Aggregation Kinetics Research Articles

Nanofibrillated cationic cellulose derivatives as flocculants for domestic wastewater treatment

Natural-based coagulants have emerged as a reliable option to implement more sustainable operations and management of wastewater treatment plants. This work aims at evaluating the use of cationic nanofibrillated celluloses (cNFC) as novel bio-based flocculants to treat domestic wastewaters by the most widely employed treatment process – coagulation/flocculation. Two cNFC samples were prepared with different charge densities and tested as coagulant/flocculants using different water characteristics. The effect of cNFCs was studied by measuring the residual turbidity and dissolved organic carbon. The aggregation mechanism and kinetics of flocculation were also evaluated. Results show that cNFC can be used as an efficient flocculant to treat medium and high DOC waters since they considerably reduce turbidity (turbidity removals varied between 66.0 % and 85.7 % for the waters and cNFCs tested) without increasing dissolved organic carbon. Instead, cNFC removed dissolved organic carbon from domestic wastewaters (between 22.1 % and 65.5 % of DOC removals for the waters and cNFCs tested), which is a novel remarkable finding and a step forward in this knowledge area. High density charged cNFC revealed superior removal capacity at lower doses than the commercial coagulant FeCl3.

Coupling scales in process‐based soil organic carbon modeling including dynamic aggregation

AbstractBackgroundCarbon storage and turnover in soils depend on the interplay of soil architecture, microbial activities, and soil organic matter (SOM) dynamics. For a fundamental understanding of the mechanisms that drive these processes, not only the exploitation of advanced experimental techniques down to the nanoscale is necessary but also spatially explicit and dynamic image‐based modeling at the pore scale.AimWe present a modeling approach that is capable of transferring microscale information into macroscale simulations at the profile scale. This enables the prediction of future developments of carbon fluxes and the impact of changes in the environmental conditions linking scales.MethodWe consider a mathematical model for CO2 transport across soil profiles (macroscale), which is informed by a pore‐scale (microscale) model for C turnover. It allows for the dynamic, self‐organized re‐arrangement of solid building units, aggregates and particulate organic matter (POM) based on surface interactions, realized by a cellular automaton method, and explicitly takes spatial effects on POM turnover such as occlusion into account. We further include the macroscopic environmental conditions water saturation, POM content, and oxygen concentration.ResultsThe coupled simulations of macroscopic transport and pore‐scale carbon and aggregate turnover reveal the complex, nonlinear interplay of the underlying processes. Limitations by diffusive transport, oxygen availability, texture‐dependent occlusion and turnover of OM drive CO2 production and carbon storage.ConclusionsThis emphasizes the need for such micro–macro models exchanging information on different scales to investigate and quantify the effects of structural changes, variations in environmental conditions, or degradation processes on carbon turnover.

Identifying the mechanisms behind the stability of silica nano- and micro-particles: Effects of particle size, electrolyte concentration and type of ionic species

Silica nanofluids have emerged as promising agents for various applications, such as water treatment, CO2 absorption, and chemical enhanced oil recovery (CEOR). However, their effectiveness can be hindered when transported through porous media, especially in challenging downhole environments. Aggregation of nanofluid suspensions can damage formation integrity, and brine fluids may unintentionally transport fine micro silica particles, warranting an in-depth examination of micro-particle behavior and stability. This study synthesized silica particles of different sizes (10 nm, 50 nm, 1 μm and 2 μm) and meticulously characterized them using advanced techniques of transmission electron microscopy (TEM), field emission scanning electron microscopy (FE-SEM), fourier-transform infrared spectroscopy (FTIR), X-Ray diffraction (XRD), thermal gravimetric analysis (TGA), and Brunauer–Emmett–Teller (BET) analysis. Subsequently, 560 combinations (in 5 series of 112 experiments) of micro- and nano-fluids in the presence of four different salts (NaCl, CaCl2, MgSO4 and MgCl2) were prepared. The stability and sedimentation of these combinations were investigated based on zeta potential, particle size, pH, electrical conductivity, and UV-spectrophotometry. The results indicated that sodium ions were effective for maintaining suspension stability at lower salt concentrations, while magnesium ions were preferred for higher salinities. Particle size increase led to instability at low salinities but stability at high salinities, where enthalpy dominated over entropy. Derjaguin-Landau-Verwey-Overbeek (DLVO) modeling accurately predicted particle interactions and aggregation tendencies. This investigation enhanced the understanding of the mechanisms governing silica particle stability through diverse analytical methods, addressing their limitations. The analysis of UV data over time revealed insights into sedimentation and aggregation kinetics. In summary, this study comprehensively explored the behavior and stability of silica particles in nano- and micro-fluids, shedding light on their performance in applications ranging from enhanced oil recovery to water treatment.

