Aggregation Model Research Articles

A Hybrid Strategy Two‐Dimensional Concrete Aggregate Filling Algorithm

ABSTRACTAccurate and highly efficient construction of numerical concrete models with different gradations is important to analyze the acoustic properties of ultrasonic waves in concrete. The random aggregates‐based method is a classic approach to meso‐structural modeling of concrete and is particularly suitable for generating models with low‐density aggregates. However, when facing the simulation requirements of concrete models with large volumes, small particle sizes, and high aggregate content, traditional random aggregates‐based methods encounter challenges in satisfying aggregate packing density and modeling efficiency. In order to improve the efficiency of aggregate placement in modeling concrete meso‐models, we proposed a hybrid‐strategy point cloud placement algorithm that combines the re‐placement and sedimentation strategies. Then, the algorithm's effectiveness in improving the modeling efficiency is verified by reconstruction experiments on random aggregate models with different aggregate grades. Finally, the model's reliability and validity is further confirmed by simulation analysis of the ultrasonic attenuation characteristics of the model. The results show that the algorithm can rapidly generate more than 75% aggregate packing density concrete specimens and significantly outperforms similar algorithms regarding aggregate placement efficiency and model generation time. In addition, the model generated by the proposed algorithm can accurately simulate the attenuation characteristics of ultrasonic waves in concrete.

Research Development and Key Issues of Pervious Concrete: A Review

In recent years, various aspects of research related to pervious concrete (PC) have progressed rapidly, and it is necessary to summarise and generalise the latest research results. This paper reviews and compares the raw materials of pervious concrete, examining elements such as porosity, permeability, mechanical properties, and durability. According to comparisons, we put forward an ideal aggregate model with Uneven Surface, which may reinforce the mechanical properties. By summarising the important issues of aggregate, particle size, water–cement ratio, additives and admixtures, mixing ratio design, mixing and moulding, and other factors that affect porosity, new design methods are proposed. A new effective stress model of pervious concrete based on continuous porosity and Terzaghi effective stress is developed which may fit the effective stress principle better. Finally, by summarising the research frontiers of pervious concrete, key issues that need to be addressed in future scientific research on pervious concrete are raised.

Taxonomy and Survey of Collaborative Intrusion Detection System using Federated Learning

This review paper looks at recent research on Federated Learning (FL) for Collaborative Intrusion Detection Systems (CIDS) to establish a taxonomy and survey. The motivation behind this review comes from the difficulty of detecting coordinated cyberattacks in large-scale distributed networks. Collaborative anomalies are one of the network anomalies that need to be detected through robust collaborative learning methods. FL is promising collaborative learning method in recent research. This review aims to offer insights and lesson learn for creating a taxonomy of collaborative anomaly detection in CIDS using FL as a collaborative learning method. Our findings suggest that a taxonomy is required to map the discussion area, including an algorithm for training the learning model, the dataset, global aggregation model, system architecture, security, and privacy. Our results indicate that FL is a promising approach for collaborative anomaly detection in CIDS, and the proposed taxonomy could be useful for future research in this area. Overall, this review contributes to the growing knowledge of FL for CIDS, providing insights and lessons for researchers and practitioners. This research also concludes significant challenges, opportunities, and future directions in CIDS based on collaborative anomaly detection using FL.

Transport in cellular aggregates described by fluctuating hydrodynamics

Biological functionality of cellular aggregates is largely influenced by the activity and displacements of individual constituent cells. From a theoretical perspective this activity can be characterized by hydrodynamic transport coefficients of diffusivity and conductivity. Motivated by the clustering dynamics of bacterial microcolonies we propose a model of active multicellular aggregates and use recently developed macroscopic fluctuation theory to derive a fluctuating hydrodynamics for this model system. Both semianalytic theory and microscopic simulations show that the hydrodynamic transport coefficients are affected by nonequilibrium microscopic parameters and significantly decrease inside of the clusters. We further find that the Einstein relation connecting the transport coefficients and fluctuations breaks down in the parameter regime where the detailed balance is not satisfied. This study offers valuable tools for experimental investigation of hydrodynamic transport in other systems of cellular aggregates such as tumor spheroids and organoids. Published by the American Physical Society 2024

