Journal of Experimental and Theoretical Analyses

Roll-to-roll production of thin organic and large-area electronic (TOLAE) devices often involves a two-step process per functional layer: a continuous, un-pattered deposition of the film and subsequent structuring process, such as laser ablation. Thin-film organic devices should be protected using ultra-barrier films. To perform laser ablation of functional layers on top of such barrier films, in particular that of transparent electrodes, highly selective laser ablation is required to completely remove the layers without damaging the thin-film barrier layers underneath. When targeting highly selective laser ablation of indium tin oxide (ITO) on top of silicon nitride (SiN) barrier layers with a 1064 nm picosecond or 1030 nm femtosecond laser, we observed the emergence of visible large-scale patterns due to local variations in ablation quality. Our investigations using a very sensitive Raman spectroscopy setup show that the observed ablation variations stem from subtle differences in optical path length within the heat-stabilized plastic substrates. These variations are likely caused by minute, localized changes in the refractive index, introduced during the bi-axial stretching process used in film fabrication. Depending on the optical path length, these variations lead to either constructive or destructive interference between the incoming laser beam and the light reflected from the back surface of the substrate. By performing laser ablation under an angle such that the reflected and incoming laser beam do not spatially overlap, highly selective uniform laser ablation can be performed, even for two stacked optically transparent layers. Full article

In this paper, an integrated geometric and thermal-induced errors prediction approach for active error compensation in machine tools is proposed and evaluated. The proposed approach is based on a hybrid of physical and neural network predictive modeling to drive an adaptive position controller for real-time error compensation including geometric and thermal-induced errors. Error components are formulated as a three-dimensional error field in the time-space domain. This approach involves four key steps for its development and implementation: (i) simplified experimental procedure combining a multicomponent laser interferometer measurement system and sixteen thermal sensors for error components measurement, (ii) artificial neural network-based predictive modeling of both position-dependent and position-independent error components, (iii) tridimensional volumetric error mapping using rigid body kinematics, and finally (iv) implementation of the real-time error compensation. Assessed on a turning center, the proposed approach conducts a significant improvement of the machine accuracy. The maximum error is reduced from 30 µm to less than 3 µm under thermally varying conditions. Full article

Natural fiber-reinforced unidirectional composites are increasingly adopted in modern industries due to their superior mechanical performance and desirable properties from both material and engineering perspectives. Among various approaches, representative volume element (RVE) generation and analysis is considered one of the most suitable and convenient methods for predicting the elastic moduli of composites. The main aim of this study is to investigate and compare the elastic moduli of natural fiber–reinforced unidirectional composite RVEs using theoretical, numerical, and machine learning models. The numerical predictions in this study were generated using the ANSYS Material Designer tool (version ANSYS 19). A comparison was made between experimental results reported in the literature and different theoretical models, showing high accuracy in validating these numerical outcomes. A dataset comprising 1600 samples was generated from numerical models in combination with the well-known theory of RVE, namely rule of mixture (ROM), to train and test two machine learning algorithms: Random Forest and Linear Regression, with the goal of predicting three major elastic moduli—longitudinal Young’s modulus (E₁₁), in-plane shear modulus (G₁₂), and major Poisson’s ratio (V₁₂). To evaluate model performance, mean squared error (MSE), mean absolute error (MAE), mean absolute percentage error (MAPE), and coefficient of determination (R²) were calculated and compared against datasets with and without the theoretical values as input variables. The performance metrics revealed that with the theoretical values, both Linear Regression and Random Forest predict E₁₁, G₁₂, and V₁₂ well, with a maximum MSE of 0.033 for G₁₂ and an R² score of 0.99 for all cases, suggesting they can predict the mechanical properties with excellent accuracy. However, the Linear Regression model performs poorly when theoretical values are not included in the dataset, while Random Forest is consistent in accuracy with and without theoretical values. Full article

