Science Citation Index Expanded (Web of Science)

Understanding the plasmonic behavior of materials with large-scale inhomogeneity (like negative index metamaterial) is crucial because these materials cannot be modeled as simple scatterers. This is due to the emergence of plasmonic materials other than those based on noble metals and the advancement of sophisticated materials processing techniques. Propagation of surface plasmons can be achieved using a metamaterial and dielectric interface. Control of the surface plasmons opens the wide avenues for a broad range of applications. Similarly, the shape of individual surface nanostructures or the phase control of individual elements in an array of such structures can be used to regulate the propagation of surface plasmons at the interface between metal films and dielectric materials. Here, we show how to regulate surface plasmon propagation at the negative index metamaterial-dielectric interface. The negative index metamaterial enables the surface modes, while the adjacent dielectric is what tunes their properties.

This article attempted to study the impact of acetylene induction on a PCCI engine powered with simarouba glauca oil-blended diesel. The experiment is carried out in the PCCI computerised engine by varying the load from 25 to 100% at a constant speed of 1500 rpm. The acetylene was induced in the air stream entering the inlet manifold at flow rates of 2, 4, and 6 LPM when the engine operated with 25% simarouba glauca blended diesel fuel, which are designated as S25, S25A2, S25A4, and S25A6. Experiments results show the hike in thermal efficiency, exhaust gas temperature, exergy efficiency, sustainability index, cooling water and exhaust gas exergy, brake specific nitrogen monoxide, and drop in brake specific energy consumption, brake specific hydrocarbon, brake specific carbon monoxide, brake specific carbon dioxide, smoke opacity, destructed exergy, entropy generation, fuel exergy, thermoeconomic loss, environmental loss, and exergoeconomic loss. On the whole, acetylene addition is a good PCCI engine attribute except for brake-specific nitrogen monoxide, which puts a limitation and needs an emission reduction technique. Hence, the selective catalytic reduction method can be selected for the scope of future work.

This research examines the feasibility of using waste cooking oil (WCO) as a substitute for traditional diesel fuel in internal combustion engines, with a focus on biodiesel production. The aim of this research is to evaluate the effects of WCO–diesel blends on engine performance, with particular emphasis on critical metrics including brake specific fuel consumption (BSFC) and brake thermal efficiency (BTE). The study utilizes artificial neural networks (ANNs) to model and forecast the performance and emission characteristics of engines operating with different fuel combinations. The study employs a methodology that involves conducting experiments to evaluate the mixtures of waste cooking oil (WCO) and diesel fuel in diesel engines. Furthermore, artificial neural networks (ANNs) are employed to develop models for predicting engine performance. The analysis focuses on critical metrics, including BSFC and BTE, under various operating conditions. This research aims to improve sustainable energy solutions by demonstrating the benefits of alternative fuels and advanced artificial intelligence (AI) prediction models in automotive applications.

The rapid development of electromobility increases the need for fast, accessible and robust charging stations devoted to EVs (electric vehicles). Planning a network of such stations poses new challenges—amongst others, a power supply that may power such chargers. One major concept is to utilise wind energy as a power source. The paper analyses meteorological data gathered since 2001 in several stations across Poland to achieve quantitative indexes, which summarise (a) wind power density (WPD) as a metric of energy amount, (b) long-term (multiannual) time trends of amount of energy, (c) short-term stability (and thus predictability) of the wind power. The indexes that cover the abovementioned factors allow the authors to answer the research questions, where the local wind conditions allow the authors to consider the integration of a wind powerplant and a network of EV chargers. Additionally, we investigated locations where the amount of available energy is sufficient, but the variability of wind power impedes its practical exploitation. In such cases, the power system may be extended by an energy storage system that acts as a buffer, smoothing power fluctuations and thereby improving the robustness and reliability of downstream charging systems.

