Multiple Aggregation Research Articles

Dynamic Connectivity Hub: Multiple ISPs Smart Aggregation for Optimized IoT Connectivity

Dynamic Connectivity Hub: Multiple ISPs Smart Aggregation for Optimized IoT Connectivity

Discrete state model of a self-aggregating colloidal system with directional interactions.

The construction of coarse-grained descriptions of a system's kinetics is well established in biophysics. One prominent example is Markov state models in protein folding dynamics. In this paper, we develop a coarse-grained, discrete state model of a self-aggregating colloidal particle system inspired by the concepts of Markov state modeling. The specific self-aggregating system studied here involves field-responsive colloidal particles in orthogonal electric and magnetic fields. Starting from particle-resolved (Brownian dynamics) simulations, we define the discrete states by categorizing each particle according to its local structure. We then describe the kinetics between these states as a series of stochastic, memoryless jumps. In contrast to other works on colloidal self-assembly, our coarse-grained approach describes the simultaneous formation and evolution of multiple aggregates from single particles. Our discrete model also takes into account the changes in transition dynamics between the discrete states as the size of the largest cluster grows. We validate the coarse-grained model by comparing the predicted population fraction in each of the discrete states with those calculated directly from the particle-resolved simulations as a function of the largest cluster size. We then predict population fractions in the presence of noise-averaging and in a situation where a model parameter is changed instantaneously after a certain time. Finally, we explore the validity of the detailed balance condition in the various stages of aggregation.

Kinetic Profiling of Oxidoreductase-Mimicking Nanozymes: Impact of Multiple Activities, Chemical Transformations, and Colloidal Stability.

In contrast to homogeneous enzyme catalysis, nanozymes are nanosized heterogeneous catalysts that perform reactions on a rigid surface. This fundamental difference between enzymes and nanozymes is often overlooked in kinetic studies and practical applications. In this article, using 14 nanozymes of various compositions (core@shell, metal-organic frameworks, metal, and metal oxide nanoparticles), we systematically demonstrate that nontypical features of nanozymes, such as multiple catalytic activities, chemical transformations, and aggregation, need to be considered in nanozyme catalysis. Ignoring these features results in the inaccurate quantification of catalytic activity. Neglecting the multiple activities led to a six-time underestimation of Mn2O3 oxidation activity and mischaracterization of this material as a low-active peroxidase-mimicking nanozyme. Additionally, overlooking chemical stability during catalytic reactions led to the reporting of high peroxidase-mimicking activity for Au@Ag nanoparticles, which, in reality, exhibited no intrinsic activity and oxidized the substrate through the leakage of Ag+ ions. Ignoring the chemical stability of Au@Prussian Blue nanoparticles may lead to more than four times overestimation of peroxidase-mimicking activity after just 24 h of storage. Finally, disregarding the colloidal stability of nanozymes led to a five-time inaccuracy in catalytic activity. These findings underscore the necessity of optimizing procedures to account for these factors in nanozyme kinetic measurements, which will in turn ensure more reliable biosensors and the success of other practical applications.

Self-Assembly of a Perfluorinated Amphiphilic Cyanine Dye into Branched Tubular J-Aggregates.

The self-assembly process is governed by the individual constituents of molecules through precise non-covalent interactions. Amphiphilic cyanines are intriguing in supramolecular chemistry due to the large polarizability of their delocalized π-electron systems, their tuneable optical properties and their ability to form well-defined self-assembled structures in different media. Here we present the synthesis of a novel tetrahydroxy amphiphilic carbocyanine dye with perfluoro alkylated chains -(CH2)2-(CF2)5-CF3 as hydrophobic segments and aminoproanediol as hydrophilic segment. The target molecule was synthesized in a multi-step process, which illustrates the complexity and precision required to achieve the desired structure. This study focuses on the comparison of the influence of C8H17 and C8H4F13 tails and the effects of carboxylated and non-ionic aminopropanediol head groups as substituents on self-assembly of the TBC dye. Absorption and fluorescence measurements show similar spectroscopic properties to cyanine dyes studied previously. Cryogenic transmission electron microscopy (cryo-TEM) reveals formation of multiple supramolecular aggregates. As supramolecular assembly is very sensitive to sample preparation, multilamellar or multivesicular vesicles are obtained preferentially in vigorously vortexed solutions. Moreover, time-dependent tube formation was observed in gently mixed solutions. Thereby, we could follow the growing mechanism of the unprecedented Y-junctions of supramolecular tubes.

