Inherent Fluctuations Research Articles

Super-resolution microscopy based on the inherent fluctuations of dye molecules

Fluorescence microscopy is a critical tool across various disciplines, from materials science to biomedical research, yet it is limited by the diffraction limit of resolution. Advanced super-resolution techniques such as localization microscopy and stimulated-emission-depletion microscopy often demand considerable resources. These methods depend heavily on elaborate sample-staining, complex optical systems, or prolonged acquisition periods, and their application in 3D and multicolor imaging presents significant experimental challenges. In the current work, we provide a complete demonstration of a widely accessible super-resolution imaging approach capable of 3D and multicolor imaging based on super-resolution optical fluctuation imaging (SOFI). We replace the confocal pinhole with an array of single-photon avalanche diodes and use the microsecond-scale fluctuations of dye molecules as a contrast mechanism. This contrast is transformed into a super-resolved image using a robust and deterministic algorithm. Our technique utilizes natural fluctuations inherent to organic dyes, thereby it does not require engineering of the blinking statistics. Our robust, versatile super-resolution method opens the way to next-generation multimodal imaging and facilitates on-demand super-resolution within a confocal architecture.

Stochastic optimization of integrated electricity‐heat‐gas energy system considering uncertainty of indirect carbon emission intensity

AbstractThe inherent intermittency and fluctuation of renewable energy generation introduce uncertainty in electricity carbon emission intensity, posing challenges to the low‐carbon and economic scheduling of integrated energy systems. To this end, this paper proposes an optimization model for integrated electricity‐heat‐gas energy system operation considering uncertain indirect carbon emission intensity. First, considering the tiered carbon trading mechanism, an optimization framework for a park‐level model is developed. Then, polyhedral uncertainty sets are introduced to capture the uncertainties in renewable energy generation and carbon emission intensity, offering a flexible and intuitive approach to handling diverse uncertainty types. These uncertainty sets are integrated into the optimization problem to enhance robustness. Finally, the uncertainty set‐based stochastic optimization method is introduced to improve the model's robustness, addressing both cost minimization and carbon reduction under uncertain conditions. Case studies using data from a Mediterranean‐region hospital demonstrate the model's effectiveness in addressing uncertainties at park‐level systems and providing robust solutions that balance cost efficiency and environmental impact.

Granular memristors with tunable stochasticity.

Most realizations of memristive devices exhibit characteristic noise sometimes described as random telegraph noise. These fluctuations in current, ubiquitous in nature, carry significant implications for device performance, reliability, and the broader landscape of memristor technology applications. Here, we study inherent random fluctuations observed in silver based granular memristive devices operating under steady bias conditions. Random telegraph noise observed in our system is characterized in terms of distributions of ON and OFF times of the current flow at a particular bias. We find that these fluctuations adhere to power law statistics with , where τOFF/ON denotes the time during which the output value remains below or above a specified threshold. We follow the fluctuations for up to four decades. Significantly, unlike previous studies, we find the emergence of a new regime of behavior where the power law exponent varies as a function of applied bias. We find that our results are best described by the Marcus-Tang expression for diffusion along intersecting parabolae with bias as the driving force. The predictions of this picture of dynamics also provide a satisfactory explanation for the quiescence of the OFF/ON state of our devices.

Metamodeling of turbofan engine compressor characteristics using SVM-based improved Kriging method

PurposeThe purpose of this paper is to overcome the inherent lack of precision in commonly used interpolation procedures when solving the mathematical model of turbofan engines, as well as to address the issue that the theoretical variogram model in traditional Kriging models is prone to subjective selection bias, which makes it impossible to accurately capture the inherent fluctuation patterns in compressor data.Design/methodology/approachTo mitigate this challenge, based on the spatial distribution characteristics of the compressor characteristic data of a certain type of turbofan engine, the input and output dimensions of the model are defined. By determining the stable operating region from the original component data, the authors use the proposed Kriging method improved with a support vector machine model to reconstruct the characteristics at unknown speeds within this region. The effectiveness of the proposed method is evaluated using the established assessment metrics.FindingsExperimental results demonstrate that the proposed method exhibits significant advantages over the conventional Kriging approach. Specifically, it leads to a substantial reduction in root mean square error and mean absolute error by 0.0153/0.0118 (low speed), 0.1306/0.0362 (medium speed) and −0.0066/0.2366 (high speed).Originality/valueThis refined approach not only offers notable engineering applicability but also contributes significantly to the enhancement of aerospace engine model solutions’ precision.