Improving solar panel recycling processes increases energy efficiency while minimizing negative environmental impacts. Recycling processes often require high-cost investments and complex processes. It is critical to determine which criteria and investment areas are prioritized for the most efficient use of these limited resources. The purpose of this study is to generate effective investment strategies to improve the performance of the recycling processes in solar panel projects by establishing a new model. Firstly, more significant indicators are selected by classifying with zero-shot learning (ZSL) analysis. Secondly, expert weights are calculated via machine learning approach. Thirdly, selected indicators are evaluated with p, q-quasirung orthopair fuzzy sets (p, q-QROFS)-based entropy. Fourthly, investment alternatives are ranked by p, q-QROFS simple additive weighting (SAW) technique. Finally, a comparative examination is conducted for alternative ranking with the help of technique for order preference by similarity to ideal solution (TOPSIS) methodology. The main contribution of this study is the identification of the effective investment strategies for the improvements of recycling processes in solar panel projects by establishing a novel decision-making model. The main superiority of this proposed model is the integration of ZSL to the fuzzy decision-making modelling by classifying the criteria. Reducing the number of criteria with this technique minimizes uncertainties in the analysis process. It is concluded that improving recycling technologies and providing incentives and support are the most essential items. Moreover, China and Russia are the most successful countries with respect to the recycling performance of solar panel projects.

In this study, a mathematical model of a railroad excavator was developed, along with a program to determine the spectrum of allowable loads in the entire working space of the excavator, according to which determines the spectrum of possible track loads at the excavator's supports on the rails. The results show that the track loads vary and depend on the position of the excavator and the size of the load. The study's findings can inform the structural analysis of rail elements and the selection of manipulator tools to ensure excavator stability.

This research investigates the influence of hydrogen and oxygen (HHO) gas on the ecological and energy efficiency characteristics of a spark ignition (SI) engine, employing a range of fuel blends, such as gasoline–bioethanol blends (E10 and E70). A systematic exploration was undertaken at a steady velocity of 2000 rpm and a throttle opening of 15%, spanning a broad range of excess-air ratios (λ = 1.0–1.4) and variations in ignition timing (IT), with the aim of assessing the impact of HHO on brake thermal efficiency (BTE), fuel economy, and emissions. The HHO gas blend, produced by water electrolysis, was supplied at 3.7 L min−1, corresponding to a hydrogen delivery rate of 0.0133 kg h−1 and a hydrogen fraction of about 1/400 by volume in the inducted air charge. The HHO stream was introduced downstream of the throttle into the intake-manifold plenum and oriented approximately 90° to the main intake flow direction to promote rapid turbulent mixing and avoid localized stratification. Polynomial regression models were developed to describe the dependence of BTE and normalized pollutant emissions (CO/BTE, CO2/BTE, HC/BTE and NOx/BTE) on IT for each fuel blend and λ value, yielding high coefficients of determination (R2 up to 0.98) and statistically significant fits (p < 0.001). At λ = 1.3, regression-based optimization shows that HHO addition shifts the BTE-optimal ignition timing by approximately 1.4–1.6 °CA toward earlier phasing for both E10 and E70 fuels, while maintaining comparable or slightly improved brake thermal efficiency and simultaneously reducing CO and HC emissions on a brake-specific basis. The results demonstrate that, under lean blend conditions (λ ≥ 1.2), the incorporation of HHO consistently shifts the optimal IT to slightly lower advance values and enhances BTE compared with operation without HHO. Under the investigated operating conditions, the combined application of ignition timing optimization and HHO supplementation increased the brake thermal efficiency by up to 17% compared to the baseline gasoline operation. At the same time, the use of HHO reduces CO and HC emissions per unit brake energy and lowers the CO2/BTE ratio, indicating more efficient utilization of the fuel chemical energy. The combination of ethanol-rich E70 gasoline and HHO gas demonstrated an optimal balance between elevated brake thermal efficiency and minimized emissions of carbon monoxide, hydrocarbons, and carbon dioxide during lean-burn operation, thereby affirming its viability as a sustainable approach to enhance engine performance while mitigating environmental repercussions. The amalgamation of real-time emission analysis with programmable control systems afforded a dependable and precise evaluation of combustion dynamics, thereby clarifying the significance of HHO gas as a beneficial and ecologically sustainable adjunct for internal combustion engines.