Study on the morphological characteristics of coal particle flocs under fractal theory

Based on fractal theory, the geometric size, surface morphology, and spatial structure of flocs in the hydrophobic flocculation process of coal particles were examined via particle size distribution detection, image analysis technology, and numerical simulation. The floc size distribution curve was classified and discussed via the dual-domain fractal rule, and the variation law of the composition and distribution of the coarse- and fine-grained coal particle flocs with the flocculation process was clarified. The various levels and characteristics of the floc growth geometry were explained by the analysis of the fractal dimension of the individual floc surface and the multiple spectra of the floc groups in the metallographic microscopy image. The hydrophobic flocculation process of coal particles was simulated via the diffusion-limited cluster aggregation model. The gyration fractal dimension was used to examine floc fragmentation and reconstruction and clarify the growth law of the floc spatial structure during the hydrophobic flocculation process.

Experimental and numerical study on the chloride ions penetration in recycled aggregate concrete

Recycled aggregate concrete (RAC) offers a practical solution for improving resource efficiency and reducing environmental impact. Durability degradation in concrete structures is primarily caused by the corrosion of internal reinforcement due to chloride ion penetration. Thus, the resistance of RAC to chloride ion penetration is a crucial durability indicator. This study investigates the effects of recycled coarse aggregate (RCA) and mineral powder on RAC's chloride penetration resistance using rapid chloride ions migration (RCM) tests. Results show that increasing the RCA replacement ratio reduces its resistance to chloride ions penetration. The addition of slag improves resistance to chloride ions of RAC by up to 30 %, while fly ash addition shows an initial increase followed by a decline in resistance to chloride ions penetration of RAC. To address the variability in the spatial distribution and thickness of old mortar within RCA, a two-dimensional random aggregate model incorporating ellipsoids and internal polygons was developed. Five distinct phases were considered in the model: natural aggregate, new motar, old mortar, old interface transition zone (ITZ) between natural aggregate and old mortar, new ITZ between old mortar and new mortar. The model simulated the random placement of aggregates by randomizing geometric shapes, considering factors such as the RCA replacement ratio and particle size. COMSOL software was used to simulate chloride ions penetration in RAC with 30 % RCA, and the simulation model's accuracy was verified through ion chromatography. This study takes into account the random distribution of old mortar on the surfaces of RCA to provide a deeper investigation into chloride transport within RAC.

Ultraprocessed Food Consumption and Hypertension Risk in the REGARDS Cohort Study.

This study examined the longitudinal association between ultraprocessed food (UPF) consumption and overall hypertension risk and explored the contribution of UPF to racial disparities in risk for hypertension. We analyzed data from 5957 participants from the REGARDS cohort study (Reasons for Geographic and Racial Disparities in Stroke) free from hypertension during visit 1 (2003-2007), had complete dietary information at visit 1, and completed visit 2 (2013-2016). UPF consumption was measured using the Nova classification system and operationalized percent calories and grams. The main outcome was incident hypertension. Logistic regression was used for analysis. Thirty-six percent of participants developed hypertension at visit 2. Results showed a positive linear relationship between UPF and hypertension incidence. Aggregate model results showed that those in the highest UPF consumption quartile had 23% greater odds of incident hypertension compared with the lowest quartile. Multivariable results showed that Black and White participants in the highest consumption quartile had 1.26 (95% CI, 0.92-1.74) and 1.22 (95% CI, 1.01-1.47) greater odds of hypertension compared with those in the lowest quartile, respectively. Analyses using UPF consumption as percent grams showed similar aggregate results; however, race-stratified results differed. Findings were no longer statistically significant among White participants (odds ratio, 1.09 [95% CI, 0.89-1.33]) but showed significant differences in incident hypertension between Black participants in the highest versus lowest UPF quartiles (odds ratio, 1.43 [95% CI, 1.01-2.02]). This study demonstrated that high consumption of UPF is associated with increased hypertension risk. Further research is warranted to better understand differences in the intakes of UPF subgroups that may underpin the racial differences in hypertension incidence observed with different UPF metrics.