Data-Driven Voltage Control Method of Active Distribution Networks Based on Koopman Operator Theory

The advent of large-scale distributed generation (DG) has introduced several challenges to the voltage control of active distribution networks (ADNs). These challenges include the heterogeneity of control devices, the complexity of models, and their inherent fluctuations. To maintain ADN voltage stability more economically and quickly, a data-driven ADN voltage control scheme is proposed in this paper. Firstly, based on the multi-run state sensitivity matrix, buses with similar voltage responses are clustered, and critical buses are selected to downsize the scale of the model. Secondly, a linear voltage-to-power dynamics model in high-dimensional state space is trained based on the offline data of critical bus voltages, DGs, and energy storage system (ESS) outputs, utilizing the Koopman theory and the Extended Dynamic Mode Decomposition (EDMD) method. A linear model predictive voltage controller, which takes ADN stability and control cost into account, is also proposed. Finally, the effectiveness and applicability of the method are verified by applying it to an improved 33-bus ADN system. The proposed control method can respond more quickly and accurately to the voltage fluctuation problems caused by source-load disturbances and short-circuit faults.

Development of a novel multifunctional superconducting device for performance enhancement of DC microgrids

DC microgrids have become an important infrastructure for increasing the integration of renewable energy sources in power grids. However, these DC microgrids suffer from inherent fluctuations from renewable energy sources as well as high fault currents during grid disturbances. Addressing these challenges simultaneously with a single device is a significant technical issue. This paper proposes a novel Multifunctional Superconducting Device (MSCD) with dual functionality to overcome these challenges. The MSCD can operate as a Superconducting Magnetic Energy Storage (SMES) system during normal operation, providing mitigation of renewable energy fluctuations through its shunt connection. During grid faults, the MSCD transits to operate as a Superconducting Fault Current Limiter (SFCL) in series with the grid, limiting the fault current. The proposed MSCD has only an additional controlled switch to the conventional power electronic converter of SMES. The dual functionality of the proposed MSCD has been evaluated using the PSCAD/EMTCD computer package under various operating conditions, validating its ability to seamlessly transition between SMES and SFCL modes. The proposed MSCD could effectively smooth DC bus voltage fluctuations caused by variations in wind speed and solar radiation and could significantly limit the fault current contribution from the DC microgrid under fault conditions. Furthermore, a superconducting coil with a pancake structure has been built and integrated with a control circuit to experimentally validate the functionality of the MSCD. Two experimental case studies were conducted - the first testing the charging and discharging behavior, and the second evaluating the current limiting capability of the MSCD prototype.

Efficient resource allocation for generative AI workloads in cloud-native infrastructures: A multi-tiered approach

Resource management becomes essential in ensuring that generative AI workloads in cloud-native infrastructures deliver the best results. The architecture described in this article targets such workloads due to their inherent fluctuations in resource usage and the difficulties in scaling them. The proposed framework divides resources into groups to guarantee that applications are given support based on difficulty level. The features of the proposed methodology are the performance assessment of resource distribution effectiveness, taking into account metrics, including latency, throughput, and utilization rates. Furthermore, examples have been provided to support the use of this approach and its efficiency in real-life situations. Based on these, applying the multi-tiered approach to resource management improves the organization's operations performance and minimizes expenses connected with resource provisioning. Such a study also emphasizes the importance of developing flexible and effective resource management tools that can be especially useful in modern generative AI development environments.