DScPA: A Dynamic Subcluster Privacy-Preserving Aggregation Scheme for Mobile Crowdsourcing in Industrial IoT

DScPA: A Dynamic Subcluster Privacy-Preserving Aggregation Scheme for Mobile Crowdsourcing in Industrial IoT

Perspectives on the misconception of levitating soil aggregates

Abstract. Soil aggregation is an important process in nearly all soils across the globe. Aggregates develop over time through a series of abiotic and biotic processes and interactions, including plant growth and decay, microbial activity, plant and microbial exudation, bioturbation, and physicochemical stabilization processes, and are greatly influenced by soil management practices. Together, and through feedback with organic matter and primary soil particles, these processes form dynamic soil aggregates and pore spaces, which jointly constitute a soil's structure and contribute to overall soil functioning. Nevertheless, the concept of soil aggregates is hotly debated, leading to confusion about their function or relevancy to soil processes. We argue here that the opposition to the concept of soil aggregation likely stems from the fact that the methods for the characterization of soil aggregates have largely been developed in the context of arable soils, where tillage promotes the formation of distinct soil aggregates that are easily visible in the topsoil. We propose that the widespread use of conceptual figures showing detached and isolated aggregates can be misleading and has contributed to the skepticism towards soil aggregates. However, the fact that we do not always see discrete aggregates within soils in situ does not mean that aggregates do not exist or are not relevant to the study of soil processes. Given that, by definition, soil aggregates consist of any group of soil particles that cohere more strongly to each other than neighboring particles, aggregates may, but do not necessarily need to be, bordered by pore space. Here, we illustrate how aggregates can form and dissipate within the context of undisturbed, intact soils, highlighting the point that aggregates do not necessarily need to have a discrete physical boundary and can exist seamlessly embedded in the soil. We hope that our contribution helps the debate on soil aggregates and supports the foundation of a shared understanding on the characterization and function of soil structure.

Heat-induced agglomeration of water-soluble cod proteins toward gelled structures

Cod proteins (CPs) have potential applications in designing desirable gel-based products, and this study aimed to unravel their heat-induced aggregation pattern and further probe the roles in protein gels. SDS-PAGE analysis indicated that high-precipitation-coefficient aggregates (HPCAs) of CPs aggregates were composed of considerable polymers of myosin heavy chains and actin, and their low-precipitation-coefficient aggregates (LPCAs) contained myosin light chains and tropomyosin. Studies from correlation analysis between the structure and aggregation kinetics revealed that the generation of β-sheet and SS bonds were responsible for their spontaneous thermal aggregation induced by heating temperature and protein concentration, respectively. Additionally, as protein denaturation ratio increased, more and larger HPCAs were formed, which was evidenced driving the network formation of protein gels and resulting in higher storage modulus (G') values. These novel findings may be applicable to other animal proteins for better tailoring the manufacturing of muscle gel-based products.

Dynamic Aggregation Method for Load Aggregators Considering Users’ Deviation Electricity

Constructing a new energy-based power system is not only an important direction for the transformation and upgrade of China’s power system, but also a key to achieving peak carbon and carbon neutrality. How to fully utilize situation awareness technology to adapt to diverse and differentiated scenarios has become a crucial breakthrough point for ensuring the reliable, safe, high-quality, low-carbon, and economical operation of the new power system. Starting from the distribution network demand resources, this paper proposes a dynamic aggregation method for load aggregators considering the user deviation quantity, to deal with the current situation that the adjustable load-side resource points are multi-faceted and wide, and the operating subjects are complex and difficult to participate directly in the grid dispatch. First, considering there is subjectivity in the electricity behavior of users under the jurisdiction of the load aggregator, a deviation amount may be generated during the actual aggregation process, which reduces the profit of the load aggregator. Therefore, a load aggregator-level user deviation dynamic volume forecasting method based on the Markov chain is proposed, which is used to predict the deviation quantity of users during the dispatch cycle and achieve a dynamic status estimate on the load side of the new power system. On this basis, a dynamic aggregation model for load aggregators based on the deviation volume was constructed with the objective of maximizing the revenue of load aggregators. The examples, by comparing the aggregation results of users under three scenarios, show the proposed method can effectively guarantee the income of load aggregators, verify the effectiveness of the proposed dynamic aggregation strategy, and provide technical support for the operation situation awareness of the load side of the new power system.

