Eng

Solar energy is one of the promising renewable energies; it is clean, green, and accepted worldwide for targeting sustainable development through applications such as power generation, desalination, food preservation, etc. Solar-powered desalination has received more attention in recent times to meet the demand of pure water in the rural places of many countries where solar energy is abundant. In the present work, a double-slope passive solar desalination system was fabricated with readily available materials that can be installed and used in rural places, either for domestic purposes or in small-scale industries. The capacity of the desalination system fabricated to be filled with saline water is ~15 L. The performance of the desalination system is continuously monitored by recording the temperatures at various locations around the system, such as the outer surface of the glass, the inner surface of the glass, inside the basin, and outside the basin, through DHT11 sensors controlled by Arduino programming fed in the Arduino UNO board. The influence of solar radiation intensity and temperatures at various locations on the solar still on the thermal performance and production of desalination unit is analyzed by the data recorded by the Arduino program. A cumulative yield of fresh water of around 0.7–0.9 L is recorded every day, and the lowest yield of around 0.55 L was obtained on the third day of experimentation. Full article

This study focuses on the development of efficient and environmentally friendly Lead-free Perovskite solar cells (PSCs) using Tin and Germanium as absorber materials. The study was performed using SCAPS-1D simulations (version 3.11) to explore the performance of PSCs. The investigation took into consideration optimizing the electron transport layer’s (ETL) material and thickness, and TiO₂, ZnO, and WO₃ were investigated for this purpose. The current results show that Sn-based PSCs achieved a maximum power conversion efficiency of 23.19% with TiO₂ as the ETL, while Ge-based PSCs reached a power conversion efficiency of 14.83%. Additionally, the ETL doping concentration optimization revealed that the doping concentration had little impact on the device performance. These results emphasize the potential of Sn- and Ge-based PSCs as sustainable alternatives to Lead-based technologies, offering a pathway toward safer and more efficient solar energy solutions. Full article

Fuzzy Cognitive Maps (FCMs) have emerged as powerful tools for addressing diverse engineering challenges, leveraging their cognitive nature and ability to encapsulate causal relationships. This paper provides a comprehensive review of FCM applications across 15 engineering sub-domains, categorizing 80 studies by their learning family, task type, and case-specific application. We analyze the methodological advancements and practical implementations of FCMs, showcasing their strengths in areas such as decision-making, classification, time-series, diagnosis, and optimization. Qualitative criteria are systematically applied to classify FCM-based methodologies, highlighting trends, practical implications of varying complexity, and human intervention across task types and learning families. However, this study also identifies key limitations, including scalability challenges, reliance on expert knowledge, and sensitivity to data distribution shifts in real-world settings. To address these issues, we outline key areas and directions for future research focusing on adaptive learning mechanisms, hybrid methodologies, and scalable computational frameworks to enhance FCM performance in dynamic and evolving contexts. The findings of this review offer a structured roadmap for advancing FCM methodologies and broadening their application scope in both contemporary and emerging engineering domains. Full article

Prestressed concrete structures, designed to enhance the compressive strength of concrete through internal pretension, are increasingly susceptible to serviceability issues caused by rising live loads, material degradation, and environmental impacts. Strengthening or retrofitting offers a practical and cost-effective alternative to full replacement. This study investigated the flexural strengthening of prestressed concrete T-beams in the negative moment region using near-surface mounted (NSM) carbon-fiber-reinforced polymer (CFRP) rods. Validation against experimental results from the literature demonstrated high accuracy, with an average numerical-to-experimental ultimate load ratio of 0.97 for reinforced concrete T-beams strengthened with NSM-CFRP rods, a negligible difference of 0.49% for prestressed concrete I-beams, and a minimal error of 1.30% for prestressed concrete slabs strengthened with CFRP laminates. Parametric studies examined the effects of CFRP rod embedment depths and initial prestressing levels. In certain cases, achieving the minimum embedment depth is not feasible due to design or construction constraints. The results showed that fully embedded CFRP rods increased the ultimate load by up to 14.02% for low prestressing levels and 16.36% for high levels, while half-embedded rods provided comparable improvements of 11.20% and 15.76%, respectively. These findings confirm the effectiveness of NSM-CFRP systems and highlight the potential of partial embedment as a practical solution in design-constrained scenarios. Full article