Hierarchical Federated Learning-Based Intrusion Detection for In-Vehicle Networks

Intrusion detection systems (IDSs) are crucial for identifying cyberattacks on in-vehicle networks. To enhance IDS robustness and preserve user data privacy, researchers are increasingly adopting federated learning (FL). However, traditional FL-based IDSs depend on a single central aggregator, creating performance bottlenecks and introducing a single point of failure, thereby compromising robustness and scalability. To address these limitations, this paper proposes a Hierarchical Federated Learning (H-FL) framework to deploy and evaluate the performance of the IDS. The H-FL framework incorporates multiple edge aggregators alongside the central aggregator, mitigating single-point failure risks, improving scalability, and efficiently distributing computational load. We evaluate the proposed IDS using the H-FL framework on two car hacking datasets under realistic non-independent and identically distributed (non-IID) data scenarios. Experimental results demonstrate that deploying the IDS within an H-FL framework can enhance the F1-score by up to 10.63%, addressing the limitations of edge-FL in dataset diversity and attack coverage. Notably, H-FL improved the F1-score in 16 out of 24 evaluated scenarios. By enabling the IDS to learn from diverse data, driving conditions, and evolving threats, this approach substantially strengthens cybersecurity in modern vehicular systems.

A Game Theory Based Scheduling Approach for Charging Coordination of Multiple Electric Vehicles Aggregators in Smart Cities

In this paper, a game theory-based coordination approach is presented to find the minimum operation cost of multiple electric vehicle (EV) aggregators that are connected to the upstream network. In order to provide a comprehensive study various EVs aggregators including residential, commercial, official, university, and industry parking lots have been considered and the proposed game theory is utilized to coordinate the charging power from/to different microgrids and/or upstream network. In addition of operation cost, a reliability index, service charge and power loss are included in the objective function. The proposed approach is applied on an electricity network with 7 connected microgrids and different scenarios have been investigated. The simulation results show the overall operation cost in the coalition operation mode is globally minimized in comparison with the non-coalition operation mode. In addition, the reliability index, service charge and power loss force the aggregators to buy their necessary power from the nearby microgrids. The average network loss in the coalition and non-coalition modes are 2.88% and 4.28%, respectively. Moreover, the average reliability cost in coalition and non-coalition modes are $2.65 and $4.25, respectively.

Chronic gastroesophageal reflux dysregulates proteostasis in esophageal epithelial cells

Background and aimsGastroesophageal reflux disease (GERD) is a common digestive disorder that is characterized by esophageal tissue damage produced by exposure of the esophageal lining to the gastric refluxate. GERD can raise the risk of multiple serious complications including esophageal tumors. At the molecular levels, GERD-affected tissues are characterized by strong oxidative stress and the formation of reactive isolevuglandins (isoLGs). These products of lipid peroxidation rapidly interact with cellular proteins forming protein adducts. Here, we investigated the interrelationship between isoLG adduction and aggregation of cellular proteins. MethodsProtein misfolding and aggregation were analyzed using multiple protein misfolding and aggregation assays. Pathological consequences of protein adduction and aggregation were studied using human and murine esophageal tissues. Surgical model of esophageal reflux injury and L2-IL1β transgenic mice were employed to investigate the mechanisms of protein misfolding and aggregation. ResultsOur studies demonstrate that gastroesophageal reflux causes protein misfolding and aggregation that is associated with severity of GERD. Dysregulation of proteostasis induces ferroptotic cell death and is mediated by modification of cellular proteins with reactive isoLGs that can be prevented by isoLG scavengers. ConclusionsGERD causes dysregulation of cellular proteostasis, accumulation of isoLG protein adducts, misfolded and aggregated proteins that promote ferroptotic cell death. Taken together, this study suggests that GERD has similarities to other known pathological conditions that are characterized by protein misfolding and aggregation.

