Search Results (137)

The decarbonization of electricity generation by 2030 and the realization of a net-zero economy by 2050 are central to the United States’ climate strategy. However, large-scale renewable integration introduces operational challenges, including extreme ramping, unsafe dispatch, and price volatility. This review investigates how demand–response (DR) aggregators and distributed loads can support these climate goals while addressing critical operational challenges. We hypothesize that current DR aggregator frameworks fall short in the areas of distributed load operational flexibility, scalability with the number of distributed loads (prosumers), prosumer privacy preservation, DR aggregator and prosumer competition, and uncertainty management, limiting their potential to enable large-scale prosumer participation. Using a systematic review methodology, we evaluate existing DR aggregator and prosumer frameworks through the proposed FCUPS criteria—flexibility, competition, uncertainty quantification, privacy, and scalability. The main results highlight significant gaps in current frameworks: limited support for decentralized operations; inadequate privacy protections for prosumers; and insufficient capabilities for managing competition, uncertainty, and flexibility at scale. We conclude by identifying open research directions, including the need for game-theoretic and machine learning approaches that ensure privacy, scalability, and robust market participation. Addressing these gaps is essential to shape future research agendas and to enable DR aggregators to contribute meaningfully to US climate targets. Full article

As the integration of renewable energy sources into microgrid operations deepens, their inherent uncertainty poses significant challenges for dispatch scheduling. This paper proposes a hybrid game-theoretic optimization strategy to address the uncertainty of renewable energy in microgrid scheduling. An energy trading framework is developed, involving integrated energy microgrids (IEMS), shared energy storage operators (ESOS), and user aggregators (UAS). A mixed game model combining master–slave and cooperative game theory is constructed in which the ESO acts as the leader by setting electricity prices to maximize its own profit, while guiding the IEMs and UAs—as followers—to optimize their respective operations. Cooperative decisions within the IEM coalition are coordinated using Nash bargaining theory. To enhance the generality of the user aggregator model, both electric vehicle (EV) users and demand response (DR) users are considered. Additionally, the model incorporates renewable energy output uncertainty through distributionally robust chance constraints (DRCCs). The resulting two-level optimization problem is solved using Karush–Kuhn–Tucker (KKT) conditions and the Alternating Direction Method of Multipliers (ADMM). Simulation results verify the effectiveness and robustness of the proposed model in enhancing operational efficiency under conditions of uncertainty. Full article

Traffic flow prediction, crucial for intelligent transportation systems, has seen advancements with graph neural networks (GNNs), yet existing methods often fail to distinguish between the importance of different intersections. These methods usually model all intersections uniformly, overlooking significant differences in traffic flow characteristics and influence ranges between ordinary and important nodes. To tackle this, this study introduces a dynamic regional-aggregation-based heterogeneous graph neural network (DR-HGNN). This model categorizes intersections into two types—ordinary and important—to apply tailored feature aggregation strategies. Ordinary intersections aggregate features based on local neighborhood information, whereas important intersections utilize deeper neighborhood diffusion and multi-hop dependencies to capture broader traffic influences. The DR-HGNN model also employs a dynamic graph structure to reflect temporal changes in traffic flows, alongside an attention mechanism for adaptive regional feature aggregation, enhancing the identification of critical traffic nodes. Demonstrating its efficacy, the DR-HGNN achieved 19.2% and 15.4% improvements in the RMSE over 50 min predictions in the METR-LA and PEMS-BAY datasets, respectively, offering a more precise prediction method for traffic management. Full article

Human milk is a complex biofluid containing a diverse array of cells crucial for infant health. Despite their importance, our understanding of these cells remains incomplete due to technical challenges. To fully comprehend human milk cells, high-resolution imaging technologies that can directly measure biological processes are required. We have developed a specialized imaging platform combining light and electron microscopy for human milk cell imaging. To identify different cell types, human milk cells were first stained with several specific cell markers (e.g., EpCAM and MUC1 for lactocytes, CD16 and CD66b for neutrophils, and HLA-DR and CD68 for macrophages) prior to light (confocal) microscopy. Following this, the same cells were processed with osmium staining, resin embedding, and sectioning for electron microscopy, allowing us to observe ultrastructural details. Our imaging workflow has enabled nanoscale visualization of human milk cells, resulting in a first-of-its-kind comprehensive database profiling the organelle-level ultrastructure of different cell types present in human milk. The cells in the human milk are highly heterogenous, featuring a large proportion of lactocytes and lipid droplets, binucleated lactocytes, neutrophil aggregation, neutrophil extracellular traps, dendritic cells/macrophages with bacteria, and immunophagocytosis. This study provides valuable cellular insights contributing to a deeper understanding of human milk biology. Full article

