Search Results (230)

Accreting neutron stars exhibit pulsed X-rays and complex temporal variability across multi-wavelengths and different timescales. This variability could be driven by various physical processes including instability or inhomogeneous motions within the accretion flow, thermonuclear bursts on the neutron star surface. In this review, we present a concise overview of the observational features for millihertz (mHz) quasi-periodic oscillations (QPOs) at a frequency range of ∼1–1000 mHz observed in light curves of X-ray pulsars for both low-mass X-ray binaries and high-mass X-ray binaries, based on recent X-ray missions, e.g., NICER, Insight-HXMT and NuSTAR. We further summarize current theoretical interpretations, discuss remaining challenges and propose potential directions for future studies to advance the understanding of the nature and physical origin of these QPOs. Full article

State-of-charge (SoC) balancing across multiple battery energy storage systems (BESS) is a central challenge in renewable-rich mini-grids. Heterogeneous battery capacities, differing states of health, stochastic renewable generation, and variable loads create a high-dimensional uncertain control problem. Conventional droop-based SoC balancing strategies are decentralized and computationally light but fundamentally reactive and limited, whereas model predictive control (MPC) is insightful but computationally intensive and prone to modeling errors. This paper proposes a Hierarchical Predictive–Adaptive Control (HPAC) framework for SoC balancing in mini-grids using deep reinforcement learning. The framework consists of two synergistic layers operating on different time scales. A long-horizon Predictive Engine, implemented as a federated Transformer network, provides multi-horizon probabilistic forecasts of net load, enabling multiple mini-grids to collaboratively train a high-capacity model without sharing raw data. A fast-timescale Adaptive Controller, implemented as a Soft Actor-Critic (SAC) agent, uses these forecasts to make real-time charge/discharge decisions for each BESS unit. The forecasts are used both to augment the agent’s state representation and to dynamically shape a multi-objective reward function that balances SoC, economic performance, degradation-aware operation, and voltage stability. The paper formulates SoC balancing as a Markov decision process, details the SAC-based control architecture, and presents a comprehensive evaluation using a MATLAB-(R2025a)-based digital-twin simulation environment. A rigorous benchmarking study compares HPAC against fourteen representative controllers spanning rule-based, MPC, and various DRL paradigms. Sensitivity analysis on reward weight selection and ablation studies isolating the contributions of forecasting and dynamic reward shaping are conducted. Stress-test scenarios, including high-volatility net-load conditions and communication impairments, demonstrate the robustness of the approach. Results show that HPAC achieves near-minimal operating cost with essentially zero SoC variance and the lowest voltage variance among all compared controllers, while maintaining moderate energy throughput that implicitly preserves battery lifetime. Finally, the paper discusses a pathway from simulation to hardware-in-the-loop testing and a cloud-edge deployment architecture for practical, real-time deployment in real-world mini-grids. Full article

We report measurements of ultrafast photoinduced charge separation and recombination processes in the conjugated donor–acceptor (D-A) polymer PSBTBT, both as pure film and blended in various polymer matrices. Using time-resolved terahertz spectroscopy (TRTS), time-dependent photoconductivity is measured for samples with PSBTBT weight fractions (W_PSBTBT:W_PE/PEG/PS) of 2.0% dispersed in high-density polyethylene (HDPE), polyethylene glycol (PEG), and polystyrene (PS). Charge carrier generation is an intrinsic feature of conductive polymers that occurs on sub-picosecond and longer timescales and is attributed to initially generated dissociation of bound polaron pairs into free carriers that reside on polymer chains, or to adjacent interchain charge transfer and migration. Both interchain and interfacial charge transfer contribute to the measured photoconductivity of the samples, which is found to increase as a function of increasing local polarity and an increasingly hydrogen-bonded environment. Pure-PSBTBT polymer film, PSBTBT dispersed in PS, and PSBTST dispersed in HDPE were all found to exhibit shorter photoconductive free-carrier long-time signal decay than PSBTBT in a hydrogen-bonded, semi-crystalline PEG environment. Full article