The demand for electricity in Albania has risen significantly in recent years, accompanied by a growing emphasis on sustainable and environmentally friendly development. As a result, the focus of electricity generation is increasingly shifting towards renewable sources, particularly solar energy. In recent years, several large-scale solar plants have been installed across the country. This research examines four different scenarios and evaluates various technical parameters related to electrical power quality to assess the effects of integrating solar plants into the power system. Specifically, the analysis focuses on the active power losses and voltage fluctuations in the electrical distribution network following the connection of solar plants through the main distribution grid. Simulations were conducted using the Electrical Transient Analyzer Program (ETAP) software platform. The results suggest that a substantial penetration of solar energy into the grid may lead to increased losses in both active and reactive power. Full article

The stress level of a rotating component is of vital importance in order to ensure its safe operation. The primary source of stress for this type of component is the induced centrifugal stress, which depends on the material, rotational speed, and the distribution of the mass. The reduction of stress has been a topic of study for some time; however, the advent of additive technologies has prompted a new wave of research into the design and manufacture of centrifugal impellers for gas turbine engines, incorporating internal lattice structures (LSs). These structures offer benefits in terms of material savings and load reduction by decreasing the centrifugal force. The present work analyzes the stress–strain state of a turbine centrifugal impeller for six different designs, distinguished by the presence or absence of LSs of various geometries, achievable only through additive technologies. The analysis was conducted on a turbine impeller, which serves as an example of a promising small-scale gas turbine engine (SSGTE). The effectiveness of LSs was assessed through their unloading effect; furthermore, an approach to identify their optimal location within the impeller was demonstrated. Full article

The Ateneo Sueco del Socorro, built in 1927 in Sueca, Spain, is a prime example of the 20th-century architectural transformation, using reinforced concrete. Designed by architect Juan Guardiola, it reflects the Art Deco style, incorporating ornamental elements from Eastern civilizations. The building’s structure includes masonry walls, concrete columns, and vaulted ceilings. The building displayed a high level of damage due to the oxidation and corrosion of the reinforcements that compose the façade, which led to the definition of the most appropriate study and intervention methodology, applying contemporary tests for reinforced concrete. The original project’s structural design reflects the construction methods of its time, with sculptural elements using Fallas modeling techniques, resulting in various concrete and mortar types. After the façade presented a pathological condition in the early 21st century that made its restoration urgent, a study methodology was followed with current tests to accurately determine the lesions, their degree of damage, and compatible materials for restoration. Corrosion on the façade is mainly triggered by carbonation and the depassivation of reinforcements, exacerbated by environmental issues like moisture retention and oxygen permeability. Repairs should use compatible pre-mixed mortars, with surface inhibitors recommended to extend the lifespan of reinforcements. Full article

When testing soft biological samples using the Atomic Force Microscopy (AFM) nanoindentation method, data processing is typically based on equations derived from Hertzian mechanics. To account for the finite thickness of the samples, precise extensions of Hertzian equations have been developed for both conical and parabolic indenters. However, these equations are often avoided due to the complexity of the fitting process. In this paper, the determination of Young’s modulus is significantly simplified when testing soft, thin samples on rigid substrates. Using the weighted mean value theorem for integrals, an ‘average value’ of the correction function (symbolized as g(c)) due to the substrate effect for a specific indentation depth is derived. These values (g(c)) are presented for both conical and parabolic indentations in the domain 0 < r/H ≤ 1, where r is the contact radius between the indenter and the sample, and H is the sample’s thickness. The major advantage of this approach is that it can be applied using only the area under the force–indentation curve (which represents the work performed by the indenter) and the correction factor g(c). Examples from indentation experiments on fibroblasts, along with simulated data processed using the method presented in this paper, are also included. Full article

