Aggregation Model Research Articles

Judgement Aggregation of Preference-Evaluation

In response to an Arrovian impossibility in combining ranking and evaluation, it is often asked whether we could embed this impossibility result into judgment aggregation. I.e. preference-evaluation aggregation is a special case of judgment aggregation. We argue for this claim, after proving this result as a corollary of Dietrich’s (2015) work. We thereby provide a new proof of the impossibility result in preference-evaluation aggregation and clarify the relation between judgment and preference-evaluation aggregation, and to illustrate the generality of the judgment aggregation model.

Aggregation unveiled: A sequential modelling approach to bark beetle outbreaks

Tree-killing bark beetle infestations are a cause of massive coniferous forest mortality impacting forest ecosystems and the ecosystem services they provide. Models predicting bark beetle outbreaks are crucial for forest management and conservation, necessitating studies of the effect of epidemiological traits on the probability and severity of outbreaks. Due to the aggregation behaviour of beetles and host tree defence, this epidemiological interaction is highly non-linear and outbreak behaviour remains poorly understood, motivating questions about when an outbreak can occur, what determines outbreak severity, and how aggregation behaviour modulates these quantities. Here, we apply the principle of distributed delays to create a novel and mathematically tractable model for beetle aggregation in an epidemiological framework. We derive the critical outbreak threshold for the beetle emergence rate, which is a quantity analogous to the basic reproductive ratio, R0, for epidemics. Beetle aggregation qualitatively impacts outbreak potential from depending on the emergence rate alone in the absence of aggregation to depending on both emergence rate and initial beetle density when aggregation is required. Finally, we use a stochastic model to confirm that our deterministic model predictions are robust in finite populations.

Detecting and Tracking β-Amyloid Oligomeric Forms and Dynamics In Vitro by a High-Sensitivity Fluorescent-Based Assay.

Aggregation of β-amyloid protein is a hallmark pathology of the neurodegenerative disorder Alzheimer's disease and proceeds from monomers to insoluble misfolded fibril forms via soluble and highly toxic oligomeric intermediates. Given the dual feature of being the most toxic form of the Aβ aggregate proteome and an early marker of pathogenesis, there is a need for sensitive methods that can be used to detect Aβ oligomers and investigate the dynamics of aggregation. Herein, we describe a method based on the application of an oligomer-sensitive fluorescent chemical probe pTP-TFE combined with the use of a QIAD (Quantitative determination of Interference with Aβ Aggregate Size Distribution) assay to correctly identify Aβ oligomers in high sensitivity. pTP-TFE was evaluated and compared to thioflavin T and pFTAA, the two most widely used amyloid fibril dyes, and shown to be the only probe capable of detecting significant differences across all oligomeric species of β-amyloid. Furthermore, by observing changes in pTP-TFE fluorescence emission over time, we could track the dynamics of oligomer populations and thereby obtain kinetic information on the Aβ42 dynamic aggregation model. Therefore, we have established a highly sensitive, readily available, and simple method for studying β-amyloid protein aggregation dynamics.

Distributed Malicious Traffic Detection

With the wide deployment of edge devices, distributed network traffic data are rapidly increasing. Traditional detection methods for malicious traffic rely on centralized training, in which a single server is often used to aggregate private traffic data from edge devices, so as to extract and identify features. However, these methods face difficult data collection, heavy computational complexity, and high privacy risks. To address these issues, this paper proposes a federated learning-based distributed malicious traffic detection framework, FL-CNN-Traffic. In this framework, edge devices utilize a convolutional neural network (CNN) to process local detection, data collection, feature extraction, and training. A server aggregates model updates from edge devices using four federated learning algorithms (FedAvg, FedProx, Scaffold, and FedNova) to build a global model. This framework allows multiple devices to collaboratively train a model without sharing private traffic data, addressing the “Data Silo” problem while ensuring privacy. Evaluations on the USTC-TFC2016 dataset show that for independent and identically distributed (IID) data, this framework can reach or exceed the performance of centralized deep learning methods. For Non-IID data, this framework outperforms other neural networks based on federated learning, with accuracy improvements ranging from 2.59% to 4.73%.

