Engineering reports : open access最新文献

The growing need for secure, high-capacity wireless communication has spurred interest in hybridizing Free Space Optics (FSO) with Dense Wavelength Division Multiplexing (DWDM). A simulation-based framework for analyzing FSO-DWDM system performance under real-world atmospheric impairments, such as Gamma-Gamma turbulence, pointing errors, and rain and fog attenuation, is introduced in this paper. Different modulation schemes like NRZ, OOK, and PPM are investigated to establish efficient configurations for FSO links. Dispersion compensation methods precompensation, postcompensation, and symmetric compensation are compared in terms of Bit Error Rate (BER) performance using dispersion compensating fibers (DCF). Simulations are carried out in Opti-System and MATLAB with environmental parameters typical of real-world conditions. Results show that postcompensation provides better BER performance in moderate turbulence with values ranging from around 10E⁻³ to 10E⁻⁹, which is appropriate for FEC-based systems. The system maintains stable operation for up to 5 km FSO link distances. Among various systems with varying numbers of DWDM channels (30, 40, 50, and 60), the best BER performance is from the 30-channel system. Implementing dispersion compensation techniques raises the possible transmission distance from 2 km (uncompensated) to 5 km, emphasizing their potential to improve FSO-DWDM links. For a 30-channel DWDM configuration, postcompensation achieved a BER as low as 10E⁻⁶³ at a 1 km FSO link, outperforming precompensation 10E⁻⁵⁷, symmetric compensation 10E⁻¹⁸, and the uncompensated system 10E⁻²⁹. The work proves that postcompensation is superior under the conditions examined herein, specifically under moderate atmospheric turbulence. Simulation yields reveal that postcompensation always produces lower BER than pre- and symmetric compensation methods, especially for 30-channel DWDM configurations. For example, in a 1 km FSO link, postcompensation had the lowest BER at 10E⁻⁶³ compared to precompensation 10E⁻⁵⁷, symmetric compensation 10E⁻¹⁸, and the uncompensated system 10E⁻²⁹. These results identify postcompensation as the most successful dispersion mitigation method under simulated atmospheric conditions of Gamma-Gamma turbulence, pointing errors, and weather-related attenuation.

This paper optimized the hyperparameters of the Light Gradient Boosting Machine (LightGBM) model. Then, a simulation experiment was carried out using financial report data collected from publicly listed companies. The performance of the improved LightGBM model was evaluated in comparison with the support vector machine, back-propagation neural network, random forest, and traditional LightGBM models. Finally, the Shapley Additive Explanations algorithm was employed to quantify the importance of each feature in the improved LightGBM model. It was found that the improved LightGBM model exhibited superior capability in detecting anomalies, and features such as return on capital, liquidity ratio, and cash ratio were particularly influential in predicting the anomalies of financial information.

Newspapers serve as a vital source for various types of advertisements. Individuals eagerly await and search for advertisements relevant to them in newspapers. However, both printed newspapers and online newspapers lack the ability to provide category-wise advertisement search options. As a result, searching a newspaper advertisement in a specific category becomes very time-consuming and cumbersome due to sequential manual search across multiple newspapers. To address this problem in online newspapers, a classification model is needed that can classify advertisement images into predefined categories and hence allow users to perform category-wise advertisement searches with much ease. This research introduces and compares two sets of classification models for advertisement images in online English newspapers in India. The first set utilizes visual features to train seven different classification models by fine-tuning different layers of the Residual Network with 50 layers (ResNet50) pretrained model and achieves a maximum classification accuracy of 71.41%. The second set utilizes textual features to train 14 different classification models by fine-tuning different layers of the pretrained Bidirectional Encoder Representations from Transformers (BERT) base model and achieves maximum classification accuracies in the range from 96.88% to 97.34%. This significant enhancement of more than 25% underscores the superiority of textual features over visual ones in understanding Indian online English newspaper advertisement images and holds promise for practical applications, including categorized advertisement searches.

