Aggregation Model Research Articles

An Econometric Study Using Panel Data Model of Factors Affecting Women's Economic Empowerment for Some Countries During the Period (2000-2023)

Objectives: The study aimed to use the analytical and standard method in analyzing the results of the study based on the analysis of factors affecting the economic empowerment of women in some countries during the period (2000-2023). Methods: To investigate the relationships between variables, the Panel Data model has Been Described to express the relationship between the female employment rate as a Dependent variable and women's empowerment indicators as independent variables. The study sample consisted of five countries, including Saudi Arabia, Sudan, Egypt, India and South Africa. The data were estimated using three models: the aggregate regression model, the static effects, and the random effects model. Results: The results showed that the fixed effects model is the most appropriate model for this study. Using the statistical program E-views12. Conclusion: The study found the following: It is expected to have an inverse relationship between the female unemployment rate and the female employment rate (FE) - as a dependent variable - and a positive relationship between the female labor force rate and FE. Contrary to expectations, it was found that there is a positive relationship between the female mortality rate and the dependent variable, in addition to the dependent variable not affected by the age dependency ratio of males.

Multi-temporal assessment of wind, solar, and hydropower resources for off-grid microgrid

For a proposed multi-source all-renewable microgrid in Nigeria’s Middle-belt region, this paper presents a multi-temporal approach to the investigation of the uncertainty in the potential of renewable energy resources. The wind, solar, and hydropower resources for a proposed multi-source all-renewable off-grid community microgrid are considered using an array of probabilistic techniques. The peculiar variances in the location’s climate throughout the year make the more common method of annual models of renewable resources unsuitable for power system planning. Consequently, a more granular model of its renewable resources over time is needed. Therefore, for the chosen location, for each renewable resource, a composite multitemporal maximum-likelihood estimation-based (MLE) probabilistic model for characterization is developed. A total of 39 probabilistic models are developed. Up to 40% improvement in the accuracy of the statistical measures for renewable resource uncertainty was observed. Multi-temporal approach provides more accurate information for power system planning over time than the conventional approach of single aggregate models, especially for hydropower, which is strongly affected by the relatively sporadic occurrence of rainfall. The study shows that solar energy is promising, hydropower potential is seasonal and complementary, and wind potential is low at the location considered in this study.

Catalytic coagulation.

We introduce an autocatalytic aggregation model in which the rate at which two clusters merge is controlled by the third "catalytic" cluster, whose mass must equal the mass of one of the reaction partners. The catalyst is unaffected by the joining event and can participate in or catalyze subsequent reactions. This model is meant to mimic the self-replicating reactions that occur in models for the origin of life. We solve the kinetics of catalytic coagulation for the case of mass-independent reaction rates and show that the total cluster density decays as t^{-1/3}, while the density of clusters of fixed mass decays as t^{-2/3}. These behaviors contrast with the corresponding t^{-1} and t^{-2} scalings for classic aggregation. We extend our model to mass-dependent reaction rates, to situations where only "magic" mass clusters can catalyze reactions, and to include steady monomer input.

3D numerical study of axial compression behavior of concrete-filled steel tubular columns from mesoscopic perspective

This study investigates the axial compression behavior of Concrete-Filled Steel Tubular (CFST) short columns from a mesoscopic perspective. Concrete is regarded as a three-phase composite material consisting of aggregates, mortar, and interfacial transition zones (ITZs). The Concrete Damaged Plasticity (CDP) model was employed to simulate the nonlinear mechanical response and fracture characteristics of concrete. Verified against experimental results, the numerical model excels in realistically simulating the uniaxial compression behavior of CFST short columns. Subsequently, factors influencing the macroscopic behavior of CFST short columns were investigated. Results indicate that the random aggregate model adopted for concrete enables a more realistic prediction of the behavior of CFST short columns. Additionally, circular section steel tubes demonstrate a stronger confinement effect compared to square ones. With increasing lateral confinement, the properties of CFST short columns, such as bearing capacity and ductility, are significantly enhanced.