Aggregation Modeling for Integrated Energy Systems Based on Chance-Constrained Optimization

Integrated energy systems (IESs) strengthen electricity–gas–heat multi-energy coupling and reduce wind and light abandonment. For grids with superior distribution, IESs are similar to virtual energy storage systems and are able to realize efficient interaction with the grid by synergizing the operating status of the internal equipment and improving the security, economy, and flexibility of the grid’s operation. However, the internal equipment coupling of an IES is complex, and determining how to evaluate its adjustable capacity range (that is, the upper and lower boundaries of its external energy demand) considering the uncertainty and volatility of wind power and photovoltaic output is a problem to be solved. To solve this problem, this paper presents a chance-constrained evaluation method for the adjustable capacity of IESs. Firstly, mathematical models and operational constraints of each device within the IES are established. Secondly, based on the mathematical model of chance-constrained planning, an adjustable capacity range assessment model considering the uncertainty of wind and photovoltaic output is established. Finally, the MATLAB/Yalmip/Gurobi solver is used for the optimization solution, and the adjustable capacity range interval of the constructed IES model is solved using an arithmetic example to analyze and verify the correctness and validity of the method and to study the influencing factors of its adjustable range.

Selection of Internet of Things-enabled sustainable real-time monitoring strategies for manufacturing processes using a disc spherical fuzzy Schweizer–Sklar aggregation model

The emergence of the Internet of Things (IoT) for monitoring in real-time is geared towards sustainable energy consumption practices by taking control over energy loss. The promising potential of current IoT real-time monitoring systems paves the way for future developments in monitoring devices with eco-friendly sensing capabilities. As a result, the creation of effective IoT real-time monitoring devices targeted at decreasing energy loss becomes crucial. This modeling procedure falls under the realm of multiple-attribute group decision-making (MAGDM), aiming to integrate the Schweizer–Sklar (SS) τ-norm and τ-conorm within the disc spherical fuzzy (D-SF) framework. The objective is to enhance the flexibility of D-SF in dealing with intricate and uncertain data. The core focus of this research is on deriving SS τ-norm and τ-conorm for D-SF data, consequently introducing innovative aggregation operators. The article offers the fundamental D-SF operations using SS aggregation operators in a systematic manner, with thorough theorem justifications. A new MAGDM tool is presented, created simply to manage ambiguous and imprecise data utilizing the suggested operators. Our model is specifically designed to tackle the critical issue of reducing energy loss in IoT real-time monitoring systems. The research not only focuses on model accuracy but also emphasizes its effectiveness in solving this pressing problem, demonstrating significant advancements in sustainable energy practices. Moreover, the proposed aggregation operators are subjected to a comparative analysis. This comprehensive comparison not only enhances the operators’ efficacy but also underscores their relevance in real-world decision-making scenarios.

Modelling the impacts of en-route ride-pooling service in a mixed pooling and non-pooling market

En-route ride-pooling services, e.g. Didi Pinche, UberPool, have been considered a promising way to alleviate traffic congestion during rush hours. Yet in reality, its market share is still small compared to non-pooling service. For a ride-hailing platform that provides both en-route pooling and non-pooling services, this paper endeavors to answer under what circumstance would it have incentive to encourage ride-pooling, and whether drivers, riders, the platform and the society can simultaneously benefit from the introduction of en-route ride-pooling service. We propose an aggregate model to characterize the equilibrium of a ride-hailing market where a platform operates a fleet of vehicles to provide en-route pooling and non-pooling services. The model captures the complex interactions among riders’ mode choices between pooling and non-pooling, the waiting times of pooling and non-pooling users, the pairing probability and the expected detour time of pooling users, the matching rates of cruising and halfly occupied vehicles with different types of riders, and the numbers of cruising, halfly occupied and fully occupied vehicles at each instant. Based on the model, we theoretically reveal the impacts of the total ride-hailing demand, vehicle fleet size, and the platform’s pricing and vehicle dispatching strategies on the market equilibrium performance through partial derivative-based sensitivity analysis, and establish conditions under which the introduction of en-route ride-pooling service improves platform profit, driver income, rider utility and social welfare.

Development of Time-Aggregated Machine Learning Model for Relapse Prediction in Pediatric Crohn's Disease.