Effects and mechanisms of different carbonaceous aggregates on coal tar pitch wettability behavior

The wettability of coal tar pitch is significantly influenced by the type of carbonaceous aggregate, directly impacting its effectiveness as a binder in industrial applications. This study systematically investigates the wetting behavior between various carbonaceous aggregates and coal tar pitch, providing critical insights into the physicochemical properties that influence the interaction. Specifically, we examine the potential of utilizing residual anodes as alternative aggregates. The rheological property of coal tar pitch was characterized to evaluate its suitability as a binder. Subsequently, the wettability of coal tar pitch on multiple carbonaceous aggregates was characterized by single particle wettability experiments, static wettability tests, and dynamic contact angle measurements. The results indicate that coal tar pitch exhibits the highest wettability with calcined coke, followed by residual anode, anthracite coal, and spent pot lining. Static wettability experiment revealed an inverse relationship between the specific surface area of the carbonaceous aggregates and their wettability. The Super-Depth-of-Field Microscope denoted that decreasing surface roughness improves the wettability of car tar pitch when the composition of aggregates is similar. X-ray diffraction and X-ray photoelectron spectroscopy tests showed that higher ideal graphite carbon content (Ig), decreased height of the crystallite (Lc), and increased surface functional groups (CN/CO/CS) all contribute to improve wettability. This study not only provides a comprehensive framework for selecting optimal carbonaceous aggregates but also delivers theoretical support for the reuse of residual anodes in industrial processes.

Dynamic localization based-utility decision approach under type-2 Pythagorean fuzzy set for developing internet of modular self-reconfiguration robot things

The Internet of Modular Robot Things (IoMRT) has emerged through the integration of robotic systems into the Internet of Things (IoT), offering a wide range of solutions to meet continuously growing demands. Six self-reconfiguration functionalities/criteria have been proposed for developing IoMRT. However, no study has fully developed an IoMRT that satisfies all the necessary functionalities. Additionally, there is a lack of scholarly research proposing a decision-based approach for evaluating and ranking IoMRT, which highlights a significant research gap. A complex multiple-criteria decision-making (MCDM) problem has arisen in evaluating and ranking IoMRT due to the diversity of functionalities, the need to prioritize these functionalities based on their importance, and data variability. To address this issue, the study proposes a novel decision-based approach for evaluating and ranking IoMRT, which consists of three phases: (i) Developing a novel weighting method called T2PFS-FWZICbIP (Type-2 Pythagorean Fuzzy Set–Fuzzy Weighted Zero Inconsistency based on Interrelationship Process) to measure the importance of the identified functionalities; (ii) Formulating a decision matrix by cross-referencing potential IoMRT developments with the six self-reconfiguration functionalities resulted in the selection of a random sample of 50 IoMRTs as proof of concept. Following this, the DLbU (Dynamic Localization-based Utility) method was proposed, integrating dynamic localization and utility procedures to manage binary data within the decision matrix; (iii) Developing a novel ranking method, T2PFS-DNMA (Type-2 Pythagorean Fuzzy Set–Double Normalization-based Multiple Aggregation), to address the diversity of functionalities and concerns regarding data variance. The results revealed that the Distributed functionality (C1) received the highest weight value of 0.4060 according to T2PFS-FWZICbIP, indicating its high importance in the ranking of IoMRT. In contrast, the High-Fidelity functionality (C5) received a weight value of 0.0733, indicating its very low importance in the ranking. IoMRT2 and IoMRT35 were identified as the most and least favored, respectively, according to T2PFS-DNMA. The robustness of the proposed approach was assessed through sensitivity analysis and comparative studies.

Cooperative price-based demand response program for multiple aggregators based on multi-agent reinforcement learning and Shapley-value

Demand response (DR) plays an essential role in power system management. To facilitate the implementation of these techniques, many aggregators have appeared in response as new mediating entities in the electricity market. These actors exploit the technologies to engage customers in DR programs, offering grid services like load scheduling. However, the growing number of aggregators has become a new challenge, making it difficult for utilities to manage the load scheduling problem. This paper presents a multi-agent reinforcement Learning (MARL) approach to a price-based DR program for multiple aggregators. A dynamic pricing scheme based on discounts is proposed to encourage residential customers to change their consumption patterns. This strategy is based on a cooperative framework for a set of DR Aggregators (DRAs). The DRAs take advantage of a reward offered by a Distribution System Operator (DSO) for performing a peak-shaving over the total system aggregated demand. Furthermore, a Shapley-Value-based reward sharing mechanism is implemented to fairly determine the individual contribution and calculate the individual reward for each DRA. Simulation results verify the merits of the proposed model for a multi-aggregator system, improving DRAs’ pricing strategies considering the overall objectives of the system. Consumption peaks were managed by reducing the Peak-to-Average Ratio (PAR) by 15%, and the MARL mechanism’s performance was improved in terms of reward function maximization and convergence time, the latter being reduced by 29%.

