Search Results (251)

Ammonia is a highly promising zero-carbon fuel for engines. However, it exhibits high ignition energy, slow flame propagation, and severe pollutant emissions, so it is usually burned in combination with highly reactive fuels such as hydrogen. An accurate understanding and modeling of ammonia–hydrogen combustion is of fundamental and practical significance to its application. Deep Neural Networks (DNNs) demonstrate significant potential in autonomously learning the interactions between high-dimensional inputs. This study proposed a deep neural network-based method for optimizing chemical reaction mechanism parameters, producing an optimized mechanism file as the final output. The novelty lies in two aspects: first, it systematically compares three DNN structures (Multi-layer perceptron (MLP), Convolutional Neural Network, and Residual Regression Neural Network (ResNet)) with other machine learning models (generalized linear regression (GLR), support vector machine (SVM), random forest (RF)) to identify the most effective structure for mapping combustion-related variables; second, it develops a ResNet-based surrogate model for ammonia–hydrogen mechanism optimization. For the test set (20% of the total dataset), the ResNet outperformed all other ML models and empirical correlations, achieving a coefficient of determination (R²) of 0.9923 and root mean square error (RMSE) of 135. The surrogate model uses the trained ResNet to optimize mechanism parameters based on a Stagni mechanism by mapping the initial conditions to experimental IDT. The results show that the optimized mechanism improves the prediction accuracy on laminar flame speed (LFS) by approximately 36.6% compared to the original mechanism. This method, while initially applied to the optimization of an ammonia–hydrogen combustion mechanism, can potentially be adapted to optimize mechanisms for other fuels. Full article

To address the global plastic crisis, recycled plastics from food packaging were used as road materials by the dry method for practical application research. First, the main components of the recycled plastics were identified based on FTIR, and their thermal stability was evaluated through DSC, TG, and microscopic analysis. Then, the workability of the plastic–asphalt mixture was evaluated using the gyratory compaction indicator, void content, and compaction energy index (CEI). Finally, the effect of reused plastics on the cracking resistance of bituminous mixtures was examined with the Superpave IDT test. The results indicate that recycled plastics from food packaging are polyolefin composite materials, primarily consisting of Low-Density Polyethylene (LDPE), Linear Low-Density Polyethylene (LLDPE), High-Density Polyethylene (HDPE), and Polypropylene (PP), and that their thermal stability meets production requirements. Good compaction performance was observed with plastic content below 2% of the aggregate weight, while higher contents reduced void content due to the space occupied by plastics. When the plastic content increased from 0.5% to 2.0%, creep compliance decreased from 68.4% to 77.87%, while the m-value, tensile strength, and elastic energy maximum decreased by 30.77%, 5.6%, and 7%, respectively. In contrast, the failure strain, fracture energy, and maximum DSCE increased by 25.86%, 87.43%, and 133.05%, respectively. The recycled plastic enhanced the toughness of the asphalt mixture, increasing the dissipated energy during crack propagation and improving its resistance to permanent deformation. Moreover, the plastics hindered crack propagation through a bridging effect, leading to fewer cracks within plastic zones compared with surrounding areas. This study provides actionable guidance for the application of composite plastics in asphalt pavements and supports their sustainable development. Full article

This paper presents a study on the propagation of acoustic waves in gallium nitride (GaN) layers deposited on sapphire substrate. The influence of GaN layer thickness and the configuration of interdigital transducers (IDTs) on the generation and propagation of different surface wave modes, including Rayleigh, Sezawa, and Love waves, was analyzed. Experimental measurements in the 100 MHz–6 GHz range were complemented by numerical simulations using the finite element method (FEM). The results demonstrated a strong dependence of wave characteristics on technological parameters, particularly the quality of the GaN–sapphire interface. The data obtained can be utilized for optimizing the design of acoustic sensors, resonators, and RF filters. Full article

Hypergolic propellants have long been central to spacecraft propulsion because of their storability, reliability and rapid ignition. Conventional systems such as hydrazine derivatives paired with oxidisers like nitrogen tetroxide deliver ignition delays in the order of a few milliseconds but pose serious risks due to extreme toxicity and handling hazards. The search for safer and environmentally friendlier alternatives has therefore become a priority in recent years. This review examines ignition delay times reported in the literature for both conventional and green propellants under ambient experimental conditions. Data were collected from published studies between 2000 and 2025 using major scientific databases, including Scopus, Web of Science, and Google Scholar, and are compared across three categories of propellants: traditional hydrazine-based systems, self-igniting ionic liquids and amines, and systems enhanced with catalytic or reactive promoters. The analysis shows that while conventional propellants remain benchmarks with ignition delays typically between 1 and 5 ms, some new formulations, particularly those containing reactive additives such as borohydrides or iodide salts, are achieving similar or improved performance in laboratory tests. The review also highlights that variability in reported ignition delays often stems from differences in test methods, droplet size, oxidiser concentration, and diagnostic approaches. Beyond performance considerations, attention is given to safety and environmental aspects since several green candidates reduce acute toxicity but introduce other challenges, such as instability or corrosive byproducts. By bringing together data in a comparative format and emphasising methodological limitations, this review aims to support the future design and evaluation of practical green hypergolic propellants. Full article