Optimizing Renewable Strategies for Emission Reduction Through Robotic Process Automation in Smart Grid Management

The integration of renewable energy into Intelligent Distribution Networks (IDNs) is challenged by the inherent variability and fluctuations in energy supply, particularly with photovoltaic (PV) generation as the primary form of distributed generation (DG). However, managing the fluctuations and variability in renewable energy supply presents significant challenges. To address these complexities, it is vital to optimally coordinate flexible resources from source–network–storage–load (SNSL) in a manner that aligns with cross-sectoral emission reduction strategies while enhancing grid stability and efficiency. This paper addresses these challenges by proposing a strategy that optimizes the coordination of PV-based DG, storage, and load resources through Robotic Process Automation (RPA) to enhance grid stability and support emissions reduction. We use a two-layer dispatching framework: the lower-layer model, formulated as a quadratic programming problem, maximizes PV utilization for individual users, while the upper-layer model, based on a second-order cone relaxation approach, manages the overall IDN to minimize operational costs. The iterative solution leverages tie-line power flow as boundary information to ensure convergence across the network. Validated on an enhanced IEEE 33-bus system, the approach demonstrates a 62% increase in PV-based DG consumption and a 25% reduction in active power losses, highlighting its potential to improve grid efficiency and contribute to emission reduction goals.

Enhancing PV feed-in power forecasting through federated learning with differential privacy using LSTM and GRU

Given the inherent fluctuation of photovoltaic (PV) generation, accurately forecasting solar power output and grid feed-in is crucial for optimizing grid operations. Data-driven methods facilitate efficient supply and demand management in smart grids, but predicting solar power remains challenging due to weather dependence and data privacy restrictions. Traditional deep learning (DL) approaches require access to centralized training data, leading to security and privacy risks. To navigate these challenges, this study utilizes federated learning (FL) to forecast feed-in power for the low-voltage grid. We propose a bottom-up, privacy-preserving prediction method using differential privacy (DP) to enhance data privacy for energy analytics on the customer side. This study aims at proving the viability of an enhanced FL approach by employing three years of meter data from three residential PV systems installed in a southern city of Germany, incorporating irradiance weather data for accurate PV power generation predictions. For the experiments, the DL models long short-term memory (LSTM) and gated recurrent unit (GRU) are federated and integrated with DP. Consequently, federated LSTM and GRU models are compared with centralized and local baseline models using rolling 5-fold cross-validation to evaluate their respective performances. By leveraging advanced FL algorithms such as FedYogi and FedAdam, we propose a method that not only predicts sequential energy data with high accuracy, achieving an R2 of 97.68%, but also adheres to stringent privacy standards, offering a scalable solution for the challenges of smart grids analytics, thus clearly showing that the proposed approach is promising and worth being pursued further.

Challenges with Literature-Derived Data in Machine Learning for Yield Prediction: A Case Study on Pd-Catalyzed Carbonylation Reactions.

The application of machine learning (ML) to predict reaction yields has shown remarkable accuracy when based on high-throughput computational and experimental data. However, the accuracy significantly diminishes when leveraging literature-derived data, highlighting a gap in the predictive capability of the current ML models. This study, focusing on Pd-catalyzed carbonylation reactions, reveals that even with a data set of 2512 reactions, the best-performing model reaches only an R2 of 0.51. Further investigations show that the models' effectiveness is predominantly confined to predictions within narrow subsets of data, closely related and from the same literature sources, rather than across the broader, heterogeneous data sets available in the literature. The reliance on data similarity, coupled with small sample sizes from the same sources, makes the model highly sensitive to inherent fluctuations typical of small data sets, adversely impacting stability, accuracy, and generalizability. The findings underscore the inherent limitations of current ML techniques in leveraging literature-derived data for predicting chemical reaction yields, highlighting the need for more sophisticated approaches to handle the complexity and diversity of chemical data.

Nanosilver-Enhanced Far-Field Fluorescence Fluctuations for Super-Resolution Microscopy.