Pediatric Crohn's disease (CD) easily progresses to an active disease compared to adult CD, making it important to predict and minimize CD relapses. However, prediction of relapse at various time points (TPs) during pediatric CD remains understudied. We aimed to develop a real-time aggregated model to predict pediatric CD relapse in different TPs and time windows (TWs). This retrospective study was conducted on children diagnosed with CD between 2015 and 2022 at Severance Hospital. Laboratory test results and demographic data were collected starting at 3 months after diagnosis, and cohorts were formed using data from six different TPs at 1-month intervals. Relapse-defined as a pediatric CD activity index ≥30 points-was predicted, and TWs were 3-7 months with 1-month intervals. The feature importance of the variables in each setting was determined. Data from 180 patients were used to construct cohorts corresponding to the TPs. We identified the optimal TP and TW to reliably predict pediatric CD relapse with an area under the receiver operating characteristic curve score of 0.89 when predicting with a 3-month TW at a 3-month TP. Variables such as C-reactive protein levels and lymphocyte fraction were found to be important factors. We developed a time-aggregated model to predict pediatric CD relapse in multiple TPs and TWs. This model identified important variables that predicted relapse in pediatric CD to support real-time clinical decision making.

Erucin, a Natural Isothiocyanate, Prevents Polyglutamine-Induced Toxicity in Caenorhabditis elegans via aak-2/AMPK and daf-16/FOXO Signaling.

Several neurodegenerative diseases (NDDs), such as Huntington's disease, six of the spinocerebellar ataxias, dentatorubral-pallidoluysian atrophy, and spinobulbar muscular atrophy, are caused by abnormally long polyglutamine (polyQ) tracts. Natural compounds capable of alleviating polyQ-induced toxicity are currently of great interest. In this work, we investigated the modulatory effect against polyQ neurotoxic aggregates exerted by erucin (ERN), an isothiocyanate naturally present in its precursor glucoerucin in rocket salad leaves and in its oxidized form, sulforaphane (SFN), in broccoli. Using C. elegans models expressing polyQ in different tissues, we demonstrated that ERN protects against polyQ-induced toxicity and that its action depends on the catalytic subunit of AMP-activated protein kinase (aak-2/AMPKα2) and, downstream in this pathway, on the daf-16/FOXO transcription factor, since nematodes deficient in aak-2/AMPKα2 and daf-16 did not respond to the treatment, respectively. Although triggered by a different source of neurotoxicity than polyQ diseases, i.e., by α-synuclein (α-syn) aggregates, Parkinson's disease (PD) was also considered in our study. Our results showed that ERN reduces α-syn aggregates and slightly improves the motility of worms. Therefore, further preclinical studies in mouse models of protein aggregation are justified and could provide insights into testing whether ERN could be a potential neuroprotective compound in humans.

Leveraging Large Language Models for Efficient Alert Aggregation in AIOPs

Alerts are an essential tool for the detection of anomalies and ensuring the smooth operation of online service systems by promptly notifying engineers of potential issues. However, the increasing scale and complexity of IT infrastructure often result in “alert storms” during system failures, overwhelming engineers with a deluge of often correlated alerts. Therefore, effective alert aggregation is crucial in isolating root causes and accelerating failure resolution. Existing approaches typically rely on either semantic similarity or statistical methods, both of which have significant limitations, such as ignoring causal relationships or struggling to handle infrequent alerts. To overcome these drawbacks, we propose a novel two-phase alert aggregation approach. We employ temporal–spatial clustering to group alerts based on their temporal proximity and spatial attributes. In the second phase, we utilize large language models to trace the cascading effects of service failures and aggregate alerts that share the same root cause. Experimental evaluations on datasets from real-world cloud platforms demonstrate the effectiveness of our method, achieving superior performance compared to traditional aggregation techniques.