Study on the spectral characteristics and chemical structure of Taixi Anthracite

Taixi anthracite is a rare form of coal in both China and the world, characterized by low ash, sulfur, phosphorus, high carbon content, conductivity, and calorific value. Our team employed a solid-phase microwave method [1] to graphitize Taixi anthracite and graft it with natural rubber to create a composite with outstanding properties. However, since the molecular structure of Taixi anthracite was unknown, establishing a molecular model for the reaction becomes crucial for the functionalization of anthracite. In this study, By combining elemental analysis, nuclear magnetic resonance carbon spectroscopy, Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy, the molecular carbon content of Taixi anthracite is 89 %, the XBP value is 0.37, and the chemical formula of the lowest relative molecular weight of Taixi anthracite is C181H77O4NS0.1. The results of computer simulation show that Taixi anthracite is assembled into coal macromolecular structure by 10 molecular structure units of 5 kinds of coal according to 2:1:2:2:3 ratio, and the geometric optimization, annealing simulation and inverse verification are carried out. The aggregation model is established by using X-ray diffraction data and computer simulation techniques.we innovatively constructed five groups of macromolecular structure units of Taixi anthracite to characterize the results and simulate the minimum energy configuration of single chains and aggregate states. This research provides a molecular-level theoretical basis for the functionalization of Taixi anthracite.

ARIMA-SVR-based risk aggregation modeling in the financial behavior

ARIMA-SVR-based risk aggregation modeling in the financial behavior

Enhancing Ovarian Tumor Diagnosis: Performance of Convolutional Neural Networks in Classifying Ovarian Masses Using Ultrasound Images.

Background/Objectives: This study aims to create a strong binary classifier and evaluate the performance of pre-trained convolutional neural networks (CNNs) to effectively distinguish between benign and malignant ovarian tumors from still ultrasound images. Methods: The dataset consisted of 3510 ultrasound images from 585 women with ovarian tumors, 390 benign and 195 malignant, that were classified by experts and verified by histopathology. A 20% to80% split for training and validation was applied within a k-fold cross-validation framework, ensuring comprehensive utilization of the dataset. The final classifier was an aggregate of three pre-trained CNNs (VGG16, ResNet50, and InceptionNet), with experimentation focusing on the aggregation weights and decision threshold probability for the classification of each mass. Results: The aggregate model outperformed all individual models, achieving an average sensitivity of 96.5% and specificity of 88.1% compared to the subjective assessment's (SA) 95.9% sensitivity and 93.9% specificity. All the above results were calculated at a decision threshold probability of 0.2. Notably, misclassifications made by the model were similar to those made by SA. Conclusions: CNNs and AI-assisted image analysis can enhance the diagnosis and aid ultrasonographers with less experience by minimizing errors. Further research is needed to fine-tune CNNs and validate their performance in diverse clinical settings, potentially leading to even higher sensitivity and overall accuracy.

Seismic response of unreinforced masonry building aggregates: Investigation on the “aggregate-effect” based on an elementary building aggregate

Masonry building aggregates constitute a significant portion of Italian and Mediterranean countries built heritage. These structures originate from the progressive transformation and interconnection of originally isolated structural units over time. Masonry aggregate seismic performance is affected by significant uncertainty arising from the lack of knowledge of their complex structural arrangement, which is also reflected in the reliability of modeling assumption adopted for the analysis and the descending outcomes. This paper presents a numerical study investigating the seismic behavior of an elementary building aggregate made of three almost identical structural units. Different boundary conditions are explored in the analysis, extracting and comparing the responses of the individual structural units modeled both in aggregate and isolated and with rigid or flexible floors. Homogenized masonry approach is implemented for the building aggregate models with 2D layered inelastic shell elements. The structural models are realized with the STKO software platform for OpenSees, enabling parallel computing to analyze large scale models. Results will highlight the extent of the so-called “aggregate effect” for the elementary aggregate building and its potential benefits to the structural units, showing also that the behavior of structural units in aggregate strongly depends on their position and the polarity of the seismic forces.

Macrocyclic peptides derived from AcPHF6* and AcPHF6 to selectively modulate the Tau aggregation

Ten macrocyclic peptides, each comprising 14 amino acids, were designed and synthesized based on the Tau aggregation model hexapeptides AcPHF6* and AcPHF6. The design took into account the aggregation tendencies of each residue in AcPHF6* and AcPHF6, their aggregation models, while employing peptide-based structural design principles including N-methylation to promote turns and to block hydrogen bond propagation and elongation of the aggregation chain. NMR analysis supported that all these peptides adopted an antiparallel β-sheet conformation. Self-aggregation studies characterized the aggregation properties of these peptides, identifying two peptides with the highest (P3) and lowest (P8) aggregation tendencies. In cross-aggregation studies with the parent peptides AcPHF6* and AcPHF6, P3 and P8 were found to promote and reduce aggregation, respectively. Furthermore, P3 and P8 demonstrated an enhancement and diminution effect on the aggregation of K18wt, indicating their capacity to modulate aggregation even at the macromolecular level. Thus, the two simple peptides, P3 and P8 selectively exhibit pro- or anti-aggregation effects on PHF peptides and Tau. This study, has thus developed structurally well-defined non-complex peptides, derived from AcPHF6* and AcPHF6, to modulate Tau aggregation as desired, offering applications in Tau model studies and the development of Tau aggregation inhibitors or promoters.

