Search Results (247)

The aim of this study was to estimate trends and multiscale variability in sea level along the southern coast of the Baltic Sea based on tide gauge and altimetry data, using Harmonic Analysis (HA) and Continuous Wavelet Transform (CWT). Particular consideration was given to the influence of time series length, data type, seafloor depth and distance from the coastline on the stability and consistency of the estimated trends and amplitudes. The results indicated that, for coastal stations, trends derived from tide gauge data averaged 2.2 mm/yr for the 1993–2024 period and 2.0 mm/yr for the 1951–2025 series, with lower estimation errors for series with an extended time range of data. Satellite altimetry data indicated a higher rate of sea level rise, averaging 4.3 mm/yr, and higher spatial consistency, particularly at virtual stations away from the coast. As the distance from the coastline increased, a more stable trend and a decrease in the influence of local hydrodynamic processes were observed. A comparison of methods demonstrated that Harmonic Analysis significantly improves the consistency of trends derived from altimetry and tide gauge data compared to classical linear regression—the correlation coefficient increased from 0.72–0.80 to 0.92–0.95. CWT confirmed the reliability of these results, while also allowing the identification of temporal modulation in cycle amplitudes and periods of increased signal nonstationarity. The results confirm that a correct interpretation of sea level changes requires that we take both long-term trends and natural variability across various timescales into account. The proposed approach provides a universal tool for analyzing nonstationary environmental signals. It can support risk assessment in coastal zones and the development of adaptation strategies in the context of climate change. Full article

Dust devils (DDs) are dust-filled vortices that loft dust from the surface to the atmosphere in dry daytime convective conditions. Although they are a common sight in many arid regions on Earth, as well as Mars and possibly Titan, little is known about surface properties and atmospheric processes that influence the vortices’ dust-lofting capacity, thus limiting what is known about their contribution to the atmospheric dust budget. We present measurements of DD width, relative dustiness, and shape from a 1.67 h long data sample obtained during a field campaign conducted in Smith Creek Valley, Nevada USA. DDs were localized through stereogrammetric triangulation from time-lapse stereo images. A total of 245 individual DDs were found, consisting of 220 sequences and 25 solo instances. DDs translated downwind ~1.9 times faster than that of the 10 m mean wind speed, a ratio higher than that measured in previous studies. DD widths did not vary significantly over the study period, contrasting with various hypotheses regarding DD response to a growing convective boundary layer (CBL). Rather, the measured DD characteristics were most influenced by changes in surface properties, which varied considerably over the study area. Notably, DDs were most likely to broaden, increase in dustiness, and develop a coherent cylindrical shape as they traversed from a vegetated hummocky area onto a bare compact playa. Observed episodes of growth, dustiness, and coherence were limited to <~1 min periods that were often a small fraction of a DD’s lifetime. As a result, instantaneous observations of DDs should be used with caution when estimating size distributions and vertical dust fluxes. Finally, DD width measurements are dependent on instantaneous DD structure, which can vary considerably at timescales of seconds or less. Previous studies have used conflicting or unspecified criteria to make width measurements, affecting DD size statistics and estimations of vertical dust flux—both of which are necessary to determine atmospheric dust loading from DDs. In summary, we find that DD physical characteristics are more variable and more reliant on surface properties than have previously been reported. Full article

The increasing penetration of renewable energy sources and converter-interfaced loads has intensified the need for fast and reliable grid-support services. Although electric vehicle (EV) battery chargers have emerged as promising resources for Vehicle-to-Grid (V2G) applications, existing solutions typically focus on individual services such as virtual inertia or frequency regulation, while limited attention has been given to the coordinated provision of multiple ancillary services within a unified framework. Furthermore, the use of batteries alone for fast frequency support may accelerate battery degradation due to frequent high-power transients. To address these challenges, this paper proposes a hybrid energy storage-based EV battery charger architecture and a coordinated multi-timescale control strategy capable of simultaneously providing virtual inertia support, long-term frequency regulation, reactive power compensation, and harmonic mitigation. The proposed approach utilizes a DC-link capacitor to deliver fast inertial response while the battery supplies sustained frequency support, thereby reducing battery stress and improving energy management efficiency. An enhanced frequency estimation method based on a phase-locked loop combined with a low-pass filter is also introduced to improve dynamic performance. Simulation results demonstrate the effectiveness of the proposed strategy under various grid disturbances. The system achieves an equivalent virtual inertia constant of approximately 1.85 s and delivers up to 786 W of transient inertial support within 80 ms during frequency events. The enhanced frequency estimation method significantly reduces transient overshoot, while harmonic compensation limits the grid current and voltage total harmonic distortion to 1.50% and 3.23%, respectively. In addition, the controller provides up to 400 VAR of reactive power support during voltage disturbances while maintaining stable battery operation. These results demonstrate that the proposed EV battery charger can function as a multifunctional grid-support resource, enhancing frequency stability, voltage regulation, power quality, and overall V2G capability in future smart grids. Full article