The literature reveals that transportation and energy consumption significantly affect CO₂ emissions, with globalization potentially increasing emissions in developing countries. Using data from Türkiye for the period of 1970–2016, the study employs Autoregressive Distributed Lag (ARDL) and Ordinary Least Squares (OLS) models to examine the short- and long-term effects of globalization, energy use and passenger transportation on carbon emissions. This study aims to analyze the determinants of carbon emissions in Türkiye by examining the impacts of globalization, energy consumption and passenger transportation on environmental sustainability. Specifically integrates air passenger transportation with energy consumption and the KOF Globalization Index to investigate their short- and long-term effects on per capita CO2 emissions in Türkiye within a unified ARDL framework, in contrast to earlier research concentrating on aggregate transportation indicators. The findings indicate that energy consumption has a notable impact on carbon emissions, while globalization indirectly increases emissions through rising energy demand. This study provides essential insights for policymakers aiming to develop sustainable environmental policies, highlighting the importance of reducing energy consumption and promoting eco-friendly transportation policies.

The integration of metaverse technology into higher education institutions (HEIs) offers transformative potential for enhancing pedagogical innovation and addressing evolving educational demands. However, its effective implementation requires a systematic assessment of enablers and implementation feasibility across diverse HEI contexts. In this study, a novel T-spherical fuzzy (T-SF) hybrid model is developed to address this issue. The proposed model first collects expert evaluation information using T-spherical fuzzy sets (T-SFSs) and utilizes a similarity measure to determine expert weights. The weighted Heronian mean aggregation (WHMA) operator is then integrated to derive conservative aggregated values. Furthermore, the T-SF-WHMA-MULTIMOOSRAL hybrid model evaluates and ranks the metaverse integration across different types of HEIs. Finally, a case study is presented to illustrate the application of the T-SF-WHMA-MULTIMOOSRAL hybrid model. The results indicate that the continual feedback for teachers and students ( E s 7 ) and the sustainability and environmental impact ( E s 6 ) as the most influential enablers of metaverse integration. This study evaluates and ranks the potential for metaverse integration across four distinct types of HEIs, with the results showing that higher vocational colleges ( O 1 ) ranked highest. The findings of this study provide practical insights for guiding the adoption of metaverse technology in diverse and complex educational environments.

Digital image-based determination of aggregate and rock gradation has been only limitedly addressed in the existing literature despite its considerable potential to transform conventional material characterization practices in civil engineering. Rapid and accurate estimation of aggregate and rock particle size distributions using advanced image-based analytical methods can significantly improve efficiency, consistency, and scalability in design, construction, and quality control processes, particularly in large-scale structural and geotechnical engineering projects where traditional sieve analysis is time-consuming, labor-intensive, and difficult to apply under field conditions. In this study, an image-based methodology is proposed to rapidly detect aggregate particles and determine their size-based proportions within a pile by employing image enhancement, segmentation, and boundary detection algorithms. The results obtained from digital image processing are comparatively evaluated against experimental sieve analysis data, demonstrating a strong correlation between the two approaches. Low RMSE values achieved for larger aggregate sizes, such as 25.4 mm and 19 mm, indicate high detection accuracy, while the relatively higher yet acceptable RMSE values obtained for smaller particles, including 12.7 mm and 9.5 mm, confirm that the method maintains practical sensitivity across different size ranges. By analyzing samples collected from various aggregate and rock piles, the study further demonstrates the originality, robustness, and effectiveness of the proposed approach in evaluating heterogeneous material groups. Overall, the findings highlight that digital image-based determination offers a fast, reproducible, and non-destructive alternative to traditional sieve analysis, making it particularly valuable for reinforced concrete aggregate assessment and port fill rock characterization in large-scale structural and geotechnical engineering applications.