Natural convection in enclosures heated from both sides is a topic of interest in various space and safety applications in nuclear power reactors. The transient dynamics during natural convection in enclosures is critically dependent on micro-scaled boundary layers and also the timescales of micromixing. In the present work, a square enclosure operating at two high Rayleigh numbers (Ra = 3.27 × 10¹⁰ and Ra = 6.55 × 10¹⁰, with water as the working fluid) have been chosen for study. First, the velocity and timescales were found using Computational Fluid Dynamic (CFD) simulations for the square enclosure with Ra 3.27 × 10¹⁰ and compared with scaling laws that presently define them. An empirical correlation for heat transfer is then developed for the Ra range (1.3 × 10¹⁰ < Ra < 6.55 × 10¹⁰). Then, an existing DL framework (Proper Orthogonal Decomposition and Long Short-Term Memory (POD-LSTM)) network) is compared qualitatively and quantitatively with the CFD data. The transient data Ra = 6.55 × 10¹⁰ was chosen for this purpose. The scaling laws show a 30% deviation for the predictions of the transient length and time scales as compared to CFD and DL model predictions. Further, accurate results up to 99.6% have been obtained by the DL model when compared with the CFD model. The DL model is also found to require an order of magnitude less time than the one required for a CFD simulation. Full article

This study integrates surface renewal theory (SR) with computational fluid dynamics (CFD) models to explore the estimation of sensible heat flux in tea plantations. SR describes the turbulent transport processes at the air–water interface and has been widely applied in sensible heat flux estimation. However, its practical application faces challenges, such as determining the calibration coefficient (α) and the reliability of high-frequency temperature sensors (10 hz). This research addresses these issues by combining large eddy simulation (LES) models with CFD simulations to simulate high-frequency temperature variations in flat tea plantation fields. The results indicate that the LES model accurately simulates temperature fluctuations across different timescales (1 min, 30 min), with R² values ranging from 0.72 to 0.99, suggesting its suitability for precise sensible heat flux calculations. Furthermore, a new method for determining the calibration coefficient α using CFD simulations is proposed, which accounts for variations in atmospheric stability and terrain, thus improving the accuracy and applicability of SR in heterogeneous environments. The findings demonstrate that the CFD-based approach offers a cost-effective alternative to traditional eddy covariance systems, simplifying field measurements and enhancing the precision of sensible heat flux calculations under various atmospheric conditions (sunny, cloudy, overcast, nighttime), thereby broadening the potential applications of surface renewal theory in crop water requirement research. Full article

Effective integration of photovoltaic (PV) systems into electric power grids presents significant challenges due to the inherent variability in solar energy. Therefore, accurate PV power forecasting in various timescales is critical for the reliable operation of modern electric power systems. For short-term horizons, the primary source of solar power stochasticity is cloud movement and deformation, which are typically captured at high spatiotemporal resolutions using ground-based sky images. In this paper, we propose a novel multi-step sky image prediction framework for improved cloud tracking, which can be deployed for short-term PV power forecasting. The proposed method is based on deep learning, but instead of being purely data-driven, we propose a hybrid approach where we combine Auto-Encoder-like Convolutional Neural Networks (AE-like CNNs) with physics-informed sky image clustering to enhance robustness towards fast-varying sky conditions and effectively model non-linearities without adding to the computational overhead. The proposed method is compared against several state-of-the-art approaches using a real-world case study comprising minutely sky images. The experimental results show improvements of up to 17.97% on structural similarity and 62.14% on mean squared error, compared to persistence. These findings demonstrate that by combining effective physics-informed preprocessing with deep learning, multi-step ahead sky image forecasting can be reliably achieved even at low temporal resolutions. Full article

