Eng

This study intended to examine and assess the performance of raw and treated Eriobotrya Japonica seed waste for the adsorption-based removal of methylene blue dye from an aqueous solution. The effects of several factors, including pH, adsorbent dose, and initial concentration, on the elimination of this dye were examined. To optimize the removal process, a Box-Behnken design (BBD) based on response surface methodology (RSM) was employed to evaluate the influence of key variables, adsorbent dose, initial dye concentration, and pH, along with their interactions. The findings demonstrated that the statistical analysis reveals a high significance of the dye for raw and treated Eriobotrya Japonica seed waste, with extremely weak probability values (p < 0.0001). The optimal conditions achieved were the adsorbent dose = 21.21 mg, initial dye concentration = 7.54 mg/L, and pH = 10.92 for the raw waste and adsorbent dosage = 21.75 mg, initial dye concentration = 7.5 mg/L, and pH = 11.7 for the extracted waste. These conditions result in a dye removal efficiency of 99.48% and 99.88% for raw and treated Eriobotrya Japonica seed waste, respectively. The methylene blue adsorption kinetics on the adsorbent can be precisely represented by an effective pseudo-second-order equation. The Freundlich model showed a significantly better fit to the experimental results compared to the Langmuir model. Full article

This paper presents a standardized apparatus and method for testing the accuracy of mobile phone apps designed to measure concrete crack widths. The apparatus comprises a standardized crack-width calibration plate (CWCP) and a simulated wall (SW), along with a pose adjusting and fixing device (PAFD) and a spatial distance measuring assemblage (SDMA). The test method employs an innovative two-stage procedure associated with the SDMA to calculate the distances (K_i) from the phone’s four corners to the SW. The phone’s position is adjusted using the PAFD until the four monitored K_i values match the target K_i. An app installed on the phone then measures crack widths on the CWCP. A standard experimental procedure was established to assess the accuracy of a preliminary Android app in measuring concrete crack widths, with results presented and discussed. This apparatus and method, grounded in their underlying physical meaning, can realistically simulate actual engineering conditions precisely and cost-effectively. Full article

Acoustic methods are a promising direction when determining the position of buried non-metallic pipelines. Under difficult soil conditions, one of the most effective methods is the vibroacoustic method, which has a maximum range of application when acoustic waves propagate through the transported medium. However, due to limited energy input into the pipeline, signal detection at significant distances from the source becomes challenging. This article considers the mechanism of acoustic oscillations attenuation in pipes and suggests possible directions for optimization of the investigated technology. The evaluation of mathematical modeling methods for the investigated process is conducted, and the key signal attenuation relationships are presented. The analysis allowed us to establish that the vibroacoustic method has the potential of increasing the efficiency by approximately 10–20%. Full article

This paper presents the optimal sizing of a hybrid renewable energy system (HRES) for an isolated residential building using modified smell agent optimization (mSAO). The paper introduces a time-dependent approach that adapts the selection of the original SAO control parameters as the algorithm progresses through the optimization hyperspace. This modification addresses issues of poor convergence and suboptimal search in the original algorithm. Both the modified and standard algorithms were employed to design an HRES system comprising photovoltaic panels, wind turbines, fuel cells, batteries, and hydrogen storage, all connected via a DC-bus microgrid. The components were integrated with the microgrid using DC-DC power converters and supplied a designated load through a DC-AC inverter. Multiple operational scenarios and multi-objective criteria, including techno-economic metrics such as levelized cost of energy (LCOE) and loss of power supply probability (LPSP), were evaluated. Comparative analysis demonstrated that mSAO outperforms the standard SAO and the honey badger algorithm (HBA) used for the purpose of comparison only. Our simulation results highlighted that the PV–wind turbine–battery system achieved the best economic performance. In this case, the mSAO reduced the LPSP by approximately 38.89% and 87.50% over SAO and the HBA, respectively. Similarly, the mSAO also recorded LCOE performance superiority of 4.05% and 28.44% over SAO and the HBA, respectively. These results underscore the superiority of the mSAO in solving optimization problems. Full article

