Eng

Increasing the process efficiency of agricultural tasks is a key measure to decrease overall costs and CO₂ emissions. However, optimizing tractor–implement combinations is challenging due to the variety of processes and implements and the complexity of the powertrains in modern tractors. In addition, overall process efficiency is an ambiguous optimization objective in agricultural processes as it relates resource consumption to harvest yields, which are only known at the end of a harvest season. The presented approach defines process constraints, ensuring optimization does not negatively affect harvest yield. These constraints allow for the formulation of explicit objective functions that are observable during the operation. The method establishes a mathematical foundation for the optimization of agricultural processes. The mathematical principles of the theoretical framework and the techniques used to define control constraints are explored, whereby the applicability to alternative objectives like optimizing the overall process cost is highlighted. To demonstrate the practical utility of the proposed approach, an optimization cycle is applied to a real-world scenario: adapting the working speed during the tillage process using a cultivator to maximize energy efficiency. The approach simplifies the optimization problem by formulation as a constraint optimization problem, allowing for improving the operating point of tractor–implement combinations with respect to observable process objective functions. The results underline the importance of advanced control strategies in agricultural machinery, advancing precision agriculture and promoting sustainable farming practices. Full article

Research on the effects of magnetic fields on water and aqueous solutions has produced various findings, such as the suppression of scale formation in pipes and boilers, inhibition of metal corrosion, enhancement of concrete strength, and changes in properties like viscosity and electrical conductivity. However, the challenges in quantifying these effects, the issues with reproducibility affected by trace elements in the water used in the experiments, and the involvement of complex parameters and mechanisms have led to ongoing debates, with some questioning the very existence of magnetic field effects. The “memory effect”, where the impact of magnetic exposure persists for a certain period, further complicates explanations of these phenomena. To fully elucidate and enable practical applications of these effects, further research is essential. In this study, we aimed to investigate the magnetic field effects on water, including memory effects, where the quantification and elucidation potentially lead to various applications, including environmentally friendly solutions on scale suppression and life science issues. The results revealed that the vaterite phase precipitation ratio significantly increased in magnetically treated water, reaching up to 51%, from 26% without the treatment, which is high reproducibility; furthermore, a reduction in mean particle size was observed when using magnetically treated water, suggesting that it may help prevent scaling. Furthermore, when solutions of calcium carbonate, calcium chloride, and sodium bicarbonate were individually subjected to magnetic treatment, the most notable increase in the vaterite phase precipitation ratio was observed when calcium chloride and sodium bicarbonate solutions were magnetically treated separately and then reacted to precipitate calcium carbonate. Full article

The development of urban areas has led to an increase in the use of subsoil for installing transportation networks. These systems usually comprise the construction of side-by-side twin running tunnels built sequentially and in close proximity. Different studies have demonstrated that under such conditions, there is an interaction between tunnels, leading to greater settlements compared with those obtained if the tunnels were excavated separately. Supported by those findings, several analytical methods have been proposed to predict the settlements induced by the excavation of the second tunnel. This paper examines the applicability of these proposals across multiple case studies published in the literature by comparing the analytical predictions with the reported monitoring data of 57 sections. The results indicate that, regardless of the different soil conditions and geometrical characteristics of the tunnels, a Gaussian curve accurately describes the settlements in greenfield conditions and those induced by the second tunnel excavation, although with the curve becoming eccentric in this case. Despite some significant scatter observed, most methods predict the settlements induced by the second tunnel with reasonable accuracy, with Hunt’s method presenting the best fit metrics. The obtained findings confirm that existent methods can be a valid tool to predict the settlements induced by twin tunnelling during the early stages of design, although do also contain limitations and pitfalls that are identified and discussed throughout the paper. Full article

This note aims for a non-local extension of the Johnson–Mehl–Avrami–Kolmogorov (JMAK) kinetic equation, describing solid phase transformation through the implementation of the time-fractional Caputo derivative and Mittag-Leffler function instead of the exponential Avrami kinetics. These are preliminary results that include tests on some published data and a clarification of the concept. Full article