The increasing complexity of energy grids, driven by rising demand and unpredictable residential consumption, highlights the need for efficient demand response (DR) strategies and data-driven services. This paper proposes a machine learning-based framework for DR that clusters users based on their consumption patterns and categorizes individual usage into distinct profiles using K-means, Hierarchical Agglomerative Clustering, Spectral Clustering, and DBSCAN. Key features such as statistical, temporal, and behavioral characteristics are extracted, and the novel Household Daily Load (HDL) approach is used to identify residential consumption groups. The framework also includes context analysis to detect daily variations and peak usage periods for individual users. High-impact users, identified by anomalies such as frequent consumption spikes or grid instability risks using IsolationForest and kNN, are flagged. Additionally, a classification service integrates new users into the segmented portfolio. Experiments on real-world datasets demonstrate the framework’s effectiveness in helping energy managers design tailored DR programs. Full article

Aceclofenac (ACF), a non-steroidal anti-inflammatory drug, was obtained in its amorphous state by cooling from melt. The glass transition was investigated using dielectric and calorimetric techniques, namely, dielectric relaxation spectroscopy (DRS), thermally stimulated depolarization currents (TSDC), and conventional and temperature-modulated differential scanning calorimetry (DSC and TM-DSC). The dynamic behavior in both the glassy and supercooled liquid states revealed multiple relaxation processes. Well below the glass transition, DRS was able to resolve two secondary relaxations, γ and β, the latter of which was also detectable by TSDC. The kinetic parameters indicated that both processes are associated with localized motions within the molecule. The main (α) relaxation was clearly observed by DRS and TSDC, and results from both techniques confirmed a non-Arrhenian temperature dependence of the relaxation times. However, the glass transition temperature (T_g) extrapolated from DRS data significantly differed from that obtained via TSDC, which in turn showed reasonable agreement with the calorimetric T_g (T_g-DSC = 9.2 °C). The values of the fragility index calculated by the three experimental techniques converged in attributing the character of a moderately fragile glass former to ACF. Above the α relaxation, TSDC showed a well-defined peak. In DRS, after “removing” the high-conductivity contribution using ε’ derivative analysis, a peak with shape parameters α_HN = β_HN = 1 was also detected. The origin of these peaks, found in the full supercooled liquid state, has been discussed in the context of structural and dynamic heterogeneity. This is supported by significant differences observed between the FTIR spectra of the amorphous and crystalline samples, which are likely related to aggregation differences resulting from variations in the hydrogen bonds between the two phases. Additionally, the pronounced decoupling between translational and relaxational motions, as deduced from the low value of the fractional exponent x = 0.72, derived from the fractional Debye–Stokes–Einstein (FDSE) relationship, further supports this interpretation. Full article