Specific ions effect on aggregation behaviors and structural changes of amyloid fibrils from rice glutelin

Metal ions have been considered as an important factor on fibrils assembly. Herein, a comprehensive analysis of specific ions effect on fibril formation and structural changes was investigated. The addition of ions (except Zn2+) accelerated the aggregation kinetics of rice glutelin fibrils (RGFs) from 0.93 to 1.28–2.19 h−1. In addition, the fibrillization rate followed the order of NH4+ > Li+ > Na+ > K+ > Cu2+ > Mg2+ > Ca2+ > Zn2+. The highest yield and length of fibrils were observed with Ca2+, probably due to the ionic bridging effect and hydrated capacity of Ca2+. However, Cu2+ reduced the fibrils yield, which was attributable to the fact that Cu2+ disrupted β-sheet structure and inhibited the transition of monomer to fibrils. The polymorphism of fibrils was observed with different salts, and the light metals presented a superior effect on fibrils formation than heavy metals. Overall, this work will provide a further information into how to tune the structure of RGFs using various ions.

Unraveling the interplay between root exudates, microbiota, and rhizosheath formation in pearl millet

BackgroundThe rhizosheath, a cohesive soil layer firmly adhering to plant roots, plays a vital role in facilitating water and mineral uptake. In pearl millet, rhizosheath formation is genetically controlled and influenced by root exudates. Here, we investigated the impact of root exudates on the microbiota composition, interactions, and assembly processes, and rhizosheath structure in pearl millet using four distinct lines with contrasting soil aggregation abilities.ResultsUtilizing 16S rRNA gene and ITS metabarcoding for microbiota profiling, coupled with FTICR-MS metabonomic analysis of metabolite composition in distinct plant compartments and root exudates, we revealed substantial disparities in microbial diversity and interaction networks. The ß-NTI analysis highlighted bacterial rhizosphere turnover driven primarily by deterministic processes, showcasing prevalent homogeneous selection in root tissue (RT) and root-adhering soil (RAS). Conversely, fungal communities were more influenced by stochastic processes. In bulk soil assembly, a combination of deterministic and stochastic mechanisms shapes composition, with deterministic factors exerting a more pronounced role. Metabolic profiles across shoots, RT, and RAS in different pearl millet lines mirrored their soil aggregation levels, emphasizing the impact of inherent plant traits on microbiota composition and unique metabolic profiles in RT and exudates. Notably, exclusive presence of antimicrobial compounds, including DIMBOA and H-DIMBOA, emerged in root exudates and RT of low aggregation lines.ConclusionsThis research underscores the pivotal influence of root exudates in shaping the root-associated microbiota composition across pearl millet lines, entwined with their soil aggregation capacities. These findings underscore the interconnectedness of root exudates and microbiota, which jointly shape rhizosheath structure, deepening insights into soil–plant-microbe interactions and ecological processes shaping rhizosphere microbial communities. Deciphering plant–microbe interactions and their contribution to soil aggregation and microbiota dynamics holds promise for the advancement of sustainable agricultural strategies.46mkfZGLAk7WD-UFCK_cLiVideo

COVID-19 impairs oxygen delivery by altering red blood cell hematological, hemorheological, and oxygen transport properties.

Introduction: Coronavirus disease 2019 (COVID-19) is characterized by impaired oxygen (O2) homeostasis, including O2 sensing, uptake, transport/delivery, and consumption. Red blood cells (RBCs) are central to maintaining O2 homeostasis and undergo direct exposure to coronavirus in vivo. We thus hypothesized that COVID-19 alters RBC properties relevant to O2 homeostasis, including the hematological profile, Hb O2 transport characteristics, rheology, and the hypoxic vasodilatory (HVD) reflex. Methods: RBCs from 18 hospitalized COVID-19 subjects and 20 healthy controls were analyzed as follows: (i) clinical hematological parameters (complete blood count; hematology analyzer); (ii) O2 dissociation curves (p50, Hill number, and Bohr plot; Hemox-Analyzer); (iii) rheological properties (osmotic fragility, deformability, and aggregation; laser-assisted optical rotational cell analyzer (LORRCA) ektacytometry); and (iv) vasoactivity (the RBC HVD; vascular ring bioassay). Results: Compared to age- and gender-matched healthy controls, COVID-19 subjects demonstrated 1) significant hematological differences (increased WBC count-with a higher percentage of neutrophils); RBC distribution width (RDW); and reduced hematocrit (HCT), Hb concentration, mean corpuscular volume (MCV), and mean corpuscular hemoglobin concentration (MCHC); 2) impaired O2-carrying capacity and O2 capacitance (resulting from anemia) without difference in p50 or Hb-O2 cooperativity; 3) compromised regulation of RBC volume (altered osmotic fragility); 4) reduced RBC deformability; 5) accelerated RBC aggregation kinetics; and (6) no change in the RBC HVD reflex. Discussion: When considered collectively, homeostatic compensation for these RBC impairments requires that the cardiac output in the COVID cohort would need to increase by ∼135% to maintain O2 delivery similar to that in the control cohort. Additionally, the COVID-19 disease RBC properties were found to be exaggerated in blood-type O hospitalized COVID-19 subjects compared to blood-type A. These data indicate that altered RBC features in hospitalized COVID-19 subjects burden the cardiovascular system to maintain O2 delivery homeostasis, which appears exaggerated by blood type (more pronounced with blood-type O) and likely plays a role in disease pathogenesis.

