Search Results (4,568)

The widespread adoption of electric vehicles (EVs) has posed significant challenges to the security of distribution grid loads. To address issues such as increased grid load fluctuations, rising user charging costs, and rapid load surges around midnight caused by uncoordinated nighttime charging of household electric vehicles in communities, this paper first models electric vehicle charging behavior as a Markov Decision Process (MDP). By improving the state-space sampling mechanism, a continuous space mapping and a priority mechanism are designed to transform the charging scheduling problem into a continuous decision-making framework while optimizing the dynamic adjustment between state and action spaces. On this basis, to achieve synergistic load forecasting and charging scheduling decisions, a forecast-augmented deep reinforcement learning method integrating Gated Recurrent Unit and Twin Delayed Deep Deterministic Policy Gradient (GRU-TD3) is proposed. This method constructs a multi-objective reward function that comprehensively considers time-of-use electricity pricing, load stability, and user demands. The method also applies a single-objective pre-training phase and a model-specific importance-sampling strategy to improve learning efficiency and policy stability. Its effectiveness is verified through extensive comparative and ablation validation. The results show that our method outperforms several benchmarks. Specifically, compared to the Deep Deterministic Policy Gradient (DDPG) and Particle Swarm Optimization (PSO) algorithms, it reduces user costs by 11.7% and the load standard deviation by 12.9%. In contrast to uncoordinated charging strategies, it achieves a 42.5% reduction in user costs and a 20.3% decrease in load standard deviation. Moreover, relative to single-objective cost optimization approaches, the proposed algorithm effectively suppresses short-term load growth rates and mitigates the “midnight peak” phenomenon. Full article

Validation of ICON model configurations optimized over a limited domain is essential before accepting new semi-empirical parameters that influence the behavior of subgrid-scale schemes. Because such parameters can modify the dynamics of a numerical weather prediction (NWP) model in highly nonlinear ways, we analyze one season of forecasts (December 2022, January and February 2023) generated with the NWP ICON-LAM through the lens of machine learning–based diagnostics as a complement to traditional evaluation metrics. The goal is to extract physically interpretable information on the model behavior induced by the optimized parameters. This work represents the first part of a wider study exploring machine learning tools for model validation, focusing on two specific approaches: Empirical Orthogonal Functions (EOFs), which are widely used in meteorology and climate science, and autoencoders, which are increasingly adopted for their nonlinear feature extraction capability. In this first part, EOF analysis is used as the primary tool to decompose weather fields from observed reanalysis and forecast datasets. Hourly 2-m temperature forecasts for winter 2022–2023 from multiple regional ICON configurations are compared against downscaled ERA5 data and in situ observations from ground station. EOF analyses revealed that the optimized configurations demonstrate a high skill in predicting surface temperature. From the signal error decomposition, the fourth EOF mode is effective particularly during night-time hours, and contributes to enhancing the performance of ICON. Analyses based on autoencoders will be presented in a companion paper (Part II). Full article

On 7 January 2025, an Mw7.1 earthquake struck Dingri County, Tibet, causing severe damage in a high-altitude, sparsely instrumented region where traditional damage assessment methods are limited. To address this, we developed an integrated "source simulation–nighttime light validation" framework. First, a kinematic source model (constrained by InSAR and teleseismic data) and the Unified Seismic Tomography models for continental China lithosphere 2.0 (USTClitho2.0) velocity model were used with the curved-grid finite difference method to simulate high-resolution ground motion and intensity fields. Second, NASA Black Marble (VNP46A2) nighttime light data, processed with the Block-Matching and 3D filtering (BM3D) algorithm, were analyzed to compute pixel-level radiance changes and township-level total nighttime light loss rates (TNLR). The results reveal a high spatial consistency between simulated high-intensity zones and areas of significant light loss. For instance, Mangpu Township, within a simulated high-intensity zone, exhibited a TNLR of 44.7%. This demonstrates that nighttime light remote sensing can effectively validate physical simulations in areas lacking dense seismic networks. Our framework provides a novel, complementary methodology for rapid and reliable post-earthquake damage assessment in high-mountain, data-sparse regions. Full article

