Search Results (3,105)

Orbit determination for non-cooperative targets represents a significant focus of research within the domain of space situational awareness. In contrast to cooperative targets, non-cooperative targets do not provide their orbital parameters, necessitating the use of observation data for accurate orbit determination. The increasing prevalence of low-cost, low-thrust spacecraft has heightened the demand for advancements in real-time orbit determination and parameter estimation for low-thrust maneuvers. This paper presents a novel dual-layer filter approach designed to facilitate real-time acceleration estimation for non-cooperative targets. Initially, the method employs a square-root cubature Kalman filter (SRCKF) to handle the nonlinearity of the system and a Jerk model to address the challenges in acceleration modeling, thereby yielding a preliminary estimation of the acceleration produced by the thruster of the non-cooperative target. Subsequently, a specialized filtering structure is established for the estimated acceleration, and two filtering frameworks are integrated into a dual-layer filter model via the cubature transform, significantly enhancing the estimation accuracy of acceleration parameters. Finally, to adapt to the potential on/off states of the thrusters, the Interacting Multiple Model (IMM) algorithm is employed to bolster the robustness of the proposed solution. Simulation results validate the effectiveness of the proposed method in achieving real-time orbit determination and acceleration estimation. Full article

Distributed energy systems (DES), such as photovoltaics (PV), heat pumps (HPs), and electric vehicles (EVs), are being rapidly integrated into low-voltage (LV) grids, while measurement coverage remains limited. This paper presents a concept for an LV grid digital twin designed to enable real-time state estimation (SE) and operation-oriented studies under constrained measurement availability. Based on this concept, an exemplary digital twin is developed and demonstrated for a test area with a high PV penetration. The proposed digital twin integrates a topology-aware grid model, realistic parameterization, standardized IEC 61850 data modeling, and a real-time estimation pipeline that processes heterogeneous measurement data, including PV inverter power and voltage as well as transformer and feeder measurements. The approach is demonstrated through software-in-the-loop (SIL) experiments using historical playback and accelerated simulations, as well as hardware-in-the-loop (HIL) tests for real-time operation. The SIL results show that the digital twin enables continuous grid monitoring, enhances transparency for distribution system operators (DSOs), and leverages existing infrastructure to increase the effective PV hosting capacity. Selective PV curtailment mitigates congestion and restores normal operation, indicating a potentially cost-effective alternative to grid reinforcement. The HIL experiments emphasize the importance of high-quality, standardized data. The achieved accuracy depends on data availability and synchronization, highlighting the need for improved data integration. Overall, the proposed approach provides a viable pathway toward data-driven planning and operation of LV grids with high DES penetration. Full article

Microbial contamination of aviation fuels is a persistent operational and safety challenge, compromising fuel quality and accelerating material degradation. The global transition toward sustainable aviation fuels (SAF) amplifies the need to reassess microbial risks across both conventional and alternative fuel systems. Here, we present the first systematic review and meta-analysis to synthesize evidence on microbial prevalence in jet fuel environments and to evaluate implications for SAF deployment. Of 2837 records screened, 37 studies fulfilled the inclusion criteria. Microorganisms were detected in up to 87% of jet fuel systems worldwide (95% CI: 76–100%); however, this pooled estimate was associated with substantial heterogeneity (I² = 96%) and should therefore be interpreted with caution as reflecting an overall trend rather than a precise global value. Taxonomic analysis identified consistently reported bacterial genera (Actinomycetes, Halomonas, Mycobacterium, Nocardioides, Rhodococcus, Stenotrophomonas) and fungal genera (Aspergillus, Alternaria, Amorphotheca, Byssochlamys, Candida, Fusarium, Saccharomyces, Schizosaccharomyces, Talaromyces, Trichocomaceae). Deteriorative organisms dominated (bacteria 57%; fungi 75%) relative to non-deteriorative taxa (12% and 32%, respectively). These findings highlight microbial spoilage as a pervasive and underrecognized threat to fuel integrity. Importantly, they suggest that risks currently documented in conventional systems are likely to extend to SAF, reinforcing the urgent need for proactive monitoring frameworks and bio-contamination mitigation strategies to ensure aviation fuel reliability. Full article

