Eng, Volume 6, Issue 9 (September 2025) – 37 articles

This study aims to evaluate the effectiveness of five analytical and semi-analytical methods for estimating Lamb wave dispersion in viscoelastic plates—the Rayleigh–Lamb solution, the Global Matrix Method (GMM), the Semi-Analytical Finite Element (SAFE) method, the Scaled Boundary Finite Element Method (SBFEM), and the Legendre Polynomial Method (LPM). The Rayleigh–Lamb equations are solved using an optimized Newton–Raphson algorithm, enhancing computational efficiency while maintaining comparable accuracy. The SAFE method exhibited a remarkable balance between computational efficiency and physical accuracy, outperforming SBFEM at high frequencies. For epoxy and high-performance polyethylene (HPPE) plates, the SAFE method and the LPM significantly outperform the GMM in relation to computational efficiency, with errors below $1\%$ for fundamental symmetric and antisymmetric modes across the tested frequency range of 0 to 100 kHz. In addition, the ability of the SAFE method to accurately predict both phase velocity and attenuation in viscous media supports their use in guided-wave-based structural health monitoring applications. Among the investigated approaches, the SAFE method emerges as the most robust and accurate for viscoelastic plates, while the SBFEM and LPM show limitations at higher frequencies. This study provides a quantitative and methodological foundation for selecting and implementing numerical methods for guided wave analysis, emphasizing the dual necessity of physical fidelity and numerical stability. Full article

Rapid urbanization has led to substantial changes in land use, resulting in challenges related to the urban microclimate across multiple scales. Given the strong relationship between urban morphology and microclimatic conditions, designing appropriate urban fabric models plays a key role in supporting sustainable urban development. The spatial form and geometry of buildings influence external environmental conditions and affect the performance of urban architecture. This study investigates how morphological and geometric characteristics of urban form influence microclimate, using a case study approach. Data were obtained through a literature review and existing urban development plans. ENVI-met software was used to simulate microclimatic variables, which were treated as dependent factors. In parallel, morphological components—treated as independent variables—were analyzed using GIS Pro software. Findings reveal that the configuration of urban fabric has a notable impact on microclimate. Specifically, higher building density is associated with greater heat accumulation around structures. Urban areas with fragmented and highly granular layouts tend to trap more heat, thereby intensifying the urban heat island effect. Conversely, when buildings are spaced apart, increased wind flow helps reduce temperatures in central urban zones of urban development in District 9, Mashhad, Iran. The results also emphasize the importance of vegetation placement. While greenery can mitigate heat in ventilated areas, dense vegetation in wind-restricted zones may raise ambient temperatures. Overall, the study offers a simulation-based understanding of how urban form influences microclimate. These insights can assist urban planners and designers in creating environments that promote more favorable local climatic conditions. Full article

This study aimed to optimize the urban public transportation system in Queretaro, Mexico, while meeting passenger demand by using Linear Programming (LP) and Goal Programming (GP) models to reduce redundant routes, minimize fuel consumption and ${\mathrm{CO}}_2$ emissions, and balance costs with service coverage. Operational data from 316 drivers were collected on diesel consumption, working hours, and vehicle availability while incorporating twelve technical, labor, and regulatory constraints. The LP model reduced the number of routes from 148 to 124, achieving daily savings of 13,789 L of diesel, a reduction of 36,816 kg in ${\mathrm{CO}}_2$ emissions, and an economic benefit of USD 17,071.90, equivalent to 13,253 tons of ${\mathrm{CO}}_2$ avoided annually; these results demonstrate LP’s ability to deliver quantifiable improvements in efficiency and sustainability. The GP model integrated multiple and often conflicting objectives, such as maintaining a maximum fuel cost of USD 9312/day for 1944 buses distributed across five zones while ensuring a minimum coverage of 145 routes and 450,000 daily passengers, showing that it is possible to meet service targets with marginal cost overruns (USD 4118.66) when balancing both coverage and budget. The novelty of this paper lies in combining mathematical optimization models with real operational data and simultaneously reporting both economic and environmental impacts. This allows us to offer a replicable and highly interpretable tool with low computational cost for use in medium-sized cities seeking to align mobility planning with sustainability policies and operational efficiency. Full article

Wireless sensor networks (WSNs) are a key enabling technology for modern e-Health applications. However, their deployment in clinical environments faces critical challenges due to dynamic network topologies, signal interference, and stringent energy constraints. Static resource allocation schemes often prove inadequate in these mission-critical settings, leading to communication failures that can compromise data integrity and patient safety. This paper proposes a novel hybrid framework for intelligent, dynamic resource allocation that addresses these challenges. The framework combines classical graph-coloring heuristics—Greedy and Recursive Largest First (RLF) for efficient initial channel assignment with the adaptive power of metaheuristics, specifically Simulated Annealing and Genetic Algorithms, for localized refinement. Unlike conventional approaches that require costly, network-wide reconfigurations, our method performs targeted adaptations only in interference-affected regions, thereby optimizing the trade-off between network reliability and energy efficiency. Comprehensive simulations modeled on dynamic, hospital-scale WSNs demonstrate the effectiveness of various hybrid strategies. Notably, our results demonstrate that a hybrid strategy using a Genetic Algorithm can most effectively minimize interference and ensure high data reliability, validating the framework as a scalable and resilient solution. These results validate the proposed framework as a scalable, energy-aware solution for resilient, real-time healthcare telecommunication infrastructures. Full article