This paper presents a first-of-its-kind evaluation of integrating Large Language Models (LLMs) within a Human-In-The-Loop (HITL) framework for risk analysis in machinery functional safety, adhering to ISO 12100. The methodology systematically addresses LLM limitations, such as hallucinations and lack of domain-specific expertise, by embedding expert oversight to ensure reliable and compliant outputs. Applied to four diverse industrial case studies—motorized gates, autonomous transport vehicles, weaving machines, and rotary printing presses—this study assesses the applicability of ChatGPT in routine risk analysis tasks central to machinery functional safety workflows, such as hazard identification and risk assessment. The results demonstrated substantial improvements: during HITL involvement and the subsequent iterations of risk assessment with expert feedback, a complete agreement with ground truth was achieved across all four use cases. ChatGPT also identified additional scenarios and edge cases, enriching the risk analysis. Efficiency gains were notable, with time efficiency rated at 4.95 out of 5, on average, across case studies. Overall accuracy (4.7 out of 5) and usability (4.8 out of 5) ratings demonstrated the robustness of the HITL framework in ensuring reliable and practical outputs. Likert scale evaluations reflected high confidence in the refined outputs, emphasizing the critical role of HITL in enhancing both trust and usability. The study also highlights the importance of prompt design, revealing that longer initial prompts improve accuracy, while shorter iterative prompts maintain usability without compromising efficiency. The iterative HITL process further ensures that refined outputs align with safety standards and practical requirements. This evaluation underscores the transformative potential of generative AI in functional safety workflows, enhancing routine activities while ensuring rigorous human oversight in safety-critical, regulated industries. Full article

This work proposes a nonlinear modeling of a permanent magnet synchronous motor (PMSM) based on state space neural networks. The state space neural network is trained and the state variables (currents in a direct–quadrature frame and the rotational speed) are estimated by considering a robust Unscented Kalman Filter (UKF). Two contributions are presented in this work: the fist one is a nonlinear modeling structure for a PMSM based on a state space neural network that allows real-time parameter identification, and the second one is PMSM neural network training and state estimation based on a robust UKF. The robustness of the UKF is obtained by using a singular value decomposition of the covariance matrix. A comparison analysis is performed over a real PMSM motor by considering the proposed approach and a linear approximation of the nonlinear model where the states and parameters are computed by using an Extended Kalman Filter. The identified model is validated in closed loop by considering a nonlinear control strategy based on state feedback linearization. Full article

This paper presents the design, kinematics, and development of a tendon-driven continuum robot for surgical applications. The continuum robot has a flexible and adaptable construction that imitates the movements of natural organisms. The robot’s unique structure comprises disk members, springs, and a continuum backbone member, enabling it to bend, contract, and deform in complex ways. The robot is operated by pulling tendons, giving it the agility and flexibility necessary to bend in confined spaces. This study discusses the main design considerations and challenges in creating a tendon-driven continuum robot, including the kinematics of the four-tendon mechanism. The developed tendon-driven continuum robot is categorized into two modules: the distal end and the proximal end. The distal end consists of the continuum robot structure, whereas the proximal module consists of the actuating unit that actuates the distal end. The experimental results demonstrate the continuum robot’s ability to be used in medical fields and pipe inspections because of the miniaturized design of the distal end, which allows it to enter confined spaces. This paper provides valuable insights into the design, kinematics, and appropriate materials to build a tendon-driven continuum robot; its bending and deformation capabilities can be used in many fields, especially surgical applications and confined space explorations. Full article

In recent years, there has been significant investigation into the high efficiency of perovskite solar cells. These cells have the capacity to attain efficiencies above 14%. As the perovskite materials that include lead pose a substantial environmental risk, components that are free from lead are used during the process of solar cell development. In this work, we use a lead-free double-perovskite material, namely Cs₂TiBr₆, as the main absorbing layer in perovskite solar cells to enhance power conversion efficiency (PCE). This work is centered on the development of solar cell structures with materials such as an ETL (electron transport layer) and an HTL (hole transport layer) to enhance the PCE. In this theoretical work, we perform simulations and analysis on double-perovskite Cs₂TiBr₆ to assess its efficacy as an absorber material in various HTLs like Cu₂O and CuI, with a fixed ETL of C60 using SCAPS (Solar Cell Capacitance Simulator, SCAPS 3.3.10) Software. This is a one-dimensional solar cell simulation program. In this work, the thickness of the double-perovskite material is also varied between 0.2 and 2.0 µm, and its efficiency is observed. The effect of temperature variation on efficiency in the range of 300 K to 350 K is observed. The effect of defect density on efficiency is also observed in the range of 1 × 10¹¹ to 1 × 10¹⁶. In this theoretical work, perovskite solar cells, including their absorbing layer, demonstrate outstanding ETLs and HTLs, respectively. As a result, the cells’ achieved PCE is improved. This work demonstrates the effectiveness of this lead-free double-perovskite structure that absorbs light in perovskite solar cells. Full article

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