Background: In-hospital cardiac arrest (IHCA) constitutes a high-impact clinical event, associated with substantial mortality, frequent neurological and functional impairment. There is a pressing need for primary IHCA studies that evaluate risk predictors, given the inherent challenges of IHCA data collection, previously unharmonized reporting frameworks, and the predominant focus of prior investigations on other domains. Among potential contributors, the “off-hours effect” has consistently been linked to poorer IHCA outcomes. Accordingly, we sought to examine whether in-hospital mortality after IHCA varies according to the time and day of occurrence within a tertiary academic center in Northern Greece. Methods: We conducted a single-center observational cohort study using a prospectively maintained in-hospital resuscitation registry at AHEPA University General Hospital, Thessaloniki. All adults with an index IHCA between 2017 and 2019 were included, and definitions followed Utstein-style recommendations. Results: Multivariable logistic regression adjusted for organizational, patient, and process-of-care factors demonstrated that afternoon/night arrests, weekend arrests, heart failure comorbidity, and need for mechanical ventilation were independent predictors of higher in-hospital mortality. Conversely, arrhythmia as the cause of IHCA and arrests occurring in the intensive care unit or operating room were associated with improved survival. Subgroup analyses confirmed consistent off-hours differences, with weekend events showing reduced 30-day and 6-month survival and worse functional status at discharge. Afternoon/night arrests were more frequent, characterized by longer response intervals and lower survival at both time points. Conclusions: Organizational factors during nights and weekends, rather than patient case mix, drive poorer IHCA outcomes, underscoring the need for targeted system-level improvements. Full article

The thermosphere serves as a pivotal region for Sun–Earth interactions, and thermospheric winds are of great scientific importance for deepening insights into atmospheric dynamics, climate formation mechanisms, and space environment evolution. This study designed and developed a Ground-based Doppler Asymmetric Spatial Heterodyne Interferometer (GDASHI). Targeting the nightglow of the oxygen atomic red line (OI 630.0 nm), this instrument enables high-precision observation of thermospheric winds. The GDASHI was deployed at Gemini Astronomical Manor (26.7°N, 100.0°E), and has obtained one year of nighttime meridional and zonal wind data. To verify the reliability of GDASHI-derived winds, a collocated observation comparison was performed against the Dual-Channel Optical Interferometer stationed at Binchuan Station (25.6°N, 100.6°E), Yunnan. The winds of the two instruments are basically consistent in both their diurnal variation trends and amplitudes. Further Deming regression and correlation analysis were conducted for the two datasets, with the meridional and zonal winds yielding fitting slopes of 0.808 and 0.875 and correlation coefficients of 0.754 and 0.771, respectively. An uncertainty analysis of the inter-instrument comparison was also carried out, incorporating instrumental measurement uncertainties, instrumental parameter errors, and small-scale perturbations induced by observational site differences; the synthesized total uncertainties of zonal and meridional winds are determined to be 20.24 m/s and 20.77 m/s, respectively. This study not only verifies the feasibility and reliability of GDASHI for ground-based thermospheric wind detection but also provides critical observational support for analyzing the spatiotemporal variation characteristics of mid-low latitude thermospheric wind fields and exploring their underlying physical mechanisms. Full article

Background: Sleep is a key determinant of recovery and performance in elite athletes, yet its optimization extends beyond sleep duration alone and encompasses multiple subjective and physiological dimensions. Environmental factors, including the sleep surface, represent modifiable components of sleep that may influence perceived sleep quality. This study aimed to examine whether an individually adjustable modular sleep system improves subjective sleep quality in elite athletes and whether alterations in objective sleep metrics, circadian timing, or nocturnal autonomic physiology accompany such changes. Methods: Forty-three elite athletes participated in this pre–post-intervention study (without a control group). Subjective sleep quality was assessed using the Pittsburgh Sleep Quality Index (PSQI), while objective sleep and physiological parameters were recorded using a wearable device (Oura Ring, 3rd generation). Outcomes were averaged across three consecutive nights at baseline (T0) and post-intervention (T1). Baseline values were derived from the final three nights of a standardized pre-intervention monitoring period (minimum 7 nights), and post-intervention values from the final three nights following a standardized intervention exposure period (minimum 14 nights). Statistical analyses included paired frequentist tests and complementary Bayesian paired-sample analyses. Results: Subjective sleep quality improved significantly following the intervention, with a mean reduction in PSQI score of 0.67 points (p < 0.001). In contrast, no meaningful changes were observed in total sleep time (p = 0.28), REM duration (p = 0.26), circadian timing (p = 0.47), or nocturnal minimum heart rate (p = 0.42), as supported by the absence of physiological changes in these parameters. Conclusions: It seems that an individually adjustable sleep system can be able to improve perceived sleep quality in elite athletes without disrupting sleep architecture, circadian regulation, or nocturnal autonomic function. In athletes whose sleep duration and physiological sleep metrics are already near optimal, such micro-environmental interventions may offer a feasible, low-risk means of enhancing recovery by targeting subjective sleep quality. This dimension dissociates from objective sleep measures. Optimizing the sleep surface may therefore represent a practical adjunct to existing recovery strategies in high-performance sport. Full article