Coastal zones are dynamic interfaces where land, ocean, and atmosphere interact, making them sensitive indicators of environmental change. However, quantifying shoreline movement across long distances and over multi-year timescales remains challenging using traditional ground-based methods alone. We conducted an analysis of environmental factors and shoreline dynamics along a 58 km stretch of the arid Cabo Pulmo shoreline in Mexico from 2020 to 2026 using the CoastSat tool. The landscape is characterized by a diverse array of geographical features, including sandy beaches, granite cliffs, estuarine systems, and various anthropogenic structures. Results indicated a sea-level rise of 2 mm/year over the last 27 years, which is consistent with the reported range for the Pacific (1.8 to 3.8 mm/year). Notably, we observed an increasing trend of Category 4 and 5 hurricanes in the Mexican Pacific, with an average of 1 additional hurricane per decade (1950–2023). A total of 457 Sentinel-2 satellite images were used for automated analysis using the CoastSat platform, all of which were acquired under tidal conditions not exceeding 1 m. Our findings indicate that the granite cliffs show no detectable horizontal changes in the satellite images; however, their minimal vertical erosion contributes sediment to adjacent beaches. The most significant shoreline erosion was observed north of a marina breakwater, measuring −19.7 m, attributed to the disruption of littoral transport toward the southeast. In contrast, sandy beaches located in front of streams and estuaries—characterized by a lack of infrastructure (houses and breakwaters) and gentle slopes of 2° to 4°—demonstrated positive accretion of up to 5.9 m. According to the autoregressive distributed lag model, wave energy and hurricane-driven wind gusts are the primary agents of shoreline retreat, displacing sediment seaward to the continental shelf. Sea level rise exacerbates this retreat, while rainfall plays a minor but contributing role by transporting sediment during hurricanes in this arid region. This study highlights the effectiveness of CoastSat as a neural network-based tool for analyzing shoreline changes; however, we faced certain limitations, such as the absence of in situ beach profiles due to restricted access. Full article