The interaction of post-explosion supernova ejecta with the surrounding circumstellar medium creates emissions across the electromagnetic spectrum. Since the circumstellar medium is created by the mass lost from the progenitor star, it carries tell-tale signatures of the progenitor. Consequently, observations and modeling of radiation produced by the interaction in various types of supernovae have provided valuable insights into their progenitors. Detailed studies have shown that the interaction in supernovae begins and sustains over various timescales and lengthscales, with differing mass-loss rates in distinct sub-classes. This reveals diverse progenitor histories for these stellar explosions. This review paper summarizes various supernova subtypes, linking them to stellar death pathways, and presents an updated supernova classification diagram. We then present a multi-wavelength study of circumstellar interaction in different supernova classes. We also present unpublished X-ray as well as radio observations of a type IIn supernova, SN 2010jl, which allow us to extend its circumstellar interaction studies to about 7 years post-explosion. The new data indicates that the extreme mass-loss rate (∼0.1 M_⊙ yr⁻¹) in SN 2010jl, reported by Chandra et al. commenced within the last 300 years before the explosion. We summarize the current status of the field and argue that via detailed studies of the circumstellar interaction, a.k.a. “Time Machine” technique, one of the big mysteries of stellar evolution, i.e., mapping supernovae progenitors to their explosive outcomes can be solved. Full article

Digital Twin (DT) technology has emerged as a valuable tool for researchers and engineers, enabling them to optimize performance and enhance system efficiency. This paper presents a comprehensive Systematic Literature Review (SLR) following the PRISMA framework to explore current applications of DT technology in the power generation sector while highlighting key advancements. A new framework is developed to categorize DTs in terms of time-scale horizons and applications, focusing on power plant types (emissive vs. non-emissive), operational behaviors (including condition monitoring, predictive maintenance, fault detection, power generation prediction, and optimization), and specific components (e.g., power transformers). The time-scale is subdivided into a six-level structure to precisely indicate the speed and time range at which it is used. More importantly, each category in the application is further subcategorized into a three-level framework: component-level (i.e., fundamental physical properties and operational characteristics), system-level (i.e., interaction of subsystems and optimization), and service-level (i.e., value-adding service outputs). This classification can be utilized by various parties, such as stakeholders, engineers, scientists, and policymakers, to gain both a general and detailed understanding of potential research and operational gaps. Addressing these gaps could improve asset longevity and reduce energy consumption and emissions. Full article

Meteorological droughts have been occurring with greater frequency and intensity, impacting water security in various regions. Between 2013 and 2015, the Paraopeba River Basin in southeast Brazil experienced its most severe drought in the last 70 years, resulting in low levels in the Paraopeba system reservoirs, which supplies 53% of the Metropolitan Region of Belo Horizonte, the third largest metropolitan area in Brazil. This study evaluated the climate models’ performance from the NEX-GDDP-CMIP6 through drought indices projections, specifically the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI). The results showed that seven climate models can represent the current climate in the basin. For the drought’s projection, the indices were used in two time scales (six and twelve months) for both the current climate and two future scenarios (SSP245 and SSP585). Our results highlight the intensification of droughts throughout the twenty-first century, with greater intensification in the SSP585 scenario. The SPEI indicated trends towards drier conditions, particularly under the SSP585 scenario and on the twelve-month timescale. These findings demonstrate the relevance of climate change and drought indices on the projections, supporting public policies for mitigation and adaptation, especially in strategic regions for water supply and hydro-electric generation. Full article

