Search Results (260)

Delays caused by motor vehicle traffic, accidents, and environmental pollution present considerable challenges to sustainable urban mobility. To address these issues, transportation system users are encouraged to adopt active transportation methods, micromobility options, and public transit. Electric scooters have become a notably popular micromobility choice, especially following the emergence of vehicle-sharing companies in 2018, a trend that gained further momentum during the COVID-19 pandemic. This study explored the demographic characteristics, attitudes, and behaviors of e-scooter users in Istanbul through an online survey conducted from 1 September 2023 to 1 May 2024. A total of 462 e-scooter users participated, providing valuable insights into their preferred modes of transportation across 24 different scenarios specifically designed for this research. The responses were analyzed using various machine learning techniques, including Artificial Neural Networks, Decision Trees, Random Forest, and Gradient Boosting methods. Among the models developed, the Decision Tree model exhibited the highest overall performance, demonstrating strong accuracy and predictive capabilities across all classifications. Notably, all models significantly surpassed the accuracy of discrete choice models reported in existing literature, underscoring the effectiveness of machine learning approaches in modeling transportation mode choices. The models created in this study can serve various purposes for researchers, central and local authorities, as well as e-scooter service providers, supporting their strategic and operational decision-making processes. Future research could explore different machine learning methodologies to create a model that more accurately reflects individual preferences across diverse urban environments. These models can assist in developing sustainable mobility policies and reducing the environmental footprint of urban transportation systems. Full article

Hybrid Gas–Magnetic Bearings (HGMBs) are an emerging technology ready to completely change high-speed oil-free rotor support in aerospace electric motors. Because HGMBs combine the stiffness and load capacity of gas bearings with the active control of magnetic bearings, enabling oil-free, contactless rotor support from zero to ultra-high speeds. They offer more load capacity of standalone magnetic bearings while maintaining full levitation across the entire speed range. Dual-mode operation, magnetic at low speeds and gas film at high speeds, minimizes control power and thermal losses, making HGMBs ideal for high-speed aerospace systems such as cryogenic turbopumps, electric propulsion units, and hydrogen compressors. While not universally optimal, HGMBs excel where extreme speed, high load, and stringent efficiency requirements converge. Advances in modeling, control, and manufacturing are expected to accelerate their adoption, marking a shift toward hybrid electromagnetic–aerodynamic rotor support for next-generation aerospace propulsion. This review provides a thorough overview of emerging HGMBs, emphasizing their design principles, performance metrics, application case studies, and comparative advantages over conventional gas or magnetic bearings. We include both a historical perspective and the latest developments, supported by technical data, experimental results, and insights from recent literature. We also present a comparative discussion including future research directions for HGMBs in aerospace electrical machine applications. Full article

Electrical insulation systems (EISs) are the principal reliability bottleneck of modern electrical machines (EMs). Among the many stresses acting on insulation, thermal stress is the most pervasive because it accelerates chemical reactions that progressively erode dielectric and mechanical integrity, ultimately dictating service life. As EMs migrate into compact, high-power-density platforms—automotive, aerospace, and industrial drives—designers need lifetime models that are not merely explanatory but actionable, linking operating temperatures and missions to quantified ageing and risk. This review article traces the evolution of thermal-ageing modelling from fundamental chemistry to a practical design tool. The historical empirical lineage of Arrhenius equation, Arrhenius–Dakin model, and Montsinger model is first revisited, clarifying their assumptions, parameter definitions, and the construction of thermal endurance curves. A discussion then follows on extensions that address deviations from first-order kinetics and demonstrate how variable temperature histories can be incorporated through cumulative damage formulations suitable for duty-cycle analysis. Since models are required to be anchored in data, accelerated thermal ageing (ATA) practices on representative specimens are outlined, alongside a description of the Weibull post-processing for deriving percentile lifetimes aligned with design targets. Building upon these foundations, the Physics-of-Failure (PoF) approach is introduced as a reliability-oriented design (ROD) methodology, in which validated lifetime models guide material selection and geometry optimisation while supporting prognostics and health management during operation. The emerging trend towards a hybrid PoF–AI approach is also discussed, which integrates artificial intelligence to identify nonlinear degradation patterns and drifting parameter relationships beyond the reach of empirical models, with physical constraints ensuring that predictions remain consistent with known ageing mechanisms. Such integration enables the learning process to adapt to operational variability and coupled stress effects, thereby improving both the accuracy and physical interpretability of lifetime estimation. The review aims to provide a concise view of models, tests, and workflows that convert thermal-ageing knowledge into robust, design-time decisions. By linking empirical and physics-based insights with modern data-driven learning, these developments support proactive maintenance, sustainable asset management, and extended operational lifetimes for next-generation EMs. Full article