This research presents a detailed Life Cycle Assessment (LCA) of 26 mm Crown cork metal closures used in glass bottle packaging, with the objective of quantifying and comparing their environmental impacts across all life cycle stages. This study adheres to ISO 14040 and ISO 14044 standards and utilizes Microsoft Excel for structuring and documenting input–output data across each phase. The LCA encompasses three primary stages: raw material production (covering iron ore extraction and steel manufacturing), manufacturing processes (including metal sheet printing, forming, and packaging of closures), and the transport phase (distribution to bottling facilities). During the Life Cycle Inventory (LCI), steel production emerged as the most environmentally burdensome phase. It accounted for the highest emissions of carbon dioxide (CO₂), carbon monoxide (CO), nitrogen oxides (NO_x), and sulphur oxides (SO_x), while emissions of heavy metals and volatile organic compounds were found to be negligible. The Life Cycle Impact Assessment (LCIA) was carried out using the Eco-Indicator 99 methodology, which organizes emissions into impact categories related to human health, ecosystem quality, and resource depletion. Final weighting revealed that steel production is the dominant contributor to overall environmental impact, followed by the manufacturing stage. In contrast, transportation exhibited the lowest relative impact. The interpretation phase confirmed these findings and emphasized steel production as the critical stage for environmental optimization. This study highlights the potential for substantial environmental improvements through the adoption of low-emission steel production technologies, particularly Electric Arc Furnace (EAF) processes that incorporate high percentages of recycled steel. Implementing such technologies could reduce CO₂ emissions by up to 68%, positioning steel production as a strategic focus for sustainability initiatives within the packaging sector. Full article

We describe the problem of estimating the speaker’s head orientation from the asynchronous multi-channel waveforms observed by microphones distributed in a room. In particular, we address a novel problem of estimating head orientation from sound captured by fewer microphones than the number of distinct head orientations to be distinguished. This is because the head orientation is an important clue indicating the speaker’s intended conversational partners. Head orientation estimation technology is an essential technology within environmental intelligence technology, which uses sensors embedded in rooms to monitor and support people’s activities. We propose a head orientation estimation method that aims to achieve high angular resolution using a small number of microphones. The proposed method achieves high estimation accuracy by using the spatial radiation pattern of the sound source as clues and by integrating information from multiple frequency bands. We conducted an experiment to estimate head orientation with an angular resolution of $15\mathrm{degrees}$ under observation conditions using six microphones. Experimental results showed that higher estimation accuracy was obtained than the conventional method using distributed microphone arrays (Oriented Global Coherence Field method) and the conventional method using distributed microphones (Radiation Pattern Matching method). The proposed method utilizing multiple frequency bands achieved the best performance with a mean absolute error of $10.58\mathrm{degrees}$ in the task of classifying 24 distinct head orientations. Full article

Conventional thermomechanical models recently failed to reproduce the temperature profile measured during rapid annular laser heating of a disk, with discrepancies of up to 150 K. One might have thought that these discrepancies resulted from neglecting the so-called “strong” thermomechanical coupling. However, the discrepancies seemed too large to be explained in this way, suggesting that another more significant phenomenon was involved. In this paper, we first present the laser heating experiment that highlights the failure of conventional models. We then demonstrate that the established strong coupling thermomechanical theory cannot account for the observed divergences, as its impact on temperature does not exceed about 1 K. To address this limitation, we propose a new, more comprehensive thermomechanical coupling formalism based on the thermodynamics of irreversible processes (TIP). Its originality lies in the explicit consideration of spatial strain transport, introduced through the notion of strain flux. This approach reveals a previously unrecognized coupling term representing mechanical work production by heat-to-work conversion. Finally, we provide a quantitative estimate of the influence of this new term by reconsidering the heating experiment. The calculation shows that it could explain the discrepancies between theory and measurement. Although applied here to a specific case, this result supports the validity of our approach. It demonstrates that such coupling must be considered whenever a system is subjected to rapid thermal and mechanical transients. Full article

Traditional empirical correlations for predicting saturated flow boiling heat transfer coefficients (HTC) often struggle with accuracy and generalizability, particularly across different refrigerants, heat exchanger geometries, and operating conditions. To address these limitations, this study investigates the application of machine learning for more robust HTC prediction. A comprehensive dataset was compiled, consisting of 22,608 data points from over 140 published studies, covering 18 pure refrigerants under diverse experimental setups. The primary goal was to evaluate the performance of different machine learning approaches—Wide Neural Network (WNN), Linear Regression (LR), and Support Vector Machine (SVM)—in predicting HTCs across varying tube types and heat exchanger configurations. The results indicate that the WNN model achieved the highest predictive accuracy, with a Root Mean Square Error (RMSE) of 1.97 and a coefficient of determination (R²) of 0.91, corresponding to less than 5% prediction error for all refrigerants. These outcomes confirm that machine learning models can effectively capture the complex thermofluid interactions involved in boiling heat transfer. This work demonstrates that data-driven methods provide a reliable and generalizable alternative to empirical correlations. Full article