This study investigates the thermo-mechanical performance of fly ash-based geopolymer composites reinforced with bagasse and human hair fibers at varying fiber contents (1%, 1.5%, and 2% by weight). Composites were exposed to elevated temperatures (200°C, 400°C, 600°C, and 800°C) and fabricated using sodium hydroxide and sodium silicate as alkali activators. Compressive strength (CS) and flexural strength (FS) were evaluated across all compositions and thermal conditions. Results indicate a significant reduction in mechanical performance with increasing temperature. For 2% fiber content, the CS of bagasse fiber composites decreased from 25.87 MPa at 200°C to 7.12 MPa at 800°C, while human hair composites showed a decrease from 24.45 MPa to 13.62 MPa. Flexural strength followed a similar trend, with human hair composites exhibiting superior retention of strength across the temperature range. SEM analysis revealed stronger fiber–matrix bonding and reduced porosity in human hair composites, contributing to enhanced thermal stability. Despite thermal degradation effects, composites with up to 1.5% fiber content demonstrated sufficient mechanical integrity for moderate-temperature applications. The findings highlight the potential of utilizing sustainable, low-cost fiber reinforcements in geopolymer systems, offering viable alternatives to synthetic composites in thermally demanding environments between 200°C and 600°C.

To address the low efficiency, high energy consumption, and poor adaptability of traditional denitrification processes in the low-pressure environment of the plateau, an improved short-cut nitrification-anaerobic ammonium oxidation (SCN-ANAMMOX) process significantly outperformed conventional systems in terms of total nitrogen removal efficiency, energy consumption, and oxygen utilization efficiency. Results showed that when the influent nitrogen concentration increased from 50 to 200 mg/L, the total nitrogen removal efficiency (NRE) remained at 69.4%–84.9%, significantly higher than that of the conventional full-process nitrification–denitrification process A (55.7%–68.9%) and other control systems. The oxygen utilization efficiency reached 85.6%, with a specific energy consumption of only 0.42 kWh/m³, a 6.7% increase and a 0.09 kWh/m³ reduction in energy consumption compared to the optimal control process C (78.9%, 0.51 kWh/m³). Under low temperature (10°C) and low pressure (75 kPa), the NO₂⁻ generation rate was significantly increased compared to the other control processes, and the system stability period was extended. To adapt to the plateau environment, this study employed a polytetrafluoroethylene (PTFE) membrane and microbubble synergistic mass transfer system in the reactor, combined with solar heating and multi-parameter PID intelligent control to achieve stable low-oxygen operation and adaptive energy consumption regulation. This integrated process significantly reduced energy consumption while maintaining a high denitrification rate, demonstrating its feasibility and potential for application in plateau wastewater treatment.

Piled raft foundations play a critical role in ensuring the structural integrity of buildings, particularly in seismic regions where lateral and dynamic loads pose significant challenges. The effects of kinematic and inertial interactions on the dynamic responses of soil-pile-structure are still not fully understood and are not considered in the analysis of the foundation and superstructure. In this research, the seismic performance of piled raft foundations with and without superstructure resting on soft and stiff clayey soil has been investigated using a three-dimensional finite difference model. The study has been carried out by analyzing the seismic response of the low-, mid-, and high-rise buildings. This study considers the nonlinear hysteresis behavior of the clayey soil through the implementation of the advanced nonlinear kinematic hardening constitutive model. The results show that soil properties, pile geometries, loading frequency, and the number of superstructure stories are closely related parameters that should be investigated through a parametric study to understand the seismic behavior of the soil-pile-structure systems. This study's primary contribution is a systematic analysis that clarifies how the interplay of these parameters dictates the seismic response across low-, mid-, and high-rise structures, offering insights into when inertial or kinematic interactions dominate.

The growing demand for efficient and reliable human–robot collaboration (HRC) in industrial environments has driven the integration of augmented reality (AR) and real-time robotic control systems. This paper presents an AR-enabled HRC system where operators use Almer Arc2 AR headsets to issue voice commands that control the movement of the xArm 7 robotic arm. The system leverages ROS 2 and MoveIt for motion planning, augmented by a decision tree-based optimization model for trajectory calculation. A key performance metric, response time, is evaluated to assess the system's efficiency in translating voice commands into precise robotic actions. Experimental results show a mean response time of 1.45 s, indicating stable and rapid responses suitable for real-time industrial applications. The system's accuracy and repeatability were also confirmed, with minimal variations in pick and drop locations, demonstrating high precision in task execution. These findings highlight the system's potential to enhance workflow efficiency, reduce cognitive load on operators, and ensure seamless interaction between human and robot. By integrating intuitive AR interfaces and real-time feedback, this study contributes to advancing intelligent and adaptable human–robot workspaces for industrial automation.