Accurate traffic input data are essential for reliable road noise mapping within the CNOSSOS-EU framework. However, European countries often rely on heterogeneous data sources and measurement practices, which may introduce uncertainties in noise estimates and reduce the comparability of results across regions. This study evaluates the performance of three traffic data collection methods, specifically microwave radar traffic counters, artificial intelligence-based cameras, and Google API-derived flows, in three representative test sites located in Italy and Romania. Traffic flows and vehicle category distributions obtained from each method were used as inputs for noise simulations, and predicted levels were compared with in situ noise measurements. A second analytical approach was developed to estimate short-term noise levels at a 10’ resolution by combining CNOSSOS-EU power models with propagation matrices computed using commercial sound propagation software. The results show that both radar counters and cameras provide reliable inputs for day/evening/night indicators, although counters may miss flows under complex traffic conditions, and cameras may overestimate counts at high volumes. Google API-derived flows perform well only when traffic exceeds approximately 150 vehicles per hour and when the traffic model is carefully calibrated. Manual counting confirmed that all three input data collection methods exhibit non-negligible traffic loss, which contributes to a systematic underestimation of simulated noise levels when using average flow-based modeling. Differences between methods become more pronounced when analyzing short time intervals rather than aggregated indicators. Overall, this study highlights the strengths and limitations of each data source and provides guidance on their appropriate use for road noise assessment and strategic mapping. Full article

Rural self-constructed homes in China’s cold-temperate regions often exhibit poor energy performance due to limited budgets and substandard construction, leading to a high reliance on active systems and low climate resilience. This study assesses four passive cooling strategies, nighttime natural ventilation (NNV), envelope retrofitting (ER), window shading (WS), and window-to-wall ratio adjustment (WWR), under 2040–2080 representative future climate conditions using energy simulation, multi-objective optimization, sensitivity analysis, and life-cycle cost assessment. Combined measures (COM) cut annual cooling demand by ~43% and representative peak cooling loads by ~50%. NNV alone delivers ~37% cooling reduction with rapid payback, while ER primarily mitigates heating demand. WS provides moderate cooling but slightly increases winter energy use, and WWR has minimal impact. Economic and sensitivity analyses indicate that COM and NNV are robust and cost-effective, making them the most suitable strategies for low-energy, climate-resilient retrofits in cold-climate rural residences. Since statistically extreme heat events are not explicitly modeled, the findings reflect relative performance under representative climatic conditions rather than guaranteed resilience under extreme heatwaves. Full article

Clonal propagation often must incorporate heaters to warm stock plants and stabilize growth. This study investigates the impact that different temperature regimes for stock plants have on the rooting capacity of mini-cuttings derived therefrom. Experiments were conducted in growth chambers using two clones of Eucalyptus dunnii Maiden, with clone A’s rooting being moderately better that that of clone B in commercial production. Root primordia differentiation and elongation were faster in clone A than clone B. Stock plants were maintained for one month under two temperature conditions: Δ0 (26/26 °C day/night) and Δ10 (26/16 °C). The main results indicate that rooting significantly decreased with the reduction in nocturnal temperature. Clone A exhibited a 38% reduction in rooting, whereas clone B showed a more pronounced decrease of 65%. In cold nights, soluble carbohydrates at the cutting bases dropped by approximately 25% considering both clones, and overall foliar nutrients also decreased. Cutting base transcript profiles revealed that cold nights decreased the expression of efflux auxin transporter PIN1, increased expression of auxin catabolism-related enzyme DAO, and that expression of auxin nuclear receptor TIR1 remained stable. Fine management of clonal gardens by adjusting thermal conditions can optimize the physiological status of donor plants and enhance the rooting potential and establishment of the derived cuttings. Full article