Stress, workload, and uncertainty characterize occupational tasks across sports, healthcare, military, and transportation domains. Emerging theory and empirical research suggest that coordinated whole-body movements may reflect these transient mental states. Wearable sensors and optical motion capture offer opportunities to quantify such movement dynamics and classify mental states that influence occupational performance and human–machine interaction. We tested this possibility in a small pilot study (N = 10) designed to test feasibility and identify preliminary movement features linked to mental states. Participants performed a perceptual decision-making task involving facial emotion recognition (i.e., deciding whether depicted faces were happy versus angry) with variable levels of stress (via a risk of electric shock), workload (via time pressure), and uncertainty (via visual degradation of task stimuli). The time series of movement trajectories was analyzed both holistically (full trajectory) and by phase: lowered (early), raising (middle), aiming (late), and face-to-face (sequential). For each epoch, up to 3844 linear and non-linear features were extracted across temporal, spectral, probability, divergence, and fractal domains. Features were entered into a repeated 10-fold cross-validation procedure using 80/20 train/test splits. Feature selection was conducted with the T-Rex Selector, and selected features were used to train a scikit-learn pipeline with a Robust Scaler and a Logistic Regression classifier. Models achieved mean ROC AUC scores as high as 0.76 for stress classification, with the highest sensitivity during the full movement trajectory and middle (raise) phases. Classification of workload and uncertainty states was less successful. These findings demonstrate the potential of movement-based sensing to infer stress states in applied settings and inform future human–machine interface development. Full article

Pulsed liquid-based nanoparticle synthesis has emerged as a versatile and environmentally friendly approach for producing a wide range of nanomaterials with tunable properties. Unlike conventional chemical methods, pulsed techniques—such as pulsed laser ablation in liquids (PLAL), electrical discharge, and other energy-pulsing methods—enable the synthesis of high-purity nanoparticles without the need for toxic precursors or stabilizing agents. This review provides a comprehensive overview of the fundamental mechanisms driving nanoparticle formation under pulsed conditions, including plasma–liquid interactions, cavitation, and shockwave dynamics. We discuss the influence of key synthesis parameters, explore different pulsed energy sources, and highlight the resulting effects on nanoparticle size, shape, and composition. The review also surveys a broad spectrum of material systems and outlines advanced characterization techniques for analyzing synthesized nanostructures. Furthermore, we examine current and emerging applications in biomedicine, catalysis, sensing, energy, and environmental remediation. Finally, we address critical challenges such as scalability, reproducibility, and mechanistic complexity, and propose future directions for advancing the field through hybrid synthesis strategies, real-time diagnostics, and machine learning integration. By bridging mechanistic insights with practical applications, this review aims to guide researchers toward more controlled, sustainable, and innovative nanoparticle synthesis approaches. Full article

This study presents an advanced, interpretability-focused machine learning framework for forecasting electricity consumption in Turkey over the period 2016–2024. The proposed approach is based on a high-dimensional dataset that incorporates a diverse set of variables, including sector-specific electricity usage (residential, industrial, lighting, agricultural, and commercial), electricity production, trade metrics (imports and exports in USD), and macroeconomic indicators such as the Industrial Production Index (IPI). A comprehensive set of eight state-of-the-art regression algorithms—including ensemble models such as CatBoost, LightGBM, Random Forest, and Bagging Regressor—were developed and rigorously evaluated. Among these, CatBoost emerged as the most accurate model, achieving R² values of 0.9144 for electricity production and 0.8247 for electricity consumption. Random Forest and LightGBM followed closely, further confirming the effectiveness of tree-based ensemble methods in capturing nonlinear relationships in complex datasets. To enhance model interpretability, SHAP (SHapley Additive exPlanations) and traditional feature importance analyses were applied, revealing that residential electricity consumption was the dominant predictor across all models, accounting for more than 70% of the variance explained in consumption forecasts. In contrast, macroeconomic indicators and temporal variables showed marginal contributions, suggesting that electricity demand in Turkey is predominantly driven by internal sectoral consumption trends rather than external economic or seasonal dynamics. In addition to historical evaluation, scenario-based forecasting was conducted for the 2025–2030 period, incorporating varying assumptions about economic growth and population trends. These scenarios demonstrated the model’s robustness and adaptability to different future trajectories, offering valuable foresight for strategic energy planning. The methodological contributions of this study lie in its integration of high-dimensional, multivariate data with transparent, interpretable machine learning models, making it a robust and scalable decision-support tool for policymakers, energy authorities, and infrastructure planners aiming to enhance national energy resilience and policy responsiveness. Full article