DEVELOPMENT OF SYSTEMIC MASTOCYTOSIS AFTER BONE MARROW TRANSPLANTATION FOR AN ACUTE MYELOID LEUKEMIA: DONOR DERIVEN DISEASE OR SYSTEMIC MASTOCYTOSIS WITH AN ASSOCIATED MYELOID NEOPLASM

Abstract Introduction/Objective Systemic mastocytosis may have variable presentation ranging from an indolent disease to an aggressive form. Systemic mastocytosis with an associated myeloid neoplasm can occur simultaneously, or within an interval of a myeloid neoplasm and should meet the diagnostic criteria of both entities. Methods/Case Report Herein, we present a patient with history of acute myeloid leukemia with KMT2A(11q23) amplification who initially underwent an allogeneic hematopoietic stem cell transplantation from a matched sibling donor. One year later, the patient developed isolated central nervous system relapse, as well as evidence of molecular relapse in the bone marrow and underwent a second allogeneic transplantation, but this time, from a matched unrelated donor. The 100 days post second transplant bone marrow biopsy was negative for involvement by acute myeloid leukemia and the chimerism study revealed evidence of total engraftment with 100% donor cells. Additionally, Fluorescence in situ hybridization analysis detected no evidence of KMT2A amplification. However, morphologic examination of the bone marrow biopsy demonstrated involvement by systemic mastocytosis with multiple dense aggregates of CD117 and tryptase positive mast cells showing spindled cell morphology. Mast cells were positive for CD25 and demonstrated negative expression of CD2 and CD30. PCR analysis of the peripheral blood did not detect evidence of KIT mutation, but serum tryptase was elevated. Results (if a Case Study enter NA) NA Conclusion This case highlights a rare instance of systemic mastocytosis in the setting of remission from an acute myeloid leukemia in a post transplantation bone marrow and represents a diagnostic challenge. While development of systemic mastocytosis in a patient with prior history of an acute myeloid leukemia suggests the possibility of systemic mastocytosis with an associated myeloid neoplasm, some other features including the complete remission and total engraftment of the post transplantation bone marrow with 100% donor cells, raise the possibility of a donor driven disease.

Robust day-ahead scheduling of cooperative energy communities considering multiple aggregators

Future cities must play a vital role in reducing energy consumption and decarbonizing the electricity sector, thus evolving from passive structures towards more efficient smart cities. This transition can be facilitated by energy communities. This emerging paradigm consists of collectivizing a set of residential installations equipped with onsite renewable generators and storage assets (i.e., prosumers), which can eventually share resources to pursue collective welfare. This paper focuses on cooperative communities, where prosumers share resources without seeking selfish monetary counterparts. Despite their apparent advantages, energy management and scheduling of energy communities suppose a challenge for conventional tools due to the high level of uncertainty (especially due to intermittent renewable generation and random demand), and privacy concerns among prosumers. This paper addresses these issues. Specifically, a novel management structure based on multiple aggregators is proposed. This paradigm preserves users' confidential features while allowing them to extract the full potential of their assets. To efficiently manage the variety of assets available under uncertainty, an adaptive robust day-ahead scheduling model is developed, which casts as a solvable and portable Mixed Integer Linear Programming framework, which eases its implementation in real-world cases. The new proposal concerns uncertain generation and demand using a polyhedral representation of the uncertainty set. A case study is conducted to validate the developed model, showing promising results. Moreover, different results are obtained and analysed. Finally, it is worth remarking on how the level of robustness impacts the collective bill, incrementing it by 75 % when risk-averse conditions are assumed. In addition, the role of storage assets under pessimistic conditions is remarked, pointing out that these assets rule the scheduling plan of the community instead of renewable generators.

Wax crystal dynamics and viscosity abnormal reduction in asphaltene-containing waxy oil at low temperatures

A micro-rheological integration experiment was conducted on model oils without asphaltene and with 0.05 wt% asphaltene. The study examined the viscosity-temperature characteristics of both model oils under different cooling rates and shear rates. Significant changes were observed in the viscosity-temperature curve of the asphaltene-containing model oil. At low shear rates, a distinct wavelet peak was identified in the temperature range of 37-30 °C, where viscosity initially increased, then decreased, and subsequently increased again. This wavelet peak diminished with the increasing shear rate, eventually forming a “buffer platform”. The underlying mechanism was attributed to asphaltene altering the wax crystal behavior from “uniform precipitation network structure formation” to “formation of unstable large aggregates - dissociation into multiple small stable aggregates - growth into larger stable aggregates.” In addition, the wax precipitation rate remained constant in the model oil without asphaltene, while in the asphaltene-containing model oil, the rate significantly accelerated between 34.5 °C and 32.5 °C. And the asphaltenes promoted the formation of small wax crystal particles but inhibited the growth of wax crystal particles.