This paper examines the impact of two storage policies—dedicated storage (D-SLAP) and randomized storage (R-SLAP)—on warehouse operational efficiency. It integrates the Storage Location Assignment Problem (SLAP) with the unrelated parallel machine scheduling problem (UPMSP), which represents the scheduling of the material handling equipment (MHE). This integration is intended to elucidate the interplay between storage strategies and scheduling performance. The considered evaluation metrics include transportation cost, average waiting time, and total tardiness, while accounting for product arrival and demand schedules, precedence constraints, and transportation expenses. Additionally, considerations such as MHE eligibility, resource requirements, and available storage locations are incorporated into the analysis. Given the complexity of the combined problem, a tailored Non-dominated Sorting Genetic Algorithm (NSGA-II) was developed to assess the performance of the two storage policies across various randomly generated test instances of differing sizes. Parameter tuning for the NSGA-II was conducted using the Taguchi method to identify optimal settings. Experimental and statistical analyses reveal that, for small-size instances, both policies exhibit comparable performance in terms of transportation cost and total tardiness, with R-SLAP demonstrating superior performance in reducing average waiting time. Conversely, results from large-size instances indicate that D-SLAP surpasses R-SLAP in optimizing waiting time and tardiness objectives, while R-SLAP achieves lower transportation cost. Full article

Laminated Veneer Lumber (LVL) presents an alternative material for offshore wind turbine towers and blades for an energy sector whose greenhouse gas emissions are substantial. In compliance with AS/NZS 4536, this case study facilitates a specifications’ selection framework that embraces a validated, cost–benefit determination via life cycle cost analyses (LCCA) specification comparisons. A structured consultation with three key Western Australian offshore industry experts, compliant with a standard phenomenological qualitative approach, further facilitates offshore wind turbine (OWT), LCCA cost comparisons between traditional steel and fibreglass components and LVL wooden components. LVL is found to have a higher capital cost but can generate long-term savings of AUD 30,400 per comparable unit less than Traditional OWT specifications, noting a 5% lower LVL operation and maintenance cost. Where decommissioning recycling facilities exist, OWT LVL specification components are encouraged. This work argues that LVL options uptake in Western Australia (WA) is both practicable and whole-cost effective. Full article

The aim of this study is to develop a simple and reliable laboratory testing procedure for evaluating the bond strength of cement–formation sheaths that considers cement slurry composition and contamination as well as formation strength and formation surface conditions (roughness and contamination). Additionally, a simple and practical empirical correlation is developed for predicting cement–rock bond strength based on the routine mechanical properties of hard-set cement and formation rock. Cement slurries composed of Yamama cement type 1 and 25% local Saudi sand, in addition to 40% fresh water, are used for all investigations in this study. Oil well cementing is a crucial and essential operation in the drilling and completion of oil and gas wells. Cement is used to protect casing strings, isolate zones for production purposes, and address various hole problems. To effectively perform the cementing process, the cement slurry must be carefully engineered to meet the specific requirements of the reservoir conditions. In oil well cementing, the cement sheath is a crucial component of the wellbore system, responsible for maintaining structural integrity and preventing leakage. Shear bond strength refers to the force required to initiate the movement of cement from the rock formation or movement of the steel casing pipe from the cement sheath. Cement–formation sheath bond strength is a critical issue in the field of petroleum engineering and well cementing. Cement plays a crucial role in sealing the annulus (the space between the casing and the formation) and ensuring the structural integrity of the well. The bond strength between the cement and the surrounding geological formation is key to preventing issues such as fluid migration, gas leaks, and wellbore instability. To achieve the study objectives, sandstone and sandstone–cement composite samples are tested using conventional standard mechanical tests, and the results are used to predict cement–formation sheath bond strength. The utilized tests include uniaxial compression, direct tensile, and indirect tensile (Brazilian) tests. The predicted cement–rock sheath bond strength is compared to the conventional laboratory direct cement–formation sheath strength test outcomes. The results obtained from this study show that the modified uniaxial compression test, when used to evaluate cement–formation shear bond strength using cement–rock composite samples, provides reliable predictions for cement–formation sheath bond strength with an average error of less than 5%. Therefore, modified uniaxial compression testing using cement–rock composite samples can be standardized as a practical laboratory method for evaluating cement–formation sheath bond strength. Alternatively, for a simpler and more reliable prediction of cement–formation sheath bond strength (with an average error of less than 5%), the empirical correlation developed in this study using the standard compressive strength value of hard-set cement and the standard compressive strength value of the formation rock can be employed separately. For the standardization of this methodology, more generalized research should be conducted using other types of oil well cement and formation rocks. Full article

