Search Results (1,404)

This paper presents a comparative study of the applicability and accuracy of optimal control methods and neural-network-based estimators in the context of porkchop plots for preliminary asteroid rendezvous mission design. The scenario considered involves a deep-space CubeSat equipped with a low-thrust engine, departing from Earth and rendezvousing with a near-Earth asteroid within a three-year launch window. A low-thrust trajectory optimization model is formulated, incorporating variable specific impulse, maximum thrust, and path constraints. The optimal control problem is efficiently solved using Sequential Convex Programming (SCP) combined with a solution continuation strategy. The neural network framework consists of two models: one predicts the minimum fuel consumption ($\Delta v$), while the other estimates the minimum flight time ($\Delta t$) which is used to assess transfer feasibility. Case results demonstrate that, in simplified scenarios without path constraints, the neural network approach achieves low relative errors across most of the design space and successfully captures the main structural features of the porkchop plots. In cases where the SCP-based continuation method fails due to the presence of multiple local optima, the neural network still provides smooth and globally consistent predictions, significantly improving the efficiency of early-stage asteroid candidate screening. However, the deformation of the feasible region caused by path constraints leads to noticeable discrepancies in certain boundary regions, thereby limiting the applicability of the network in detailed mission design phases. Overall, the integration of neural networks with porkchop plot analysis offers an effective decision-making tool for mission designers and planetary scientists, with significant potential for engineering applications. Full article

Glyoxalase I (GLYI) is the key regulatory enzyme in the glyoxalase pathway. This pathway enables plants to neutralize methylglyoxal (MG) using glutathione (GSH), a mechanism significant for their acclimation to environmental stress. While functionally significant, the specific functions of GLYI genes in Amaranthus palmeri remain unexplored. In this study, integrated bioinformatics and expression analysis was used to identify five GLYI genes in A. palmeri. The results indicate that ApGLYI proteins are hydrophilic and slightly acidic, localized to scaffolds 1, 11, 13, and 16 of the A. palmeri genome. Phylogenetic analysis grouped ApGLYIs with other plant GLYI proteins into three distinct clades, each exhibiting conserved motif patterns. Expression analyses demonstrate that ApGLYI genes participate in both early and late regulatory phases of MG detoxification and signaling, responding to diverse stimuli including high temperature, NaCl, osmotic stress, exogenous methylglyoxal, abscisic acid (ABA), and methyl jasmonate (MeJA). Conversely, glufosinate ammonium treatment appears to compromise this cellular detoxification system. These results offer the evolutionary trajectory and functional significance of the ApGLYI gene. They establish a foundation for subsequent studies toward managing A. palmeri infestation and using these genes to improve stress resilience in cultivated crops through breeding strategies. Full article

Measuring national progress toward green transformation remains challenging due to fragmented assessment frameworks. This study develops and validates a Green Transformation Index that captures the capacity for sustainability transitions by integrating resource efficiency, innovation systems, and policy instruments. Using OECD Green Growth Indicators covering 58 economies from 2017 to 2025, we construct a composite index from 47 standardized indicators organized into three theoretically grounded dimensions. The GTI measures transformation capacity through innovation investment and policy frameworks rather than environmental outcomes. Results reveal substantial heterogeneity in transformation capacity with a Gini coefficient of 0.283, indicating persistent global inequality. Temporal analysis identifies a three-phase trajectory: consolidation from 2017 to 2019, acceleration during 2021 to 2023 driven by green recovery investments, and marked reversal in 2024 to 2025, highlighting vulnerability to economic shocks. Cluster analysis identifies four distinct pathways: innovation-driven, balanced integration, resource-first, and policy-led approaches. Critical findings show only 19 percent of countries demonstrate strong coordination between innovation investments and policy instruments, revealing significant governance fragmentation. Validation tests confirm the index effectively measures innovation capacity but shows weak correlation with emissions outcomes, underscoring the distinction between transformation inputs and environmental performance. Full article