Fluctuation-based super-resolution microscopy enhances image resolution using signal fluctuations, yet the inherent fluctuations of fluorophores limit its spatiotemporal resolution. In this work, we reveal a far-field enhancement (FFE) effect via a nanosilver film that significantly boosts fluorescence fluctuations of fluorophores positioned up to 10 μm away. The FFE effect arises from the interference of scattered light from the nanosilver film and photothermal-induced refractive index changes in the imaging medium, which create periodic auxiliary illumination on the sample. This phenomenon enabled the development of far-field enhanced super-resolution microscopy (FFE-SRM), a technique compatible with commonly used fluorophores. FFE-SRM improves temporal resolution up to 10-fold and enhances spatial resolution by about 2-fold over various SRM methods, including stochastic optical fluctuation imaging, super-resolution radial fluctuation, mean-shift super-resolution, and direct stochastic optical reconstruction microscopy. We demonstrated the potential of FFE-SRM by revealing mitochondrial dynamics in live-cell imaging, advancing super-resolution imaging, and cellular process exploration.

Demands and challenges of energy storage technology for future power system

AbstractThis paper addresses the pressing necessity to align the regulatory capacity of renewable energy sources with their inherent fluctuations across various time scales. Emphasising the pivotal role of large‐scale energy storage technologies, the study provides a comprehensive overview, comparison, and evaluation of emerging energy storage solutions, such as lithium‐ion cells, flow redox cell, and compressed‐air energy storage. It outlines three fundamental principles for energy storage system development: prioritising safety, optimising costs, and realising value. Through analysis of two case studies—a pure photovoltaic (PV) power island interconnected via a high‐voltage direct current (HVDC) system, and a 100% renewable energy autonomous power supply—the paper elucidates the critical role of energy storage in facilitating high levels of renewable energy integration. Furthermore, it delves into the challenges inherent in the development of energy storage systems.

A stochastic approach to tumor modeling incorporating macrophages

Macrophages are essential components of the immune system’s response to tumors, engaging in intricate interactions shaped by factors such as tumor type, progression, and the surrounding microenvironment. These dynamic relationships between macrophages and cancer cells have become a focal point of research, as scientists seek innovative ways to harness the immune system, including macrophages, for cancer immunotherapy. In this study, we introduce a novel model that examines the interaction between tumor and macrophage cells. We provide an in-depth analysis of the equilibrium points and their stability, as well as a thorough investigation into the solution properties of the model. Moreover, by incorporating a stochastic approach, we account for inherent randomness and fluctuations within the system, offering a more comprehensive understanding of tumor-immune dynamics. Numerical simulations further validate the model, providing key insights into how stochastic elements may influence tumor progression and immune response.

Statistics of Solar White-light Flares. I. Optimization and Application of Identification Methods

White-light flares (WLFs) are energetic activity in the stellar atmosphere. However, observed solar WLFs are relatively rare compared to stellar WLFs or solar flares observed at other wavelengths, which limits our further understanding of solar/stellar WLFs through statistical studies. By analyzing flare observations from the Solar Dynamics Observatory, here we improve WLF identification methods to obtain more solar WLFs and their accurate light curves from two aspects: (1) imposing constraints defined by the typical temporal and spatial distribution characteristics of WLF-induced signals; and (2) setting the intrinsic threshold for each pixel in the flare ribbon region according to its inherent background fluctuation rather than a fixed threshold for the whole region. Applying the optimized method to 90 flares (30 C-class flares, 30 M-class flares, and 30 X-class flares) for a statistical study, we identified a total of nine C-class WLFs, 18 M-class WLFs, and 28 X-class WLFs. The WLF identification rate of C-class flares reported here reaches 30%, which is the highest to date to our best knowledge. It is also revealed that in each GOES energy level the proportion of WLFs is higher in confined flares than that in eruptive flares. Moreover, a power-law relation is found between the WLF energy (E) and duration (τ): τ ∝ E 0.22, similar to those of solar hard/soft X-ray flares and other stellar WLFs. These results indicate that we could recognize more solar WLFs through optimizing the identification method, which will lay a base for future statistical and comparison study of solar and stellar WLFs.