Surface roughness is a quality-critical attribute in industrial zinc phosphating, directly affecting sealing performance, coating uniformity, dimensional tolerances, and first-pass production yield in automotive pretreatment lines. While the chemical mechanisms of phosphate coating formation are well understood, the translation of this knowledge into statistically defensible, production-scale prioritization of bath chemistry control levers under real manufacturing constraints remains limited, particularly with respect to surface roughness stability and its environmental implications. This study investigates surface roughness control in a fully operational industrial zinc phosphating line by systematically evaluating the effects of pickling acid chemistry (H₂SO₄ versus H₃PO₄), dissolved ferrous iron (Fe²⁺) levels in pickling and phosphating baths, and nitrate accelerator dosage. A Taguchi L16 (2⁴) experimental design was implemented under real manufacturing constraints. Surface roughness (Rz) was measured in accordance with ISO 4287 and analyzed using a general linear model supported by partial effect size estimation (ηp²) and bootstrap confidence intervals. This approach enables statistically robust ranking of dominant and secondary control parameters, rather than qualitative trend confirmation alone. The robustness of statistically identified trends was independently verified using paired measurements from 25 production components, while scanning electron microscopy provided qualitative mechanistic support. The results demonstrate that pickling acid chemistry and nitrate accelerator dosage are the dominant control parameters governing surface roughness stability, whereas Fe²⁺ concentration does not act as a primary independent driver within the defined Fe²⁺ concentration ranges investigated in this study, but contributes through interaction-dependent mechanisms. Phosphoric acid pickling combined with nitrate acceleration consistently yields lower and more stable roughness values. In addition, roughness-related nonconformities were translated into product carbon footprint outcomes using an ISO 14067–aligned, gate-to-gate framework with Monte Carlo uncertainty analysis, explicitly quantifying the carbon footprint penalties associated with quality-driven rework and external return logistics under industrial production conditions. Full article

Human-in-the-loop (HIL) immersive simulators integrate a human operator into the simulation loop, enabling real-time interaction with virtual environments. To expose users to controlled acceleration fields, they employ parallel kinematic machines (PKMs), including reduced-degree-of-freedom (DoF) configurations when compact and cost-effective systems are required. These reduced-DoF platforms frequently exhibit kinematic crosstalk, whereby motion along one axis causes unintended displacements or rotations along others. Among compact PKMs, the four-legged, three-DoF platform is widely used, particularly in driving simulators. However, to the best of the authors’ knowledge, its kinematics have never been systematically analyzed in the literature. It is an over-actuated system with specific constraint conditions characterized by actuators that are not fully grounded. As a result, kinematic crosstalk accelerations are not fully determined by kinematic relationships. They also depend on friction at the constraints; thus, they are also determined by the dynamic behavior of the machine, which is difficult to predict during operation. To address this issue, this paper introduces a simplified modeling approach to estimate kinematic crosstalk whose usability is evaluated experimentally both with mono-harmonic, combined DoF tests and in a real-world engineering application on an actual driving simulator. Results show that kinematic crosstalk on the platform is likely to generate acceleration levels up to 4 $\mathrm{m}/{\mathrm{s}}^2$, exceeding the vestibular perception threshold of 0.17 $\mathrm{m}/{\mathrm{s}}^2$ defined by Reid and Nahon. This result is relevant with respect to enabling a comprehensive assessment of the acceleration field to which the user is actually subjected, which determines the actual quality and immersiveness of the simulation. Full article

Global extreme poverty has fallen dramatically over the past two centuries, yet hundreds of millions remain impoverished, underscoring the need for scalable monitoring tools. In Mexico, poverty metrics are available only sporadically in terms of time and space (e.g., every 5 years at the municipal level), making it difficult for decision-makers to access reliable, up-to-date, and sufficiently detailed information, highlighting the need for higher-resolution, timely methods. To address this problem, we propose a two-stage approach that combines socioeconomic and Earth Observations-based data. Initially, a machine learning model maps census variables to official poverty indicators belonging to a multidimensional model, yielding fine-scale poverty estimates. A census-based model trained with eXtreme Gradient Boosting (XGBoost) achieved a determination coefficient ($R^2$) of approximately $0.842$, indicating strong agreement with official poverty figures and providing high-resolution proxies. Afterward, we use features based on remote observations to predict these poverty estimates at a 469 m grid scale. In this case, advanced foundation models outperformed other machine learning (ML) approaches, achieving an $R^2$ of $0.683$. While foundation models enable more accurate, fine-scale poverty mapping and could accelerate poverty assessments, their use comes at a heavy price in terms of carbon emissions. Full article