The quality change process of table grapes during cold chain logistics is complex and highly susceptible to vibration-induced damage. Traditional monitoring techniques not only consume significant human and material resources but also cause destructive effects on the fruit structure of table grapes, making them difficult to apply in practical scenarios. Based on this, this paper focuses on table grapes in cold chain business processes and designs a flexible wireless vibration sensor for monitoring the quality of table grapes during cold chain transportation. The hardware component of the system fabricates a flexible wireless vibration sensing for monitoring the quality of the table grape cold chain. In contrast, the software component develops corresponding data acquisition and processing functionalities. Using Summer Black table grapes purchased from Tianjin Hongqi Agricultural Market as the research subject, correlation and quality monitoring models for the cold chain process of table grapes were constructed. After Z-score standardization, the prediction results based on the MLR model achieved R² values all greater than 0.87 and RPD values all exceeding 2.7. Comparisons with other regression models demonstrated its optimal fitting performance for monitoring the quality of the cold chain for table grapes. This achieves non-destructive and high-precision data acquisition and processing during the cold chain process of table grapes, wirelessly transmitting results to terminal devices for real-time visual monitoring. Full article

Focusing on the issues of severe mining pressure and discontinuous surface deformation caused by the large-scale mining of multiple coal seams, and taking into account the research background of Shigetai Coal Mine in Shendong Mining Area, this study adopts physical similarity simulation, theoretical analysis, and on-site verification methods to carry out research on rock migration, stress evolution, and overlying rock fracture mechanism at shallow burial depths and in multiple-coal-seam mining. The research results indicate that as the working face advances, the overlying rock layers break layer by layer, and the intact rock mass on the outer side of the main fracture forms an arched structure and expands outward, showing a pattern of layer-by-layer breaking of the overlying rock and slow settlement of the loose layer. The stress of the coal pillars on both sides in front of and behind the workplace shows an increasing trend followed by a decreasing trend before and after direct top fracture. The stress on the bottom plate of the goaf increases step by step with the collapse of the overlying rock layer, and its increment is similar to the gravity of the collapsed rock layer. When mining multiple coal seams, when the fissures in the overlying strata of the current coal seam penetrate to the upper coal seam, the stress in this coal seam suddenly increases, and the pressure relief effect of the upper coal seam is significant. Based on the above laws, three equilibrium structural models of overlying strata were established, and the maximum tensile stress and maximum shear stress yield strength criteria were used as stability criteria for overlying strata structures. The evolution mechanism of mining damage caused by layer-by-layer fracturing and the upward propagation of overlying strata was revealed. Finally, the analysis of the hydraulic support working resistance during the backfilling of the 31,305 working face in Shigetai Coal Mine confirmed the accuracy of the similarity simulation and theoretical model. The above research can provide support for key theoretical and technological research on underground mine safety production, aquifer protection, surface ecological restoration, and source loss reduction and control. Full article

This study offers a comprehensive petrophysical evaluation and reservoir characterization of the Srikail Gas Field, situated on the Tripura Uplift in the east-central Bengal Basin. Utilizing well log data from four wells (Srikail-1 to Srikail-4), the analysis targets the Bhuban and Bokabil formations of the Surma Group. Standard log suites, including gamma ray, spontaneous potential, caliper, resistivity, neutron, density, and sonic logs, were interpreted using both manual techniques and digital analysis through software. Key petrophysical properties, including shale volume, effective porosity, fluid saturations, permeability, and bulk volume of water, were estimated using a combination of empirical modeling and automated interpretation workflows. Cross-plot methodologies were applied to assist in reservoir evaluation. The study integrated both qualitative and quantitative approaches to characterize each reservoir unit in detail. Results demonstrate significant heterogeneities in reservoir quality across the field. While some intervals exhibit favorable properties suitable for commercial gas production, others are characterized by high carbonate content, poor porosity, and very low permeability (Sand C with 0.05 to 0.08 mD), indicative of tight to semi-conventional reservoirs. The most productive zones, identified as the D sands, are cleaner sands with excellent permeability (102 mD to 355 mD). In contrast, deeper intervals generally exhibit tighter characteristics, with DST-derived permeability values ranging from 0.6 to 0.01 mD. The study recommends integrating core analysis, advanced petrophysical modeling, and 3D seismic interpretation with well log data to enhance reservoir delineation in the Srikail Gas Field. This combined approach would reduce uncertainties, improve input parameter accuracy, and offer a more comprehensive understanding of the Bhuban Formation’s heterogeneity, ultimately supporting more effective reservoir evaluation and hydrocarbon recovery planning. Full article