Architecturing ultra-stable multi-dimensional MXene/MAX self-supporting electrode anchored with Low-Pt for efficient hydrogen evolution reaction in harsh environments

The design of self-supporting structure is particularly important to improve the stability and electrochemical performance of hydrogen evolution reaction electrode. Here, a facile strategy for building novel ultra-stable 0D-2D-3D integrated self-supporting electrode with high conductivity, sufficient diffusion channels and large reactive surface area was proposed. In the heterostructure, 2D Ti3C2Tx flakes in-situ synthesized on 3D network porous Ti3AlC2 surface which provides the multiple reactive surface areas and aggregation resistance to facilitating 0D ultrafine Pt nanoparticles uniform anchorage. Combined with structural characterization and first-principles calculations revealed that, the highly dispersed ultrafine Pt synergistically coupling strong metal-support interactions creates a unique multifunctional catalytic interface with high stability and atomic utilization efficiency of Pt to promote the HER in acidic and seawater. The resultant self-supporting electrode (support 0.48 wt% Pt) exhibits much superior activity to 10 % commercial Pt/C (loaded on foam nickel) in 0.5 M H2SO4 (55 mV@10 mA cm−2) and simulate seawater (196 mV@10 mA cm−2) while reducing the Pt usage by 15 times. Meanwhile, the electrode also illustrates outstanding stability under high current densities (100 h@100 mA cm−2). This study provides a new design idea for developing integrated self-supporting catalytic electrodes to meet the durability of hydrogen evolution reaction applications in harsh environments.

Improving the forecast accuracy of wind power by leveraging multiple hierarchical structure

Renewable energy generation is of utmost importance for global decarbonization. Forecasting renewable energies, particularly wind energy, is challenging due to the inherent uncertainty in wind energy generation, which depends on weather conditions. Recent advances in hierarchical forecasting through reconciliation have demonstrated a significant increase in the quality of wind energy forecasts for short-term periods. We leverage the cross-sectional and temporal hierarchical structure of turbines in wind farms and build cross-temporal hierarchies to further investigate how integrated cross-sectional and temporal dimensions can add value to forecast accuracy in wind farms. We found that cross-temporal reconciliation was superior to individual cross-sectional reconciliation at multiple temporal aggregations. Additionally, machine learning based forecasts that were cross-temporally reconciled demonstrated high accuracy at coarser temporal granularities, which may encourage adoption for short-term wind forecasts. Empirically, we provide insights for decision-makers on the best methods for forecasting high-frequency wind data across different forecasting horizons and levels.

An efficient decision-making model for evaluating irrigation systems under uncertainty: Toward integrated approaches to sustainability

Agriculture is essential for many countries that depend on crops for food production and security. So, evaluating irrigation systems and selecting the best one is critical and gives various benefits such as water use efficiency, productivity, and agricultural development. This paper proposed a framework for conserving water and increasing effectiveness by using a suitable irrigation system. We used the multi-criteria decision-making (MCDM) methodology to deal with conflict criteria in the evaluation process. We used two MCDM methods such as Criteria Importance Through Inter-Criteria Correlation (CRITIC) method to compute the weights of the irrigation system criteria, and the spherical fuzzy double normalization-based multiple aggregations (DNMA) method to rank the irrigation systems (alternatives). The main advantage of CRITIC method computes the conflict and variability of the criteria by calculate weights of them. The main advantage of DNMA is used the two normalization method to rank the alternatives. These methods are integrated with spherical fuzzy set (SFS) fuzzy information in the assessment process. It has three values: membership, non-membership, and hesitant degrees to overcome uncertainty in the assessment steps. The proposed methodology is applied to a case study to show its performance. This study used 20 criteria of irrigation systems and 10 irrigation systems (alternatives) to select the best alternative. The results are discussed from the perspective of five experts. The sensitivity analysis is conducted to show the stability of the results. The comparative analysis is performed to show the validity and effectiveness of the proposed methodology. The results show the proposed methodology is more robust compared to other methods.