Tire–road friction is a fundamental factor in vehicle safety, energy efficiency, and environmental sustainability. This narrative review synthesizes current knowledge on the tire–road friction coefficient (TRFC), emphasizing its dynamic nature and the interplay of factors such as tire composition, tread design, road surface texture, temperature, load, and inflation pressure. Friction mechanisms, adhesion, and hysteresis are analyzed alongside their dependence on environmental and operational conditions. The study highlights the challenges posed by emerging mobility paradigms, including electric and autonomous vehicles, which demand specialized tires to manage higher loads, torque, and dynamic behaviors. The review identifies persistent research gaps, such as real-time TRFC estimation methods and the modeling of combined environmental effects. It explores tire–road interaction models and finite element approaches, while proposing future directions integrating artificial intelligence and machine learning for enhanced accuracy. The implications of the Euro 7 regulations, which limit tire wear particle emissions, are discussed, highlighting the need for sustainable tire materials and green manufacturing processes. By linking bibliometric trends, experimental findings, and technological innovations, this review underscores the importance of balancing grip, durability, and rolling resistance to meet safety, efficiency, and environmental goals. It concludes that optimizing friction coefficients is essential for advancing intelligent, sustainable, and regulation-compliant mobility systems, paving the way for safer and greener transportation solutions. Full article

Electricity tariffs in emerging economies are often influenced by macroeconomic volatility and regulatory design, affecting both affordability and system stability. Understanding these interactions is crucial for anticipating price fluctuations and ensuring sustainable energy policy. This paper examines the influence of macroeconomic conditions on electricity tariff dynamics in Colombia by integrating econometric and machine learning approaches. Using monthly data from 2009 to 2024 and a set of 153 macroeconomic indicators condensed via principal component analysis (PCA), we assess the predictive performance of vector autoregressive (VAR), SARIMAX, and XGBoost models, as well as a hybrid VAR–XGBoost specification. Impulse-response analysis reveals that tariff components exhibit limited sensitivity to macroeconomic shocks, underscoring the buffering role of regulation and sector-specific drivers. However, forecasting exercises demonstrate that accuracy is highly component-specific: SARIMAX performs best for transmission and restrictions, and VAR dominates for distribution and losses, while the hybrid model outperforms for generation and commercialization. These findings highlight that although macroeconomic pass-through into tariffs is weak, hybrid approaches that combine structural econometric dynamics with nonlinear learning can deliver tangible forecasting gains. The study contributes to the literature on electricity pricing in emerging economies and offers practical insights for regulators and policymakers concerned with tariff predictability and energy affordability. Full article

The increased energy efficiency of electrified vehicles and their potential to reduce CO₂ emissions through the use of environmentally friendly materials are highlighted as reasons for the shift to electrified vehicles. Brief trends on the development of electric vehicles (EVs) have been discussed, presenting outstanding improvement towards the actualization of the green economy. The state of the art in lubrication has been thoroughly investigated as one of the factors influencing energy efficiency and the lifespan of machine components. As a result, many reports on the effectiveness of specific lubricants in electric vehicle applications have been developed. Good thermal and corrosion-resistant lubricants are necessary because of the emergence of several new tribological difficulties, especially in areas that interact with greater temperatures and currents. To avoid fluidity and frictional problems that may be experienced over its lifetime, a good viscosity level of lubricant was also mentioned as a crucial component in the formulation of EV lubricant. New lubricants are also necessary for the gearbox systems of electric vehicles. Furthermore, battery electric vehicles (BEVs) require a suitable cooling system for the batteries; thus, a compatible nano-fluid is recommended. Sustainable battery cooling options support global energy efficiency and carbon emission reduction while extending the life of EV batteries. The path for future advancements or the creation of the most useful and efficient EV lubricants is provided by this review study. Full article