Bifacial Photovoltaic (bPV) technology is rapidly becoming the standard in the solar photovoltaic (PV) industry due to its ability to capture reflected radiation and generate additional energy. This experimental study analyses the electrical performance of bPV modules under specific installation conditions, including varying heights, module tilt angles (MTA), and surface reflectivity. The methodology combines controlled indoor testing with outdoor experiments that replicate real-world operating environments. The outdoor test setup was carefully designed and included dual data acquisition systems: one with independent sensors and another with wireless telemetry for data transfer from the inverter. A thermal performance model was used to estimate energy output and was benchmarked against experimental measurements. All electrical parameters were obtained in accordance with international standards, including current-voltage characteristic (I–V curve) corrections, using calibrated instruments to monitor irradiance and temperature. Indoor measurements under Standard Test Conditions yielded at bifaciality coefficient $\phi =0.732$, a rear bifacial power gain $BiFi=0.285$, and a relative bifacial gain $BiF{i}_{rel}=9.4\%$. The outdoor configuration employed volcanic red stone (Tezontle) as a reflective surface, simulating a typical mid-latitude installation with modules mounted 1.5 m above ground, tilted from 0° to 90° regarding floor and oriented true south. The study was conducted at a site located at 18.8° N latitude during the early summer season. Results revealed significant non-uniformity in rear-side irradiance, with a 32% variation between the lower edge and the centre of the bPV module. The thermal model used to determine electrical performance provides power values higher than those measured in the time interval between 10 a.m. and 3 p.m. Maximum energy output was observed at a MTA of $0°$, which closely aligns with the optimal summer tilt angle for the site’s latitude. Bifacial energy gain decreased as the MTA increased from $0°$ to $90°$. These findings offer practical, data-driven insights for optimizing bPV installations, particularly in regions between $15°$ and $30°$ north latitude, and emphasize the importance of tailored surface designs to maximize performance. Full article

Since the Great East Japan Earthquake, floriculture has expanded in Namie Town, Fukushima Prefecture, as part of agricultural recovery. Growth surveys are essential for floriculture production, cultivation management, and trials as they help assess plant growth. However, these surveys are labor-intensive, and the standards used can vary owing to subjective judgments and individual differences. To address this issue, image-processing technologies are expected to enable more consistent and objective evaluations. In this study, we explored image processing in growth surveys by estimating plant growth parameters from three-dimensional (3D) point clouds acquired using a smartphone-based 3D scanner. Focusing on lisianthus (Eustoma grandiflorum), we estimated the plant height and the number of nodes above the bolting. The results showed that plant height could be estimated with high accuracy, with a root mean square error (RMSE) of 1.2 cm. By contrast, the node number estimation showed a mean error exceeding one node. This error was attributed to the challenges in handling variations in point cloud density, which stem from the 3D point cloud generation method and leaf occlusion caused by dense foliage. Future work should focus on developing analysis methods that are robust to point-cloud density and capable of handling complex vegetative structures. Full article

This paper presents the design and implementation of a control algorithm for power converters in a microgrid, with the main objective of providing the flexibility to adjust the system inertia. The increasing integration of renewable energy sources in microgrids has driven the development of advanced control techniques to ensure stability and power quality. The proposed algorithm combines droop control, synchronverter dynamics, and virtual impedance to achieve a robust and efficient control strategy. Simulations were conducted to validate the algorithm’s performance, demonstrating its capability to maintain voltage within acceptable limits and improve the inertial response of the microgrid. The results contribute to the advancement of intelligent and resilient microgrid development, which is essential for the transition towards a more sustainable energy system. Full article

This study explores the early-age performance of eco-efficient alkali-activated slag–fly ash (AASF) mixtures using high-calcium fly ash (HCFA) and low-calcium fly ash (LCFA) at varying alkali activator-to-slag cement (AL/SC) ratios (15%, 20%, and 25%) under steam, water, and ambient curing conditions. Mix designs were developed with a fixed water-to-slag cement ratio of 50%, while fly ash partially replaced fine aggregate at a 20% substitution level. Fresh and hardened properties were investigated. The results revealed that increasing the AL/SC ratio led to reduced workability and increased flow loss, especially in HCFA mixtures, due to their higher calcium content and finer particle size, which promoted early stiffening. In contrast, LCFA mixtures exhibited greater slump flow and better workability retention owing to their slower dissolution rate. Regarding compressive strength, steam curing produced the highest performance. At 25% AL/SC, HCFA mixtures achieved 70 MPa at 28 days, while LCFA mixtures reached 68 MPa. Water curing showed moderate strength development, whereas ambient curing resulted in slower gains. These findings emphasize the influence of fly ash type, AL/SC ratio, and various curing conditions in enhancing the performance of eco-efficient AASF mixtures. Full article