The shadow economy weakens fiscal sustainability, hampers the financing of public goods, and impedes the achievement of sustainable development goals. The informal sector remains a persistent challenge for policymakers, as it distorts competition, reduces transparency, and undermines the effectiveness of economic and fiscal policies. The aim of this article is to identify the key factors determining the size of the shadow economy in European Union countries and to provide policy-relevant insights. The analysis covers data on the share of the informal economy in GDP and macroeconomic variables such as GDP per capita, consumer price index, average wages, household consumption, government expenditure, and unemployment, as well as indicators of digital development in society and the economy (DESI, IDT), the share of cashless transactions in GDP, and information on the implementation of digital tax administration tools and restrictions on cash payments. Five hypotheses (H1–H5) are formulated concerning the effects of income growth, labour market conditions, digitalisation, cashless payments, and tax administration tools on the shadow economy. The research question addresses which factors—macroeconomic conditions, economic and social digitalisation, payment structures, and fiscal innovations in tax administration—play the most significant role in determining the size of the shadow economy in EU countries and whether these mechanisms have broader implications for fiscal sustainability and sustainable development. The empirical strategy is based on multilevel models with countries as clusters, complemented by correlation and comparative analyses. The results indicate that the most significant factor in limiting the size of the shadow economy is the level of GDP per capita and its growth, whereas the impact of card payments appears to be superficial, reflecting overall increases in wealth. Higher wages, household consumption, and digital development as measured by the DESI also play an important role. The implementation of digital solutions in tax administration, such as SAF-T or e-PIT/pre-filled forms, along with restrictions on cash transactions, can serve as complementary measures. The findings suggest that sustainable strategies to reduce the shadow economy should combine long-term economic growth with digitalisation and improved tax administration, which may additionally foster the harmonisation of economic systems and support sustainable development. Full article

This paper presents a design method for a continuous tension detection sensor based on a cantilever beam structure and compensates for the temperature drift of a SAW sensor based on a neural network algorithm. Firstly, a novel cantilever beam roller structure is proposed to significantly enhance the sensitivity of the transmission of silk thread tension to a SAW tension sensor. Secondly, to improve the sensitivity of the SAW tension sensor, the COMSOL finite element method (FEM) is employed for simulation to determine the optimal IDT placement. An unbalanced split IDT design is utilized to suppress potential parasitic responses. Finally, the designed sensor is tested, and a GA-PSO-BP algorithm is employed to fit the test data for temperature compensation. The experimental results demonstrate that the temperature sensitivity coefficient of the data optimized by the GA-PSO-BP algorithm is reduced by an order of magnitude compared to the raw data, with reductions of $6.0409\times {10}^{-3}{°C}^{-1}$ and $3.0312\times {10}^{-3}{°C}^{-1}$ compared to the BP neural network and the PSO-BP algorithm, respectively. The average output error of the optimized data is reduced by 5.748% compared to the sensor measurement data, and it is also lower than both the BP neural network and the PSO-BP algorithm. It provides new design ideas for the development of tension sensors. Full article

Ultrasonic guided wave interdigital transducers realized with piezoelectric materials are of interest for structural health monitoring systems because of their capability of performing Lamb wave mode selection with respect to single-element transducers. Besides this advantage, the coverage of large areas with a minimum number of elements is an important challenge and the problem of efficient excitation with integrated electronics must be solved. This work proposes an electrical matching network topology made of L and C passive components that can be designed for the trade-off between electrical to mechanical conversion efficiency and bandwidth. The network circuit is analyzed considering the equivalent transducer impedance and the output impedance of the driving electronics. The design rules derived by the transfer function analysis are described and a case study for a piezopolymer IDT is presented. Finally, with the implementation of the integrated matching network with the connector of the IDT, the effect of cable capacitance is minimized. In conclusion this article is a contribution to the study of using IDT efficiently and in a versatile mode for different electronic front-ends that usually operate at low power supply voltage. Full article