Toxic cyanobacterial blooms are a growing environmental problem, persisting in freshwater bodies globally, and potentially hazardous to populations that rely on surface freshwater supplies. Nature-based solution units (NBSUs) are effective and sustainable approaches for water treatment, with sorption being an important process. The purpose of this study was to evaluate unmodified agro-based waste materials (rice husks, olive pulp pomace pellets (OP), cork granules) and the benchmark NBSU substrates (biochar, light expanded clay aggregate (LECA), and sand) for their microcystin-LR (MC-LR) and cylindrospermopsin (CYN) sorption potential. The kinetics and sorption mechanism of the two best sorbent materials were studied for future incorporation into NBSUs. Pre-screening of the sorbents showed highest sorption with biochar (>86% MC-LR and >98% CYN) and LECA (78% MC-LR and 80% CYN) and lower sorption with rice husk (<10%), cork (<10%), and sand (<26%). Leaching from OP made them unsuitable for further use. The sorption of both the cyanotoxins onto biochar was rapid (8 h), whereas onto LECA it was steadier (requiring 48 h for equilibrium). The pseudo-second-order kinetic model fit the sorption of both cyanotoxins onto biochar and LECA (R²: 0.94–0.99), suggesting that the sorption rate is limited by chemisorption. The sorption of MC-LR and CYN to biochar and LECA fit the Freundlich and D–R models better, suggesting multilayer sorption, high heterogeneity, and porosity in the sorbents (which was also confirmed by SEM/EDS). The sorption capacity was observed to be higher for biochar (K_f: MC-LR = 0.05, CYN = 0.16) than LECA (K_f: MC-LR = 0.02, CYN = 0.01). Full article

Lomami National Park, located in the Democratic Republic of the Congo (DR Congo), is renowned for the integrity of its forest ecosystems, safeguarded by the absence of agricultural activities and limited road access. However, these ecosystems remain under-researched, particularly in terms of forest cover dynamics. This research gap poses a significant challenge to establishing rigorous monitoring systems, which are essential for ensuring the long-term preservation of these valuable ecosystems. This study utilized Google Earth Engine to preprocess Landsat images from 2008, 2016, and 2024, employing techniques such as atmospheric correction and cloud masking. Random Forest classification was applied to analyze land cover changes, using training datasets curated through ground-truthing and region-of-interest selection. The classification accuracy was evaluated using metrics such as overall accuracy, producer’s accuracy, and user’s accuracy. To assess landscape configuration, metrics such as class area, patch number, largest patch index, disturbance index, aggregation index, and edge density were calculated, distinguishing between the park’s core and peripheral zones. Spatial transformation processes were analyzed using a decision tree approach. The results revealed a striking contrast in forest cover stability between Lomami National Park and its surrounding periphery. Within the park, forest cover has been preserved and even showed a modest increase, rising from 92.60% in 2008 to 92.75% in 2024. In contrast, the peripheral zone experienced a significant decline in forest cover, decreasing from 79.32% to 70.48% during the same period. This stability within the park extends beyond maintaining forested areas; it includes preserving and enhancing the spatial structure of forest ecosystems. For example, edge density, a key indicator of forest edge compactness, remained stable in the park, fluctuating between 8 m/ha and 9 m/ha. Conversely, edge density in the peripheral zone exceeded 35 m/ha, indicating that forest edges within the park are considerably more cohesive and intact than those in the surrounding areas. The spatial transformation processes also underscored these contrasting dynamics. In the park, the primary process was the aggregation of primary forest patches, reflecting a trend toward continuous and connected forest landscapes. By contrast, the peripheral zone exhibited dissection, indicating fragmentation and the breakdown of forest patches. These findings highlight the park’s critical role in maintaining both the extent and structural integrity of forest ecosystems, setting it apart from the more degraded periphery. They underscore the resilience of forest ecosystems in the face of limited anthropogenic pressures and the crucial importance of effective land management and rigorous conservation strategies in addressing the challenges posed by urbanization and rural expansion. Additionally, the results emphasize that well-adapted conservation measures, combined with specific demographic and socio-economic conditions, can play a pivotal role in achieving long-term forest preservation and ecological stability. Full article

A demand response (DR) aggregator is a specialized entity designed to collaborate with electricity producers, facilitating the exchange of energy for numerous stakeholders. This application is a pivotal development within the Russian Energy System as it transitions to a Smart Grid. Its successful operation relies on the advancement and implementation of more efficient strategies to manage emerging energy assets and structures. The holonic approach is a distributed management model used to handle systems characterized by random and dynamic changes. This paper analyzes the specific aspects of the electricity demand management mechanism in Russia, primarily aimed at reducing peak load in the energy system by engaging active consumers who are outside the wholesale market. The DR-Aggregator is considered both a cyber-physical system (CPS) with a cluster structure and a business process. The DR-Aggregator exhibits essential holonic properties, enabling the application of a holonic approach to enhance the efficiency of the DR-Aggregator mechanism. This approach will facilitate greater flexibility in managing the load schedules of individual holon consumers, bolster the reliability of power supply by aligning load schedules among holon consumers within the super-holon cluster, and improve the fault tolerance of the DR-Aggregator structure, providing greater adaptability of demand management services. Full article