In the last decade, the incorporation of Artificial Intelligence (AI) with autonomous vehicles (AVs) has transformed transportation, mobility, and smart mobility systems. The present study provides a systematic review of global trends, applications, and challenges at the intersection of AI, including Machine Learning (ML), Deep Learning (DL), and autonomous vehicle technologies. Using data extracted from Clarivate Analytics’ Web of Science Core Collection and a set of specific keywords related to both AI and autonomous (electric) vehicles, this paper identifies the themes presented in the scientific literature using thematic maps and thematic map evolution analysis. Furthermore, the research topics are identified using both thematic maps, as well as Latent Dirichlet Allocation (LDA) and BERTopic, offering a more faceted insight into the research field as LDA enables the probabilistic discovery of high-level research themes, while BERTopic, based on transformer-based language models, captures deeper semantic patterns and emerging topics over time. This approach offers richer insights into the systematic review analysis, while comparison in the results obtained through the various methods considered leads to a better overview of the themes associated with the field of AI in autonomous vehicles. As a result, a strong correspondence can be observed between core topics, such as object detection, driving models, control, safety, cybersecurity and system vulnerabilities. The findings offer a roadmap for researchers and industry practitioners, by outlining critical gaps and discussing the opportunities for future exploration. Full article

This study examines how green finance drives sustainable economic development in Saudi Arabia, focusing on the Hail region, with comparisons across seven major cities (Riyadh, Jeddah, Mecca, Medina, Dammam, Tabuk, and Taif). Through an XGBoost machine learning approach enhanced by SHAP interpretation, we analyze how Green Finance Investment (IGF), Alternative and Nuclear Energy (ANE), Electricity Access (ATE), and Logarithmic Carbon Emissions (LCDE) influence New Business Registrations (NBR), Employment rates (EM), and Circular Economy outcomes measured via Combustible Renewables and Waste (CRW). Results reveal that ANE is the dominant predictor of employment across all cities, with SHAP values ranging from 76.9% in Hail to 96.6% in Jeddah. Entrepreneurial drivers vary regionally: ANE leads in Riyadh (63.1%) and Jeddah (73.3%), LCDE dominates in Hail (45.0%) and Taif (48.6%), and IGF is primarily evident in Tabuk (39.5%). Model accuracy varies, with RMSE being the highest in Hail (58.97) and lowest in Jeddah (433.86), highlighting structural differences across urban economies. Circular economy pathways diverge between LCDE-driven industrial modernization (e.g., Dammam, 62.9%) and IGF-driven greenfield development (e.g., Tabuk, 81.1%). These findings support a threefold city classification and provide actionable insights into Saudi Arabia’s Vision 2030 implementation. They inform targeted policy interventions, including green infrastructure investments in energy hubs, industrial modernization programs in manufacturing centers, and entrepreneurial financing mechanisms in emerging regions. Full article

Rapid decarbonization relies on knowing which structural and energy factors affect national carbon dioxide emissions. Much of the literature leans on linear and additive assumptions, which may gloss over curvature and interactions in this energy–emissions link. Unlike previous studies, we take a different approach. Using a panel of 80 high- and upper-middle-income countries from 2011 to 2020, we model emissions as a function of fossil fuel energy consumption, methane, the food production index, renewable electricity output, gross domestic product (GDP), and trade measured as trade over GDP. Our contribution is twofold. First, we evaluate how different modeling strategies, from a traditional Generalized Linear Model to more flexible approaches such as Support Vector Machine regression and Random Forest (RF), influence the identification of emission drivers. Second, we use Double Machine Learning (DML) to estimate the incremental effect of fossil fuel consumption while controlling for other variables, offering a more careful interpretation of its likely causal role. Across models, a clear pattern emerges: GDP dominates; fossil fuel energy consumption and methane follow. Renewable electricity output and trade contribute, but to a moderate degree. The food production index adds little in this aggregate, cross-country setting. To probe the mechanism rather than the prediction, we estimate the incremental role of fossil fuel energy consumption using DML with RF nuisance functions. The partial effect remains positive after conditioning on the other covariates. Taken together, the results suggest that economic scale and the fuel mix are the primary levers for near-term emissions profiles, while renewables and trade matter, just less than is often assumed and in ways that may depend on context. Full article