Determining the Size of Representative Volume Elements for a Two-Dimensional Random Aggregate Numerical Model of Asphalt Mortar without Damage.

The size of the representative volume element (RVE) for the two-dimensional (2D) random aggregate numerical model of asphalt mortar in a non-destructive state, which directly affects the time required to simulate the linear viscoelastic behavior from asphalt mastic to asphalt mortar. However, in the existing literature, limited research has been conducted on the size determination of the numerical model RVE for asphalt mortar. To provide a recommended size for the typical 2D random aggregate numerical model RVE of asphalt mortar in a nondestructive state, this paper first applies the virtual specimen manufacturing method of asphalt concrete 2D random aggregate to asphalt mortar. Then, it generates numerical model RVEs of asphalt mortar with different maximum particle sizes, after which geometric and numerical analyses are conducted on these models. Finally, based on the geometric and numerical analysis results, the recommended minimum sizes of RVE for the 2D asphalt mortar numerical model are provided.

Development of an equivalent system frequency response model based on aggregation of distributed energy storage systems

Energy storage systems (ESSs) installed in distribution networks have been widely adopted for frequency regulation services due to their rapid response and flexibility. Unlike existing ESS design methods which focus on control strategies, this paper proposes a new method based on an ESS equivalent aggregated model (EAM) for calculating the capacity and the droop of an ESS to maintain the system frequency nadir and quasi-steady state frequency using low-order functions. The proposed method 1) uses first-order functions to describe the frequency response (FR) of synchronous generators (SGs); 2) ignores the control strategies of SGs, making the method systematic and allowing it to avoid analyzing complex high-order functions; and 3) is suitable for low inertia systems. The applicability and accuracy of the method is demonstrated using a modified four-generator two-area (4G2A) system.

Simulation method of asphalt mixture based on fracturable digital aggregate model

This study proposed an approach to construct a fracturable aggregate model to make the fracture patterns consistent with the real situation. Firstly, this study utilized three-dimensional structured light technology to obtain the geometric shell of aggregate and divide them into four categories of spherical, angular, elongated and flaky aggregates according to the minimum bounding box algorithm. Secondly, this study proposed a discrete-spheres filling algorithm based on the Bubble Pack Filling algorithm to fill the shell accurately. Thirdly, this study established the conversion equations between the macro and micro parameters of aggregate by using the linear parallel bonding model, and used it to establish the fracturable aggregate model. Through the indoor and virtual static fracture tests, this study found that the fracture patterns of the fracturable aggregate models are similar to the real situation and characterized the fracture patterns of four types of aggregates. Further analyzing the virtual static fracture tests, this study found that the bearing capacity of aggregates is determined by the compressive strength of host rocks and the shape of aggregate. Through the indoor and virtual uniaxial compression creep tests, this study investigated the behavior of the mixture specimens consisted by the fracturable aggregate model. The stress-strain curves of the fracturable and unfracturable aggregate model in this test were plotted in this study, and the results showed that the relative error between the results obtained by the fracturable model and those of the indoor test is much smaller than that of the unfracturable model. Considering that existing simulation tests on asphalt mixture often ignore aggregate deformation and fracture, so this study used the method mentioned above to obtain the fracturable aggregate model, which can improve the confidence of asphalt mixture simulation results.

Excitatory synaptic structural abnormalities produced by templated aggregation of α-syn in the basolateral amygdala