The use of Reclaimed Asphalt Pavement (RAP) in asphalt mixtures offers environmental and economic advantages by reducing reliance on virgin aggregates and minimizing construction waste. However, the aged binder in RAP increases mixture stiffness, which can compromise fatigue resistance. This systematic review evaluates the influence of RAP content on fatigue performance compared to conventional mixtures, with a focus on the Indirect Tensile Test (IDT) as the primary assessment method. Following the parameters of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, five studies published between 2014 and 2024 were identified through searches in Web of Science, ScienceDirect, ASCE, and Scopus. Study quality was assessed using the Cochrane Risk of Bias tool. The results indicate that although RAP enhances rutting resistance, higher contents (>30%) often lead to reduced fatigue performance due to binder hardening and reduced mixture flexibility. The incorporation of rejuvenators—such as heavy paraffinic extracts—and modifiers, including high-modulus agents, polymers, and epoxy binders, can partially restore aged binder properties and improve performance. Sustainable innovations, such as lignin-based industrial by-products and warm-mix asphalt technologies, show promise in balancing mechanical performance with reduced environmental impact. Variability in material sources, modification strategies, and test protocols limits direct comparability among studies, underscoring the need for standardized evaluation frameworks. Overall, this review highlights that optimizing RAP content and selecting effective rejuvenation or modification strategies are essential for achieving durable, cost-effective, and environmentally responsible asphalt pavements. Future research should integrate advanced laboratory methods with performance-based design to enable high RAP utilization without compromising fatigue resistance. Full article

The United States produces approximately 500 million tons of asphalt mixtures annually, while generating vast amounts of waste materials that could be repurposed for sustainable infrastructure. Each year, 1.4 billion gallons of lubricating oils are available for reuse and recycling. Additionally, 280 million tires are discarded, contributing to significant environmental challenges. Given the critical role of the roadway network in economic growth, mobility, and infrastructure sustainability, there is a pressing need for innovative material solutions that integrate recycled materials without compromising performance. This study introduces a Rubberized-Aerogel Composite (RaC), a novel asphalt binder modifier combining crumb rubber, recycled oil, and a silica-based aerogel to enhance the sustainability and durability of asphalt pavements. The research methodology involved blending the RaC with the PG70-10 asphalt binder at a 5:1 ratio and conducting comprehensive laboratory tests on binders and mixtures, including rheology, thermal conductivity (TC), specific heat capacity (Cp), the Hamburg Wheel-Tracking Test (HWTT), and indirect tensile strength (IDT). Pavement performance was simulated using AASHTOWare Pavement ME under hot and cold climates with thin and thick pavement structures. Results showed that RaC-modified binders reduced thermal conductivity by up to 30% and increased specific heat capacity by 15%, improving thermal stability. RaC mixtures exhibited a 50% reduction in rut depth in the HWTT and lower thermal expansion/contraction coefficients. Pavement ME simulations predicted up to 40% less permanent deformation and 60% reduced thermal cracking for RaC mixtures compared to the controls. RaC enhances pavement lifespan, reduces maintenance costs, and promotes environmental sustainability by repurposing waste materials, offering a scalable solution for resilient infrastructure. Full article

Reduced chemical kinetic mechanisms are essential for enabling the use of complex fuels in 3D CFD combustion simulations. This study presents the development and optimization of a compact mechanism capable of accurately modeling ethanol–gasoline blends, including Brazilian Type-C gasoline (27% ethanol by volume) and up to pure ethanol (E100). An initial mechanism was constructed using the Directed Relation Graph with Error Propagation (DRGEP) method applied to detailed mechanisms selected for each surrogate component. The resulting mechanism was then refined through three global iterations of a genetic algorithm targeting ignition delay time (IDT) and laminar flame speed (LFS) performance. Five candidate versions (Mec1 to Mec5), each containing 179 species and 771 reactions, were generated. Mec4 was identified as the optimal configuration based on quantitative error analysis across all tested conditions and blend ratios. The final mechanism offers a balance between predictive accuracy and computational feasibility, making it well-suited for high-fidelity simulations in complex geometries involving multi-component ethanol–gasoline fuels. Full article

In recent years, various devices utilizing surface acoustic waves (SAW) have emerged as powerful tools for manipulating particles and fluids in microchannels. Although they demonstrate a wide range of functionalities across diverse applications, existing devices still face limitations in flexibility, manipulation efficiency, and spatial resolution. In this study, we developed a dual-sided standing surface acoustic wave (SSAW) device that simultaneously excites acoustic waves through two piezoelectric substrates positioned at the top and bottom of a microchannel. By fully exploiting the degrees of freedom offered by two pairs of interdigital transducers (IDTs) on each substrate, the system enables highly flexible control of microparticles. To explore its capability on particle aggregation, we developed a two-dimensional numerical model to investigate the influence of the SAW phase modulation on the established acoustic fields within the microchannel. Single-particle motion was first examined under the influence of the phase-modulated acoustic fields to form a reference for identifying effective phase modulation strategies. Key parameters, such as the phase changes and the duration of each phase modulation step, were determined to maximize the lateral motion while minimizing undesired vertical motion of the particle. Our dual-sided SSAW configuration, combined with novel dynamic phase modulation strategy, leads to rapid and precise aggregation of microparticles towards a single focal point. This study sheds new light on the design of acoustofluidic devices for efficient spatiotemporal particle concentration. Full article