ПОСТРОЕНИЕ МАТЕМАТИЧЕСКОЙ МОДЕЛИ ПРОГНОЗА ДИНАМИКИ ДЕНЕЖНЫХ АГРЕГАТОВ

This article is devoted to the development of a mathematical model for forecasting the dynamics of monetary aggregates M0–M3. The relevance of this topic is due to the demand for monetary aggregates as one of the key indicators for the purposes of substantiating the monetary policy of the Bank of Russia. Based on the forecasts of these macroeconomic indicators, the issues of interest rates, inflation and lending volume regulation are solved. The research use such methods as the method of comparison, relative and average values, graphical and tabular presentation of data, grouping, method of correlation and regression analysis, etc. Modeling based on the apparatus of neural networks was also used to build a forecast of the dynamics of monetary aggregates. Scientific novelty of the work lies in the fact that the unique models of forecasting the dynamics of monetary aggregates were built, as well as the forecast of key indicators for the last quarter of 2023. The practical significance of this study lies in the possibility of applying the best of the developed multifactor models, as well as models using ANN, for further forecasting of liquidity indicators.

Knowledge-informed neuro-integrators for aggregation kinetics

We report a novel approach for the efficient computation of solutions of a broad class of large-scale systems of non-linear ordinary differential equations, describing aggregation kinetics. The method is based on a new take on the dimensionality reduction for this class of equations which can be naturally implemented by a cascade of small feed-forward artificial neural networks. We show that this cascade, of otherwise static models, is capable of predicting solutions of the original large-scale system over large intervals of time, using the information about the solution computed over much smaller intervals. The computational cost of the method depends very mildly on the temporal horizon, which is a major improvement over the current state-of-the-art methods, whose complexity increases super-linearly with the system’s size and proportionally to the simulation time. In cases when prior information about the values of solutions over a relatively small interval of time is already available, the method’s computational complexity does not depend explicitly on the system’s size. The successful application of the new method is illustrated for spatially-homogeneous systems, with a source of monomers, for a number of the most representative reaction rates kernels.

Liquid-liquid phase separation induced by crowding condition affects amyloid-β aggregation mechanism.

Liquid-liquid phase separation (LLPS) is common in the aggregation of proteins associated with neurodegenerative diseases. Many efforts have been made to reproduce crowded conditions with artificial polymeric materials to understand the effect of LLPS in physiological conditions with significantly highly concentrated proteins, such as intrinsically disordered proteins. Although the possibility that LLPS is involved in intracellular amyloid-β (Aβ) aggregation, a protein related to the pathogenesis of Alzheimer's disease, has been investigated, the relationship between LLPS and the aggregation of Aβ is poorly characterized. Thus, in this study, we mimicked the intracellular crowding environment using polyethylene glycol and dextran, used commonly as model polymers, to examine the relationship of Aβ with LLPS and aggregation dynamics in vitro. We confirmed that Aβ undergoes LLPS under specific polymer coexistence conditions. Moreover, the addition of different electrolytes modulated LLPS and fibril formation. These results suggest that hydrophobic and electrostatic interactions are the driving forces for the LLPS of Aβ. Similar to the role of the liposome interface, the interface of droplets induced by LLPS functioned as the site for heterogeneous nucleation. These findings offer valuable insights into the complex mechanisms of Aβ aggregation in vivo and may be useful in establishing therapeutic methods for Alzheimer's disease.

