Engineering reports : open access最新文献

This study presents a four-parameter distribution which is called the Harris generalized Kappa distribution, which is flexible and tractable. The new distribution extends the two-parameter Kappa distribution, and it can be explored to model highly skewed data, especially those exhibiting decreasing, increasing, reversed J, and non-monotone (bathtub) failure rates. Mathematical expressions were derived for the statistical properties of the Harris generalized Kappa distribution and studied in detail, namely: moments, incomplete moments, moment generating functions, characteristic function, mean residual life, average waiting time, Bonferroni and Lorenz curves, Gini index, Renyi and q-entropies, and stress-strength reliability function. The estimate of the parameters of the proposed model is obtained using the maximum likelihood estimation procedure with the adequacy model in R. The application of the Harris generalized Kappa model to two lifetime data sets, namely the taxes revenue's data and the fracture toughness data sets, demonstrated its applicability in modeling real-life data, as it provides the best fit among other competitive models considered in the study.

Demand-side management in conjunction with plug-in electric vehicles, along with renewable energy sources, has been explored in relation to dynamic optimal power flow (DOPF). The renewable energy sources considered here are solar PV plants (SPVPs) along with hydropower plants (HPPs). All of the bottlenose dolphin optimizer (BDO), gray wolf optimization (GWO), and the self-organizing Hierarchical particle swarm optimizer with time-varying acceleration coefficients (HPSO-TVAC) are the techniques utilized to solve DOPF. 57-bus, 118-bus, as well as IEEE 30-bus systems are employed for authentication. The cost acquired with DSM is about 4.35%, 12.98%, and 5.25% less than the cost obtained without DSM in the case of the IEEE 30-bus, 57-bus, and 118-bus systems, respectively. The cost derived by BDO is less than that of the other two methods.

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.