Fluctuation-driven morphological patterning: An unconventional approach to morphogenesis

Recent experimental investigations into regeneration revealed a remarkable phenomenon: the morphological transformation of a tissue fragment from the incipient spherical configuration to a tubelike structure—the hallmark of a mature —has the dynamical characteristics of a first-order phase transition, with calcium field fluctuations within the tissue playing an essential role. This morphological transition was shown to be generated by activation over a barrier within an effective morphological potential that underlies morphogenesis. Inspired by this intriguing insight, we propose an unconventional mechanism where stochastic fluctuations drive the emergence of morphological patterns. Thus, the inherent fluctuations determine the nature of the dynamics and are not incidental noise in the background of the otherwise deterministic dynamics. Instead, they play an important role as a driving force that defines the attributes of the pattern formation dynamics and the nature of the transition itself. Here, we present a simple model that captures the essence of this mechanism for morphological pattern formation. Specifically, we consider a one-dimensional tissue arranged as a closed contour embedded in a two-dimensional space, where the local curvature of the contour is coupled to a non-negative scalar field. An effective temperature parameter regulates the strength of the fluctuations in the system. The tissue exhibits fluctuations near a circular shape at sufficiently low coupling strengths, but as the coupling strength exceeds some critical value, the circular state becomes unstable. The nature of the transition to the new state, namely whether it is a first-order-like or second-order-like transition, depends on the effective temperature and the effective cutoff on the wavelength of the spatial variations in the system. The former sets the level of noise, while the latter determines the height of the potential barrier between the initial circular state and the final deformed state of the tissue. It is also found that entropic barriers separate various metastable states of the system. Published by the American Physical Society 2024

Optimization of solid oxide electrolysis cells using concentrated solar-thermal energy storage: A hybrid deep learning approach

The Solid Oxide Electrolysis Cell (SOEC) represents a cutting-edge solution for the conversion of CO2 and H2O into syngas, offering significant economic and environmental benefits. However, the process requires substantial high-temperature heat inputs, traditionally supplied by electricity. This study introduces a novel approach leveraging concentrated solar radiation as a renewable heat source for SOEC, addressing the challenge of its inherent fluctuations through the integration of Thermal Energy Storage (TES) systems. We propose a hybrid model that combines multi-physics simulation with a deep learning algorithm, enabling rapid optimization of the electrolysis process under real-time direct normal irradiance conditions. Our findings demonstrate that the inclusion of TES within the system architecture results in a remarkable 53 % reduction in temperature variation rate at the SOEC inlet, ensuring operational stability and efficiency. Furthermore, by fine-tuning capacity parameters, we have developed a control strategy that harmonizes efficiency with safety performance. The robustness of our system is underscored by its resilience to step changes, achieving a 75 % reduction in temperature fluctuations. This research contributes a pioneering method for the real-time optimization and control of SOEC systems, harnessing the power of TES to drive sustainable energy conversion with enhanced reliability and economic viability, facilitating precise and swift predictive capabilities even under dynamic operating conditions.

Anchoration of hydrothermally developed FeSe nanoparticles on rGO nanosheets as electrode for energy storage devices

A perpetual discrepancy exists between demand and supply due to the inherent fluctuation of renewable energy sources. A possible approach for addressing this persistent issue involves the advancement of appropriate materials for consumption in energy storage systems. An example of a device that can potentially serve as energy storage is a supercapacitor equipped with highly effective electrode materials. This study synthesizes FeSe, rGO and FeSe@rGO electrodes via hydrothermal route. Specific capacitance (Cs) of FeSe, rGO and FeSe@rGO electrode was measured by GCD analysis as 517, 799, and 1408 F g−1 at 1 A g−1. The synthesized FeSe@rGO nanohybrid exhibits outstanding performance and high cycling stability up to 10000th cycles. Furthermore, the enhanced supercapacitance of the nanohybrid has been ascribed to the synergistic interaction between FeSe and rGO, which delivers higher surface area and enhanced active sites. Further, symmetric two-electrode study revealed excellent electrode material with specific capacitance (Cs) of 553 at 1 A g−1 with capacitive retention of 66.36 % after 1000th cycle. However, the electrochemical investigation demonstrated that the FeSe@rGO nanohybrid structure exhibited superior performance and could be used in different electrochemical applications.