Physical activity (PA) and quality sleep are essential for cognitive health, providing synergistic protection against age-related cognitive decline. However, the shared molecular pathways that explain their combined and interactive benefits remain poorly understood. This review suggests that lactate, long dismissed as a metabolic waste product, is a unifying mechanism. We introduce the “Lactate Nexus”, a conceptual framework that proposes lactate functions as a key signalling molecule, mechanistically linking the pro-cognitive effects of both daytime exercise and nighttime sleep. We begin by outlining lactate’s evolving role—from an energy substrate shuttled from astrocytes to neurons (the Astrocyte–Neuron Lactate Shuttle) to a pleiotropic signal. As a signal, lactate influences neuroplasticity via NMDA receptors, neuroinflammation via the HCAR1 receptor, and gene expression through the epigenetic modification of histone lactylation. We then compile evidence demonstrating how PA provides a substantial lactate signal that activates these pathways and primes the brain’s metabolic infrastructure. Crucially, we integrate this with proof that lactate levels naturally increase during slow-wave sleep to support memory consolidation and glymphatic clearance. The “Lactate Nexus” framework offers a comprehensive molecular explanation for the synergy between PA and sleep, positioning lactate as a key signalling mediator and a promising biomarker and therapeutic target for fostering lifelong cognitive resilience. Full article

Rapid urbanization has significantly affected urban ecological environments, necessitating accurate and scientific quality assessments. In this study, we develop an enhanced remote sensing ecological index (WRSEI) for water network cities using Linyi City, China, as a case study. Key innovations include (1) introducing a water–vegetation index to better represent aquatic ecosystems; (2) incorporating nighttime light data to quantify the intensity of human activity; and (3) employing hierarchical PCA to rationally weight ecological endowment and stress indicators. The model’s effectiveness was rigorously validated using independent land use data. The results show that (1) the WRSEI accurately captures Linyi’s “water–city symbiosis” pattern, increasing the assessed ecological quality of water bodies by 15.78% compared to the conventional RSEI; (2) hierarchical PCA provides more ecologically reasonable indicator weights; and (3) from 2000 to 2020, ecological quality exhibited a pattern of “central degradation and peripheral improvement”, driven by urban expansion. This study establishes a validated technical framework for ecological assessment in water-rich cities, offering a scientific basis for sustainable urban management. Full article

Sensor degradation poses a significant challenge in autonomous driving. During heavy rainfall, interference from raindrops can adversely affect the quality of LiDAR point clouds, resulting in, for instance, inaccurate point measurements. This, in turn, can potentially lead to safety concerns if autonomous driving systems are not weather-aware, i.e., if they are unable to discern such changes. In this study, we release a new, large-scale, multi-modal emulated rain dataset, REHEARSE-3D, to promote research advancements in 3D point cloud de-raining. Distinct from the most relevant competitors, our dataset is unique in several respects. First, it is the largest point-wise annotated dataset (9.2 billion annotated points), and second, it is the only one with high-resolution LiDAR data (LiDAR-256) enriched with 4D RADAR point clouds logged in both daytime and nighttime conditions in a controlled weather environment. Furthermore, REHEARSE-3D involves rain-characteristic information, which is of significant value not only for sensor noise modeling but also for analyzing the impact of weather at the point level. Leveraging REHEARSE-3D, we benchmark raindrop detection and removal in fused LiDAR and 4D RADAR point clouds. Our comprehensive study further evaluates the performance of various statistical and deep learning models, where SalsaNext and 3D-OutDet achieve above $94\%$ IoU for raindrop detection. Full article