Aluminum 7075 alloys are widely utilized in aerospace, transportation, and marine industries due to their high strength and low density. However, further research is needed to understand their mechanical, thermomechanical, and vibrational behaviors when reinforced. This study focuses on the development of Al 7075 composites reinforced with zirconium silicate (ZrSiO₄), processed via sand stir casting. The mechanical properties, including tensile, compression, and impact strength, as well as thermomechanical and vibrational behaviors, were thoroughly investigated. A planetary ball mill was used to mix ZrSiO₄ with a wettability agent, and the results indicated that the addition of ZrSiO₄ with the wettability agent significantly enhanced the mechanical properties. Fourier Transform Infrared Spectroscopy (FTIR) was employed to identify the compounds formed after adding the reinforcement and wettability agent. Scanning Electron Microscope (SEM) images and Energy-dispersive X-ray (EDX) analysis revealed a uniform distribution of the particles within the matrix. The tensile, compression, and impact strengths increased by 20%, 21%, and 19%, respectively, with the addition of 8 wt% ZrSiO₄; however, strain decreased. Additionally, heat treatment further enhanced the mechanical properties of the composites. The thermomechanical properties showed improvement even at elevated temperatures, and the damping factor was enhanced with the addition of ZrSiO₄. The elemental composition of the reinforced composites was analyzed using EDX, confirming the presence of the reinforcement. This research highlights the potential of Al 7075-ZrSiO₄ composites for improved performance in various applications. Full article

The rising demand for housing continues to outpace traditional construction processes, highlighting the need for innovative, efficient, and sustainable delivery models. Off-site construction (OSC) has emerged as a promising alternative, offering faster project timelines and enhanced cost management. However, current research on cost models for OSC, particularly in automating material take-offs and optimising cost performance, remains limited. This study addresses this gap by proposing a new cost model integrating Digital Twin (DT) technology and AI-driven decision models for modular housing in the UK. The research explores the role of DTs in enhancing cost estimation and decision-making processes. By leveraging DTs and AI, the proposed model evaluates the impact of emergent technologies on cost performance, material efficiency, and sustainability across social, environmental, and economic dimensions. As proposed, this integrated approach enables a cost model tailored for OSC systems, providing a data-driven foundation for cost optimisation and material take-offs. The study’s findings highlight the potential of combining DTs and AI decision models to enhance cost modelling in modular construction, offering new capabilities to support sustainable and performance-driven housing delivery. The paper introduces a dynamic, data-driven cost model integrating real-time data acquisition through DTs and AI-powered predictive analytics. This dynamic approach enhances cost accuracy, reduces lifecycle cost variability, and supports adaptive decision-making throughout the OSC project lifecycle. Full article

Green synthesis of nanocomposites offers an eco-friendly and viable solution to overcome the limitations of conventional chemical and physical methods as it uses biological agents to act as reducing and stabilizing agents. The current study’s novelty is phyto-fabricated manganese (Mn)-doped zinc oxide (ZnO) nanocomposites using aqueous extract of H. enneaspermus by a biological method. Mn-doped ZnO nanocomposites were synthesized using manganese acetate and zinc acetate. The synthesized nanocomposites were characterized by XRD, FTIR, SEM, and EDX analysis. XRD shows the crystalline nature of nanocomposites with particle sizes of 30–40 nm, and FTIR reveals the presence of functional groups responsible for capping and stabilization. SEM analysis indicates spherical morphology with minor aggregation due to phytochemical interactions. EDX analysis of Mn-doped ZnO nanocomposites was used to verify the elemental composition, including Mn, Zn, O, and C. The anti-bacterial property of Mn-doped ZnO nanocomposites was assessed using the agar well-diffusion method against pathogens. The results of the anti-bacterial investigation proved that Mn-doped ZnO nanocomposites inhibit the growth of pathogens at different concentrations. The research concludes that the extract of H. enneaspermus acts as a capping and reducing agent in the synthesis process. The process can offer bio-compatible nanocomposites for new drug development against pathogens. Full article