Public transit systems are critical to the quality of urban life, and enhancing their efficiency is essential for building cost-effective and sustainable smart cities. Historically, researchers sought reinforcement learning (RL) applications to mitigate bus bunching issues with holding strategies. Nonetheless, these attempts often led to oversimplifications and misalignment with the goal of reducing the total time passengers spent in the system, resulting in less robust or non-optimal solutions. In this study, we introduce a novel setting where each bus, supervised by an RL agent, can appropriately form aggregated policies from three strategies (holding, skipping station, and turning around to serve the opposite direction). It’s difficult to learn them all together, due to learning complexity, we employ domain knowledge and develop a gradually expanding action space curriculum, enabling agents to learn these strategies incrementally. We incorporate Long Short-Term Memory (LSTM) in our model considering the temporal interrelation among these actions. To address the inherent uncertainties of real-world traffic systems, we impose Domain Randomization (DR) on variables such as passenger demand and bus schedules. We conduct extensive numerical experiments with the integration of synthetic and real-world data to evaluate our model. Our methodology proves effective, enhancing bus schedule reliability and reducing total passenger waiting time by over 15%, thereby improving bus operation efficiency and smoothering operations of buses that align with sustainable goals. This work highlights the potential of robust RL combined with curriculum learning for optimizing public transport in smart cities, offering a scalable solution for real-world multi-agent systems. Full article

The purpose of this paper is to analyze the profitability of wind energy and demand response (DR) resources participating in the energy and frequency regulation markets. Since wind power producers (WPPs) must reduce their output to provide up-regulation and DR aggregators (DRAs) have to purchase additional power to facilitate down-regulation, this may result in revenue loss. If WPPs coordinate with DRAs, these two costs could be reduced. Thus, it would be profitable for WPPs and DRAs to form a coalition to participate in the regulation market. To better utilize the frequency response characteristics of wind and DR resources, this paper proposes a cooperation scheme to optimize the bidding strategy of the coalition. Furthermore, cooperative game theory methods, including Nucleolus- and Shapley-value-based models, are employed to fairly allocate additional benefits among WPPs and DRAs. The uncertainties associated with wind power and the behavior of DR customers are modeled through stochastic programming. In the optimization process, the decision-maker’s attitude toward risks is considered using conditional value at risk (CVaR). Case studies demonstrate that the proposed bidding strategy can improve the performance of the coalition and lead to higher benefits for both WPPs and DRAs. Specifically, the expected revenue of the coordinated strategies increased by 12.1% compared to that of uncoordinated strategies. Full article

Due to the hydraulic sorting effect in the hydraulic filling process, a fine-grained aggregate layer dominated by silty fine sand with uneven distribution is easily formed in reclamation projects, which triggers issues with the bearing capacity and nonuniform settlement of calcareous sand foundations. In this study, a series of one-dimensional compression tests were conducted to investigate the effect of different fines contents (f_c) on the compression behavior of calcareous sand. The results show that at the same relative density (medium-density, D_r = 50%), the addition of fine particles leads to a reduction in the initial void ratio (for f_c ≤ 40%). Furthermore, while the compressibility of the soil samples increases with the rising of fines content, it begins to decrease with further addition of fine particles beyond a threshold value of fines content (f_c-th). Additionally, particle crushing contributes to the compressive deformation of calcareous sand, and the particle relative breakage of calcareous sand increases at the initial stage of adding fine particles. Moreover, a comparison of the compression test results between calcareous silty sand (f_c = 10%) and clean sand reveals that the addition of fine particles accentuates the compressibility differences among calcareous sands with different relative densities. These findings provide valuable insights for addressing the challenges posed by fine-grained layers in calcareous sand foundations. Full article