This study assesses the innovation performance and convergence dynamics across 237 European regions (NUTS 2 level) from 2016 to 2023, explicitly accounting for the structural and behavioural changes triggered by the COVID-19 pandemic. The article provides a novel regional-level assessment of how an unprecedented external shock reshaped innovation trajectories before and after the pandemic. To this end, the analysis combines Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), sigma-convergence measures, and a Difference-in-Differences (DiD) framework within an integrated multi-method empirical approach to evaluate shifts in regional innovation patterns over time. The results reveal a highly uneven distribution of innovation activities, with increasing polarization in the post-pandemic period. Northern and Western European regions strengthened their competitive advantage through robust digital infrastructure, strong human capital, and substantial R&D investments. In contrast, many Southern and Eastern European regions faced heightened structural barriers, leading to a widening innovation gap. Nevertheless, several regions exhibited notable resilience and achieved significant innovation catch-up, providing new empirical evidence on heterogeneous regional adaptive dynamics supported by targeted regional policies and improved local capabilities. The sigma-convergence analysis indicates a general increase in overall disparities, as reflected by rising dispersion in the Regional Innovation Index (RII) during 2020–2023. However, according to the DiD estimation, regions most severely affected by COVID-19 experienced a statistically significant relative increase (approximately 2.17%) in innovation performance, highlighting the pandemic’s role as a catalyst for accelerated digital transformation and innovation adjustment at the regional level. Full article

This study develops an integrated panel econometric framework for modeling investment–output dynamics in circular economy sectors, explicitly addressing dynamic propagation, long-run equilibrium relationships, endogeneity, and nonlinear responses. Building on the Samuelson–Hicks Multiplier–Accelerator model, the analysis combines two complementary approaches. A dynamic panel specification estimated by the Generalized Method of Moments (Arellano–Bond) is employed to capture output inertia, intertemporal transmission of investment shocks, and stability properties of the dynamic system. In parallel, a nonlinear panel ARDL model estimated using the Pooled Mean Group (PMG/NARDL) methodology is used to identify cointegration and to distinguish between the long-run and short-run effects of positive and negative investment variations. The empirical analysis relies on a balanced panel of 28 European economies (EU-27 and the United Kingdom) over the period 2005–2023, using sectoral circular economy data, with gross value added as the output variable and gross private investment as the main regressor. The results indicate the existence of a stable cointegrated relationship between investment and output, characterized by significant asymmetries, with expansionary investment shocks exerting larger and more persistent effects than contractionary shocks. Dynamic GMM estimates further confirm delayed investment effects and a stable autoregressive structure. Overall, the paper contributes to mathematical economic modeling by providing a unified dynamic–equilibrium panel framework and by extending the empirical relevance of Multiplier–Accelerator dynamics to circular economy systems. Full article

Owing to the low cost, small size, and convenience for installation, 2D LiDAR has been widely used in mobile robots for simultaneous positioning and mapping (SLAM). However, traditional 2D LiDAR SLAM methods have low robustness and accuracy in LiDAR-degenerated environments. To improve the robustness of the SLAM method in such environments, an innovative SLAM method is developed, which mainly includes two parts, i.e., the front-end positioning and the back-end optimization. Specifically, in the front-end part, the AKF (adaptive Kalman filter) method is applied to estimate the pose of the mobile robot, zero bias of acceleration and gyroscope, lever arm length, and the mounting angle. The adaptive factor of the AKF can dynamically adjust the variance of the process and measurement noises based on the residual. In the back-end part, a particle filter (PF) is employed to optimize the pose estimation and build the map, where the pose domain constraint from the output of the front-end is introduced in the PF to avoid mismatch and enhance positioning accuracy. To verify the performance of the method, a series of experiments is carried out in four typical environments. The experimental results show that the positioning precision has been improved by about 61.3–97.9%, 35.7–99.0%, and 43.8–93.0% compared to the Karto SLAM, Hector SLAM, and Cartographer, respectively. Full article