Against the backdrop of global warming and the ‘warming and wetting’ trend in north-western China, changes in seasonal snowpack and glacial ice in high-altitude cold regions directly impact water security in inland river basins. At present, there is a paucity of systematic research concerning the long-term evolution of snow and ice cover, multi-scale climate responses and future trends in the source region of the Keriya River on the northern slope of the Kunlun Mountains. To address this, this study utilised Landsat remote sensing imagery and meteorological station data from 2005 to 2024. Employing a multi-model fusion framework that integrates various machine learning and time-series models—including random forests, gradient boosting trees and ARIMA—the research incorporated trend factors, climate cycle identification and probabilistic modelling of extreme events to systematically analyse the spatiotemporal variability of snow/ice coverage and its multiscale coupling relationships with air temperature and precipitation. Given the inherent limitations of optical remote sensing methods in distinguishing between seasonal snow and glacial ice, this study defines the extracted coverage type as snow/ice coverage. Given the inherent limitations of optical remote sensing methods in distinguishing between seasonal snow and glacial ice, this study defines the extracted coverage type as snow/ice coverage. The results indicate that: (1) the annual average snow/ice cover percentage in the study area shows a non-significant decreasing trend (−0.69%/year, p > 0.1); within the year, it exhibits a pattern of accumulation in winter and melting in summer, with a peak in January (average 63.2%) and a trough in August (average 11.6%); (2) snow/ice cover percentage increases significantly with altitude; the annual average SICP in the <2000 m elevation zone is 5.2%; in the 2000–3000 m and 3000–4000 m altitude ranges, this rises to 5.7% and 8.3%, respectively, representing the primary seasonal snow/ice distribution zones; in areas above 6000 m, the annual average reaches 70.3%, constituting a zone of perennial stable snow/ice cover; (3) the relationship between snow/ice and temperature and precipitation exhibits significant time-scale dependence: correlations are weak on an annual scale (temperature R = −0.25, precipitation R = −0.14), but significantly strengthen on a monthly scale and exhibit seasonal differentiation; during the melting season, temperature exerts a dominant negative influence (August R = −0.35), whilst during the accumulation season, solid precipitation provides a positive supplement (February R = 0.34), with the strongest correlation with temperature occurring in September (R = −0.50); (4) it is projected that between 2025 and 2044, snow and ice cover will follow a fluctuating downward trend (averaging an annual decrease of roughly −0.12%), falling to approximately 29% by 2044; at the same time, temperatures are expected to continue rising (+0.035 °C per year), whilst precipitation will increase slightly (+0.4% per year). The results of this study provide a sound scientific basis for formulating sustainable water resource management strategies for the northern flank of the Kunlun Mountains and optimising measures to regulate snowmelt runoff. They are of great importance for safeguarding the stability of the oasis ecological systems in the Keriya River basin and ensuring the sustainable development and utilisation of water resources. Full article

Under the continuous impact of global warming, the water cycle has undergone significant changes, causing a series of problems such as water shortage, frequent climate disasters and ecological environment deterioration. Therefore, understanding the evolution of regional historical and future drought and wet conditions is crucial for adapting and mitigating disasters. This paper discusses the evolution of drought and pluvial events in the Henan section of the Yellow River from 1970 to 2014, projects the future evolution of drought and wet conditions, and assesses the performance of various climate models from Coupled Model Intercomparison Project Phase 6 in simulating precipitation and temperature. Subsequently, future drought and wet conditions in the Henan section were projected for the 2015–2100 period across four SSP-RCP scenarios using Standardized Precipitation and Evapotranspiration Index (SPEI) and run theory. The results indicate that the Henan section of the Yellow River exhibited a significant drying trend during the historical period, with a rate of 0.15 per decade. Looking ahead, a wetting tendency is projected under the SSP1-2.6 scenario, with an increasing rate of 0.02 per decade, whereas the other three scenarios consistently show drying trends, with rates of −0.11, −0.15, and −0.23 per decade, respectively. Across all scenarios, drought and wetness variations exhibit pronounced periodicity, particularly at timescales of approximately 20–30 years, suggesting the persistence of multi-decadal hydroclimatic oscillations. Furthermore, drought and wetness events are projected to become more persistent and severe during the mid-to-late 21st century. Compared with the historical baseline, increasing radiative forcing is associated with an expansion in drought-affected areas, accompanied by reduced event frequency but longer duration and greater severity. In terms of risk, the SSP3-7.0 scenario presents the highest overall drought and wetness risk with the widest spatial extent, whereas the SSP2-4.5 scenario shows relatively lower risk levels and a more balanced spatial distribution. Full article

The development of a new-type power system requires addressing the long-timescale imbalance between electricity supply and demand caused by the high penetration of wind and solar energy, which places higher demands on the secure and stable operation of power systems. Conventional single-type energy storage cannot simultaneously satisfy short-term power regulation and medium- to long-term energy balancing requirements. Therefore, coordinated optimal allocation of multi-type energy storage is necessary. This study investigates the optimal capacity allocation of short- and long-duration energy storage in high-renewable-penetration power grids to improve renewable energy accommodation, enhance system flexibility, and optimize life-cycle cost. A mathematical model of a Multi-Type Energy Storage Coupled System (MTESCS) considering both power and energy balance is first established, together with a life-cycle economic model. Then, a source-load time-series reduction method based on Ward’s method is adopted to preserve the original temporal trends while reducing optimization complexity, and an optimal capacity allocation model is developed with the objective of minimizing system life-cycle cost. Finally, different storage configuration scenarios are constructed for comparative analyses under various renewable energy penetration levels. Results show that the proposed MTESCS can effectively improve renewable energy accommodation and economic performance, providing useful support for system design and engineering applications. Full article