Developing structural materials capable of maintaining integrity under extreme irradiation conditions is a cornerstone challenge for advancing sustainable nuclear energy technologies. The complexity and severity of radiation-induced microstructural changes—spanning multiple length and timescales—pose significant hurdles for purely experimental approaches. This review critically evaluates recent advancements in atomistic modeling, emphasizing its transformative potential to decipher fundamental mechanisms driving microstructural evolution in irradiated alloys. Atomistic simulations, such as molecular dynamics (MD), have successfully unveiled initial defect formation processes at picosecond scales. However, the inherent temporal limitations of conventional MD necessitate advanced methodologies capable of exploring slower, thermally activated defect kinetics. We specifically traced the development of powerful potential energy landscape (PEL) exploration algorithms, which enable the simulation of high-barrier, rare events of defect evolution processes that govern long-term material degradation. The review systematically examines point defect behaviors in various crystal structures—BCC, FCC, and HCP metals—and elucidates their characteristic defect dynamics, respectively. Additionally, it highlights the pronounced effects of chemical complexity in concentrated solid-solution alloys and high-entropy alloys, notably their sluggish diffusion and enhanced defect recombination, underpinning their superior radiation tolerance. Further, the interaction of extended defects with mechanical stresses and their mechanistic implications for material properties are discussed, highlighting the critical interplay between thermal activation and strain rate in defect evolution. Special attention is dedicated to the diverse mechanisms of dislocation–obstacle interactions, as well as the behaviors of metastable grain boundaries under far-from-equilibrium environments. The integration of data-driven methods and machine learning with atomistic modeling is also explored, showcasing their roles in developing quantum-accurate potentials, automating defect analysis, and enabling efficient surrogate models for predictive design. This comprehensive review also outlines future research directions and fundamental questions, paving the way toward autonomous materials’ discovery in extreme environments. Full article

With the rising automation and complexity level of industrial systems, the efficiency and accuracy of fault diagnosis have become a critical challenge. The convolutional neural network (CNN) has shown some success in the fault diagnosis field. However, typical convolutional kernels are commonly fixed-sized, which makes it difficult to capture multi-scale features simultaneously. Additionally, the use of numerous fixed-size convolutional filters often results in redundant parameters. During the feature extraction process, the CNN often struggles to take inter-channel dependencies and spatial location information into consideration. There are also limitations in extracting various time-scale features. To address these issues, a fault diagnosis method on the basis of a dual-path mixed convolutional attention-BiGRU network (DMCA-BiGRUN) is proposed for industrial processes. Firstly, a dual-path mixed CNN (DMCNN) is designed to capture features at multiple scales while effectively reducing the parameter count. Secondly, a coordinate attention mechanism (CAM) is designed to help the network to concentrate on main features more effectively during feature extraction by combining the channel relationship and position information. Finally, a bidirectional gated recurrent unit (BiGRU) is introduced to process sequences in both directions, which can effectively learn the long-range temporal dependencies of sequence data. To verify the fault diagnosis performance of the proposed method, simulation experiments are implemented on the Tennessee Eastman (TE) and Continuous Stirred Tank Reactor (CSTR) datasets. Some deep learning methods are compared in the experiments, and the results confirm the feasibility and superiority of DMCA-BiGRUN. Full article

This article provides a comprehensive review of gas flow behavior in low-permeability geological media, focusing on its implications for the long-term performance of engineered barriers in underground radioactive waste repositories. Key mechanisms include two-phase flow and gas-driven fracturing, both critical for assessing repository safety. Understanding the generation and migration of gas is crucial for the quantitative assessment of repository performance over extended timescales. The article synthesizes the current research on various types of claystone considered as potential host rocks for repositories, providing a comprehensive analysis of gas transport mechanisms and constitutive models. In addressing the challenges related to multi-field coupling, the article provides practical insights and outlines potential solutions and areas for further research, underscoring the importance of interdisciplinary collaboration to tackle these challenges and push the field forward. In addition, the article evaluates key research projects, such as GMT, FORGE, and DECOVALEX, shedding light on their methodologies, findings, and significant contributions to understanding gas migration in low-permeability geological media. In this context, mathematical modeling becomes indispensable for predicting long-term repository performance under hypothetical future conditions, enhancing prediction accuracy and supporting long-term safety assessments. Finally, the growing interest in gas-driven fracturing is explored, critically assessing the strengths and limitations of current numerical simulation tools, such as TOUGH, the phase-field method, and CODE_BRIGHT. Noteworthy advancements by the CODE_BRIGHT team in gas injection simulation are highlighted, although knowledge gaps remain. The article concludes with a call for innovative approaches to simulate gas fracturing processes more effectively, advocating for advanced modeling techniques and rigorous experimental validation to address existing challenges. Full article