Background and Objectives: The combination of resistant hypertension (RHTN) and type 2 diabetes mellitus (T2DM) accelerates the development of chronic kidney disease (CKD), which may be largely associated with sympathetic hyperactivity. Distal renal denervation (dRDN) effectively reduces sympathetic flow to the kidneys, causing renal vasodilation and increased renal perfusion. However, this effect may be limited by nephrotoxicity due to the multiple increase in the number of contrast injections, as well as a significant blood pressure (BP) reduction, which naturally worsens renal perfusion. This study aimed to test the hypothesis that dRDN prevents the progressive decline in kidney function in patients with RHTN and T2DM. Materials and Methods: The prospective interventional study (REFRAIN, NCT04948918) included men and women > 20 y.o. with true RHTN. Eligible patients underwent dRDN. The primary endpoint was a change in eGFR from baseline to 12 months. Secondary endpoints were changes in 24 h BP, serum lipocalin-2, cystatin C, 24 h urinary albumin excretion, renal blood flow, and kidney volumes (by MRI). Multiple regression analysis was used to find independent predictors of individual estimated glomerular filtration rate (eGFR) change. Results: A total of 29 patients with RHTN and T2DM were included in the study (61.6 ± 7.2 y.o., 10 males, mean 24 h ambulatory BP: 158.1 ± 21.4/81.8 ± 12.4 mmHg (systolic/diastolic, respectively)), HbA1c: 7.8 ± 1.4%, and eGFR 56.7 ± 19.9 mL/min/1.73 m², 23 (79%) patients with CKD, and 2 patients with albuminuria only. There were no perioperative complications. Twenty-seven (93%) participants completed 12 month follow-up. eGFR did not change from baseline: +1.3 mL/min/1.73 m² [95% CI: −9.6, 12.1], despite the expected decrease due to a significant decrease in 24 h systolic BP (−18.2 mmHg [95% CI: −28.6, −7.8]). No changes in other secondary endpoints were observed. Independent predictors of individual eGFR change were baseline 24 h pulse pressure (p = 0.030) and HbA1c (p = 0.010). Conclusions: Distal RDN demonstrates a substantial nephroprotective effect in patients with RHTN and T2DM, which may be partly mediated by a reduction in arterial stiffness and is negatively dependent on baseline hyperglycemia. Full article

Against the backdrop of accelerating global digitalization and mounting climate pressures, enabling digital-economy growth while simultaneously controlling carbon emissions has become a critical challenge for China. This study constructs a Digital Economy Development Index (DEI) and a Carbon Emissions Index (CEI) to examine the spatiotemporal evolution and spatial heterogeneity of coordinated development between the digital economy and carbon emissions. We employ global and local Moran’s I, a spatial Markov chain model, and kernel density estimation to investigate spatiotemporal autocorrelation, interregional transition patterns, and the dynamic evolution of the coupling coordination degree over 2011–2022. The results indicate that China’s eastern region performs notably better in achieving coordinated development, maintaining persistently higher coupling coordination levels. In contrast, the central and western regions face substantial challenges; in particular, low-value areas exhibit considerable potential to transition toward higher-value states, suggesting substantial room for improvement. The spatiotemporal analysis further reveals pronounced regional disparities and provides a scientific basis for policymaking aimed at advancing green and low-carbon development strategies tailored to regional characteristics. Full article

In the face of accelerating climate change and urbanization, sustainable mobility infrastructure plays a critical role in reducing greenhouse gas emissions. This article assesses the Sunglider concept—an elevated, solar-powered transport system—through the New European Bauhaus (NEB) Compass, which emphasizes sustainability, inclusion, and esthetic value. Designed by architect Peter Kuczia and collaborators, Sunglider combines photovoltaic energy generation with modular, parametrically designed wooden pylons to form a lightweight, climate-positive mobility solution. The study evaluates the system’s technological feasibility, environmental performance, and urban integration potential, drawing on existing design documentation and simulation-based estimates. While Sunglider demonstrates strong alignment with NEB principles, including zero-emission operation and material circularity, its implementation is challenged by high initial investment, political and planning complexities, and integration into dense urban environments. Mitigation strategies—such as adaptive routing, visual screening, and universal station access—are proposed to address concerns around privacy, esthetics, and accessibility. The article positions Sunglider as a scalable and replicable model for mid-sized European cities, capable of advancing inclusive, carbon-neutral mobility while enhancing the urban experience. It concludes with policy and research recommendations, highlighting the importance of embedding infrastructure innovation within broader ecological and cultural transitions. Full article