Despite their significant environmental benefits, solar photovoltaic (PV) systems are susceptible to malfunctions and performance degradation. This paper addresses detecting and diagnosing faults from a dataset representing a 250 kW PV power plant with three types of faults. A comprehensive dataset analysis is conducted to improve the dataset quality and uncover intricate relationships between features and the target variable. By introducing novel feature importance averaging techniques, a two-phase fault detection and diagnosis framework employing tree-based models is proposed to identify faults from normal cases and diagnose the fault type. An ensemble of six tree-based classifiers, including decision trees, random forest, Stochastic Gradient Boosting, LightGBM, CatBoost, and Extra Trees, is trained in both phases. The results show 100% accuracy in the first phase, particularly with the Extra Trees classifier. In the second phase, Extra Trees, XGBoost, LightGBM, and CatBoost achieve similar accuracy, with Extra Trees demonstrating superior training and convergence speed. This study then incorporates Explainable Artificial Intelligence (XAI), utilizing LIME and SHAP analyzers to validate the research findings. The results highlight the superiority of the proposed approach over others, solidifying its position as an innovative and effective solution for fault detection and diagnosis in PV systems. Full article

Proton exchange membrane fuel cells (PEMFCs), as a clean energy technology, show remarkable potential for a wide range of applications. However, high altitude regions pose significant challenges for PEMFC system operation due to thin air and low oxygen partial pressure. Existing logic judgement-based controls exhibit defects such as poor robustness and poor adaptability, which seriously restrict PEMFC system operation. In order to address this issue, this paper puts forth an intelligent control of a PEMFC system air compressor (AC) and back pressure valve (BPV) using an asynchronous advantage actor-critic (A3C) algorithm and systematically compares it with the logic judgement-based control. The application of an A3C-based control under three distinct high altitude test conditions demonstrated a notable enhancement in dynamic responsiveness, with an improvement of up to 40% compared to the results for the logic judgement-based control. Additionally, an improvement of 5.8% in electrical efficiency was observed. The results demonstrate that the A3C-based control displays significant robustness and control precision in response to altitude alterations. Full article

Lubricant friction modifier additives are used on lower viscosity engine oils to mitigate boundary friction. This work presents the development of a graphene-based material as an oil friction modifier additive, from formulation to actual vehicle tests. The graphene material was initially characterized using scanning electron microscopy (SEM) and Raman spectroscopy, which revealed the predominance of graphene nanoplatelets (GNPs) with an average of nine layers. After functionalization, two GNP additive variants were initially mixed with a fully formulated SAE 0W-20 engine oil and tribologically evaluated using reciprocating sliding tests at 40 and 120 °C and Hertzian pressure up to 1.2 GPa when both variants presented friction reduction. Then, the GNP additive variant with better performance was evaluated in a vehicle emission test using a fully formulated 5W-20 SAE oil as a reference. The addition of 0.1% of GNPs reduced fuel consumption by 2.6% in urban conditions and 0.8% in highway ones. The urban test cycle was FTP75 and higher benefits of the GNP additive occurred especially on the test start, when the engine and oil were still cold and on test portions where the vehicle speed was lower. Full article

This paper explores the historical development of conventional plastics, tracing their evolution from early forms to their pervasive use in modern society. Its observations include the rise of mass plastic production during World War II and the post-war development, showcasing plastics’ economic and societal impact. The environmental repercussions of plastic pollution have led to increased global awareness and calls for sustainable alternatives. The emergence of bioplastics is investigated, including their classification, properties, applications, and challenges in scaling. This paper emphasises the urgency of adopting bioplastics for a sustainable future and discusses efforts towards homogenisation and standardisation across global markets. Full article

This article presents a framework of using MEMS sensors to investigate unsteady flow speeds of a flapping wing or the new concept of sensors on flapping wings (SOFWs). Based on the implemented self-heating flow sensor using U18 complementary metal–oxide–semiconductor (CMOS) MEMS foundry provided by the Taiwan Semiconductor Research Institute (TSRI), the compact sensing region of the flow sensor was incorporated for in situ diagnostics of biomimetic flapping issues. The sensitivity of the CMOS MEMS flow sensor, packaged with a parylene coating of 10 μm thick to prolong the lifetime, was observed as −3.24 mV/V/(m/s), which was below the flow speed of 6 m/s. A comprehensive investigation was conducted on integrating CMOS MEMS flow sensors on the leading edge of the mean aerodynamic chord (m.a.c.) of the flexible 70-cm-span flapping wings. The interpreted flow speed signals were checked and demonstrated similar behavior with the (net) thrust force exerted on the flapping wing, as measured in the wind tunnel experiments using the force gauge. The experimental results confirm that the in situ measurements using the concept of SOFWs can be useful for measuring the aerodynamic forces of flapping wings effectively, and it can also serve for future potential applications. Full article