Nonuniversal Dynamics of Hyperbranched Metallo-Supramolecular Polymer Networks by the Spontaneous Formation of Nanoparticles.

Various transient and permanent bonds are commonly combined in increasingly complex hierarchical structures to achieve biomimetic functions, along with high mechanical properties. However, there is a traditional trade-off between mechanical strength and biological functions like self-healing. To fill this gap, we develop a metallo-supramolecular polymer hydrogel based on the hyperbranched poly(ethylene imine) (PEI) backbone and phenanthroline ligands, which have unexpectedly high plateau modulus at low concentrations. Rheological measurements demonstrate nonuniversal metal-ion-specific dynamics, with significantly larger plateau moduli, longer relaxation times, and stronger temperature dependencies, compared to equivalent networks based on model-type telechelic precursors, which cannot be explained by the theory of linear viscoelasticity. TEM images reveal the in situ mineralization of metal ions, which nucleate by the ligand complexation and grow thanks to the spontaneous reducing effect of the PEI backbone. Evidently, the complex lifetime works against Ostwald ripening, resulting in the formation of thermodynamically stable smaller particles. This trend is followed by time-dependent network buildup measurements and is confirmed by a kinetic model for particle formation and aggregation. The spontaneous formation of particles with complex lifetime-dependent sizes can explain the nonuniversal dynamics through the interaction of polymer segments and particles at the nanoscale. This work describes how the polymer backbone can affect the strength and stability of supramolecular bonds, promising for combining high mechanical properties and self-healing comparable to natural tissues.

Enhancing Heart Disease Prediction with Federated Learning and Blockchain Integration

Federated learning offers a framework for developing local models across institutions while safeguarding sensitive data. This paper introduces a novel approach for heart disease prediction using the TabNet model, which combines the strengths of tree-based models and deep neural networks. Our study utilizes the Comprehensive Heart Disease and UCI Heart Disease datasets, leveraging TabNet’s architecture to enhance data handling in federated environments. Horizontal federated learning was implemented using the federated averaging algorithm to securely aggregate model updates across participants. Blockchain technology was integrated to enhance transparency and accountability, with smart contracts automating governance. The experimental results demonstrate that TabNet achieved the highest balanced metrics score of 1.594 after 50 epochs, with an accuracy of 0.822 and an epsilon value of 6.855, effectively balancing privacy and performance. The model also demonstrated strong accuracy with only 10 iterations on aggregated data, highlighting the benefits of multi-source data integration. This work presents a scalable, privacy-preserving solution for heart disease prediction, combining TabNet and blockchain to address key healthcare challenges while ensuring data integrity.

Privacy-Preserving Federated Learning-Based Intrusion Detection Technique for Cyber-Physical Systems

The Internet of Things (IoT) has revolutionized various industries, but the increased dependence on all kinds of IoT devices and the sensitive nature of the data accumulated by them pose a formidable threat to privacy and security. While traditional IDSs have been effective in securing critical infrastructures, the centralized nature of these systems raises serious data privacy concerns as sensitive information is sent to a central server for analysis. This research paper introduces a Federated Learning (FL) approach designed for detecting intrusions in diverse IoT networks to address the issue of data privacy by ensuring that sensitive information is kept in the individual IoT devices during model training. Our framework utilizes the Federated Averaging (FedAvg) algorithm, which aggregates model weights from distributed devices to refine the global model iteratively. The proposed model manages to achieve above 90% accuracies across various metrics, including precision, recall, and F1 score, while maintaining low computational demands. The results show that the proposed system successfully identifies various types of cyberattacks, including Denial-of-Service (DoS), Distributed Denial-of-Service (DDoS), data injection, ransomware, and several others, showcasing its robustness. This research makes a great advancement to the IDSs by providing an efficient and reliable solution that is more scalable and privacy friendly than any of the existing models.