Dynamic bipolar fuzzy aggregation operators: A novel approach for emerging technology selection in enterprise integration

<abstract> <p>Emerging technology selection is crucial for enterprise integration, driving innovation, competitiveness, and streamlining operations across diverse sectors like finance and healthcare. However, the decision-making process for technology adoption is often complex and fraught with uncertainties. Bipolar fuzzy sets offer a nuanced representation of uncertainty, allowing for simultaneous positive and negative membership degrees, making them valuable in decision-making and expert systems. In this paper, we introduce dynamic averaging and dynamic geometric operators under bipolar fuzzy environment. We also establish some of the fundamental crucial features of these operators. Moreover, we present a step by step mechanism to solve MADM problem under bipolar fuzzy dynamic aggregation operators. In addition, these new techniques are successfully applied for the selection of the most promising emerging technology for enterprise integration. Finally, a comparative study is conducted to show the validity and practicability of the proposed techniques in comparison to existing methods.</p> </abstract>

Towards addressing aggregation deviation for model training in resource-scarce edge environment

Federated Learning (FL) can train models in an edge environment without sending raw data. However, the performance is still constrained by data heterogeneity. To address the problems of data heterogeneity and resource scarcity in edge devices, we propose Federal Learning via Dynamic Aggregation (FedDA), which eliminates the influence of data heterogeneity and improves model accuracy. FedDA updates the impact of individual local models on the global model in real-time at different stages. It adjusts the local epoch in each round to prevent the device from dropping out while obtaining a more accurate local model. The core module is the model impact factor (MIF) that inscribes the aggregation weights to solve the impact of fixed weights on the aggregation model with improper extraction of local information. We conducted several experiments to evaluate the convergence speed using different algorithms on the MINIST. FedDA consistently outperforms the other six SOTA algorithms on MNIST, Cifar10, and Cifar100 datasets. In significant data heterogeneity, FedDA improves accuracy by up to 6% over the different algorithms and at least about 3%, especially in resource-scarce environments. To reach the specified accuracy, FedDA is 3 times faster than SCAFFOLD and at least 50% faster than other algorithms.

A mitochondria targeting, de novo designed, aggregation-induced emission probe for selective detection of neurotoxic amyloid-β aggregates.

A striking issue is the scarcity of imaging probes for the early diagnosis of Alzheimer's disease. For the development of Aβ biomarkers, a mitochondria targeting, de novo designed, aggregation-induced emission (AIE) probe Cou-AIE-TPP+ is constructed by engineering the aromatic coumarin framework into the bridge of electron donor-acceptor-donor tethered with a lipophilic cationic triphenylphosphonium (TPP+) group. The synthesized Cou-AIE-TPP+ probe exhibits biocompatibility, noncytotoxicity, and a huge Stokes shift (124 nm in PBS). Cou-AIE-TPP+ has respectable fluorescence augmentation inside the aggregated Aβ40 in comparison with monomeric Aβ40 with a high binding affinity (Kd = 83 nM) to Aβ40 aggregates, is capable of detecting the kinetics of amyloid aggregation, and is superior to the gold standard probe thioflavin T. Fluorescence lifetime and brightness are also augmented when the probe Cou-AIE-TPP+ binds with Aβ aggregates in PBS. Cou-AIE-TPP+ (λem 604 nm) selectively targets and images neuronal cell mitochondria, is useful to monitor mitochondrial morphology alteration and damage during Aβ40-induced neurotoxicity, recognizes neurotoxic Aβ fibrils, and is highly colocalized with thioflavin T, showing a decent Pearson correlation coefficient of 0.91 in the human neuroblastoma SH-SY5Y cell line. These findings indicate that the mitochondria targeting, de novo designed, functional AIE-based solvatofluorochromic Cou-AIE-TPP+ probe is a promising switch on biomarkers for fluorescence imaging of Aβ aggregates and to monitor mitochondrial morphology change and dysfunction during Aβ-induced neurotoxicity, which may offer imperative direction for the advancement of compelling AIE biomarkers for targeted early stage Aβ diagnosis in the future.

Kinetic models reveal the interplay of protein production and aggregation

We develop a new mathematical model to describe the kinetics of protein aggregation under conditions when aggregation-prone protein is being produced during the aggregation.

Small Object Detection Method Based on Global Multi-level Perception and Dynamic Region Aggregation

Small Object Detection Method Based on Global Multi-level Perception and Dynamic Region Aggregation

DynamicNet: Efficient Federated Learning for Mobile Edge Computing With Dynamic Privacy Budget and Aggregation Weights

DynamicNet: Efficient Federated Learning for Mobile Edge Computing With Dynamic Privacy Budget and Aggregation Weights

Complex Dynamic Slow Independent Symbol Aggregation Approximation for Multimodal Temporal Fuzzy Cognitive Map

Complex Dynamic Slow Independent Symbol Aggregation Approximation for Multimodal Temporal Fuzzy Cognitive Map