To demonstrate the difference between non-ideal experiments and idealized analytical models, bending vibrations of a frame structure have been analyzed in the context of hands-on teaching in structural dynamics. Both experimental modal analysis and model-based evaluation of system dynamics have been performed. The investigations have been limited to mechanical vibrations in the low-frequency range. It has been found that even simple mechanical models are very useful to explain, understand, and validate experimental results. The latter have been derived from one key principle of analytical dynamics—the Lagrange formalism. The article is written for students in mechanical engineering and related fields as well as for the academic community. The latter could use the results as a benchmark problem in academic teaching as well as in applied research. Full article

The research presents the thermal performance comparison of silicone and non-silicone thermal pads using a steady-state thermal interface material (TIM) testing apparatus. The TIM tester follows standard guidelines for testing thermal properties. TIMs are applied between two solid surfaces to improve heat transfer by eliminating air gaps that naturally occur due to surface roughness and non-flatness. Since TIMs possess significantly higher thermal conductivity than air, they effectively reduce contact resistance at the interface, thereby minimizing the risk of overheating in electronic systems. In this work, the thermal resistances of silicone and non-silicone thermal pads were compared over a pressure range of 10–50 psi. Results indicate that non-silicone pads consistently exhibit lower thermal resistance than their silicone counterparts under identical testing conditions. Full article

In this review paper, we briefly discuss the occurrence of oil spills and their behavior under natural sea conditions and clean-up methods, as well as their environmental and economic impacts. We discuss methodologies for oil spill modeling used to predict the fate of a spill under dynamic physical and chemical processes. Weathering processes such as evaporation, emulsification, spreading, dissolution, dispersion, biodegradation, and sedimentation are considered within easy-to-use modeling frameworks. We present simple models based on the principles of thermodynamics, mass transfer, and kinetics that under certain conditions can predict oil thickness, volume, area, composition, and the distribution of toxic compounds in water and air over time for various types of oil and their products. Modeling approaches for underwater oil jets, including applications related to the 2010 BP oil spill in the Gulf of Mexico, are reviewed. The influence of sea surface velocity and wind speed on oil spill mapping, spill location, oil spill trajectory over time, areas affected by light, medium, and heavy oil, and comparisons between satellite images and model predictions are demonstrated. Finally, we introduce several recently published articles on more recent oil spill incidents and the application of predictive models in different regions. We also discuss the challenges, advantages, and disadvantages of various models and offer recommendations at the end of the paper. Full article

According to the International Diabetes Federation, approximately 537 million adults suffered from diabetes in 2021, a number that is projected to rise to 783 million by 2045. Insulin is a hormone produced by the pancreas that regulates blood glucose levels; for people suffering from diabetes, insulin activity may be reduced or absent, and therefore, administration of insulin may be necessary to maintain healthy blood glucose levels. Recombinant human insulin is commercially produced using a variety of host microorganisms, such as bacteria and yeast. Nevertheless, few studies have assessed the environmental impacts associated with different upstream medium formulations and their contribution to the overall environmental footprint of recombinant insulin production. Here, Life Cycle Assessment (LCA) is conducted on various upstream media used in insulin production—including pre-cultivation, growth, feed, and induction media—capturing the impacts associated with both their supply chains and their on-site preparation. Hotspots of environmental impacts are identified, and different alternatives for input materials and process conditions are compared in terms of impacts. The findings reported here can serve to guide process and sustainability optimization of the upstream production process from an operational process perspective. Finally, the identification of hotspots enables the implementation of impact reduction measures in bioprocess design, which have the potential to significantly improve the sustainability of insulin production. Full article

This study evaluates the environmental impact of swimming goggles through a Life Cycle Assessment (LCA), comparing virgin and recycled polycarbonate models. It identifies key hotspots, assesses circular economy benefits, and examines barriers to sustainable disposal, aligning with European Union’s (EU) 2050 sustainability objectives. The LCA was modeled using SimaPro, with the Environmental Footprint (EF) 3.1 method to analyze 16 impact categories (e.g., climate change, human toxicity, resource depletion). Two scenarios were assessed: (1) virgin polycarbonate production and (2) a closed-loop system (80% recycled content, 30% reintegration). Primary data from a survey of 150 competitive swimmers quantified disposal behaviors. The lens production phase (bisphenol A processing) dominated impacts, contributing to 62% of climate change and 75% of human toxicity. The recycling scenario reduced total impact by 23.1% (119 → 91.5 mPt), with significant declines in freshwater ecotoxicity (−28.6%) and marine eutrophication (−25.1%). Survey data highlighted critical gaps: low consumer participation in recycling due to lack of awareness and inadequate disposal infrastructure. Recycled polycarbonate can substantially mitigate environmental impacts, but systemic barriers (consumer behavior, collection gaps) limit progress. Future work should explore bio-based polymers and policy incentives to accelerate circularity. Full article