Background/Objectives: Insulin resistance and ambulatory blood pressure monitoring (ABPM) abnormalities represent distinct but interrelated pathways contributing to cardiovascular risk. The triglyceride–glucose (TyG) index reflects metabolic burden, whereas arterial load—captured through arterial stiffness, blood pressure variability, and morning surge—reflects hemodynamic instability. Whether the coexistence of these domains identifies a particularly high-risk ambulatory phenotype remains unclear. To evaluate the independent and combined effects of metabolic burden (TyG) and arterial load on circadian blood pressure pattern and short-term systolic blood pressure variability. Methods: This retrospective cross-sectional study included 294 adults who underwent 24 h ABPM. Arterial load was defined using three ABPM-derived indices (high AASI, high SBP-ARV, high morning surge). High metabolic burden was defined as TyG in the upper quartile. The “double-high” phenotype was classified as high TyG plus high arterial load. Primary and secondary outcomes were non-dipping pattern and high SBP variability. Multivariable logistic regression and Firth penalized models were used to assess independent associations. Predictive performance was evaluated using ROC analysis. Results: The double-high phenotype (n = 15) demonstrated significantly higher nighttime SBP, reduced nocturnal dipping, and markedly elevated BP variability. It was the strongest independent predictor of non-dipping (adjusted OR = 42.0; Firth OR = 11.73; both p < 0.001) and high SBP variability (adjusted OR = 41.7; Firth OR = 26.29; both p < 0.001). Arterial load substantially improved model discrimination (AUC = 0.819 for non-dipping; 0.979 for SBP variability), whereas adding TyG to arterial load produced minimal incremental benefit. Conclusions: The coexistence of elevated TyG and increased arterial load defines a distinct hemodynamic endotype characterized by severe circadian blood pressure disruption and exaggerated short-term variability. While arterial load emerged as the principal determinant of adverse ambulatory blood pressure phenotypes, TyG alone demonstrated limited discriminative capacity. These findings suggest that TyG primarily acts as a metabolic modifier, amplifying adverse ambulatory blood pressure phenotypes predominantly in the presence of underlying arterial instability rather than serving as an independent discriminator. Integrating metabolic and hemodynamic domains may therefore improve risk stratification and help identify a small but clinically meaningful subgroup of patients with extreme ambulatory blood pressure dysregulation. Full article

An early indicator of future cardiovascular risk is lower levels of nighttime blood pressure (BP) dipping from day to night. Prior work has been limited to identifying health behaviors that can promote greater dipping. This pilot study proposes that one possible set of behaviors may be engagement in self-selected leisure activities (SSLAs, or freely chosen non-work activities that are performed with the purpose of relaxation and/or mental escape), which have been linked with reduced daily stress and general daily BP control. Healthy young adult college students [N = 32; 78.1% (n = 25) female, 71.9% (n = 23) white, with an average body mass index (BMI) of 26.31 (SD = 2.46)] visited our laboratory twice within approximately one week. At each visit, the participants were fitted with an ambulatory monitor to collect BP over 24 h. On each day, participants were randomly assigned to either engage in an agreed-upon SSLA or go about their day as usual, except to refrain from engaging in assigned SSLAs; compliance was verified by daily diaries. When accounting for BMI and race/ethnicity, the results showed a higher percentage of BP dipping on the SSLA versus control day for diastolic BP (d = 0.54). SSLAs may be associated with reduced future cardiovascular disease through a nighttime BP dipping effect. Full article

Accurate clutch-frequency estimates are essential for assessing population abundance and reproductive output in sea turtles. Traditional nighttime beach-monitoring approaches, however, often underestimate clutch frequency by missing nesting events occurring outside patrolled beaches. Here, we integrated long-term beach monitoring (2009–2016) with satellite telemetry to estimate the clutch frequency of loggerhead turtles (Caretta caretta) nesting at Praia do Forte, Bahia, Brazil. A total of 593 females were identified along a 5 km monitored beach segment, and transient individuals represented 42.4% ± 3.9 SD of seasonal records. A 2-year remigration interval was the most frequent. The observed clutch frequency (OCF) averaged 3.1 ± 1.2 SD clutches per female, while the estimated clutch frequency based on beach monitoring alone (ECF_BM) averaged 3.9 ± 1.5 SD. For the subset of satellite-tracked females (n = 12), integration of residency length derived from telemetry increased the estimate to 5.6 ± 0.7 SD clutches per female (ECF_BMST). Statistical comparisons confirmed significant differences among estimation methods. These findings align with previous studies, demonstrating that clutch frequency is substantially underestimated when relying solely on beach monitoring. Incorporating satellite telemetry, therefore, provides a more accurate assessment of reproductive output and has important implications for population modelling and the conservation of loggerhead turtles in Brazil. Full article