In data-scarce regions and ungauged basins, remotely sensed evapotranspiration (ET) products are increasingly employed to support hydrological model calibration. In this study, a high-resolution hydrological model was developed for the Upper Anthemountas Basin using ArcSWAT, with a focus on comparing simulated ET outputs to three freely available remote sensing-based ET products: the MODIS MOD16 Collection 5, the updated MODIS MOD16A2GF Collection 6.1, and the SSEBop Version 5 dataset. ET estimates derived from the calibrated SWAT model were compared to all remote sensing products at the basin scale, across various temporal scales over the 2002–2014 simulation period. Results indicate that the MOD16 Collection 5 product achieved the closest correspondence with SWAT-simulated ET across all temporal scales. The MOD16A2GF Collection 6.1 product exhibited moderate overall agreement, with improved performance during early summer. The SSEBop Version 5 dataset generally displayed weaker correlation, but demonstrated enhanced alignment during the driest years of the record. Strong correspondence is observed when averaging the ET values from all satellite products. These findings underscore the importance of exercising caution when utilizing remotely sensed ET products as the sole basis for hydrological model calibration, particularly given the variability in performance among different datasets. Full article

This study investigates the spatio-temporal distribution of meteorological drought in the upper Cheliff basin, Algeria, downstream of the Boughzoul dam, between September 1982 and August 2021. This research use two drought indices—the Standardised Precipitation Index (SPI) and the Standardised Precipitation and Evapotranspiration Index (SPEI)—to evaluate drought trends, frequency, duration, severity, and number of events across various time scales (1 year, 1 month, 3 months, 6 months, 9 months, and 12 months). The results identify five major drought periods (1983/84, 1993/94, 1987/88, 1999/2000–2001/2002, and 2020/21). Both the SPI and the SPEI capture the monthly variability of drought on various time scales, with different intensities. The SPEI identifies a higher number of drought events than the SPI, particularly on shorter time scales (1 and 3 months). However, at longer timescales (6, 9, and 12 months), the number of drought events detected by both indices converges. The correlation between SPI and SPEI (R ranging from 0.73 to 0.93) across the same time scales is notably high, though the lowest correlation was found in the western part of the catchment area. This suggests that for accurate meteorological drought identification in this region, particularly in its intensively irrigated agricultural areas, SPI and SPEI should be considered. Full article

Remotely sensed data, including rainfall estimates and digital elevation models (DEMs), are increasingly available at various temporal and spatial scales, offering new opportunities for rainfall regionalization in regions with limited ground-based observations. We evaluate the efficacy of NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG) rainfall estimates and SRTM-derived elevation data as alternative spatial covariates for regionalizing average and extreme rainfall patterns across Greece. Using the Bilinear Surface Smoothing (BSS) framework, we assess and compare the regionalization of average daily rainfall and average annual maximum rainfall across multiple timescales (0.5 h to 48 h) by leveraging both IMERG-derived estimates and the elevation data as covariates. Additionally, the BSS framework is herein extended to provide Bayesian credible intervals for the final estimates, using the posterior variance estimate and the equivalent degrees of freedom determined through the Generalized Cross Validation error minimization procedure. Elevation-based models outperformed IMERG, particularly for indices of extreme rainfall, capturing the differential effects of orography. The exploration of the orographic effect based on the BSS framework revealed that the average annual rainfall maxima at small timescales exhibit a negative relation to elevation, which becomes positive and more significant with increasing timescale. However, IMERG proved valuable for regionalizing average daily rainfall, demonstrating its utility as a complementary tool. The results also underscore the role of temporal scale in regionalization efficiency of extreme rainfall, with higher accuracy observed at longer timescales (24 h and 48 h) and greater uncertainty at finer scales. Full article