This study presents a comprehensive analysis of heat transfer enhancement, flow resistance, and thermal performance in rectangular channels equipped with three baffle configurations: conventional transverse baffles (TBs), in-line downward-facing notched baffles (IDF-NBs), and staggered downward-facing notched baffles (SDF-NBs). The influence of the pitch-to-baffle height ratio (P/e), ranging from 2.0 to 10, was examined across Reynolds numbers from 6000 to 24,000. Results indicate that a P/e ratio of 6.0 consistently yielded the highest Nusselt numbers across all configurations. While the TB configuration produced significant heat transfer at P/e= 6.0, it experienced a substantial friction penalty, with its best thermal enhancement factor (TEF = 1.168) observed at P/e = 8.0. The IDF-NB configuration achieved optimal performance at P/e = 6.0 with a TEF of 1.257, offering a better balance between heat transfer and flow resistance. The SDF-NB arrangement outperformed all other cases, delivering the highest Nusselt number (Nu = 116.9), TEF (1.362), and improved flow reattachment, primarily due to enhanced mixing from the staggered layout. These findings demonstrate that the staggered notched baffle configuration at P/e = 6.0 offers the most effective thermal performance enhancement among the configurations studied. Full article

Dissolved Gas Analysis (DGA) of power system transformers has emerged as one of the most effective transformer health diagnosing tools by analyzing the gases dissolved in the insulating oil. There are various traditional DGA techniques like Key Gas Method, Roger’s Ratio, IEC ratio, Dornenburg’s Ratio, and Duval Triangle method. However, these techniques have limitations such as inconsistent results, the inability to detect low-energy faults, and reliance on expert knowledge due to complex interpretation. To overcome these limitations, this paper introduces an integrated fuzzy logic system that enhances DGA interpretation by combining the diagnostic strengths of Key Gas Method, Roger’s Ratio, IEC ratio, and Duval Triangle methods. To obtain a final, human-readable diagnosis, the output of each technique is incorporated into a higher-level fuzzy inference system once each is modeled separately with fuzzy logic, having known membership functions and rule bases. To test this model, oil samples of known results of different transformers are used and compared to the results given by the proposed fuzzy inference system. The proposed method is easier and more feasible for practical use since it not only improves fault detection accuracy and reliability but also allows for easier interpretation by non-specialists. This study makes an additional contribution to a higher-level, more effective, and more accurate method for transformer fault detection by overcoming the interpretational difficulties and weaknesses of conventional DGA approaches. Full article

In the context of Industry 4.0 and the proliferation of smart buildings, elevators represent critical assets whose performance is often inadequately measured by traditional indicators that overlook energy consumption. This study addresses the need for a more holistic Key Performance Indicator (KPI) by developing the Overall Equipment Effectiveness for Elevators (OEEE), an index designed to integrate operational effectiveness with energy efficiency. The methodology involves adapting the classical OEE framework through a comprehensive literature review and an analysis of elevator energy standards. This leads to a novel structure that incorporates a dedicated energy efficiency dimension alongside the traditional pillars of availability, performance, and quality. The framework further refines the performance and energy efficiency dimensions, resulting in six distinct sub-indicators that specifically measure operational uptime, speed adherence, electromechanical conversion, fault-free cycles (as a proxy for operational quality), and energy use during both movement and standby modes. The primary result is the complete mathematical formulation of the OEEE, a single, integrated KPI derived from these six metrics and designed for implementation using data from modern IoT-enabled elevators. The study concludes that the OEEE provides a more accurate and comprehensive tool for asset management, enabling data-driven decisions to enhance reliability, optimise energy consumption, and reduce operational costs in smart vertical transportation systems. Full article

Designing efficient nanoparticle-enhanced CO₂ capture systems is challenging due to the diversity of nanoparticles, solvent formulations, reactor configurations, and operating conditions. This study presents the first ANN-based meta-analysis framework developed to predict CO₂ absorption enhancement across multiple reactor systems, including batch reactors, packed columns, and membrane contactors. A curated dataset of 312 experimental data points was compiled from literature, and an artificial neural network (ANN) model was trained using six input variables: nanoparticle type, concentration, system configuration, base fluid, pressure, and temperature. The proposed model achieved high predictive accuracy (R² > 0.92; RMSE: 4.2%; MAE: 3.1%) and successfully captured complex nonlinear interactions. Feature importance analysis revealed nanoparticle concentration (28.3%) and system configuration (22.1%) as the most influential factors, with functionalized nanoparticles such as Fe₃O₄@SiO₂-NH₂ showing superior performance. The model further predicted up to 130% enhancement for ZnO in optimized membrane contactors. This AI-driven tool provides quantitative insights and a scalable decision-support framework for designing advanced nanoparticle–solvent systems, reducing experimental workload, and accelerating the development of sustainable CO₂ capture technologies. Full article