A principled, model-agnostic framework for structural feature extraction in time series is presented, grounded in topological data analysis (TDA). The motivation stems from two gaps identified in the literature: First, compact and interpretable representations that summarise the global geometric organisation of trajectories across scales remain scarce. Second, a unified, task-agnostic protocol for evaluating structure preservation against established non-topological families is still missing. To address these gaps, time-delay embeddings are employed to reconstruct phase space, sliding windows are used to generate local point clouds, and Vietoris–Rips persistent homology (up to dimension two) is computed. The resulting persistence diagrams are summarised with three transparent descriptors—persistence entropy, maximum persistence amplitude, and feature counts—and concatenated across delays and window sizes to yield a multiscale representation designed to complement temporal and spectral features while remaining computationally tractable. A unified experimental design is specified in which heterogeneous, regularly sampled financial series are preprocessed on native calendars and contrasted with competitive baselines spanning lagged, calendar-driven, difference/change, STL-based, delay-embedding PCA, price-based statistical, signature (FRUITS), and network-derived (NetF) features. Structure preservation is assessed through complementary criteria that probe spectral similarity, variance-scaled reconstruction fidelity, and the conservation of distributional shape (location, scale, asymmetry, tails). The study is positioned as an evaluation of representations, rather than a forecasting benchmark, emphasising interpretability, comparability, and methodological transparency while outlining avenues for adaptive hyperparameter selection and alternative filtrations. Full article

The limited capacity for cardiomyocyte proliferation in the adult human heart restricts its ability to recover from injury. Building on discoveries in regenerative model systems, such as zebrafish and neonatal mice, reactivation of a latent potential for cardiomyocyte proliferation is a strategy to promote therapeutic heart regeneration. Although cardiomyocyte proliferation remains modest even with the most effective mitogenic stimuli identified to date, evidence for a potential functional benefit in pre-clinical model systems has led to the initiation of several early-phase clinical programs. Here, we review insights from model organisms that inform the potential efficacy and limitations of therapeutic cardiomyocyte proliferation, systems to study human cardiomyocyte proliferation, and the natural history of cardiomyocyte proliferation in the human heart. We also examine the translational trajectory of selected discoveries, including therapeutic delivery modalities, and attendant safety concerns. Finally, we discuss critical challenges that will need to be addressed to enable successful clinical translation. Full article

Emotions are classified into the valence dimension (positive and negative) and the arousal dimension (low and high). Using electrocardiogram (ECG) phase space diagrams and a deep learning approach, emotional states were identified in this study. The DREAMER database was utilized for training and testing the classification model developed. We examined different ECG phase space parameters and compared different deep learning models, including the Visual Geometry Group and Residual networks, and a simple convolutional neural network (CNN) with attention modules. Among the models, a simple four-layer CNN integrated with a convolutional block attention module showed the best performance. Experimental results indicate that the model achieved an accuracy of 87.89% for the valence dimension and 91.79% for the arousal dimension. Compared with existing models, the developed model demonstrates superior performance in emotion recognition. Emotional changes produce noticeable variations in the trajectory patterns of ECG phase space diagrams, which enhance the model’s ability to recognize emotions, even when using relatively simple networks. Full article

Tropical cyclones can rapidly alter watershed chemistry by shifting hydrologic pathways and mobilizing stored nutrients, yet these disturbances often remain undetected when storms cause little visible flooding or geomorphic damage. During Hurricane Helene 2024, intense rainfall across the Oconee River watershed in Georgia generated sharp increases in discharge that triggered substantial nutrient export despite minimal physical alteration to the landscape. High-frequency measurements of nitrate, phosphate, and sulfate in urban, forested, and recreational settings revealed pronounced and synchronous post-storm increases in all three solutes. Nitrate showed the strongest and most persistent response, with mean concentrations increasing from approximately 1–3 mg/L during pre-storm conditions to 6–14 mg/L post-storm across sites, and remaining elevated for several months after hydrologic conditions returned to baseline. Phosphate concentrations increased sharply during the post-storm period, rising from pre-storm means of ≤0.3 mg/L to a post-storm average of 1.5 mg/L, but declined more rapidly during recovery, consistent with sediment-associated mobilization and subsequent attenuation. Sulfate concentrations also increased substantially across the watershed, with post-storm mean values commonly exceeding 20 mg/L and maximum concentrations reaching 41 mg/L, indicating sustained dissolved-phase release and enhanced temporal variability. Recovery trajectories differed by solute: phosphate returned to baseline within weeks, nitrate declined gradually, and sulfate remained elevated throughout the winter. These findings demonstrate that substantial chemical perturbations can occur even in the absence of visible storm impacts, underscoring the importance of event-based, high-resolution monitoring to detect transient but consequential shifts in watershed biogeochemistry. They also highlight the need to better resolve solute-specific pathways that govern nutrient mobilization during extreme rainfall in mixed-use watersheds with legacy nutrient stores and engineered drainage networks. Full article