This study evaluates the effectiveness of advanced statistical and geospatial methods for analyzing radon concentration distributions in indoor environments, using the district of San Martín de Porres, Lima, Peru, as a case study. Radon levels were monitored using LR-115 nuclear track detectors over three distinct measurement periods between 2015 and 2016, with 86 households participating. Detectors were randomly placed in various rooms within each household. Normality tests (Shapiro–Wilk, Anderson–Darling, and Kolmogorov–Smirnov) were applied to assess the fit of radon concentrations to a log-normal distribution. Additionally, analysis of variance (ANOVA) was used to evaluate the influence of environmental and structural factors on radon variability. Non-normally distributed data were normalized using a Box–Cox transformation to improve statistical assumptions, enabling subsequent geostatistical analyses. Geospatial interpolation methods, specifically Inverse Distance Weighting (IDW) and Kriging, were employed to map radon concentrations. The results revealed significant temporal variability in radon concentrations, with geometric means of 146.4 Bq·${\text{m}}^{-3}$, 162.3 Bq·${\text{m}}^{-3}$, and 150.8 Bq·${\text{m}}^{-3}$, respectively, across the three periods. Up to 9.5% of the monitored households recorded radon levels exceeding the safety threshold of 200 Bq·${\text{m}}^{-3}$. Among the interpolation methods, Kriging provided a more accurate spatial representation of radon concentration variability compared to IDW, allowing for the precise identification of high-risk areas. This study provides a framework for using advanced statistical and geospatial techniques in environmental risk assessment. Full article

Nowadays, the increase in sustainable investments, especially when it comes to energy efficiency in buildings, has been recognised as an important pillar towards reductions in energy consumption. In this context, there is a need for efficient and user-friendly digital tools that can support decision-making procedures for all involved parties in the energy efficiency value chain. The scope of this paper is to present a high-level architecture and system design of the energy efficiency marketplace developed within the framework of the ENERGATE project, an EU-funded initiative aiming to assist Building Owners, Project Implementors, and Financial Institutions to collaborate and execute energy-efficient building renovations. To this end, structured data through predefined information entries will be collected. This will facilitate the interactions between heterogenous stakeholders and contribute to the standardisation of processes. The paper focuses on the functional description of the system design of the ENERGATE platform by defining the architecture, components, modules, interfaces, and structured data, as well as highlighting the requirements of potential platform users and design principles to meet the necessary requirements while ensuring security, reliability, and effectiveness. Full article

The supercritical water gasification (SCWG) and carbon dioxide gasification of agro-industrial and urban waste residues—Coffee Husk, Eucalyptus Biochar, Energy Sugarcane, and Refuse-Derived Fuel (RDF)—were studied using TeS^® v.2 software, which employs a non-stoichiometric thermodynamic model to minimize Gibbs free energy and predict equilibrium compositions. The effects of temperature (873.15–1273.15 K), pressure (220–260 bar), biomass feed (18–69%), and gasifying agents on hydrogen and methane formation were analyzed. Higher temperatures and biomass feed percentages favored hydrogen production, while lower temperatures increased methane formation. At 1273.15 K, RDF showed the highest hydrogen yield in SCWG, rising from 0.43 to 1.42 mol, followed by Energy Sugarcane (0.39 to 1.23 mol), Coffee Husk (0.34 to 0.74 mol), and Eucalyptus Biochar (0.33 to 0.62 mol). In CO₂ gasification, hydrogen yields were lower but followed a similar trend. At 873.15 K, RDF also exhibited the highest methane increase in SCWG, from 0.14 to 0.91 mol, followed by Energy Sugarcane (0.12 to 0.65 mol), Coffee Husk (0.11 to 0.36 mol), and Eucalyptus Biochar (0.11 to 0.29 mol). Methane formation in CO₂ gasification was significantly lower, with RDF increasing from 0.0035 to 0.35 mol, followed by Energy Sugarcane (0.0024 to 0.24 mol), Coffee Husk (0.0002 to 0.058 mol), and Eucalyptus Biochar (0.0002 to 0.028 mol). On the other hand, a slight increase in hydrogen formation was observed as pressure decreased, while the opposite effect was observed for methane formation, with a small increase in its production as pressure increased. The impact of pressure change on the equilibrium compositions was not as significant as the effect observed by varying temperature; this behavior was observed in both gasification processes studied. Additionally, the behavior of the H₂/CO molar ratio for each biomass in the studied gasification processes was analyzed to assess the potential uses of the produced syngas. It was observed that the SCWG resulted in significantly higher H₂/CO molar ratios compared to CO₂ gasification. Full article