The Aquila Optimizer (AO), inspired by the hunting behavior of Aquila birds, is a recent nature-inspired metaheuristic algorithm recognized for its simplicity and low computational cost. However, the conventional AO often suffers from premature convergence and an imbalance between exploration and exploitation when applied to complex engineering optimization problems. To overcome these limitations, this study proposes a modified Aquila Optimizer (m-AO) incorporating three enhancement strategies: an adaptive chaotic reverse learning mechanism to improve population diversity, an elite alternative pooling strategy to balance global exploration and local exploitation, and a shifted distribution estimation strategy to accelerate convergence toward promising regions of the search space. The performance of the proposed m-AO is evaluated using 23 classical benchmark functions, IEEE CEC 2022 benchmark problems, and a practical plate–fin heat exchanger (PFHE) design optimization problem. Numerical simulations demonstrate that m-AO achieves faster convergence, higher solution accuracy, and improved robustness compared with the original AO and several state-of-the-art metaheuristic algorithms. In the PFHE application, the proposed method yields a significant improvement in thermal performance, accompanied by a reduction in entropy generation and pressure drop under prescribed design constraints. Statistical analyses further confirm the superiority and stability of the proposed approach. These results indicate that the modified Aquila Optimizer is an effective and reliable tool for solving complex thermal system design optimization problems. Full article

The accelerating energy demands from artificial intelligence (AI) deployment introduce systemic challenges for achieving carbon neutrality. Large language models (LLMs) represent a dominant driver of AI energy consumption, with inference operations constituting 80–90% of total energy usage. Current energy benchmarks report aggregate metrics without domain-level breakdowns, preventing accurate carbon footprint estimation for workloadspecific operations. This study addresses this critical gap by introducing a carbon-aware framework centered on the carbon cost of intelligence (CCI), a novel metric enabling workload-specific energy and carbon calculation that balances accuracy and efficiency across heterogeneous domains. This paper presents a comprehensive cross-domain energy benchmark using the massive multitask language understanding (MMLU) dataset, measuring accuracy and energy consumption in five representative domains: clinical knowledge (medicine), professional accounting (finance), professional law (legal), college computer science (technology), and general knowledge. Empirical analysis of GPT-4 across 100 MMLU questions, 20 per domain, reveals substantive variations: legal queries consume 4.3× more energy than general knowledge queries (222 J vs. 52 J per query), while energy consumption varies by domain due to input length differences. Our analysis demonstrates the evolution from simple ratio-based approaches (weighted accuracy divided by weighted energy) to harmonic mean aggregation, showing that the harmonic mean, by preventing bias from extreme values, provides more accurate carbon usage estimates. The CCI metric, calculated using weighted harmonic mean (analogous to P/E ratios in finance, where A/E represents accuracy-to-energy ratio), enables practitioners to accurately estimate energy and carbon emissions for specific workload mixes (e.g., 80% medicine + 15% general + 5% law). Results demonstrate that the domain workload mix significantly impacts carbon footprint: a law firm workload (60% law) consumes 96% more energy per query than a hospital workload (80% medicine), representing 49% potential savings through workload optimization. Carbon footprint analysis using US Northeast grid intensity (320 gCO₂e/kWh) shows domain-specific emissions ranging from 0.0046–0.0197 gCO₂ per query. CCI is validated through comparison with simple weighted average, demonstrating differences up to 12.1%, confirming that the harmonic mean provides more accurate and conservative carbon estimates essential for carbon reporting and neutrality planning. Our findings provide a novel cross-domain energy benchmark for GPT-4 and establish a practical carbon calculator framework for sustainable AI deployment aligned with carbon neutrality goals. Full article

In nanometer integrated-circuit (IC) manufacturing, advanced technology scaling has intensified the effects of process variations on circuit reliability and performance. Random fluctuations in parameters such as threshold voltage, channel length, and oxide thickness further degrade design margins and increase the likelihood of functional failures. These variations often lead to rare circuit failure events, underscoring the importance of accurate yield estimation and robust design methodologies. Conventional Monte Carlo yield estimation is computationally infeasible as millions of simulations are required to capture failure events with extremely low probability. This paper presents a novel reliability-based circuit design optimization framework that leverages deep transfer learning to improve the efficiency of repeated yield analysis in optimization iterations. Based on pre-trained neural network models from prior design knowledge, we utilize model fine-tuning to accelerate importance sampling (IS) for yield estimation. To improve estimation accuracy, adversarial perturbations are introduced to calibrate uncertainty near the model decision boundary. Moreover, we propose a cascaded batch Bayesian optimization (CBBO) framework that incorporates a smart initialization strategy and a localized penalty mechanism, guiding the search process toward high-yield regions while satisfying nominal performance constraints. Experimental validation on SRAM circuits and amplifiers reveals that CBBO achieves a computational speedup of 2.02×–4.63× over state-of-the-art (SOTA) methods, without compromising accuracy and robustness. Full article