The present article compares the Proportional Directional Control Valve (PDCV) and the Stepper Motor-Driven Orbitrol Valve (SMDOV) coupled to the steering system of the articulated steered vehicles. Simulation models of both valve coupled steering systems are developed in a MATLAB (r2019b) environment, and results are well validated with the experimental data. Comparison analysis is performed between the PDCV and SMDOV steering systems by controlling the desired position demand using a conventional PID controller. From the comparative study, it is observed that the SMDOV provides almost 50% energy reduction, but the valve response is low compared to PDCV. However, the steering response provided by the SMDOV is quite enough for performing steering operations in mining conditions. Overall, the orbitrol valve-assisted steering system offers more efficient and smooth steering than the PDCV valve. The future work of the present study extends to the development of autonomous steering operation using an orbitrol valve-operated articulated steering system. Full article

The Portevin-Le Chatelier (PLC) effect has been studied for many decades, yet the influence of testing modes has received limited attention. In the past 20 years, it has become increasingly recognized that the stiffness of the testing machine can significantly affect the occurrence of jerky flow, particularly the serrations observed during tensile tests. This study addresses this issue by conducting tests on the Al-Mg alloy AA5083H111, which contains a substantial amount of diffusible magnesium in solid solution and exhibits dynamic strain aging, resulting in a pronounced PLC effect. Both electromechanical and servohydraulic testing machines were used in the tests; these machines differ in stiffness and control technology for applied strain rates. The study also explored different control modes, including stroke control for both machines and true strain control for the servohydraulic machine. The findings indicate that machine stiffness has a moderate effect on material behavior, and no single machine or testing mode can precisely control the strain rate in the sample during the PLC effect. However, it was noted that true strain rate control using a servohydraulic machine comes closest to accurately reflecting the material’s behavior during jerky flow. Full article

This study presents the electrical and optical properties of 35P₂O₅–xV₂O₅–(65–x) Sb₂O₃ glasses for 0 ≤ x ≤ 65 mol%. The direct current (DC) resistivity was measured by the Van der Pauw method and optical absorption spectra were taken in the Ultraviolet–Visible-Near-Infrared (UV–VIS–NIR) range. Electrical transport is attributed to simultaneous hopping of small polarons (SPs) between V⁴⁺ and V⁵⁺ (vanadium ion) sites and small bipolarons (SBPs) between the Sb³⁺ and Sb⁵⁺ (antimony ion) sites. The resistivity exhibits a non-linear dependence on the ionic fraction of vanadium (n_v), whereas the resistivity exhibits a minimum in the composition range 0 ≤ n_V ≤ 0.3, and a resistivity maximum was observed in the range 0.3 ≤ n_V ≤ 0.5. On further increasing n_v, the resistivity exhibits a monotonic decline. In the composition range 0 ≤ n_V ≤ 0.3, where the hopping distance between V ions decreases, while that between the Sb ions increases, the resistivity minimum has been shown to be the consequence of decreasing tunneling distance of SPs between the V⁴⁺ and V⁵⁺ ion sites. In the composition range 0.3 ≤ n_V ≤ 0.5, the resistivity, activation energy for DC conduction, glass transition temperature, and density exhibit their respective maxima even though the separation between the V⁴⁺ and V⁵⁺ sites continues to decrease. This feature is explained by enhanced localization of electrons on account of increased disorder (entropy) among the SPs and SBPs, like that of Anderson localization. This argument is further supported by a shift in the polaronic optical absorption bands associated with the SPs and SBPs toward higher energies. The transport behavior of all the glasses except the x = 0 composition has been explained by adiabatic transport, principally, by the SPs on V ions while the Sb ions contribute little to the total transport process. The results provide a clear relation between composition, polaron/bipolaron contributions, and conduction in these glasses. Full article