Accurate estimation of the State-of-Health (SOH) is essential for the reliable operation of lithium-ion batteries in electric vehicles and energy storage systems. However, conventional data-driven models often lack interpretability and show limited robustness under non-linear aging conditions. In this study, a physics-informed Transformer model is proposed for long-term SOH estimation by incorporating physically interpretable, degradation-sensitive indicators into a self-attention framework. Incremental Capacity Analysis (ICA)-derived features and thermal-gradient indicators are used as auxiliary inputs to provide physics-consistent inductive bias, enabling the model to focus on degradation-relevant regions of the charging trajectory. The proposed approach is validated using four lithium-ion battery cells exhibiting diverse aging behaviors, including severe non-linear capacity fade. Experimental results demonstrate that the proposed model consistently outperforms an LSTM baseline, achieving an RMSE below 1.5% even for the most degraded cell. Furthermore, attention map analysis reveals that the model autonomously emphasizes voltage regions associated with electrochemical phase transitions, providing clear physical interpretability. These results indicate that the proposed physics-informed Transformer offers a robust and explainable solution for battery health monitoring under practical aging conditions. Full article

Electric-powered unmanned guidance vehicles provide surface taxiing guidance for arriving and departing aircraft within the airport movement area, enabling sustained safety under complex operational conditions and improving overall operational efficiency, particularly under low-visibility scenarios. In this context, how to design scientifically rigorous operational trajectories for the three phases of unmanned guidance vehicle operations—dispatch, guidance, and recovery—remains an open and important research problem. This study proposes a three-stage trajectory-planning method for unmanned guidance vehicles, including initial trajectory planning, conflict prediction, and conflict resolution. First, the Guidance Unit—composed of the unmanned guidance vehicle and the guided aircraft—is defined, and a standard speed-profile design model is established for this unit. Then, considering airport operational-safety constraints, a conflict prediction algorithm for the guidance process is developed, which identifies potential conflicts in guidance trajectory planning based on time-window overlap analysis. Subsequently, under operational safety constraints, an optimization model aiming to minimize the maximum guidance time is formulated, and a trajectory planning algorithm for unmanned guidance vehicles based on the improved A* algorithm is designed to generate conflict-free operational trajectories. Finally, a simulation study is conducted using a major airport in Southwest China as a case study. The results show that (1) the speed-profile design and airport operational-rule constraints affect the operational trajectories of unmanned guidance vehicles; (2) the proposed algorithm enables coordinated planning of both speed control and path selection, thereby improving overall operational efficiency by 43.65% compared with conventional operations, while ensuring conflict-free airport surface taxiing, due to the adoption of an improved A* trajectory-planning algorithm for unmanned guidance vehicles; (3) under the electric-powered guidance-vehicle scheme proposed in this study, the method achieves a 34.52% reduction in total energy consumption during the guidance phase compared with traditional Follow-Me guidance, enabling the simultaneous optimization of operational efficiency and energy consumption. Full article

This study investigates the dynamics of electricity consumption in Brazil over the past two decades, with a focus on the persistent slowdown in consumption growth observed since 2013. Using segmented regression and interrupted time series (ITS) modeling, the research identifies statistically significant structural breakpoints in national and regional electricity demand. The main novelty of this study lies in the integrated use of segmented regression, ITS, and seasonal SARIMA models to systematically characterize asymmetric and phase-dependent demand behavior rather than to produce short-term forecasts. Seasonal Autoregressive Integrated Moving Average (SARIMA) models reveal that monthly seasonality plays a dominant role in electricity consumption dynamics, with seasonal specifications consistently outperforming non-seasonal alternatives. The results show that Brazil’s electricity demand evolution is best explained by three distinct phases: (i) a stagnation of industrial demand associated with deindustrialization prior to 2013; (ii) an abrupt contraction in commercial and residential demand during the 2014–2016 economic crisis; and (iii) a permanently lower growth trajectory driven by energy efficiency policies under the Brazilian National Electric Energy Conservation Program (PROCEL) and the expansion of solar distributed generation. The findings demonstrate that policy and structural interventions exert gradual, cumulative effects on electricity consumption rather than immediate shifts, providing critical insights for long-term energy planning and policy design in emerging economies. Full article

