during the innovation update. Each landmark's innovation is specifically weighted by a coefficient derived from normalized entropy, which enables the filter to discount features that are ambiguous, noisy, or aliased, while placing emphasis on landmarks that are historically stable and informative. This entropy-aware gate is integrated directly into the EKF correction equations, maintaining the prediction model intact and ensuring computational efficiency is preserved. A complete mathematical derivation supports theoretical development, and we present a detailed simulation in MATLAB featuring a mobile robot navigating a 2D environment with five landmarks. Results from experiments show enhanced accuracy in trajectory and greater robustness when faced with perceptual ambiguity and dynamic sensor noise. This change represents an important advancement in adaptive uncertainty modeling in filtering-based SLAM frameworks.
This paper investigates the dynamic response of functionally graded piezoelectric plates using the finite element method based on first-order shear deformation theory. The plates are composed of materials with properties that vary across their thickness, following a simple power-law relationship with the volume fraction of the constituents. Different boundary conditions and configurations, such as FG plates with piezoelectric layers and FGP plates with piezoelectric layers, are considered to analyze the dynamic behavior of functionally graded piezoelectric material (FGPM) plates under mechanical and electrical loadings. Comparisons with previous studies validate the accuracy of the obtained results. Furthermore, the effects of various parameters, such as the power-law index, maximum displacement from static analysis, maximum displacement in the first vibration mode, and others, on the dynamic response of FGM and FGPM plates with piezoe-lectric layers are investigated. The results demonstrate the influence of different power-law exponents and piezoelectric layers on plate behavior. The findings show that the maximum static and dynamic deflections of the plate vary with the power-law index, but their ratio remains constant for all values of "n". Additionally, the effect of electrical loading on the dynamic response of FGP plates is examined. The study reveals that the maximum dynamic displacement of the plates increases with the power-law constant under electrical loading. Overall, this study provides valuable insights into the dynamic response of functionally graded piezoelectric plates, contributing to the understanding and design of such structures in various engineering applications subjected to dynamic loading.
This study presents the design, thermodynamic analysis, and experimental validation of a condensing economizer dedicated to bi-omass-fired heating boilers. The proposed device incorporates a centrally arranged flue-gas distribution chamber and a water-cooled tube bundle that enables recovery of both sensible heat and the latent heat released during water-vapor condensation. The economizer, protected under Patent No. PL247342B1 was evaluated through analytical modelling and laboratory testing per-formed on pellet boilers with rated outputs of 25–100 kW. The heat-transfer model, which couples convective and condensation mechanisms, demonstrated that the presence of water vapor in the flue gas significantly increases the effective heat transfer coef-ficient, thereby enhancing the overall energy recovery potential. Prototype units with heat-exchange surfaces ranging from 0.63 to 2.90 m2 achieved thermal outputs between 1.8 and 7.8 kW, with stable condensation occurring when the boiler’s return-supply temperature difference was maintained at ΔT ≥ 25 °C. Experimental results confirmed a substantial reduction in flue-gas tempera-ture (down to 59 °C at nominal load and 34 °C at reduced load), which translated into an increase in gross boiler efficiency to 107% and 119%, respectively. All emission indicators complied with the Ecodesign Directive (EU) 2015/1189, while seasonal effi-ciency analysis yielded an Energy Efficiency Index of 167.8 (A+++). The findings demonstrate that the integrated counterflow–crossflow economizer significantly improves waste-heat recovery, reduces fuel consumption, and lowers particulate and gaseous emissions. Owing to its modular construction and corrosion-resistant design, the system is suitable for practical implementation in modern biomass heating installations.