Community Choice Electricity Aggregation and Solar Adoption: Evidence From Illinois

We explore whether municipalities voting on the community choice aggregation (CCA) model of electricity procurement relates to the adoption of small-scale distributed solar in the US state of Illinois. Municipalities that held a referendum for CCA were more likely to have small-scale solar installations compared to those that did not. Among municipalities with solar installations, those that held CCA referendums have lower per capita solar power generation than those without CCA referendums. The impacts of CCA referendums on small-scale solar adoption are mixed and should be combined with other strategies to promote broader solar adoption in the U.S. energy market.

Simulation of Dendrite Growth with a Diffusion-Limited Aggregation Model Validated by MRI of a Lithium Symmetric Cell during Charging

Lithium metal batteries offer high energy density but are challenged by dendrite growth, which can lead to short circuits and battery failure. Multiple models with varying degrees of accuracy and computational cost have been developed to understand and predict dendrite growth. This study presents a simple model to simulate macroscale dendrite growth on lithium metal electrodes. The model uses a 3D single-particle Diffusion-Limited Aggregation (DLA) algorithm with an electric field bias to simulate dendrite growth. The electric field bias was introduced into the model with an important parameter, namely the biasing factor c, which determines the balance between diffusion and electric field effects. Before performing the simulation with the proposed model, the dendrite growth in a lithium symmetric cell during charging was measured by sequential 3D magnetic resonance imaging (MRI). These data were then used to validate the simulation, as the dendrite structure in each measured MRI time frame was used a starting point for a new simulation, the results of which were then validated with the measured dendrite structure of the next time frame. The best agreement between the simulated and measured dendrite structures using the overlap and displacement of deposition sites metrics was obtained at the biasing factor c = 0.7. This agreement was also good in terms with the fractal dimension of the dendrite structures. The proposed method offers a simple, accurate, and scalable framework for predicting dendrite growth over long deposition periods, making it a valuable tool for studying dendrite suppression under real-world battery charging conditions.

A Robust Client Selection Mechanism for Federated Learning Environments

There is a exponential growth of data usage, specially due to the proliferation of connected applications with personalized models for different applications. In this context, Federated Learning (FL) emerges as a promising solution to enable collaborative model training while preserving the privacy and autonomy of participating clients. In a typical FL scenario, clients exhibit significant heterogeneity in terms of data distribution and hardware configurations. In this way, randomly sampling clients in each training round may not fully exploit the local updates from heterogeneous clients, resulting in lower model accuracy, slower convergence rate, degraded fairness, etc. In addition, malicious users could disseminate incorrect weights, which may decrease the accuracy of aggregated models and increase the time for convergence in FL. In this article, we introduce Resilience-aware Client Selection Mechanism for non-IID data and malicious clients in FL environment, called RICA. The proposed mechanism employs data size and entropy as criteria for client selection. In addition, RICA relies Centroid-Based Kernel Alignment (CKA) to identify and exclude potentially malicious clients. Our evaluation shows an improvement of 125% in Accuracy values in a scenario of malicious clients, which means the RICA+CKA demonstrates a more stable and resilient approach, reaching 90% accuracy in a few rounds compared to the default average approach, reached only around 30%. Therefore, results of the behavior of RICA+CKA in different datasets show the evaluation of different numbers of clients reaching around 90% while the other approach does not pass the 50% Accuracy.

Examining the indirect effects of acculturation stress on insomnia through rumination and alcohol use among Latinx women and men.