Deposits of insoluble protein plaques, which are mostly composed of fibrils from disease-specific amyloid proteins, are histological markers of various human disorders. These range from non-neuropathic amyloidosis such as light chain amyloidosis or type II diabetes to well-known neuro-degenerative diseases such as Alzheimer’s Disease and Parkinson’s Disease. There are indications that these types of fibrillar suprastructures display biological activity distinct from the individual fibrils they are composed of. Yet, little is known about the mechanisms underlying the assembly of fibrillar suprastructures. An understanding of secondary fibril self-assembly into mesoscopic and macroscopic suprastructures is also critical for their application as novel biomaterial. The paucity of experimental data and theoretical models on fibrillar supra-assembly likely relates to the experimental and conceptual challenges in following this type of assembly on multiple length- and timescales, and in characterizing the distinct morphologies formed. Here, we report that the amyloid dye thioflavin T (ThT) is augmented during self-assembly of isolated lysozyme fibrils. We provide evidence that this augmentation of ThT fluorescence results from the unquenching of fibril-bound ThT during fibril binding. Combining ThT fluorescence, optical density, and fluorescence quenching kinetics with optical and electron microscopy, we propose that fibril self-assembly is driven by a transition from reaction-limited ordered assembly to diffusion-limited random cross-linking of fibrils. Full article

With the rapid development of China’s electricity spot market, the participation of Integrated Energy Systems (IESs) with multi-energy complementarity has become an inevitable trend in future energy development. However, IESs face difficulties in effectively matching heterogeneous resource capabilities with the diverse requirements of the multi-timescale spot market. Therefore, this paper proposes an optimization strategy for integrated energy system operation based on the hierarchical dispatch of flexibility resources, aiming to enhance the adaptability of different resources to multi-period markets. Firstly, a quantitative flexibility assessment framework is established from three key dimensions—power regulation range, energy shifting capacity, and dynamic response speed—to evaluate the market adaptability of various adjustable resources. Subsequently, the flexibility assessment results are converted into dynamic market participation ratios, which are incorporated as constraints into a Model Predictive Control (MPC)-based optimization model. In the day-ahead scheduling stage, the model prioritizes meeting fundamental electricity demand while dynamically reserving a portion of flexible capacity for participation in more profitable intra-day and real-time market services. Case studies demonstrate that the proposed strategy achieves real-time computational feasibility, significantly improves the economic performance of park-level IESs, and maintains stable dispatch behavior under market uncertainties and forecast deviations. The results indicate that the proposed hierarchical flexibility-oriented dispatch framework provides a practical and scalable solution for enabling IES participation in multi-timescale electricity spot markets. Full article

Estimating future air quality under the warming climate is an urgent task for all populated regions. Often, climate models are evaluated with respect to air temperature and precipitation, but without a focus on other air quality-related meteorological variables. This study evaluated a regional ensemble system over the southeast Australian region driven by five selected CMIP6 global climate models (downscaled by two regional models, making the ensemble size ten) in terms of a range of surface variables relevant for air quality from seasonal to diurnal timescales. Results showed that the two regional climate models, although only differing in their planetary boundary layer (PBL) parameterizations, performed quite differently. In general, the regional model with the MYNN2 PBL scheme (named R3) performed better than the other. While most meteorological variables, including surface wind speed, were verified well, wind direction showed large biases and variability among models. When downscaled (~4 km resolution) atmospheric variables were applied to drive the Community Multiscale Air Quality (CMAQ) model, the ensemble members, particularly the two versions of the regional model, resulted in different chemical species concentrations. A model ranking scheme was developed based on various spatiotemporal timescales and identified slightly superior performance by the regional model R3. The findings provide a valuable reference for selecting optimized model members for future air quality projections. Full article