Sustainable building construction (SBC) contributes immensely to attaining sustainable development initiatives. Nevertheless, SBC is not fully embraced among construction organisations in developing countries due to several challenges, suggesting the need for lasting solutions. However, uncertainty remains about the most vital characteristics/enablers that construction organisations need to adopt SBC. This study investigated the organisational enablers that contribute to SBC’s successful deployment. This study employed quantitative methodology using a structured questionnaire for data collection. With a convenient sample technique, a sample size of 281 was achieved from professionals working in the built environment in the Gauteng Province of South Africa (SA). Data were analysed with a four-step approach, including the relevant descriptive and inferential statistics. Relevant reliability and validity tests of the research instrument/measuring variables were observed, including pilot testing, Cronbach’s alpha test, Kaiser–Meyer–Olkin, and Bartlett’s sphericity test. Mean rankings followed this in conjunction with standard deviations. Likewise, the Kruskal–Wallis H-test was employed to determine statistically significant differences in the responses of the study’s respondents. Furthermore, confirmatory factor analysis (CFA) was used to confirm the variables’ goodness of fit in the measurement model or latent construct (organisational enablers), indicating their significance. According to their regression values, the top five variables included commitment to innovative construction, adequate project management culture, support from top management, sound intra-organisational leadership, and social responsibility to protect the environment. Generally, the study’s findings were supported by institutional theory and resource-based view theory. The study recommends carefully considering the findings among construction organisations and policymakers. This will assist in self-assessment and decision-making regarding direct improvement initiatives and curbing unsustainable practices. Similarly, this study is positioned to encourage further investigation of organisational enablers from the perspective of the enlisted theories. Full article

Under the “Healthy China” strategy, the demand for home fitness equipment is increasing, but existing solutions face challenges such as large size, limited functionality, and lack of personalization. This study proposes an innovative integrated design framework for multifunctional home fitness equipment, combining modular design, space optimization, and intelligent health monitoring. The design integrates an exercise bike, rowing machine, and spring tensioner into a single unit, reducing equipment footprint by 30% while enabling seamless transitions between exercise modes. Multimodal sensors collect real-time physiological data, processed via Kalman filtering and adaptive algorithms to generate personalized fitness recommendations. The system achieves 95% monitoring accuracy for key metrics (heart rate: 97–147 bpm, energy consumption: 216–550 kcal) and improves user satisfaction by 40% compared to conventional equipment. This research demonstrates a scalable and intelligent solution that bridges the gap between multifunctional integration and user-centric health management, offering significant advancements over previous designs. Full article

The pavement is a complex construction subject to a range of environmental and loading conditions. Transportation organizations use pavement management systems (PMSs) to maintain satisfactory pavement performance. The pavement condition index (PCI) is a commonly used performance indicator, yet PCI evaluation is costly and time-consuming. Machine and deep learning algorithms have recently been more instrumental for forecasting pavement conditions. This research uses AI tools to develop a correlation between PCI and collected distress in urban road networks. The distresses for 15,000 pavement segments in Egypt were investigated through a desk study and field data collection. To this end, several machine learning (ML) and deep learning approaches were developed. The ML techniques include random forest (RF), support vector machine (SVM), decision tree (DT), and the deep learning approach entails artificial neural networks (ANN). The proposed techniques provide precise PCI estimates and can be seamlessly integrated with PMCs using ubiquitous spreadsheet programs. The results have shown excellent predictions of the ANN model, as demonstrated in the high coefficient of determination ($R^{2 }$ = 0.939) and the low root mean squared error (RMSE = 7.20) and the mean absolute error (MAE = 2.94). This study sets out to provide a reliable and affordable alternative to specialized tools like MicroPAVER. The ANN model exhibited greater prediction accuracy than the other developed models and can also reliably forecast PCI values by using only measured distress data. Full article