In the context of the green energy transition, the rapid expansion of flexible resources such as distributed renewable energy, electric vehicles (EVs), and energy storage has significantly impacted the operation of distribution networks. This paper proposes a multi-objective optimization approach for active distribution networks (ADNs) based on analytical target cascading (ATC). Firstly, a dynamic optimal power flow (DOPF) calculation method is developed using second-order conic relaxation (SOCR) to address power flow and voltage issues in the distribution network, incorporating active management (AM) elements. Secondly, this study focuses on aggregating the power of flexible resources within station areas connected to distribution network nodes and incorporating these resources into demand response (DR) programs. Finally, a two-layer model for collaborative multi-objective scheduling between station areas and the active distribution network is implemented using the ATC method. Case studies demonstrate the model’s effectiveness and validity, showing its potential for enhancing the operation of distribution networks amidst the increasing integration of flexible resources. Full article

Diabetic retinopathy (DR) is a leading cause of blindness, yet its molecular mechanisms are unclear. Extracellular vesicles (EVs) contribute to dysfunction in DR, but the characteristics and functions of vitreous EVs are unclear. This study investigated the inflammatory properties of type 2 diabetic (db) vitreous EVs. EVs isolated from the vitreous of db and non-db donors were used for nanoparticle tracking analysis (NTA), transmission electron microscopy (TEM), immunogold staining, Western blotting, and proteomic analysis by mass spectrometry. Intracellular uptake of vitreous EVs by differentiated macrophages was evaluated using ExoGlow membrane labeling, and the impact of EVs on macrophage (THP-1) activation was assessed by cytokine levels using RT-qPCR. NTA and TEM analysis of db and non-db vitreous EVs showed non-aggregated EVs with a heterogeneous size range below 200 nm. Western blot detected EV markers (Alix, Annexin V, HSP70, and Flotillin 1) and an upregulation of Cldn5 in db EVs. While the db EVs were incorporated into macrophages, treatment of THP-1 cells with db EVs significantly increased mRNA levels of TNFα and IL-1β compared to non-db EVs. Proteomic and gene enrichment analysis indicated pro-inflammatory characteristics of db EVs. Our results suggest a potential involvement of EC-derived Cldn5+ EVs in triggering inflammation, offering a novel mechanism involved and presenting a possible therapeutic avenue for DR. Full article

To explore the spatial distribution characteristics of soil physical properties and soil erosion in sloping farmland with ridges in the black soil areas of northeast China, sloping farmland with ridges built with woven bags (RW) along the contour lines was selected as the research object, and another sloping farmland was selected as the control (CK). Soil samples were collected from both RW and CK at uniform spatial intervals to measure key indicators of soil properties in the surface layer (0–15 cm), including soil water-holding capacity, soil structure, and annual average soil loss (A). The results showed that: (i) RW exhibited a significantly higher overall field water-holding capacity compared to CK, with soil moisture characteristics more evenly distributed spatially. Soil bulk weight, fractal dimension, and soil aggregate destruction in RW were reduced by 1.09%, 0.65%, and 4.61%, respectively, compared to CK. Additionally, soil total porosity, capillary porosity, mean weight diameter (MWD), and geometric mean diameter (GWD) were more evenly distributed spatially in RW. (ii) On the up-slope, soil water content and DR_>0.25 in RW had a higher increase than those of CK. On the mid-slope, soil field water-holding capacity, capillary porosity, MWD, and GWD in RW had a higher increase than those in CK. On the down-slope, RW had a 7.67–10.79% increase in soil water content, saturated water-holding capacity, field water-holding capacity, and capillary water-holding capacity compared to CK, with total soil porosity and soil capillary porosity increasing by 2.84% and 15.51%, respectively. (iii) Annual average soil loss (A) of RW was reduced by 61.85–99.64% compared to CK, based on the China Soil Loss Equation (CSLE). (vi) Soil water-holding capacity and soil structure characteristics of RW showed benefits compared to CK, with the benefits ranging from 1.01 to 1.09, while the benefit of A reached 2.46. This study is significant for understanding the spatial distribution of soil erosion on sloped farmland in black soil areas and for the effective application of soil and water conservation measures. Full article