This paper presents ModuLab, a modular, low-cost sensor platform designed to simplify real-time environmental monitoring for laboratory research and educational settings. Centered on the APP-All MCU 2023 development board with an AVR128DA48 microcontroller (Microchip Technology Inc., Taiwan) ModuLab supports plug-and-play integration of multiple sensor types—including temperature, pH, light, and humidity—using a robust I²C communication protocol. The system features configurable sampling rates, built-in signal conditioning, and a Python-based interface for real-time data visualization. As a proof-of-concept, ModuLab was operated continuously for 48 h to evaluate system stability and filtering capabilities. However, due to institutional data ownership and confidentiality policies, the underlying datasets cannot be disclosed in this submission. The architecture and implementation details described herein are intended to guide future users and research groups seeking accessible alternatives to conventional data acquisition solutions. Comprehensive performance validation and open-access data sharing are planned as the next steps in this ongoing project. Full article

This study presents a three-dimensional numerical analysis of natural convection during the postharvest storage of corn and wheat in a galvanized steel silo with a conical roof and floor, measuring 3 m in radius and 18.7 m in height, located in the Bajío region of Mexico. Simulations were carried out specifically for December, a period characterized by cold ambient temperatures (10–20 °C) and comparatively lower solar radiation than in warmer months, yet still sufficient to induce significant heating of the silo’s metallic surfaces. The governing conservation equations of mass, momentum, energy, and species were solved using the finite volume method under the Boussinesq approximation. The model included grain–air sorption equilibrium via sorption isotherms, as well as metabolic heat generation: for wheat, a constant respiration rate was assumed due to limited biochemical data, whereas for corn, respiration heat was modeled as a function of grain temperature and moisture, thereby more realistically representing metabolic activity. The results, obtained for December storage conditions, reveal distinct thermal and hygroscopic responses between the two grains. Corn, with higher thermal diffusivity, developed a central thermal core reaching 32 °C, whereas wheat, with lower diffusivity, retained heat in the upper region, peaking at 29 °C. Radial temperature profiles showed progressive transitions: the silo core exhibited a delayed response relative to ambient temperature fluctuations, reflecting the insulating effect of grain. In contrast, grain at 1 m from the wall displayed intermediate amplitudes. In contrast, zones adjacent to the wall reached 40–41 °C during solar exposure. In comparison, shaded regions exhibited minimum temperatures close to 15 °C, confirming that wall heating is governed primarily by solar radiation and metal conductivity. Axial gradients further emphasized critical zones, as roof-adjacent grain heated rapidly to 38–40 °C during midday before cooling sharply at night. Relative humidity levels exceeded 70% along roof and wall surfaces, leading to condensation risks, while core moisture remained stable (~14.0% for corn and ~13.9% for wheat). Despite the cold ambient temperatures typical of December, neither temperature nor relative humidity remained within recommended safe storage ranges (10–15 °C; 65–75%). These findings demonstrate that external climatic conditions and solar radiation, even at reduced levels in December, dominate the thermal and hygroscopic behavior of the silo, independent of grain type. The identification of unstable zones near the roof and walls underscores the need for passive conservation strategies, such as grain redistribution and selective ventilation, to mitigate fungal proliferation and storage losses under non-aerated conditions. Full article

As urban buildings become increasingly dense, indoor personnel are often exposed to noise disturbances from adjoining rooms which can reduce working efficiency and affect mental health. Closing the door is one of the ways to reduce noise transmission, but it can cause a decrease in indoor air circulation. This paper investigates the sound insulation effect and air ventilation performance of a door in a partially open state by numerical simulation. To acquire the effect of sound insulation, an acoustic–structural solver is employed to calculate the sound transmission losses with different door opening angles in the frequency domain. To evaluate the ventilation performance, the mass flow rates across door opening are calculated by computational fluid dynamics. The simulation results confirm the trade-off relation between the sound insulation effect and the ventilation performance. To calculate the effect of noise and ventilation on work efficiency, a comprehensive evaluation index workplace environmental score (WES) was introduced and calculated by the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. A clear sound insulation effect corresponds to an opening angle (θ_d) of less than 15° with minimum air ventilation. Good ventilation performance could be obtained when the door opening angle is larger than 45°, while the sound insulation effect is negligible. A good compromise between the sound insulation effect and the air ventilation performance is found to be in the range of θ_d = 15°~25°, which provides practical recommendations in daily routines. Full article