Forecasting extremes of football players’ performance in matches

This study evaluates the use of simple linear or piecewise linear predictive models to predict extreme performance metrics in soccer matches, based on historical training and to match data of soccer players from RKS Raków Częstochowa football club. The data were collected from January to June 2023. The collected training and matched data average is 9000 records per month. A standard workweek at the RKS Academy consisted of 5 training units and at least 1 match. The best individual models found predict selected game performance metrics with a relative error of 2.3%, suggesting an excellent model fit between prediction and the actual value. This is illustrated by input data metric called “Metabolic Time Zone 5 and 6 Per Distance”, and output data by “Decelarations Total Distance in Zone 5 and 6 Per Distance”—calculated for in 3 min sliding window and characterized by the highest value of the generated parameter based on High Metabolic Load Distance (HMLD). The result concerns models run on aggregated performance metrics developed in APEX-PRO system using expert knowledge in soccer training, while raw GPS location-based models performing worse but still acceptably. Although we believe that the accuracy of the models still has limited reliability, their clarity and up-to-date quality make them useful in the daily planning of training activities and the management of workloads that affect player performance in the upcoming match, as well as the tactical decisions of the coach. More accurate predictions are given by individual models compared to aggregated models (player position), but there are exceptions where group models also perform very well. Adding a second metric to the input did not show a significant difference in the analyzed examples (the results are very similar). Our findings indicate that the model based on metrics from the last match also effectively predict extreme motor performances occurring in the game. In the case of the analyzed player, it was at the input “Accelerations Total Time Per Distance in Zone 6” at the output “Distance in Zone 6”. Specific training or match parameters can be key in predicting exceptional soccer performance, but they can also vary depending on the analyzed player. This confirms the need for further analysis of this issue.

A numerical simulation study on the characteristics of wrap shell lightweight aggregate concrete based on random aggregate model

It is a way to reuse solid waste by using dredged silt as raw material to prepare unburned wrap shell lightweight aggregates and replacing crushed stone aggregate to prepare unburned wrap shell lightweight aggregates concrete specimen. In order to understand the characteristics of wrap shell lightweight aggregates concrete, this paper adopts the method of combining laboratory test and numerical simulation to design the mix ratio test scheme of concrete specimens with different substitution rates of wrap shell aggregate (0 %, 25 %, 50 %, 75 %, 100 %), and conducts indoor mechanical properties test and numerical simulation simulation test on them, so as to obtain and analyze the mechanical properties and failure characteristics.The results show that:(1) The peak stress intensity of concrete specimens decreases linearly with the increase of the substitution rate of wrap shell aggregate.(2) The peak stress of concrete under different wrap shell round aggregate substitution rates is normally distributed, and the dispersion of peak strength decreases with the increase of wrap shell aggregate substitution rate. When the substitution rate is 0 %, the dispersion of peak strength is the largest.(3) Under the uniaxial compression simulation test, the failure mode of concrete specimens changes with the addition of the substitution rate of wrap shell aggregate. When the crack failure occurs, natural coarse aggregate with high strength is chosen to bypass, while when it comes to wrap shell aggregate with low strength, it is chosen to penetrate. With the continuous addition of round aggregate, the stress concentration phenomenon inside concrete can be effectively improved and the number of micro-cracks can be reduced.But at the same time, the penetration cracks gradually increase and gradually tend to be straight.The fractal dimension is negatively correlated with the compressive strength and positively correlated with the substitution rate of wrap shell aggregate.The above research can provide theoretical basis for the popularization of recycled concrete with wrap shell aggregate.

Construction of a digital twin model for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints

In the multi type load information of hybrid microgrids, data loss or incompleteness may occur due to network congestion, signal interference, equipment failures, and other reasons. Especially with the continuous generation of new load data, gradually incorporating these new data into the existing aggregation process to achieve continuous updating and optimization of load information. Therefore, this article proposes a digital twin model construction method for incremental aggregation of multi type load information in hybrid microgrids under integrity constraints. The Leida criterion and cubic exponential smoothing method are used to preprocess various load data of hybrid microgrids, remove abnormal data, reduce data fluctuations, and make the data more interpretable. Establish integrity constraints for multiple load data of hybrid microgrids and extract load characteristics of hybrid microgrids. Based on these, establish a digital twin model for the incremental aggregation of multiple load information in a hybrid microgrid, and solve the model using an improved K-means algorithm to achieve continuous updating and optimization of load information. The experimental results show that the data sharing delay of this method is 0.12 s, the load is basically consistent with the actual value, and the relative error of the load data is 4%.