Optical synaptic transistors employing polymer dielectrics have emerged as promising building blocks for neuromorphic computing due to their low power consumption and rich photo-induced memory behaviors. While extensive experimental studies have demonstrated various synaptic functions, a unified physical understanding of the coupled charge trapping and ionic polarization processes governing device dynamics remains incomplete. In this work, we develop a unified physical model to investigate optical synaptic behaviors in polymer-based transistors with oxide interlayers. The model explicitly describes the time-dependent evolution of photo-induced charge trapping at the semiconductor–dielectric interface and ionic polarization within the polymer dielectric, which jointly modulate the effective threshold voltage of the transistor channel. Based on this framework, key synaptic functions including excitatory postsynaptic current (EPSC), paired-pulse facilitation (PPF), and pulse-dependent potentiation are quantitatively reproduced. The model further reveals how dielectric structure and trapping strength govern the transition between short-term and long-term plasticity. This study provides a physically intuitive and experimentally relevant modeling framework for understanding optical synaptic transistors, offering guidance for the rational design and optimization of polymer-based neuromorphic devices. Although simplified, the proposed model captures the essential physics governing optical synaptic behaviors and provides a general framework applicable to a wide class of ion–electronic neuromorphic devices. Experimental measurements are used as physically motivated proxies to validate the multi-timescale structure of the model rather than direct numerical fitting. Full article

To effectively manage and conserve ecosystems, it is crucial to understand how vegetation changes over time and space and what drives these changes. The Normalized Difference Vegetation Index (NDVI) is a key measure of plant growth that is highly sensitive to climate variations. Despite its importance, there has been limited research on vegetation changes in the upper sections of the Irtysh River. In our study, we combined various datasets, including NDVI, temperature, precipitation, soil moisture, elevation, and land cover. We conducted several analyses, such as Theil–Sen median trend analysis, Mann–Kendall trend and mutation tests, partial correlation analysis, the geographical detector model, and wavelet analysis, to reveal the region’s pronounced warming and moistening trend in recent years, the response relationship between NDVI and the climate, and the primary drivers influencing NDVI variations. We also delved into the spatiotemporal evolution of NDVI and identified key factors driving these changes by analyzing atmospheric circulation patterns. Our main findings are as follows: (1) Between 1901 and 2022, the area’s temperature rose by 0.018 °C/a, with a noticeable increase in the rate of warming around 1990; precipitation increased by 0.292 mm/a. From 1950 to 2022, soil moisture exhibited a steady increase of 0.0002 m³ m⁻³/a. Spatial trend distributions indicated that increasing trends in temperature and precipitation were evident across the entire region, while trends in soil moisture showed significant spatial variation. (2) During 1982 to 2022, the vegetation greening trend was 0.002/10a, indicating a gradual improvement in vegetation growth in the study area. The spatial distribution of monthly average NDVI values revealed that the main growing season of vegetation spanned April to November, with peak NDVI values occurring in June–August. Combined with serial partial correlation and spatial partial correlation analysis, temperatures during April to May effectively promoted the germination and growth of vegetation, while soil moisture accumulation from June to August (or January to August) effectively met the water demand of vegetation during its growth process, with a significant promoting effect. Geographical detector results demonstrate that temperature exhibits the strongest explanatory power for NDVI variation, whereas land cover has the weakest. The synergistic promotional effect of multiple climatic factors is highly pronounced. (3) Wavelet analysis revealed that the periodic characteristics of NDVI and climate variables over a 2–15-year timescale may have been associated with the impacts of atmospheric circulation. Taking NDVI and climatic factors from June to August as an example, before 2000, temperature was the dominant influencing factor, followed by precipitation and soil moisture; after 2000, precipitation and soil moisture became the primary drivers. The North Atlantic Oscillation (NAO) and Arctic Oscillation (AO) were the primary atmospheric circulation patterns influencing vegetation variability in the region. Their effects were reflected in the inverse relationship observed between NAO/AO indices and NDVI, with typical phases of high and low NDVI closely corresponding to shifts in NAO and AO activity. This study helps us to understand how plants have been changing in the upper parts of the Irtysh River. These insights are critical for guiding efforts to develop the area in a way that is sustainable and beneficial for the environment. Full article