Lead-free perovskite solar cells (PSCs) provide a viable alternative to lead-based versions, thereby reducing significant environmental issues related to toxicity. MASnIBr₂ has emerged as a very attractive lead-free perovskite material due to its environmentally friendly characteristics and advantageous optoelectronic capabilities. However, more tuning is required to achieve superior conversion efficiencies (PCEs). This study uses SCAPS-1D simulations to systematically develop and optimize the electron and hole transport layers (ETLs/HTLs) in MASnIBr₂-based perovskite solar cells (PSCs). Iterative simulations are used to carefully examine and optimize critical parameters, including electron affinity, energy bandgap, layer thickness, and doping density. Additionally, the thickness of the MASnIBr₂ absorber layer is optimized to enhance charge extraction and light absorption. Our findings showed a maximum power conversion efficiency of 20.42%, an open-circuit voltage of 1.38 V, a short-circuit current density of 17.91 mA/cm², and a fill factor of 82.75%. This study establishes a basis for future progress in sustainable photovoltaics and offers essential insights into the design of efficient lead-free perovskite solar cells. Full article

Recent progress in computer vision and embedded systems has facilitated real-time monitoring of bioprocesses; however, lightweight and scalable solutions for resource-constrained settings remain limited. This work presents a modular framework for monitoring Chlorella vulgaris growth by integrating RGB image processing with multimodal sensor fusion. The system incorporates a Logitech C920 camera and low-cost pH and temperature sensors within a compact photobioreactor. It extracts RGB channel statistics, luminance, and environmental data to generate a 10-dimensional feature vector. A feedforward artificial neural network (ANN) with ReLU activations, dropout layers, and SMOTE-based data balancing was trained to classify growth phases: lag, exponential, and stationary. The optimized model, quantized to 8 bits, was deployed on an ESP32 microcontroller, achieving 98.62% accuracy with 4.8 ms inference time and a 13.48 kB memory footprint. Robustness analysis confirmed tolerance to geometric transformations, though variable lighting reduced performance. Principal component analysis (PCA) retained 95% variance, supporting the discriminative power of the features. The proposed system outperformed previous vision-only methods, demonstrating the advantages of multimodal fusion for early detection. Limitations include sensitivity to lighting and validation limited to a single species. Future directions include incorporating active lighting control and extending the model to multi-species classification for broader applicability. Full article

Ground engaging tools (GETs) are critical consumable components on mining excavators, and their timely replacement is essential to prevent risks and excessive downtime. This paper presents a monitoring method utilising the modal properties—natural frequencies and mode shapes. The method is applied in a test case to show how the GETs on an excavator bucket could be monitored. Modal analysis and dynamic analysis are conducted with ANSYS to verify the effectiveness of the proposed method. The finite element analysis models are validated by experimental vibration experiments. The results demonstrate a strong correlation between changes in natural frequencies and the conditions of the teeth on the excavator bucket, when comparing the intact to the worn-out condition. In conclusion, the presented method offers a promising approach for real-time monitoring of the GETs on mining excavators and similar equipment. It will contribute to efficient maintenance interventions and enhancing operational efficiency and safety. Full article

Efficient labor productivity forecasting is a critical challenge in construction engineering, directly influencing scheduling, cost control, and resource allocation. In reinforced concrete projects, accurate prediction of rebar-fixing productivity enables managers to optimize workforce deployment and mitigate delays. This study proposes a machine learning-based framework to forecast rebar-fixing labor productivity under varying site and environmental conditions. Four regression algorithms—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), and k-Nearest Neighbors (KNN)—were trained, tuned, and validated using grid search with k-fold cross-validation. RF achieved the highest accuracy, with an $R^2$ of 0.901 and RMSE of 19.94 on the training set and an $R^2$ of 0.877 and RMSE of 22.47 on the test set, indicating strong generalization. Model interpretability was provided through SHapley Additive exPlanations (SHAP), revealing that larger quantities of M32 and M25 rebars increased productivity, while higher temperatures reduced it, likely due to lower labor efficiency. Humidity, wind speed, and precipitation showed minimal influence. The integration of accurate predictive modeling with explainable machine learning offers practical insights for project managers, supporting data-driven decisions to enhance reinforcement task efficiency in diverse construction environments. Full article

In order to enhance the performance of 112 Gb/s polarization-multiplexed quadrature phase-shift keying (PM-QPSK) coherent optical receivers, a novel digital signal processing (DSP) framework is presented in this study. The suggested method combines cutting-edge signal processing techniques to address important constraints in long-distance, high data rate coherent systems. The framework uses overlap frequency domain equalization (OFDE) for chromatic dispersion (CD) compensation, which offers a cheaper computational cost and higher dispersion control precision than traditional time-domain equalization. An adaptive carrier phase recovery (CPR) technique based on mean-squared differential phase (MSDP) estimation is incorporated to manage phase noise induced by cross-phase modulation (XPM), providing dependable correction under a variety of operating situations. When combined, these techniques significantly increase Q factor performance, and optimum systems can handle transmission distances of up to 2400 km. The suggested DSP approach improves phase stability and dispersion tolerance even in the presence of nonlinear impairments, making it a viable and effective choice for contemporary coherent optical networks. The framework’s competitiveness was evaluated by comparing it against the most recent, cutting-edge DSP methods that were released after 2021. These included CPR systems that were based on kernels, transformers, and machine learning. The findings show that although AI-driven approaches had the highest absolute Q factors, they also required a large amount of computing power. On the other hand, the suggested OFDE in conjunction with adaptive CPR achieved Q factors of up to 11.7 dB over extended distances with a significantly reduced DSP effort, striking a good balance between performance and complexity. Its appropriateness for scalable, long-haul 112 Gb/s PM-QPSK systems is confirmed by a complexity versus performance trade-off analysis, providing a workable and efficient substitute for more resource-intensive alternatives. Full article