The operational capability of Autonomous Underwater Vehicles (AUVs) depends on the precise modeling of their dynamic beha-viors under environmental disturbances. Traditionally, model parameter estimation processes—conducted through tank tests, empirical cal-culations, and Computational Fluid Dynamics (CFD) analyses—are giving way to data-driven approaches due to high costs, intensive com-putational loads, and real-time adaptation constraints. This study systematically reviews the machine learning (ML) techniques developed for estimating AUV dynamic model parameters over the ten-year period from 2015 to 2025. Within the scope of this review, classical methods such as Support Vector Regression (SVR), Artificial Neural Networks (ANN), and Multi-Output Gaussian Processes (MOGP) are examined alongside Physics-Informed Neural Networks (PINN), which integrate physical laws into the learning process, and Explainable Artificial In-telligence (XAI) approaches that ensure model transparency. Furthermore, LSTM and Transformer architectures, which model the temporal dependencies of AUV motions, and Reinforcement Learning (RL) based online adaptation strategies are analyzed. The reviewed methods are presented comparatively in terms of data requirements, computational complexity, physical consistency, and validation strategies. This review serves as a guide for researchers in the field of underwater robotics, highlighting the current state and future research directions of modern machine learning paradigms in AUV system identification processes.
This article presents a new approach to intelligent and efficient fault diagnosis for bearings, which are important mechanical components that are widely used in modern industry. They are are among the most common causes of induction motor failure. Traditional approaches that exploit vibration signals, have several shortcomings, such as the number of intrusive vibration sensors, complex calculations, and limited learning capability. Motor current signature analysis uses a non-intrusive sensor to easily collect stator current signals from a power source. To rapidly process rotating machine failures and automatically provide an accurate diagnosis in the face of increasing condition data, conventional deep neural network models present limitations and inaccurate fault diagnosis results. To overcome this problem, a new intelligent deep learning architecture was proposed. To enhance the predictive capability of our deep neural network model (DNN), we set out to associate specific coefficients to each level and variation, and the results obtained were compared with other models such as support vector machines, k-nearest neighbors, decision tree, long short-term memory (LSTM), and convolutional neural network (CNN). Experimental tests for the early detection of bearing faults under different loads verify the effectiveness of the proposed approach, providing a fast and, reliable diagnosis capable of achieving a high diagnostic accuracy that is superior to existing methods.
Magnesium alloys have great potential for industrial applications due to their very good properties. However, their main disadvantage limiting their use is a tendency to ignite during machining. This also eliminates the possibility of using abrasive machining to improve their quality. Precision machining may be a solution to this problem. Despite the potentially significant benefits of applying this method to magnesium alloys, the amount of research in this area remains negligible. This paper therefore focuses on the analysis of the precision milling process of AZ31B and AZ91D magnesium alloys using a double-flute carbide end mill. The aim of the research is to analyse the material removal process during precision machining, focusing on the observation of chip formation. This will enable to minimise the material ploughing phenomenon, which adversely affects the machining process and surface condition. The study also analysed the formation of burrs, which are also an undesirable effect of machining. As part of the research, temperature measurements were also taken in the cutting zone, which are particularly important in terms of safety. During the study, significant differences in the size of chips formed by cutting flutes were observed, caused by uneven tool operation. Burrs were also observed on the edges of the slot due to the low undeformed chip thickness. It was also shown that the maximum temperature in the cutting zone remained below the ignition temperature, so this machining method can be considered safe.
Thermal loading may develop in horizontal boiler components - drums, superheater inlet and outlet headers, and steam lines - when steam con-denses on their internal surfaces. The problem is typical for start-ups, shutdowns, and emergency conditions. At start-up, condensate occupies the lower region of the component, whereas steam remains in the upper region. Consequently, the circumferential temperature difference in a horizontal pressure part can become very large, in some cases approaching 200 K, because super-heated steam may heat the upper wall while condensate at saturation temperature cools the lower wall. Such non-uniform heating can produce local plastic deformation, impair drainage, and further intensify thermal stresses; deformed superheater headers are a frequent practical example. This study develops an inverse heat conduc-tion procedure for reconstructing the temperature field in the cross-section of a horizontal pressure element. The reconstructed field is then used in a finite element analysis to calculate transient thermal stresses. The component is assumed to be externally insulated, and equally spaced thermocouples are placed inside the wall near the inner surface. Numerical tests are carried out for several numbers of measurement locations distributed over half of the circumference, with detailed results reported for 7, 13, and 19 points. The temperature field between the outer surface and the thermocouple radius is obtained from a direct heat conduction solution. The temperature and heat-flux histories at the thermocouple radius are then used as input for the inverse analysis, which provides both the in-ner-wall temperature and the local heat transfer coefficient in the steam and condensate zones.