Theory of nematic-isotropic phase transitions in solutions of rodlike aggregates

ABSTRACT A mean-field theory is introduced to describe phase behaviours for solutions of rodlike aggregates. By combining the Onsager model for rodlike aggregates and the Flory-Huggins model for polydisperse polymer solutions, we derive the free energy for the solutions of the rodlike aggregates. We calculate the mean aggregation number and orientational order parameter of rodlike aggregates. The mean aggregation number jumps at the first-order nematic–isotropic transition (NIT). We also derive the Landau-de Gennes expansion of the free energy and calculate the isotropic-nematic coexistence curves on the temperature-concentration plane. The NIT curves are qualitatively consistent with the experimental results of rodlike aggregates.

Multi-level physics informed deep learning for solving partial differential equations in computational structural mechanics

Physics-informed neural network has emerged as a promising approach for solving partial differential equations. However, it is still a challenge for the computation of structural mechanics problems since it involves solving higher-order partial differential equations as the governing equations are fourth-order nonlinear equations. Here we develop a multi-level physics-informed neural network framework where an aggregation model is developed by combining multiple neural networks, with each one involving only first-order or second-order partial differential equations representing different physics information such as geometrical, constitutive, and equilibrium relations of the structure. The proposed framework demonstrates a remarkable advancement over the classical neural networks in terms of the accuracy and computation time. The proposed method holds the potential to become a promising paradigm for structural mechanics computation and facilitate the intelligent computation of digital twin systems.

Investigations on the rheological properties of cement mortar based on fine aggregate characteristic and its extended prediction model

The rheological property of mortar is significantly influenced by its contained fine aggregate morphology characteristic and capable predictive models are limited. This work aims to expand the film thickness theory by developing an index that integrally represents variations in grain shape, gradation, and mix proportions, and elucidates the impact and the mechanism of these multifactorial differences on rheological performance. The quantitative analysis of the aggregate gradation and particle shape differences were performed, which was used to assess the impact of these variations and different mix ratios on the rheological performance of mortars. A correlation between aggregate differences and film thickness was established using specific surface area and void ratio. The result shows that variations in aggregates are accurately assessed by image analysis and consistently reflected in specific surface area and void ratio metrics. Since variations in rheological performance are predominantly governed by differences in film thickness, the disparities among aggregates can be quantified in terms of their impact on rheological properties by considering specific surface area and void ratio, thus translating into variations of film thickness. The differences in rheological properties due to variations in grain shape, gradation, and mix proportions can be comprehensively represented and controlled through an expansion of the film thickness model.

A novel sigma-Mu multiple criteria decision aiding approach for mutual funds portfolio selection

A Sigma-Mu approach is proposed for mutual funds portfolio selection. The mean and variance of the overall performance of each asset are considered, according to an additive aggregation model, subject to weights’ preferences provided by the decision maker. These preferences concern two independent sets of weights, i.e., those pertaining to the investment indicators and those pertaining to the time periods associated with the estimation of the indicators. For the first time in the Sigma-Mu framework, a weighting matrix is exploited, assisting on the development of a method to appraise the sources of variance, due to the weighting scheme of either the indicators or the periods. The Mu's, Sigma's and covariances estimated according to the Sigma-Mu approach, enter as inputs to mixed-integer quadratic programming (MIQP) mean-variance portfolio optimization models, in order to implement an empirical testing procedure, for a period of 8 years. The underlying MIQP models are equipped to consider non-convex investment policy constraints, such as the number of securities to be included in the portfolio, specific binary buy-in thresholds, the desired exposure of the portfolio to each investment advisor etc. The dataset that has been chosen for the empirical testing includes European mutual funds, that offer a broad exposure to the whole span of investment strategies and styles. The results document that the suggested approach may effectively be utilized in mutual funds investment management, since the portfolios constructed by the suggested methodology are associated with superior absolute and risk-adjusted performance against benchmarks.

Flow2GNN: Flexible Two-Way Flow Message Passing for Enhancing GNNs Beyond Homophily.