In recent years, the frequent collapse of bridges has underscored the severe threats posed by ship collisions and seismic forces to bridge pile foundations, particularly under scour conditions. Scour significantly increases bending moments, weakens foundation stability, and exacerbates damage under ship impacts and seismic loading. This review systematically examines the dynamic responses of bridge pile foundations subjected to multi-hazard scenarios, focusing on how scour-induced degradation exacerbates the impacts of ship collisions and seismic events. The synthesis covers experimental studies, numerical simulations, and theoretical approaches, providing a comprehensive evaluation of methodologies and findings. Advanced bibliometric tools, such as CiteSpace and VOSviewer, are employed to identify research trends, hotspots, and collaborations in this domain. Additionally, the review highlights the integration of intelligent technologies for mitigating ship collision risks and improving bridge safety management in scour-prone environments. By consolidating existing knowledge, this paper can serve as a critical reference for understanding the compounded effects of scour and other hazards on bridge pile foundations, offering guidance for future research and engineering practices. Full article

In this study, the CrCuFeNiTiAl₁ equiatomic alloy was used as a base, which was modified by adding graphite in proportions of 0.5, 1.0, 2.5, and 5.0 mol%. The samples were obtained by powder metallurgy and sintering at 1200 °C for 2 h in a furnace with a protective argon atmosphere. Structural characterization was performed by XRD. A microstructural evaluation was conducted by SEM. The best mechanical microhardness and compressive strength results were obtained in the samples with the lowest amounts of graphite (238 μHV and 1000 MPa, respectively). The density values showed that samples with low amounts of graphite had better densification, lower porosity, and finer structural characteristics than those with graphite percentages higher than 1 mol%. The XRD studies determined the formation of a mixture of crystalline structures composed of FCC due to the presence of Cu, Ni, and Al metals; BCC due to Fe and Cr metals; and HCP due to Ti, and the formation of a Cr₇C₃ compound. SEM analysis showed the formation of cracks and porosity due to the formation of carbides. Full article

Anion exchange membrane electrolyzer (AEMEC) is a promising hydrogen production technology device. An electrochemical model is developed using MATLAB/Simulink to analyze the impact of factors such as anion exchange membrane (AEM) thickness, operating temperature, pressure, and gas diffusion layer (GDL) parameters including GDL thickness, porosity, and pore size. The results showed that as the thickness of AEM, operating pressure, and GDL decreased, the electrolysis efficiency significantly improved, and energy consumption decreased. When the thickness of AEM decreases from 70 microns to 65 microns, it will cause a decrease of 24 mV in cell voltage. This study also found that increasing pressure slightly increases voltage due to higher diffusion overpotential. In addition, changes in GDL porosity and pore size have a significant impact on performance. The lower porosity reduces ohmic loss and improves efficiency. This study highlights the importance of optimizing the design of AEMEC components to improve hydrogen production performance. Full article

This study aims to evaluate the techno-economic feasibility of implementing the IEC 62034:2012 standard, which governs automatic test systems for battery-powered emergency escape lighting, on a 52.8-m multipurpose vessel. The work is based on a detailed case study of the vessel’s lighting systems, incorporating lighting simulations, system modifications using DALI-compatible components, and an economic analysis based on net present value, internal rate of return, and discounted payback period. The results demonstrate that the implementation reduces preventive maintenance costs significantly—from 24,750 EUR to 2250 EUR over ten years—while achieving a positive net present value of 5317 EUR, an internal rate of return of 27.81%, and a discounted payback period of under five years. The findings contribute to maritime safety literature by extending the application of IEC 62034:2012 to shipboard environments, where it is not yet standard practice. Practically, it provides a cost-effective and safety-enhancing solution for ship operators, suggesting that automated testing systems can replace outdated manual maintenance procedures and improve compliance with safety regulations. Full article