In order to meet the needs of grid integration of various renewable energy sources and promote long-distance power transmission, a hybrid multi-infeed DC system architecture consisting of a line-commutated converter (LCC) and a modular multilevel converter (MMC) is constructed. Focusing on the issue of traditional differential protection refusing to operate under high-resistance grounding faults and failing under symmetrical faults, a dual-criteria protection mechanism is proposed in this paper. By integrating current differential and voltage criterion, the accurate identification of various types of AC line faults can be realized. A hybrid DC system simulation model was built on MATLAB, the sampled data was decoupled, and the differential quantity was calculated to test the dual-criteria protection mechanism. The simulation results show that the proposed protection mechanism can effectively identify various faults within the hybrid DC multi-feed system area and faults outside the area and has robustness to complex working conditions such as high-resistance grounding and three-phase short circuits, which improves the sensitivity, selectivity, and adaptability of the protection. This method is designed for AC line protection under the disturbance of multi-infeed DC systems. It is not directly applicable to pure DC microgrids. The concept can be extended to AC/DC hybrid microgrids by adding DC-side protection criteria and re-calibrating thresholds. Full article

A very important method of reducing carbon emissions is to make sure industrial plants are operated at optimal energy efficiency. The oil and petrochemical industries spend large amounts of energy in the transportation of petroleum and its various products that have high viscosities. A critical component in these plants is abrupt pipe contraction. Large amounts of energy are lost in pipe contractions. In this paper we investigate the energy losses in pipe contraction using the local entropy generation method after solving the detailed flow field around an abrupt pipe contraction. We have applied the method at various Reynolds numbers covering laminar and turbulent flow regimes. Furthermore, we have used an integral entropy analysis and found excellent agreement between the differential and integral entropy methods when the computational grid is well refined. The differential analysis was able to predict the local entropy generation and find where the large losses are located and therefore be able to minimize these losses effectively. Based on the detailed entropy generation field, it is recommended to use rounded contraction in order to reduce the losses. By introducing rounded contractions in laminar flow, the losses have been reduced by 22%. In the case of the turbulent flow regime, the losses were reduced by 96% by introducing a rounding radius to diameter ratio r/D₂ of 10%. The turbulent flow results for the case of pipe entrance, which is a special case of abrupt contraction (D₂/D₁ goes to zero) agree very well with the present results. This work addresses a large range of D₂/D₁ for laminar and turbulent flows. It is recommended that companies involved in designing petrochemical plants and installations take these findings into consideration to reduce carbon emissions. These recommendations also extend to the design of equipment and piping systems for the food industry and micro-device flows. Full article

The integration of sawmilling with prefabrication manufacturing presents a critical opportunity to enhance the efficiency, sustainability, and quality of timber construction in Australia. While prefabrication offers substantial benefits, including reduced waste, faster build times, and improved precision, its effectiveness is often constrained by inconsistencies and inefficiencies in upstream sawmilling processes. This state-of-the-art review examines the current structure of the Australian timber supply chain, identifies key challenges in aligning sawmill outputs with the requirements of prefabrication manufacturers, and explores enabling strategies for integration. The paper presents a comprehensive overview of integration pathways by reviewing insights from the academic literature, industry reports, national standards, and case studies from Australia and the European Union. It also proposes a priority-based implementation roadmap to guide coordinated technical, operational, and policy actions, thereby supporting the transformation toward a more connected timber sector in Australia. Full article

This study presents a comprehensive multi-criteria decision-making (MCDM) framework for evaluating and selecting optimal non-destructive testing (NDT) techniques for oil and gas facility maintenance operations. This research used a Fuzzy Analytic Hierarchy Process (FAHP) integrated with multiple MCDM methods to assess eight NDT techniques including radiographic testing, ultrasonic testing, and thermographic testing. The evaluation framework incorporated seven technical criteria and seven economic criteria. The FAHP results revealed spatial resolution (0.175) as the most critical technical criterion, followed by depth penetration (0.155) and defect characterization (0.143). For economic criteria, downtime costs (0.210) and operational costs (0.190) emerged as the most significant factors. This study used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), PROMETHEE (Preference Ranking Organization Method for Enrichment of Evaluations), and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje) methods to rank NDT techniques, with results consolidated using the CRITIC (CRiteria Importance Through Intercriteria Correlation) method. The final techno-economic analysis identified radiographic testing as the most suitable NDT method with a score of 0.665, followed by acoustic emission testing at 0.537. Visual testing ranked lowest with a score of 0.214. This research demonstrates the effectiveness of combining fuzzy logic with multiple MCDM approaches for NDT method selection in offshore welding operations. Full article