Markerless motion capture is increasingly used in pediatric neurorehabilitation, yet its ability to detect patient-specific gait abnormalities in small and heterogeneous cohorts remains unclear. This study evaluated a smartphone-based markerless workflow (OpenCap integrated with OpenSim) as a clinical assessment tool to support individualized planning in the context of robot-assisted gait rehabilitation (RAGT) by characterizing individualized gait deviations in four pediatric patients with neurological gait disorders, referenced against normative data from 30 healthy individuals. Sagittal hip, knee, and ankle kinematics were extracted, normalized, and converted into gait-cycle–dependent Z-scores. Group-level comparisons using one-sample Statistical Parametric Mapping (SPM) revealed no significant deviations between patient-group means and normative trajectories (p ≥ 0.05). In contrast, individualized deviation profiling—including Z-score heatmaps, phase-wise Z-score analysis, and per-patient kinematic overlays—identified distinct, clinically meaningful abnormalities in every patient, such as excessive swing-phase hip and knee flexion, mid-stance knee extension deficits, reduced terminal-stance hip extension, and markedly diminished late-stance ankle plantarflexion and push-off. Several deviations exceeded |2–5| SD from the normative dataset, indicating substantial impairments that were obscured by group averaging. These individualized patterns were consistent with each patient’s clinical presentation and could be interpreted in relation to modifiable gait features that are commonly considered during planning and phase-specific adjustment of robot-assisted gait rehabilitation, rather than serving as direct evidence of therapeutic efficacy. Overall, the findings demonstrate that smartphone-based markerless motion capture enables sensitive, individualized gait assessment even when group-level statistics remain nonsignificant, supporting its use as an exploratory, decision-support framework rather than as an outcome measure of RAGT. Full article

Grassland productivity faces heightened uncertainty under nonlinear climatic forcing. This study characterizes the spatial heterogeneity of nonlinear variations and nonstationary climate sensitivities across the Eurasian Steppe Region (EASR) to provide a scientific basis for its adaptive management. Using the aboveground net primary productivity (ANPP) and climate datasets (1982–2015), we employed piecewise linear regression, LOWESS, and sliding window partial correlation analysis to identify temporal turning points and dynamic climate–productivity relationships. We identified distinct turning points in 1994 and 2008, revealing a phased “Increasing–Decreasing–Increasing” trajectory. A key novelty is the mapping of eight phased trajectory patterns, illustrating significant spatial heterogeneity in productivity trends. Furthermore, we demonstrate temporally reversed climate sensitivities. Notably, the sensitivity of ANPP to temperature shifted from positive to negative as warming-induced water stress intensified. While precipitation remains the dominant driver (68% of the region), its influence is nonstationary and state-dependent. In the Qinghai–Tibet Plateau, the limiting factor transitioned from thermal to water availability. Overall, productivity in the EASR appears to undergo phased reorganization under shifting climatic baselines. Our findings suggest that future ecosystem models should incorporate time-varying sensitivity parameters to account for nonlinear dynamics and potential trend reversals in grassland ecosystems. Full article