Acculturation stress, a type of sociocultural stress, is positively associated with insomnia among Latinxs; however, the mechanisms of this association remain elusive. We tested the indirect effects of acculturation stress on insomnia through rumination and alcohol use, two coping strategies associated with insomnia, and explored these effects in gender-stratified models among Latinxs. We analyzed cross-sectional data from 187 Latinxs participating in the Latino Sleep and Health study in New York City in 2016-2019. We conducted bias-corrected boot-strap tests of mediation with case resampling (1,000 replications) in aggregate and gender-stratified models. Acculturation stress and insomnia were measured continuously using the Hispanic Stress Inventory and Insomnia Severity Index (ISI). Rumination was measured using a subscale of the Ruminative Response Scale. The Quantity-Frequency Index was used to measure alcohol use. Covariates included age, New York City poverty threshold, and perceived stress. Participants were Mage 37.43 (SD = 13.67). Most participants were women (64.17%). The average ISI was 6.65 (SD = 5.51). The indirect effect of acculturation stress on insomnia through rumination was statistically significant (b = 0.02, 95% BCa CI [0.01, 0.03]). Among women, this indirect effect had slightly larger coefficients than among men (b = 0.02, 95% BCa CI [0.01, 0.04]; b = 0.01, 95% BCa CI [0.004, 0.04]). Alcohol use was not a statistically significant mediator (b = -0.001, 95% BCa CI [-0.004, 0.0002]). These findings suggest that psychological interventions focused on reducing rumination in response to acculturation stress may promote healthy sleep among Latinxs, particularly among Latina women. Future studies should use longitudinal study designs to determine the causal relationships among these variables. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Survey on Federated Learning for Intrusion Detection System: Concept, Architectures, Aggregation Strategies, Challenges, and Future Directions

Intrusion Detection Systems (IDS) are essential for securing computer networks by identifying and mitigating potential threats. However, traditional IDS face challenges related to scalability, privacy, and computational demands as network data complexity increases. Federated Learning (FL) has emerged as a promising solution, enabling collaborative model training on decentralized data sources while preserving data privacy. Each participant retains local data repositories, ensuring data sovereignty and precluding data sharing. Leveraging the FL framework, participants locally train machine learning models on their respective datasets, subsequently transmitting model updates to a central server for aggregation. The central server then disseminates the aggregated model updates to individual participants, collectively striving to bolster intrusion detection capabilities. This article presents a comprehensive survey of FL applications in IDS, covering core concepts, architectural approaches, and aggregation strategies. We evaluate the strengths and limitations of various FL methodologies for IDS, addressing privacy and security concerns and exploring privacy-preserving techniques and security protocols. Our examination of aggregation strategies within the FL framework for IDS aims to highlight their effectiveness, limitations, and potential enhancements.

Study on the electromagnetic-thermal-mechanical coupling damage of concrete under microwave irradiation

The paper aims to explore the thermal response of three-dimensional random aggregate concrete model under microwave irradiation. The multi-field distribution in concrete by coupling of the electromagnetics, heat transfer and solid mechanics in COMSOL Multiphysics software under microwave irradiation was investigated. The results shown that the different in dielectric properties and thermal expansion coefficients between aggregate and mortar resulted in the generation of temperature gradients and stress concentration at interface. In addition, due to the larger expansion coefficient of aggregate compared to mortar, stress concentration was formed at interface, promoting the damage of concrete. Microwave power exhibited a notable influence on thermal accumulation and thermal stress. Higher microwave power correlating to higher growth rates of temperature and stress. The Von Mises stress in mortar was significantly greater than that in aggregate, as energy absorption reached a certain threshold, damage occurred in mortar. In addition, microwave frequency significantly influenced the distribution of electric field within the cavity, and also the energy conversion ratio of aggregate within concrete. The study could provide a comprehensive understanding of heating effects of microwave radiation on concrete, providing the mechanical basis for microwave-assisted concrete recovery.

Investigating the radiative properties of large dust aggregate particles via the Monte Carlo ray tracing method

Understanding the radiative properties of particles is essential for interpreting and analyzing atmospheric remote sensing, target detection, combustion diagnostics, etc. At present, there is a relative lack of studies and understanding of the radiative properties of large aggregate particles. In this work, we comprehensively investigate the radiative properties of large dust aggregate particles via the developed Monte Carlo ray tracing method. Large dust aggregate models with monodisperse and polydisperse monomers are constructed, respectively. The effects of various factors on the radiative properties of large dust aggregate particles are analyzed. We find that the larger geometric standard deviation and the greater number of monomers lead to slightly larger backscattering and an increase of the overall radiative energy distribution on the receiving surface. With increasing the size parameter, the scattering phase function becomes smoother and the difference between the scattering phase function of spheres and aggregates diminishes. The absorptivity is proportional to the size parameter and inversely proportional to the number of monomers. At a size parameter of 100, the absorptivity and the peak of the radiative energy distribution of monodisperse monomer aggregates are higher than those of polydisperse monomer aggregates, and gradually converge with the increase of particle size parameter. Overall, this work helps to enhance the knowledge of the radiative properties of large aggregate particles.