The conformation of a glycosaminoglycan (GAG) carbohydrate biopolymer is dependent upon the ring puckering states of its constituent monosaccharide residues and the dihedral angles (φ, ψ) of the glycosidic linkages connecting these residues. In the context of GAGs, the monosaccharide residue iduronate (IdoA; the conjugate base of iduronic acid) is able to take on both chair and boat-like ring pucker states. All-atom explicit-solvent molecular dynamics simulations were applied to determine the extent to which IdoA ring pucker state affects the conformational preferences of (φ, ψ) in 16 different IdoA-containing disaccharides derived from the GAGs heparin/heparan sulfate and dermatan sulfate. Using the extended-system adaptive biasing force (eABF) method, the complete free-energy surface ΔG(φ, ψ) was computed for each disaccharide with its IdoA ring restrained separately to the ¹C₄, ²S_O, B_3,O, or ⁴C₁ ring pucker state. Global-minimum ΔG(φ, ψ) values resided within broad ΔG(φ, ψ) basins, and both ring pucker state and sulfation status influenced basin shape and size. Various sulfoforms of the disaccharide IdoAα1–4GlcNS had prominent secondary-minimum basins distinct from the global-minimum basins, and these secondary-minimum basins may manifest as metastable states in standard (nonbiased) molecular dynamics simulations on the 1-microsecond timescale. As such, the present results provide a reference for assessing (φ, ψ) sampling in nonbiased molecular dynamics simulations of GAGs and demonstrate the interplay between IdoA ring puckering, glycosidic linkage dihedral rotation, and sulfation status in contributing to GAG conformational preferences. Full article

Today, machine learning (ML) is generally considered a potent and efficient tool for addressing studies in various diverse domains, including image processing and event prediction on a timescale. ML represents complex relations between features, and these mappings between such features may be applied in simulations of time-dependent events, such as the behavior of animals. Still, ML inherently strongly depends on extensive and consistent datasets, a fact that reveals both the benefits and drawbacks of ML. In the use of ML, insufficient or skewed data can limit the ability of algorithms to accurately predict or generalize possible states. To overcome this limitation, this work proposes an integrated hybrid approach that combines machine learning with methods from cognitive science, here especially inspired by the ACT-R model to approach cases of missing or unbalanced data. By incorporating cognitive processes such as memory, perception, and attention, the model accounts for the internal mechanisms of decision-making and environmental interaction where traditional ML methods fall short. This approach is particularly useful in representing states that are not directly observable or are underrepresented in the data, such as rare behavioral responses for animals, or adaptive strategies. Experimental results show that the combination of machine learning for data-driven analysis and cognitive ‘rule-based’ frameworks for filling in gaps provides a more comprehensive model of animal behavior. The findings suggest that this hybrid approach to simulation models can offer a more robust and consistent way to study complex, real-world phenomena, especially when data is inherently incomplete or unbalanced. Full article

Proteins are generally characterized by three-dimensional structures that are well suited for their specific function. It is much less accepted that a particular flexibility or plasticity of a protein is essential for performing its function. The latter plasticity encompasses the stochastic motions of small protein sidechains on the picosecond timescale that serve as “lubricating grease”, allowing slower functionally relevant conformational changes. Some remarkable examples of potential correlations between protein dynamics and function were observed for pigment–protein complexes in photosynthesis. For example, electron transfer and protein plasticity are concurrently suppressed in Photosystem II upon decreases in temperature or hydration, thus suggesting a prominent functional role of protein dynamics. An unusual dynamics–function correlation was observed for the major light-harvesting complex II, where the dynamics of charged protein residues affect the pigment absorption frequencies in photosynthetic light-harvesting. Generally, proteins exhibit a wide variety of motions on multiple time and length scales. However, there is an approach to characterize the plasticity of a protein as a single effective force constant that permits a straightforward comparison between different protein systems. Within this review, we determine the latter effective force constant for three photosynthetic proteins in different functional and organizational states. The force constant values determined appear to be rather different for each protein and are consistent with the requirements imposed by the various functions. These findings highlight the individual character of a protein’s flexibility and the role(s) it is playing for the specific function. Full article