Hydrogen propulsion technologies are emerging as a key enabler for decarbonizing the aviation sector, especially for regional commercial aircraft. The evolution of aircraft propulsion technologies in recent years raises the question of the feasibility of a hydrogen propulsion system for beyond regional aircraft. This paper presents a comprehensive review of hydrogen propulsion technologies, highlighting key advancements in component-level performance metrics. It further explores the technological transitions necessary to enable hydrogen-powered aircraft beyond the regional category. The feasibility assessment is based on key performance parameters, including power density, efficiency, emissions, and integration challenges, aligned with the targets set for 2035 and 2050. The adoption of hydrogen-electric powertrains for the efficient transition from KW to MW powertrains depends on transitions in fuel cell type, thermal management systems (TMS), lightweight electric machines and power electronics, and integrated cryogenic cooling architectures. While hydrogen combustion can leverage existing gas turbine architectures with relatively fewer integration challenges, it presents its technical hurdles, especially related to combustion dynamics, NOx emissions, and contrail formation. Advanced combustor designs, such as micromix, staged, and lean premixed systems, are being explored to mitigate these challenges. Finally, the integration of waste heat recovery technologies in the hydrogen propulsion system is discussed, demonstrating the potential to improve specific fuel consumption by up to 13%. Full article

The turbine mortise is a critical structural feature of turbine disks, and its manufacturing quality directly determines the performance and service life of aircraft engines. With the increasing application of advanced nickel-based superalloys, severe tool wear in conventional mechanical broaching of turbine mortises has emerged as a key limitation, substantially elevating production costs. Electrochemical broaching (ECB), which removes material through anodic dissolution reactions, eliminates tool wear and thus offers low cost and efficiency advantages, making it a promising method for turbine mortise fabrication. In this study, COMSOL Multiphysics 6.2 was employed to simulate the multiphysics field comprising the electric field, flow field, temperature field, bubble ratio, and dynamic mesh and elucidate the evolution of the electric field during the ECB process. ECB experiments of specimens on Inconel 718 were conducted under different feed speeds. On this basis, optimal processing parameters were identified. The results of the mid-position ECB experiments revealed five distinct dissolution states: pre-processing, pre-transition, stable dissolution, post-transition, and post-processing stages. A material dissolution mechanism model for the ECB process was established. Finally, fir-tree turbine mortises were successfully manufactured on Inconel 718 using a self-developed specialized electrochemical machining system at a feed speed of 70 mm/min. The mortise profile demonstrated dimensional deviations of (+16 to −21) μm, with working surface variations maintained within ±5 μm. The machined surfaces exhibited uniform and dense morphology with a surface roughness of Ra 0.275 μm. Three sets of mortise specimens processed under identical parameters showed excellent consistency, presenting a maximum deviation in profile removal thickness of +4.1 μm. The tool cathode was repeatedly reused without any detectable wear. Full article

Thermoelectric (TE) materials represent a critical frontier in sustainable energy conversion technologies, providing direct thermal-to-electrical energy conversion with solid-state reliability. The optimizations of TE performance demand a nuanced comprehension of structure–property relationships across diverse length scales. This review summarizes established and emerging spectroscopic and microscopic techniques used to characterize inorganic and polymer TE materials, specifically poly(3,4-ethylenedioxythiophene): poly(styrenesulfonate) (PEDOT:PSS). For inorganic TE, ultraviolet–visible (UV–Vis) spectroscopy, energy-dispersive X-ray (EDX) spectroscopy, and X-ray photoelectron spectroscopy (XPS) are widely applied for electronic structure characterization. For phase analysis of inorganic TE materials, Raman spectroscopy (RS), electron energy loss spectroscopy (EELS), and nuclear magnetic resonance (NMR) spectroscopy are utilized. For analyzing the surface morphology and crystalline structure, chemical scanning electron microscopy (SEM), transmission electron microscopy (TEM), and X-ray diffraction (XRD) are commonly used. For polymer TE materials, ultraviolet−visible–near-infrared (UV−Vis−NIR) spectroscopy and ultraviolet photoelectron spectroscopy (UPS) are generally employed for determining electronic structure. For functional group analysis of polymer TE, attenuated total reflectance–Fourier-transform infrared (ATR−FTIR) spectroscopy and RS are broadly utilized. XPS is used for elemental composition analysis of polymer TE. For the surface morphology of polymer TE, atomic force microscopic (AFM) and SEM are applied. Grazing incidence wide-angle X-ray scattering (GIWAXS) and XRD are employed for analyzing the crystalline structures of polymer TE materials. These techniques elucidate electronic, structural, morphological, and chemical properties, aiding in optimizing TE properties like conductivity, thermal stability, and mechanical strength. This review also suggests future research directions, including in situ methods and machine learning-assisted multi-dimensional spectroscopy to enhance TE performance for applications in electronic devices, energy storage, and solar cells. Full article