This study ground in the Innovation Diffusion Theory (IDT), explores the value of digital twin technology in cross-border logistics risk management. Using structural equation modeling, it examines how five innovation characteristics of digital twins—relative advantage, compatibility, complexity, trialability, and observability—influence risk management capabilities, specifically robustness and resilience, within cross-border logistics systems. The findings reveal that relative advantage, compatibility, trialability, and observability significantly enhance both robustness and resilience, while complexity does not show a significant negative impact. Furthermore, the study confirms that improvements in risk management capabilities contribute positively to competitive performance. This research not only enriches the theoretical understanding of digital twin applications in cross-border logistics but also offers valuable insights for practical implementation by enterprises. Full article

The propagation of interface acoustic waves (IAWs) along rotated YX-LiNbO₃/SU-8/ZX-Si structures is theoretically investigated to identify the Y-rotation angles that support the efficient propagation of low-loss modes guided along the structure’s interface. A three-dimensional finite element analysis was performed to simulate IAW propagation in the layered structure and to optimize design parameters, specifically the thicknesses of the platinum (Pt) interdigital transducers (IDTs) and the SU-8 adhesive layer. The simulations revealed the existence of two types of IAWs travelling at different velocities under specific Y-rotated cuts of the LiNbO₃ half-space. These IAWs are faster than the surface acoustic wave (SAW) and slower than the leaky SAW (LSAW) propagating on the surface of the bare LiNbO₃ half-space. The mechanical displacement fields of both IAWs exhibit a rapid decay to zero within a few wavelengths from the LiNbO₃ surface. The piezoelectric coupling coefficients of the IAWs were found to be as high as approximately 7% and 31%, depending on the Y-rotation angle. The theoretical results were experimentally validated by measuring the velocities of the SAW and LSAW on a bare 90° YX-LiNbO₃ substrate, and the velocities of the IAWs in a 90° YX-LiNbO₃/SU-8/Si structure featuring 330 nm thick Pt IDTs, a 200 µm wavelength, and a 15 µm thick SU-8 layer. The experimental data showed good agreement with the theoretical predictions. These combined theoretical and experimental findings establish design principles for exciting two interface waves with elliptical and quasi-shear polarization, offering enhanced flexibility for fluidic manipulation and the integration of sensing functionalities. Full article

This study investigated the fracture behavior of asphalt mixtures under indirect tensile loading by comparing the performance of homogenized and micromechanical finite element (FEMs) models based on the cohesive zone model (CZM). Five asphalt mixture types were tested experimentally, and both models were calibrated and validated using load–displacement curves from indirect tensile tests (IDTs). The micromechanical model, incorporating random aggregate generation and three-phase material definition, exhibited significantly higher predictive accuracy (R² = 0.86–0.98) than the homogenized model (R² = 0.66–0.77). The validated micromechanical model was further applied to quantify the impact of construction-related deficiencies—namely, increased air voids, non-continuous gradation, and aggregate segregation. The simulation results showed that higher void content (from 4% to 10%) reduced peak load by up to 35% and increased localized stress concentrations by up to 40%. Discontinuous gradation and uneven aggregate distribution also led to premature crack initiation and more complex fracture paths. These findings demonstrated the value of micromechanical modeling for evaluating sensitivity to mix design and compaction quality, providing a foundation for performance-based asphalt mixture optimization and durability improvement. Full article

This study aimed to investigate the future trajectories of last-mile delivery (LMD), and their implications for sustainable urban logistics and smart city planning. Through a Delphi-based scenario analysis targeting the year 2030, this research draws on inputs from a two-round Delphi study with 52 experts representing logistics, academia, and government. Four key thematic areas were explored: consumer demand and behavior, emerging delivery technologies, innovative delivery services, and regulatory frameworks. The projections were structured using fuzzy c-means clustering, and analyzed through the Technology Acceptance Model (TAM) and Innovation Diffusion Theory (IDT), supporting a systemic understanding of innovation adoption in urban logistics systems. The findings offer strategic insights for municipal planners, policymakers, logistics service providers, and e-commerce stakeholders, helping align infrastructure development and regulatory planning with the evolving needs of last-mile logistics. This approach contributes to advancing resilient, low-emission, and inclusive smart city ecosystems that align with global sustainability goals, particularly those outlined in the UN 2030 Agenda for Sustainable Development. Full article