This study explored an alternative approach to managing construction and demolition waste (CDW) in Greece by repurposing waste bricks and tiles as secondary raw materials for geopolymer synthesis. Alkali dissolution tests indicated that waste brick is more susceptible to alkaline attack than tile waste. The Taguchi method was employed as a design of experiments (DoE) approach to optimize synthesis and address CDW mineralogical variability, targeting maximum compressive strength. The primary influencing factors were alkali content (64%) and soluble silicon (33%). Geopolymers with a compressive strength of 42.8 MPa were synthesized at 90 °C for 3 days under optimal conditions: a soluble silicon-to-alkali molar ratio of 0.5, an alkali-to-aluminum molar ratio of 1, and a 50:50 sodium–potassium ion mixture. Full article

In this work, we present a series of experimental and computational modeling procedures related to precision controls of intermittent dispensing systems for complex fluids. With reductionist approaches, we have modeled key components of dispensing systems with different boundary conditions. With system approaches, we have also connected the pressure differential with the volume flow rate, as well as the characterization of the material properties. Finally, confirmed with a series of experiments and simulations, we demonstrate that traditional reductionist approaches and system modeling tools can be effectively and efficiently employed hand-in-hand with the help of the so-called inversed optimization approaches to derive useful information of relevance for industrial applications. Full article

Wear particle analysis, which identifies failure modes caused by the wear of various machine components, is an essential technique for monitoring machinery conditions. This analysis plays a vital role in predictive maintenance by revealing component degradation in machinery. This study proposes an automated framework to classify four standard wear particle textures—rough, striated, pitted, and fatigued—using artificial neural networks (ANNs) combined with advanced image processing techniques. Images acquired via Charged-Coupled Device (CCD) microscopy were preprocessed using sharpening, histogram stretching, and four edge detection algorithms: Sobel, Laplacian, Boie–Cox, and Canny. The Laplacian and Canny methods yielded the highest classification accuracies of 97.9% and 98.9%, respectively. By minimizing human subjectivity, this automated approach enhances diagnostic consistency and represents a scalable solution for industrial condition monitoring. Full article

The article examines the parameters of axial motion of bulk material in rotary kilns, including bed height, axial velocity, and mean residence time. The review includes summary tables of experiments from the scientific literature, detailing the conditions and ranges of operating parameter variations. Mathematical models from the literature are presented for each of the parameters discussed. The materials of the article cover studies from 1927 to 2025, including analysis of numerous works that were not published in international sources. Based on the review, the necessity of studying the impact of coating formation on the axial motion parameters is highlighted, along with the need for experiments on real facilities and pilot plants. Full article

This literature review critically examines the development and optimization of sustainable energy and exergy analysis software specifically designed for offshore wind farms, emphasizing the transformative role of machine learning (ML) in overcoming operational challenges. Offshore wind energy represents a cornerstone in the global transition to low-carbon economies due to its scalability and superior energy yields; however, its complex operational environment, characterized by harsh marine conditions and logistical constraints, necessitates innovative analytical tools. Traditional deterministic methods often fail to capture the dynamic interactions within wind farms, thereby underscoring the need for ML-integrated approaches that enhance precision in energy forecasting, fault detection, and exergy analysis. This PRISMA-ScR review synthesizes recent advancements in ML techniques, including Random Forest, Long Short-Term Memory networks, and hybrid models, demonstrating significant improvements in predictive accuracy and operational efficiency. In addition, it critically identifies current gaps in existing software tools, such as inadequate real-time data processing and limited user interface design, which hinder the practical implementation of ML solutions. By integrating theoretical insights with empirical evidence, this study proposes a unified framework that leverages ML algorithms to optimize turbine performance, reduce maintenance costs, and minimize environmental impacts. Emerging trends, such as incorporating digital twins and Internet of Things (IoT) technologies, further enhance the potential for real-time system monitoring and adaptive control. Overall, this review provides a comprehensive roadmap for the next generation of software tools to revolutionize offshore wind farm management, thereby aligning technological innovation with global renewable energy targets and sustainable development goals. Full article