Today, the machining of heat-resistant alloys based on triple, quad, or penta equilibria high-entropy alloy systems of elements (ternary, quaternary, quinary iron-, titanium-, or nickel-rich alloys), including dual-phase by Gibb’s phase rule, steels of the austenite class, and nickel- and titanium-based alloys, are highly relevant for the airspace and aviation industry, especially for the production of gas turbine engines. Cutting tools in contact with those alloys should withstand intensive mechanical and thermal loads (tense state of 1.38·10⁸–1.54·10⁸ N/m², temperature up to 900–1200 °C). The most spread material for those tools is cutting ceramics based on oxides, nitrides of the transition and post-transition metals, and metalloids. This work considers the wear resistance of the cutting insert of silicon nitride with two unique development coatings — titanium–zirconium nitride coating (Ti,Zr)N and complex quad nitride coating with TiN content up to 70% (Ti,Al,Cr,Si)N with a thickness of 3.8–4.0 µm on which microtextures were produced by the assisted electric discharge machining with the electrode-tool of ø0.25 mm. The microtextures were three parallel microgrooves of R0.13^+0.02 mm at a depth of 0.025₋₀.₀₅. The operational life was increased by ~1.33 when the failure criterion in turning nickel alloy was 0.4 mm. Full article

The presented paper analyzes the impact of limestone dust accumulation on photovoltaic (PV) panel performance, focusing on the specific surrounding conditions near quarries. The results from the performed field measurements show that high concentrations of limestone dust accumulate significantly faster in these areas, and a hard layer is formed in the presence of moisture. This layer of dust is resistant to removal, even in moderate precipitation and winds with speeds between 6 and 9 m/s, making it a significant problem for the long-term performance of the systems. The analysis revealed that the lack of systematic cleaning of the panels leads to a drop in efficiency of over 20%, with this loss pointedly limiting the return on investment. This study highlights the need for innovative maintenance approaches, such as regular cleaning, use of special coatings and adapting designs to specific environmental conditions. This is essential for the development of strategies to manage, maintain and improve PV systems in areas with high levels of dust pollution. Full article

The perception of animal emotions is key to enhancing veterinary practice, human–animal interactions, and protecting domesticated species’ welfare. This study presents a unique emotion classification deep learning-based approach for pet animals. The actual and emotional status of dogs and cats have been classified using a modified EfficientNetB5 model. Utilizing a dataset of images classified into four different emotion categories—angry, sad, happy, and neutral—the model incorporates sophisticated feature extraction methods, such as Dense Residual Blocks and Squeeze-and-Excitation (SE) blocks, to improve the focus on important emotional indicators. The basis of the second strategy is EfficientNetB5, which is known for providing an optimal balance in terms of accuracy and processing capabilities. The model exhibited robust generalization abilities for the subtle identification of emotional states, achieving 98.2% accuracy in training and 91.24% during validation on a separate dataset. These encouraging outcomes support the model’s promise for real-time emotion detection applications and demonstrate its adaptability for wider application in ongoing pet monitoring systems. The dataset will be enlarged, model performance will be enhanced for more species, and real-time capabilities will be developed for real-world implementation. Full article