Multiple financial analyst opinions aggregation based on uncertainty-aware quality evaluation

Financial analysts’ opinions are pivotal in investment decision-making, as they provide valuable expert knowledge. Aggregating these opinions offers a promising way to unlock their collective wisdom. However, existing opinion aggregation methods are hindered by their inability to effectively assess differences in opinion quality, resulting in suboptimal outcomes. This Study introduces a novel model called SmartMOA, which addresses this limitation by automatically evaluating the quality of each opinion and integrating this evaluation into the aggregation process. Our model begins with a novel Bayesian neural network that leverages the implicit knowledge embedded in the interactions between analysts and stock characteristics. This methodology produces an assessment of individual opinions that accounts for uncertainties. We then formulate a bi-objective combinatorial optimization problem to determine optimal weights for combining multiple analysts’ opinions, simultaneously minimizing the error and uncertainty of the aggregated outcome. Therefore, SmartMOA systematically highlights high-quality opinions during the aggregation process. Using a real dataset spanning eight years, we present comprehensive empirical evidence that demonstrates the superior performance of SmartMOA in heterogeneous analyst opinion aggregation.

Joint bidding strategy of multi‐virtual power plant operators

AbstractThe distributed generation of new energy can effectively use the local endemic clean new energy and provide green power. However, due to the small scale and scattered layout of distributed generation resources, it is difficult to participate in the economic dispatch of power systems and even the competition of the power market. Virtual Power Plant (VPP) can act as a carrier of Distributed Energy Resource (DER) to manage its internal energy, to carry out combined bidding in the day‐ahead energy market and the regulation market. When virtual power plants coordinate multiple distributed energy resources to participate in electricity market transactions, the degree of disclosure of their internal privacy information will produce different trading strategies. A Stackelberg game model between multiple virtual power plant aggregators and virtual power plant operators is constructed, and the Kriging model is combined to protect the privacy information in the transactions of virtual power plant operators. Energy management for distributed generation is carried out, and the profit situation of virtual power plant operators under different strategies is analyzed.

A Meta-Analysis Methodology in Stan to Estimate Population Pharmacokinetic Parameters from Multiple Aggregate Concentration-Time Datasets: Application to Gevokizumab mPBPK Model.

The aim of the present study was to develop and evaluate the performance of a methodology to estimate the population pharmacokinetic (PK) parameters along with the inter-individual variabilities (IIVs) from patients' reported aggregate concentration-time data, in particular, mean plasma concentrations and their standard deviations (SDs) versus time, such as those often found in published graphs. This method was applied to the published data of gevokizumab, a novel monoclonal anti-interleukin-1β antibody, in order to estimate the drug's population pharmacokinetic (PopPK) parameters of a second-generation minimal physiologically based pharmacokinetic (mPBPK) model. Assuming this mPBPK model, a mixed effects approach was utilized to allow accounting for the random inter-group variability (IGV) that was assumed among different dosage groups. The entire analysis was performed using R software (Rstudio) and the Bayesian software tool RStan was used for the application of Bayesian priors on the parameters. Conclusively, the proposed method could be applied to monoclonal antibodies for which the second-generation mPBPK model has been proposed as well as to other drugs with different PK models when only a published graph with aggregate concentration-time data is available. In addition, the method could be used when multiple aggregate datasets from different sources need to be combined in a meta-analysis approach in order to estimate the PopPK parameters of a drug.

Magnetically Powered Microrobotic Swarm for Integrated Mechanical/Photothermal/Photodynamic Thrombolysis.

Current thrombolytic drugs exhibit suboptimal therapeutic outcomes and potential bleeding risks due to their limited circulation time, inadequate thrombus penetration, and off-target biodistribution. Herein, a photosensitizer-loaded, red cell membrane-encapsuled multiple magnetic nanoparticles aggregate is successfully developed for integrated mechanical/photothermal/photodynamic thrombolysis. Red cell membrane coating endows magnetic particles with prolonged blood circulation and superior biocompatibility. Under a preset rotating magnetic field (RMF), the aggregate with asymmetric magnetic distribution initiates rolling motion toward the blood clot interface, and because of magnetic dipole-dipole interactions, the aggregate tends to self-assemble into longer, flexible chain-like microrobotic swarm with powerful mechanical stir forces, thereby facilitating thrombus penetration and mechanical thrombolysis. Moreover, precise magnetic control enables targeted photosensitizer accumulation, allowing effective conversion of near-infrared (NIR) light into heat and reactive oxygen species (ROS) for thrombus phototherapy. In thrombolysis assays, the weight of thrombi is massively reduced by ≈90%. The work presents a safer and more promising combination of magnetic microrobotic technology and phototherapy for multi-modality thrombolysis.