Cross-domain sentiment analysis in Arabic is still challenging due to the scarcity of labeled data and the inherent complexity of identifying sarcasm, when ostensibly negative language expresses good sentiment. In our study, we address these issues with a comprehensive semi-supervised framework that combines domain adaptation, pseudo-labeling, and contrastive learning, with MARBERT, a pretrained Arabic language model. We adapted a model originally trained on the Large Arabic Book Reviews (LABR) dataset to the ArSarcasm dataset. This was achieved through five iterative runs of pseudo-labeling, using a dual-threshold confidence filter (0.85–0.75 for general samples and 0.70–0.60 for positive samples) to ensure reliable learning. Our approach yielded strong results, achieving a macro F1 score of 66.25% (±0.49) and an overall accuracy of 70.21% (±0.43) on the ArSarcasm test set. The model demonstrated particularly robust performance in classifying neutral (F1: 75.38%) and negative (F1: 70.20%) sentiments. However, detecting positive sentiment in sarcastic expressions remains challenging, as reflected in a lower F1 score of 53.18%, underscoring the complexity of this specific linguistic phenomenon. This study ultimately shows the synergistic value of integrating domain adaptation, pseudo-labeling, and contrastive learning for semi-supervised sentiment analysis in low-resource, sarcasm-heavy Arabic contexts. It also provides empirical insight into a key limitation of current transformer-based models: accurately detecting the incongruence that defines sarcasm.

Information on road gradient and vehicle mass is critical for the performance of the automatic shift system. This study presents a real-time identification method for estimating vehicle mass and road gradient using an extended Kalman filter (EKF) enhanced with a mass pre-estimation step. As a sensorless approach, it offers considerable practical advantages for heavy-duty trucks equipped with automatic transmissions. First, a Kalman state equation is formulated based on the longitudinal vehicle dynamics model. Subsequently, an EKF-based estimation algorithm is designed to identify both vehicle mass and road gradient. To accelerate convergence in real-time identification, driving data collected during clutch disengagement in the gear-shifting phase are utilized for mass pre-estimation prior to the EKF process. This pre-estimated mass value then serves as the initial condition for the EKF, significantly reducing the convergence time. The efficacy of the proposed method is demonstrated through truck experiments. Results show that both road gradient and vehicle mass converge to near the true values within 2 s. With mass pre-estimation, the convergence time is reduced by approximately 60% compared to the standard EKF initialized with a random mass value. Moreover, the low RMSE confirms that the identification accuracy is sufficiently high for automatic shifting strategy applications. In conclusion, the mass pre-estimation-based extended Kalman filter offers an effective and rapid solution for estimating truck mass and road gradient.

This work explores the effect of bioactive glass (BG) reinforcement and aging treatment on the microstructure and mechanical performance of AZ91 magnesium alloy processed by friction stir back extrusion (FSBE). BG particles with an average diameter of 4.62 μm, synthesized via the sol–gel route, were incorporated at 2 and 4 vol.% into AZ91 billets prior to FSBE at 1200 rpm, 40 mm/min traverse speed, and an extrusion ratio of 2.86. Solution heat treatment at 450°C for 3 h, followed by aging at 180°C for 6 and 12 h, was applied to study precipitation behavior and property evolution. FSBE processing refined the microstructure through dynamic recrystallization, while BG particles further stabilized fine grains by Zener pinning and promoted heterogeneous nucleation of β-Mg₁₇Al₁₂. Aging for 12 h yielded the most significant property enhancement. The Vickers hardness increased from 66.3 ± 1.2 HV in the solution-treated alloy to 83.2 ± 0.9 HV with 4 vol.% BG after aging. Similarly, yield strength rose from 181.2 ± 8.4 MPa to 267.6 ± 12.7 MPa, and ultimate tensile strength improved from 252.7 ± 9.1 MPa to 342.1 ± 12.3 MPa under the same conditions. Ductility decreased moderately from 9.1% ± 1.4% to 6.2% ± 1.2% with BG addition and extended aging, reflecting a trade-off between strength and plasticity. Fractography revealed a transition from ductile dimpled morphologies in the solution-treated alloy to mixed ductile–brittle fracture in BG-reinforced and aged specimens, attributed to particle–matrix debonding and precipitation-induced cleavage.