Message passing (MP) is crucial for effective graph neural networks (GNNs). Most local message-passing schemes have been shown to underperform on heterophily graphs due to the perturbation of updated representations caused by local redundant heterophily information. However, our experiment findings indicate that the distribution of heterophily information during MP can be disrupted by disentangling local neighborhoods. This finding can be applied to other GNNs, improving their performance on heterophily graphs in a more flexible manner compared to most heterophily GNNs with complex designs. This article proposes a new type of simple message-passing neural network called Flow2GNN. It uses a two-way flow message-passing scheme to enhance the ability of GNNs by disentangling and redistributing heterophily information in the topology space and the attribute space. Our proposed message-passing scheme consists of two steps in topology space and attribute space. First, we introduce a new disentangled operator with binary elements that disentangle topology information in-flow and out-flow between connected nodes. Second, we use an adaptive aggregation model that adjusts the flow amount between homophily and heterophily attribute information. Furthermore, we rigorously prove that disentangling in message-passing can reduce the generalization gap, offering a deeper understanding of how our model enhances other GNNs. The extensive experiment results show that the proposed model, Flow2GNN, not only outperforms state-of-the-art GNNs, but also helps improve the performance of other commonly used GNNs on heterophily graphs, including GCN, GAT, GCNII, and H2GCN, specifically for GCN, with up to a 25.88% improvement on the Wisconsin dataset.

Developmental and physiological impacts of pathogenic human huntingtin protein in the nervous system

Huntington's Disease (HD) is a neurodegenerative disorder, part of the nine identified inherited polyglutamine (polyQ) diseases. Most commonly, HD pathophysiology manifests in middle-aged adults with symptoms including progressive loss of motor control, cognitive decline, and psychiatric disturbances. Associated with the pathophysiology of HD is the formation of insoluble fragments of the huntingtin protein (htt) that tend to aggregate in the nucleus and cytoplasm of neurons. To track both the intracellular progression of the aggregation phenotype as well as the physiological deficits associated with mutant htt, two constructs of human HTT were expressed in the Drosophila melanogaster nervous system with varying polyQ lengths, non-pathogenic-htt (NP-htt) and pathogenic-htt (P-htt), with an N-terminal RFP tag for in vivo visualization. P-htt aggregates accumulate in the ventral nerve cord cell bodies as early as 24 h post hatching and significant aggregates form in the segmental nerve branches at 48 h post hatching. Organelle trafficking up- and downstream of aggregates formed in motor neurons showed severe deficits in trafficking dynamics. To explore putative downstream deficits of htt aggregation, ultrastructural changes of presynaptic motor neurons and muscles were assessed, but no significant effects were observed. However, the force and kinetics of muscle contractions were severely affected in P-htt animals, reminiscent of human chorea. Reduced muscle force production translated to altered locomotory behavior. A novel HD aggregation model was established to track htt aggregation throughout adulthood in the wing, showing similar aggregation patterns with larvae. Expressing P-htt in the adult nervous system resulted in significantly reduced lifespan, which could be partially rescued by feeding flies the mTOR inhibitor rapamycin. These findings advance our understanding of htt aggregate progression as well the downstream physiological impacts on the nervous system and peripheral tissues.

Research on the random generation model of 2D irregular aggregates in concrete based on fast grid region division method and its ground penetrating radar characteristics

Concrete can be regarded as a composite material with aggregates embedded in hardened slurries. The size, shape and distribution of aggregates directly affect multiple properties of concrete, including mechanical properties, ultrasonic field characteristics of concrete and ground penetrating radar (GPR) wave field characteristics. Therefore, conducting in-depth research on the random generation model of two-dimensional irregular aggregates is particularly important. This article introduces a fast grid region division method (FGRDM), which is an efficient reconstruction algorithm designed for two-dimensional concrete aggregate models. The algorithm takes into consideration the random micro-aggregate structure of concrete. A two-dimensional random distribution model of irregular aggregates in concrete was successfully established by generating concrete models with different particle size distributions and aggregate contents. Furthermore, the finite difference time domain method was used to analyze the GPR wave field of the random model. The significant influence of aggregate shape and distribution on the GPR wave field was discussed. Compared with existing two-dimensional irregular aggregate random generation models, the FGRDM proposed in this paper avoids computational redundancy and generates irregular aggregates at an increasingly fast speed. In the analysis of GPR data, electromagnetic waves are greatly affected by the shape and distribution of aggregates, highlighting the critical role of two-dimensional random distribution models in GPR data analysis. The paper uses FGRDM to generate random irregular aggregates to simulate the distribution of aggregates in actual concrete. Different diameter glass fiber reinforced polymer (GFRP) bars are inserted into the actual concrete and the numerical model to investigate the effect of aggregate size on the recognition of different diameter GFRP bars. The simulation results are generally consistent with the experimental results.