Unbiased temperature-related mortality estimates using weekly and monthly health data: a new method for environmental epidemiology and climate impact studies.

Exposure to environmental factors has a high burden on human health, with millions of premature annual deaths associated with the short-term health effects of ambient temperatures and air pollution. However, direct estimations of exposure-related mortality from real data are still not available in most parts of the world, especially in low-resource settings, due to the unavailability of daily health records to calibrate epidemiological models. In this study, we have filled the crucial gap in available direct estimations by developing a method to make valid inference for the relationship between exposure and response data that uses only exposure and temporally aggregated response data. We provided the mathematical derivation of the method, and compared the results by using simulations applied to daily temperature and daily, weekly, and monthly mortality data. The method was then applied to the newly created database of the EARLY-ADAPT project. The daily and weekly models produced similar and unbiased estimates of the temperature-related relative risks and attributable mortality, with only slightly more imprecision in the weekly model. Even the estimates of the monthly model were unbiased when using enough data, although at the expense of a substantial increase in variability. The real data analysis showed that the similarity between the regional values of two aggregation models increased with the number of years and regions of the dataset, and decreased with the difference in their degree of temporal aggregation. Our method opens the door to conducting epidemiological studies in low-resource settings, where access to daily health data is not possible. Moreover, it allows accurate estimation of the short-term health effects of environmental exposures in near-real time, when daily health data are still not available, such as in the estimation of the mortality burden of recent record-breaking heat episodes. Overall, our method represents an important new approach to how the public health community can use data to create new evidence for research, translation and policy making. European Research Council (ERC).

Innovation and Practice of Diversified Business Model of New Energy Power Generation Enterprises

With the growing global demand for clean energy, new energy power generation enterprises are facing new opportunities and challenges. This paper explores the diversified business model of new energy power generation enterprises. Firstly, the theoretical basis of the business model of new energy power generation enterprises is analyzed, including the characteristics and income sources of single and multiple industrial chain models. The advantages and applications of different business models are analyzed through the study of cases at home and abroad, such as the European aggregated generation side resource model, the United States focused on controllable load demand response model, and the domestic cases of Shanghai and Shenzhen virtual power plant projects and distributed photovoltaic power generation. Then it puts forward the diversified business model innovation strategy of new energy power generation enterprises, covering the innovation of comprehensive energy services, the development of power side energy storage business, and the expansion of key businesses and profit models. Finally, the research conclusions are summarized and the future research directions are prospected. In short, the exploration of diversified business models of new energy power generation enterprises is of great significance to promote the development of clean energy and improve the competitiveness of enterprises.

Coordinated scheduling of 5G base station energy storage for voltage regulation in distribution networks

With the rapid development of 5G base station construction, significant energy storage is installed to ensure stable communication. However, these storage resources often remain idle, leading to inefficiency. To enhance the utilization of base station energy storage (BSES), this paper proposes a co-regulation method for distribution network (DN) voltage control, enabling BSES participation in grid interactions. In this paper, firstly, an energy consumption prediction model based on long and short-term memory neural network (LSTM) is established to accurately predict the daily load changes of base stations. Secondly, a BSES aggregation model is constructed by using the power feasible domain maximal inner approximation method and Minkowski summation to evaluate the charging and discharging potential and adjustable capacity of BSES clusters. Subsequently, a BSES demand assessment and optimal scheduling model for low voltage regulation in DN is developed. This model optimizes the charging and discharging strategies of BSES to alleviate low voltage problems in DN. Finally, the simulation results effectively verify the feasibility of the proposed optimal scheduling method of BSES for voltage regulation in DN.