Parkinson's disease (PD) and Dementia with Lewy bodies (DLB) are characterized by neuronal α-synuclein (α-syn) inclusions termed Lewy Pathology, which are abundant in the amygdala. The basolateral amygdala (BLA), in particular, receives projections from the thalamus and cortex. These projections play a role in cognition and emotional processing, behaviors which are impaired in α-synucleinopathies. To understand if and how pathologic α-syn impacts the BLA requires animal models of α-syn aggregation. Injection of α-syn pre-formed fibrils (PFFs) into the striatum induces robust α-syn aggregation in excitatory neurons in the BLA that corresponds with reduced contextual fear conditioning. At early time points after aggregate formation, cortico-amygdala excitatory transmission is abolished. The goal of this project was to determine if α-syn inclusions in the BLA induce synaptic degeneration and/or morphological changes. In this study, we used C57BL/6 J mice injected bilaterally with PFFs in the dorsal striatum to induce α-syn aggregate formation in the BLA. A method was developed using immunofluorescence and three-dimensional reconstruction to analyze excitatory cortico-amygdala and thalamo-amygdala presynaptic terminals closely juxtaposed to postsynaptic densities. The abundance and morphology of synapses were analyzed at 6- or 12-weeks post-injection of PFFs. α-Syn aggregate formation in the BLA did not cause a significant loss of synapses, but cortico-amygdala and thalamo-amygdala presynaptic terminals and postsynaptic densities with aggregates of α-syn show increased volumes, similar to previous findings in human DLB cortex, and in non-human primate models of PD. Transmission electron microscopy showed that asymmetric synapses in mice with PFF-induced α-syn aggregates have reduced synaptic vesicle intervesicular distances, similar to a recent study showing phospho-serine-129 α-syn increases synaptic vesicle clustering. Thus, pathologic α-syn causes major alterations to synaptic architecture in the BLA, potentially contributing to behavioral impairment and amygdala dysfunction observed in synucleinopathies.

Edge2LoRa: Enabling edge computing on long-range wide-area Internet of Things

Long-Power Wide Area Networks (LPWAN) is a low-cost solution to deploy very-large scale Internet of Things (IoT) infrastructures with minimal requirements following a classic producer/consumer model. Inevitably such deployments will require a shift towards low-latency, distributed and collaborative data aggregation models. The cloud edge computing continuum (CECC) has been proposed as an evolution of the traditional central ultra-high-end processing cloud into a continuum of collaborative processing elements distributed from the cloud to the network edge. Until today, incorporating existing centralized and monolithic LPWAN architectures in the CECC faces multiple security-related implications. We propose Edge2LoRa, a complete secure solution to incorporate LPWAN architectures in CECC enabling faster data processing while reducing the transmission of sensitive data. It improves network performance through data pre-processing, traffic flow optimization, and real-time local analysis. Edge2LoRa gradually transform existing LPWAN deployments into agile and versatile infrastructures that enable the seamless and efficient processing of data throughout the CECC while guaranteeing service continuity and full-backwards compatibility. We implement Edge2LoRa in hardware compliant with the Things Stack and the LoRaWAN v1.0.4 and v1.1. We evaluate the performance in terms of networking and computing resource utilization, quality of service and security. The results provide a clear indication of the improvements to public and private LoRaWAN infrastructures without any disruption or service degradation for existing legacy services. In public LoRaWAN deployments where large-scale IoT data streams drive big data analytics, we demonstrate core network bandwidth usage reductions of up to 90% and data processing latency improvements by a ×10 factor.

Assessing hydropower capability for accommodating variable renewable energy considering peak shaving of multiple power grids

Hydropower can play a transformative role in supporting the substantial increase in variable renewable energy (VRE) via flexible regulation ability, but the capability of cascade hydropower to buffer VRE is indistinct. This study proposes a systematic framework to assess hydropower capability for integrating VRE considering the peak shaving demands of multiple power grids. A chance-constrained multi-time aggregate model for the integrated hydro–wind–solar operation is established to determine the optimal wind and solar capacity and coordinated operational strategies. An independent hydropower peak shaving operation model is proposed for comparison. The proposed models are recast as mixed integer linear programming formulations based on the proposed solution method. Comparative situation experiments divided by different influencing factors are implemented to analyze hydropower capability for integrating VRE. The results revealed the following: (1) Integrating VRE would reduce the peak shaving effectiveness of hydropower — the lowest load rate decreases to 0.899. (2) Hydropower capability for integrating wind and solar power is affected by hydrological conditions. Hydropower capability is limited under fixed hydrological conditions, and it prioritizes expanding wind power and restricting solar power. (3) Expanding the transmission capacity will increase hydropower capability from 13300 to 17000 MW and enhance peak shaving performance through the increased hydropower contribution.

