MIMO antennas play an significant role in wireless communication by enhancing the speed of communication for different applications. However, conventional multi-port MIMO antennas suffered from strong electromagnetic coupling between closely spaced elements, resulting in degraded diversity performance and reduced radiation efficiency. To address these restrictions, this study presents a novel dual band four-port shovel shaped MIMO antenna integrated with a meta-material absorber and a T-shaped defected ground structure (DGS) for upper mid-band 6G applications. The metamaterial absorber, realized using split-ring resonator (SRR) based unit cells, is used to suppress surface waves and unwanted electromagnetic interactions. This unit cell helps to enhance antenna gain and radiation efficiency while improving port isolation. A T-shaped DGS integrated into the ground plane is used to decrease mutual coupling among antenna elements. Antenna parameters are tuned by an improved grey wolf optimization algorithm (Imp-GWO), which helps to improve gain. The overall dimension of the proposed antenna model is54 mm × 54 mm × 1.6 mm, which resonates at the frequencies of 17.0933 GHz and 18.2667 GHz. Simulated results demonstrate a reflection coefficient of -23.18 dB and -20.30 dB at respective bands, and isolation is less than -35 dB. The structure achieves peak gains of 4.5 dBi and 5.7 dBi. MIMO performance is validated with an envelope correlation coefficient (ECC) less than 0.05, diversity gain greater than 9.95, channel capacity loss less than 0.04 bits/s/Hz and low TARC values. The simulated and measured results are obtained with very close values, which shows the robustness and effectiveness of the proposed antenna design.
Accurate storm surge estimation is critical for effective coastal disaster management, particularly in cyclone-prone regions. A blended wind field derived from ERA5 reanalysis, India Meteorological Department (IMD) best track and Holland parametric winds is used to simulate the Advanced Circulation (ADCIRC) model, and the resulting surge and cyclone variables, along with astronomical tide obtained from WXTide32 are used as inputs to classical Artificial Neural Network (ANN), Support Vector Regression (SVR) and quantum machine learning models namely Quantum Neural Networks (QNN), Quantum Support Vector Regression (QSVR). The framework is evaluated across five cyclones over Bay of Bengal (BoB) Very Severe Cyclonic Storms (VSCS) Hudhud (2014), Vardah (2016), Gaja (2018), Thane (2011), and the Severe Cyclonic Storm (SCS) Phethai (2018) then compared with ADCIRC numerical model and validated with Indian National Centre for Ocean Information Services (INCOIS) observed storm surge height. Comparative assessment using regression statistics, time-series analysis, and Taylor diagrams demonstrates that the QSVR model consistently provides the most balanced performance across all cyclone cases. For Hudhud (2014), QSVR exhibits a high correlation (CRR ∼ 0.74) with moderate standard deviation and lower RMSE compare to other models. A similar behavior is observed for Phethai and Vardah, where QSVR maintains stable correlations ( ∼ 0.79–0.81) and balanced error characteristics. These findings highlight the potential of quantum-enhanced learning frameworks for next-generation coastal hazard prediction.
This study examines the most widely used earnings management techniques employed among financially distressed manufacturing companies in South Wollo, Ethiopia. The study employed a quantitative research design and utilized secondary data from audited financial statements of sampled manufacturing firms covering 2020 to 2024. A multi-stage sampling incorporating stratified and simple random sampling techniques was employed to choose 49 manufacturing companies. Data were analyzed through descriptive statistics and a panel random-effects multivariate Seemingly Unrelated Regression (SUR) model. The findings show that financial distress has a significant positive effect on Real earnings management (REM), but no significant impact on Accrual earnings management (AEM). This suggests that financially distressed firms tend to manipulate real earnings activities rather than accrual earnings, likely to meet short-term performance targets while maintaining long-term sustainability. The study recommends stronger regulatory monitoring, enhanced disclosure, and governance reforms to mitigate opportunistic real earnings management, thereby promoting transparent, accountable, and sustainable corporate practices among financially distressed firms.
The performance of a compression ignition (CI) engine could be improved by incorporating waste materials such as waste high density plastic oil (HDPO) and diesel blends by dispersing various non-metallic, carbon-based additives (graphene nanoplatelets [GNPs]) and higher alcohols (di-ethyl ether (DEE)) in recent days. In the present investigation, waste plastic oil (WPO) is prepared and blended with diesel to produce the WP20 sample. The WP20 sample is mixed with additives DEE at 10% Vol. and GNPs at concentrations of 25, 50, and 75 mg/L and tested on a four stroke, twin cylinder, common rail direct injection (CRDI), CI engine to evaluate performance, emissions and thermodynamic irreversibilities as per first and second law including energy and exergy efficiency, and sustainability index (SI). The inclusion of additives improved brake thermal efficiency (BTE) by 15.54%, and decreased brake specific fuel consumption (BSFC) by 16.5%. While emissions including carbon monoxide (CO), carbon dioxide (CO2), hydrocarbon (HC), nitrogen oxide (NOx), and smoke are decreased by 9.27%, 13%, 9.89%, 9.30%, and 8.68% respectively for WP20 + DEE10 + GNP50 sample than the WP20 mix at higher BP. Furthermore, the energy and exergy efficiencies, sustainability index (SI), and (EPC) are enhanced by 7.89, 16.7%, 18.95%, and 8.77%, respectively. According to the 2nd law of thermodynamics (TD), the energy and exergy losses (exhaust gas, cooling water, and unaccounted losses) are reduced, indicating the WP20 + DEE10 + GNP50 blend is sustainable and suitable in diesel engines as an alternative fuel.
This article presents the synergistic effect between the ZOME20 biodiesel blend (i.e., 20% ZOME +80% diesel) and metal oxide nanoadditives (CeO2, Al2O3, TiO2, and GO) in the amount of 50-200 ppm in the performance and emission characteristics of a single-cylinder CI engine and its prediction models by machine learning (ML). Out of all operable combinations in a diesel engine, the strongest enhancement of brake thermal efficiency (BTE) was found by the experimental addition of 150 ppm CeO2 and TiO2, rising from 28.9% (diesel) to 31.5%, and the drop in Brake Specific Fuel Consumption (BSFC) is from 0.29 to 0.27 kg/kWh. Emission analysis showed a 21% reduction in HC, a 16% reduction in CO, and a 24% reduction in smoke opacity, while CO2 and NOx showed reasonable increases of 9% and 7%, respectively (over baseline diesel). A series of machine learning models (Random Forest, Gradient Boosting, AdaBoost, and Ensemble Regression) were developed to predict the performance as well as emission results. In the present work obtained the highest total accuracy using Random Forest (R 2 = 0.82; RMSE = 7.91 and MAE = 4.10), followed by Gradient Boosting (R 2 = 0.78). Mean absolute percentage error (MAPE) of significant parameters was below 15%, indicating good model generalization. The combined experimental and ML efforts validated that nanoadditive-assisted biodiesel combustion improves thermal efficiency and emission control, a sustainable and model-based line of advance for optimizing future diesel engine technologies.