Background: Intrauterine Growth Restriction (IUGR) is associated with a distinct neonatal metabolic profile, attributable to chronic intrauterine nutritional deprivation and suboptimal placental nutrient exchange. Upon delivery, IUGR neonates typically present with depleted nutrient stores, dysregulated endocrine activity, and a spectrum of micronutrient deficiencies, factors that collectively compromise metabolic homeostasis and significantly influence subsequent health trajectories. Methods: This narrative review systematically synthesizes the current body of evidence from clinical, biochemical, and translational investigations pertaining to the micronutrient status and pivotal endocrine markers in neonates affected by intrauterine growth restriction. The collected findings were integrated to elucidate metabolic adaptation mechanisms, immediate clinical ramifications, and the potential pathways linking neonatal biochemical patterns to long-term metabolic programming. Results: IUGR neonates consistently exhibit reduced cord-blood concentrations of essential micronutrients, including vitamin D, iron (Fe), zinc (Zn), magnesium (Mg), folate (vitamin B9), and cobalamin (vitamin B12), reflecting compromised placental nutrient transfer and limited fetal reserves. Concomitantly, endocrine alterations—most notably reduced insulin (INS) and C-peptide (C-pep) levels—indicate suppressed pancreatic β-cell activity and a prevailing hypoanabolic adaptive state. In parallel, disturbances in mineral metabolism, characterized by lower calcium (Ca) concentrations and increased alkaline phosphatase (ALP) activity, suggest impaired bone mineralization during the critical phase of early postnatal adaptation. Collectively, these biochemical patterns increase vulnerability to early clinical complications such as neonatal hypoglycemia and bone demineralization, disrupt early growth trajectories, and are associated with an elevated long-term risk of insulin resistance and adverse cardiometabolic programming. Conclusions: IUGR neonates consistently demonstrate a synergistic interplay of micronutrient deficiencies and adaptive endocrine responses, profoundly impacting immediate postnatal metabolic stability and predisposing them to long-term health challenges. Therefore, early biochemical screening, followed by tailored nutritional and hormonal interventions, may assist restore metabolic balance, promote growth and decrease long term risk for metabolic diseases. Full article

This paper presents a unified framework for high-precision dynamic target tracking that combines phase-map-based visual servoing with Model Predictive Control (MPC). Phase maps obtained from fringe projection provide dense, subpixel geometric feedback, enabling accurate end-effector velocity computation; however, their high dimensionality leads to substantial computational overhead that hinders real-time control. To overcome this limitation, we introduce a phase-map-specific dimensionality reduction strategy that constructs a low-dimensional control subspace through gradient-guided sparsification and PCA embedding while preserving the controllability of the original interaction model. An adaptive prediction horizon is further developed to regulate MPC complexity according to the rate of phase variation, enabling real-time deployment without compromising tracking accuracy. In addition, an Extended Kalman Filter (EKF) is integrated into the control loop to compensate for system delays and improve trajectory prediction in dynamic scenarios. Experimental results on multi-axis robotic manipulation demonstrate that the proposed approach achieves superior tracking accuracy and computational efficiency compared with conventional visual servoing methods, validating the feasibility of phase-map-driven predictive control for high-speed dynamic target tracking. Full article

Rising global electricity demand and the expansion of distribution networks require a critical assessment of component-level greenhouse gas contributions. Distribution transformers, although indispensable, have significant life-cycle carbon impacts due to the use of materials, manufacturing, and in-service losses. This study conducts a life-cycle assessment of a single-phase, 75 kVA oil-immersed distribution transformer manufactured in Newfoundland, one of the provinces with the cleanest, hydro-dominated grids in Canada, and evaluates it over a 40-year lifespan. Using a cradle-to-use boundary, the analysis quantifies embodied emissions from raw material extraction, manufacturing, and transportation, alongside operational emissions derived from empirically measured no-load and load losses. All the data are collected directly during the manufacturing process, ensuring high analytical fidelity. The energy efficiency of the transformer is analyzed in MATLAB version R2023b using measured no-load and load losses to generate efficiency, load characteristics under various operating conditions. Under varying load factor scenarios and based on Newfoundland’s 2025 grid intensity of 18 g CO₂e/kWh, the lifetime operational emissions are estimated to range from 0.19 t CO₂e under no-load operation to 4.4 t CO₂e under full-load conditions. A linear regression-based decarbonization model using Microsoft Excel projects grid intensity to reach net-zero around 2037, two years beyond the provincial target, indicating that post-2037 transformer losses will remain energetically relevant but carbon-neutral. Sensitivity analysis reveals that temporary overloading can substantially elevate lifetime emissions, emphasizing the value of smart-grid-enabled load management and optimal transformer sizing. Comparative assessment with fossil fuel-intensive provinces across Canada demonstrates the dominant influence of grid generation mix on life-cycle emissions. Additionally, refurbishment scenarios indicate up to 50% reduction in cradle-to-gate emissions through material reuse and oil reclamation. The findings establish a scalable framework for integrating grid decarbonization trajectories, life-cycle carbon modelling, and circular-economy strategies into sustainable distribution network planning and transformer asset management. Full article