On the diffusive model of C<sub>60</sub> fullerene clusterization in liquids

Purpose of research. The purpose of the study is to explain the mechanisms of clustering of fullerene molecules in liquid media observed using various structural nuclear -physical methods, as well as the interpretation of experimental data within the framework of a microscopic diffusion model. Methods. The article gives a brief overview of the results of positron annihilation spectroscopy, small-angle neutron scattering, translucent electron microscopy, with which the geometric parameters of fullerene clusters in solutions were established. The theoretical method of research is the microscopic diffusion model “Limited diffusion aggregation”, which describes the kinetic processes of clusterization. Results. The diffusion limited aggregation model displays adequately the mechanism of formation of C60 molecules in the form of fractal units in the volume of fluid, observed in experiments on positron annihilation spectroscopy and small-angle neutron scattering. The structural indicators of aggregate fullerene particles in carbon disulfide, o-Xylene, toluene, water and other solvents are analyzed. The properties of various diffusion models (the reaction limited aggregation model and the diffusion limited aggregation model) are considered and their combination in relation to the assessmentsof the kinetics of fullerene aggregation in solutions. A quantitative comparison of the results of the discussed models was carried out using the example of carbon disulfide. Conclusion. The diffusion microscopic models adequately describe the phenomenon of fullerene aggregation in polar and non-polar solvents, which are recorded by various nuclear methods (positron annihilating spectroscopy and small-angle neutron scattering), the most reliable is the diffusion limited aggregation model, more than that, it is the basis of a numerical definition of structural structures parameters of units. Compared to neutron scattering, with the annihilation of positrons, in the aggregation of fullerene the [Ps–C60] molecular complexes participate in the clusterization, but this does not affect the change in the size of the cluster and the reliability of the results.

An Efficient Federated Learning Framework for Machinery Fault Diagnosis With Improved Model Aggregation and Local Model Training.

Due to device operating environment limitations and data privacy protection, it is frequently difficult to obtain sufficient high-quality labeled data from devices, resulting in an insufficient generalization ability of fault diagnosis model. Therefore, a high-performance federated learning framework is proposed in this work, which makes improvements in the procedure of model aggregation and local model training. In the model aggregation of central server, an optimization aggregation strategy in which forgetting Kalman filter (FKF) is combined with cubic exponential smoothing (CES) is proposed to improve the efficiency of federated learning. In the local model training of multiclient, a deep learning network combined with multiscale convolution, attention mechanism, and multistage residual connection is proposed, which is able to fully extract multiclient data features simultaneously. Meanwhile, experiments on two machinery fault datasets show that the proposed framework is capable of achieving high accuracy and strong generalization of fault diagnosis on the premise of protecting data privacy in actual industrial situations.

The frequency analytical method for regional power systems considering the emergency frequency control

With the continuous construction of high-voltage direct-current (HVDC) transmission projects, higher requirements are placed on the frequency security of the regional power systems. In order to ensure the frequency security of regional power systems, this paper proposes a transient frequency analytical method considering the emergency frequency control (EFC). Firstly, the aggregated system frequency response (SFR) model is constructed by reducing the order of the generator governor equation, the EFC equation, and the load model equation. Then, based on the aggregation model, the analytical solution of the transient frequency nadir of the regional power system is derived. The model can quickly and accurately calculate the transient frequency nadir of the regional power system considering EFC after the HVDC block fault. Finally, based on an actual regional power system model, the accuracy and applicability of the proposed method under HVDC block fault are verified.

Research on simulation accuracy of equivalent aggregate electromagnetic model optimization for large renewable energy stations

Abstract The traditional power grid is rapidly transforming into a new power system with high penetration of new energy. Simulation modeling plays an important role in supporting the cognition and research of the new power system. A high proportion of renewable energy is connected to the grid through power electronic components, which puts forward a higher demand for research on the accuracy and stability of power grid simulation modeling. Renewable energy plants usually have a large number of single units, and equivalent aggregation modeling is the usual way to simplify the modeling of renewable energy plants. However, the transmission line power multiplier equivalent module inevitably introduces series impedance into the network, resulting in errors between simulation and actual engineering. Therefore, the study of lossless multiplier elements based on Modified Nodal Analysis (MNA) can improve the simulation accuracy, and it is verified with the simulation results of detailed models in actual scenarios, proving that MNA lossless multiplier elements can optimize the accuracy and stability of equivalent aggregation modeling of large-scale multi-unit renewable energy stations.

Cost share-induced technological change: An analytical classical-evolutionary model

This paper builds on prior work by the author on cost share-induced technological change. The theoretical model views selection of candidate innovations as a capital budgeting exercise. In this paper it treats the case in which firms target an incremental rate of profit, which introduces a nonzero threshold into a “selection frontier”. This presents analytical challenges, which are resolved in this paper by assuming that the probability distribution of potential increases in productivity among the set of fit innovations is normal. That permits an explicit derivation of a micro-level model of cost share-induced technological change that can be taken as a candidate functional form for an aggregate model. The model is calibrated against historical data for India, China, and the United States, three large continental economies at different levels of per capita GDP. The model is able to fit the data with reasonable fidelity, and the fitted model parameters can be given a reasonable interpretation. The paper further shows that combining cost share-induced technological change with price-setting behavior produces theoretically interesting results.