Background: Different treatments may be required for paroxysmal versus non-paroxysmal atrial fibrillation. However, they may be difficult to distinguish on an electrocardiogram (ECG). Machine learning methods may aid in differentiating these conditions, yet current approaches either do not preserve patient privacy or tend to make the unrealistic assumption of uniform data. Methods: We create a non-independently and identically distributed dataset for paroxysmal versus non-paroxysmal atrial fibrillation detection. Two baselines (a centralized classifier and a federated classifier) are trained, and the performances of classifiers pretrained on shared data before federated training are compared. Results: The centralized classifier outperforms all other models ($p<0.001$), while the federated model is the worst-performing model ($p<0.0001$). Classifiers that are pretrained on 10%, 30%, and 50% of shared data (CNN-10, CNN-30, CNN-50, respectively) perform better than the purely federated model ($p<0.0001$ for all models). Furthermore, no performance difference is observed between any of the models trained on shared data (the null hypothesis of a one-way analysis of variance test between the shared data models is not rejected, $p=0.954$). Conclusions: The partial sharing of data in creating federated machine learning models may significantly improve performance. Furthermore, the volume of data required to be shared may be relatively small. Full article

Seed priming is an innovative pre-planting technique to improve germination and accelerate early seedling growth, offering a sustainable and eco-friendly alternative to chemical treatments. In this study, silver nanoparticles (AgNPs) were synthesized using flower extracts of neem plants for the first time, alongside the conventional neem leaf extract-based AgNPs, and their comparative efficacy was evaluated in wheat seed priming. The biosynthesized AgNPs were characterized through UV–Vis spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive Spectroscopy (EDS), Dynamic Light Scattering (DLS), and zeta potential analysis to confirm their formation, stability, and surface functionality. Wheat seeds were primed with varying concentrations (25, 50, 75, 100 mg/L) of flower-mediated nanoparticles (F-AgNPs) and leaf-mediated nanoparticles (L-AgNPs). Effects on seed germination, seedling growth, plant pigments, secondary metabolites, and antioxidant enzyme activities were systematically investigated. The results indicated that F-AgNP priming treatment significantly enhanced wheat seedlings’ performances in comparison to L-AgNPs, which could be attributed to the difference in phytochemical profiles in the extracts. This study contributes a comparative experimental analysis highlighting the potential of biogenic AgNPs—particularly those derived from neem flower extract—offering a promising strategy for enhancing seedling establishment in wheat through seed priming. Full article

Material extrusion additive manufacturing (MEAM) has emerged as a versatile and widely adopted 3D printing technology due to its cost-effectiveness and ability to process a diverse range of materials. However, achieving consistent part quality and repeatability remains a challenge, mainly due to variations in process parameters and material behavior during fabrication. In-situ monitoring and advanced process control systems have been increasingly integrated into MEAM to address these issues, enabling real-time detection of defects, optimization of printing conditions, reliability of fabricated parts, and enhanced control over mechanical properties. This review examines the state-of-the-art in-situ monitoring techniques, including thermal imaging, vibrational sensing, rheological monitoring, printhead positioning, acoustic sensing, image recognition, and optical scanning, and their integration with process control strategies, such as closed-loop feedback systems and machine learning algorithms. Key challenges, including sensor accuracy, data processing complexity, and scalability, are discussed alongside recent advancements and their implications for industrial applications. By synthesizing current research, this work highlights the critical role of in-situ monitoring and process control in advancing the reliability and precision of MEAM, paving the way for its broader adoption in high-performance manufacturing. Full article

Renewable fuels produced from animal- and plant-based edible oils have emerged as an alternative to oil and natural gas. Burgeoning interest in renewables can be attributed to the rapid depletion of fossil fuels caused by the global energy demand and the environmental advantages of renewables, specifically reduced emissions of greenhouse gases. An important property of the feedstock that is crucial for the conversion of edible oils to renewable fuels is the total acid number (TAN), as even a small increase in TAN for the feedstock can lead to corrosion of the catalyst in the refining process. Currently, the TAN is determined by potentiometric titration, which is time-consuming, expensive, and requires the preparation of reagents. As part of an effort to promote the use of renewable fuels, a partial least squares regression method with orthogonal signal correction to remove spectral information related to the sample background was developed to determine the TAN from the mid-infrared (IR) spectra of the feedstock. Digitally blended mid-IR spectral data were generated to fill in regions of the PLS calibration where there were very few samples. By combining experimental and digitally blended mid-IR spectral data to ensure adequate sample representation in all regions of the spectra–property calibration and better understand the spectra–property relationship through the identification of sample outliers in the original data that can be difficult to detect because of swamping, a PLS regression model for TAN (R² = 0.992, cross-validated root mean square error = 0.468, and bias = 0.0036) has been developed from 118 experimental and digitally blended mid-IR spectra of commercial feedstock. Thus, feedstock whose TAN value is too high for refining can be flagged using the proposed mid-IR method, which is faster and easier to use than the current titrimetric method. Full article