This study introduces a novel approach to enhance the sustainability of road pavement construction by utilizing palm oil clinker (POC), an industrial waste product, as a replacement for fine aggregates (passing 4.75 mm) in stone mastic asphalt (SMA) mixtures. Departing from conventional practices, this research comprehensively evaluates the feasibility of using POC at varying replacement levels (0% to 100%) across a range of binder contents (5.0% to 7.0%). A significant contribution of this work is the application of Response Surface Methodology (RSM) to optimize the proportions of POC and binder content (BC), achieving target Marshall and volumetric properties for superior pavement performance. The results demonstrate that POC can effectively substitute fine aggregates in SMA mixtures, meeting all requirements for Marshall stability, flow, stiffness, and volumetric properties, even at a 100% replacement rate. Statistical analysis using RSM confirmed the model’s validity, exhibiting a high R-squared value (>0.80), significant p-values, and an adequate precision exceeding 4. Optimization analysis revealed that a 60% POC content with a 6% BC yields the most desirable combination for achieving optimal SMA mixture characteristics. Further validation through experimental testing showed a strong correlation with the theoretical RSM predictions, with an error margin below 5%. This research underscores the potential of POC as a sustainable alternative to traditional aggregates, paving the way for more environmentally friendly and cost-effective road construction practices while simultaneously addressing waste management challenges in the palm oil industry. Full article

The strong coupling of the six-degree-of-freedom (6-DoF) electro-hydraulic Stewart parallel mechanism manifests as adjusting the elongation of one actuator potentially inducing motion in multiple degrees of freedom of the platform, i.e., a change in pose; this pose change leads to time-varying and unbalanced load forces (disturbance inputs) on the six hydraulic actuators; unbalanced load forces exacerbate the time-varying nature of the acceleration and velocity of the six hydraulic actuators, causing instantaneous changes in the pressure and flow rate of the electro-hydraulic system, thereby enhancing the pressure–flow nonlinearity of the hydraulic actuators. Considering the advantage of artificial intelligence in learning hidden patterns within complex environments (strong coupling and strong nonlinearity), this paper proposes a reinforcement learning motion control algorithm based on deep deterministic policy gradient (DDPG). Firstly, the static/dynamic coordinate system transformation matrix of the electro-hydraulic Stewart parallel mechanism is established, and the inverse kinematic model and inverse dynamic model are derived. Secondly, a DDPG algorithm framework incorporating an Actor–Critic network structure is constructed, designing the agent’s state observation space, action space, and a position-error-based reward function, while employing experience replay and target network mechanisms to optimize the training process. Finally, a simulation model is built on the MATLAB 2024b platform, applying variable-amplitude variable-frequency sinusoidal input signals to all 6 degrees of freedom for dynamic characteristic analysis and performance evaluation under the strong coupling and strong nonlinear operating conditions of the electro-hydraulic Stewart parallel mechanism; the DDPG agent dynamically adjusts the proportional, integral, and derivative gains of six PID controllers through interactive trial-and-error learning. Simulation results indicate that compared to the traditional PID control algorithm, the DDPG-PID control algorithm significantly improves the tracking accuracy of all six hydraulic cylinders, with the maximum position error reduced by over 40.00%, achieving high-precision tracking control of variable-amplitude variable-frequency trajectories in all 6 degrees of freedom for the electro-hydraulic Stewart parallel mechanism. Full article

Agrivoltaic technology holds great significance for promoting the collaborative development of new energy industries and modern agriculture. A systematic optimization design and preliminary electrical scheme for a 50 MW agrivoltaic power station in Shaanxi Province, China, were studied in this work. A combination of checkerboard and long-row layouts was adopted, considering the influence of the shading rate on agricultural production and photovoltaic power generation. The checkerboard pattern features the highest system efficiency, the smallest irradiance loss, and a slight lead in power generation, with a moderate shading rate, when compared to the other patterns. The expected energy gain from the bifacial modules’ rear side in this specific setup is 7.6%. These layouts ensure the power generation efficiency of the photovoltaic power station, while minimizing the shading impact of shading on crop growth, thereby achieving efficient comprehensive utilization of agricultural greenhouses and solar power generation. The primary electrical system was designed, including the main wiring design, main transformer selection, and type selection of major electrical equipment. The research results provide a practical reference for the large-scale application of agrivoltaic power stations, which is beneficial to promoting the high-quality development of modern agriculture. Full article