Predicting the Dynamic Interaction of Intrinsically Disordered Proteins.

Intrinsically disordered proteins (IDPs) participate in various biological processes. Interactions involving IDPs are usually dynamic and are affected by their inherent conformation fluctuations. Comprehensive characterization of these interactions based on current techniques is challenging. Here, we present GSALIDP, a GraphSAGE-embedded LSTM network, to capture the dynamic nature of IDP-involved interactions and predict their behaviors. This framework models multiple conformations of IDP as a dynamic graph, which can effectively describe the fluctuation of its flexible conformation. The dynamic interaction between IDPs is studied, and the data sets of IDP conformations and their interactions are obtained through atomistic molecular dynamic (MD) simulations. Residues of IDP are encoded through a series of features including their frustration. GSALIDP can effectively predict the interaction sites of IDP and the contact residue pairs between IDPs. Its performance in predicting IDP interactions is on par with or even better than the conventional models in predicting the interaction of structural proteins. To the best of our knowledge, this is the first model to extend the protein interaction prediction to IDP-involved interactions.

Table-Top Ultrafast Soft X-Ray Spectroscopy Toward Synchrotron-like in-Situ Photoelectrochemical Measurements

Advancements in in-situ X-ray capabilities have enabled the measurement of electrochemical systems in more realistic operating conditions. While these measurements have progressed our understanding of electrochemical reactions from carrier dynamics to reaction mechanisms to degradation processes, they have mostly been limited to synchrotron facilities. However, performing in-situ table-top measurements has remained a challenge in the field. Table-top XUV spectrometers based on high-harmonic generation (HHG) are powerful tools for measuring ultrafast electronic and structural properties of photoelectrocatalysts ex-situ. However, extending the accessible energy range of HHG-based spectrometers beyond 300 eV is difficult due to inherent physical scaling laws. Laser-produced plasma sources can extend into the soft X-ray range to cover the water window. This capability could enable in-situ studies of photocatalysts, but their inherent source fluctuations have prevented ultrafast studies in the past. In this work, we employ methods pioneered by HHG-based setups to create a novel laser-produced plasma technique for table-top time-resolved measurements in the soft X-ray regime (200 – 1000 eV). We combine EMCCD detection, background-free self-referencing, edge-referencing analysis, amongst other advances, to reduce shot-to-shot noise so that sub-mOD signals are detectable. This presentation will also describe the technical advances pioneered at synchrotron sources that have enabled in-situ X-ray measurements of electrochemical systems. These advancements have inspired the effort to achieve the first table-top soft X-ray measurements in device-relevant liquid environments, enabling the correlation of photocatalyst properties, such as electron and hole energies and polarons, with photoelectrochemical product formation.

Degradation modeling and remaining life prediction for a multi-component system under triple uncertainties

As equipment systems are gradually moving toward large-scale and complex that comprise multi-components, the correlation between components significantly impacts the system’s degradation. Meanwhile, the system degradation process is subject to aleatory uncertainty due to inherent fluctuations, epistemic uncertainty since insufficient information, and measurement uncertainty affected by measurement noise. In this paper, a system-level RUL prediction method based on deep belief network, self-organizing map neural network and uncertain random process is proposed by combining the advantages of deep learning in extracting degraded features with the advantages of statistical-based models in quantifying uncertainty. Firstly, the monitoring variables that characterize the equipment degradation trend are selected based on multi-sensor monitoring information of a single component. Deep belief network is utilized to extract component-level deep hidden degradation features. Subsequently, a system-level health index construction method combining mutual information and self-organizing map neural network is proposed according to correlation analysis between components. Finally, a health index evolution model is constructed to predict system RUL by integrating the advantages of the multi-phase degradation model based on uncertain random processes quantifying multiple uncertainties. The superiority of the proposed method is verified through the C-MAPSS dataset provided by NASA.