Aggregate Time Research Articles

Development of high-volume fly ash concrete with improved interfacial transition zone

The interfacial transition zone (ITZ) is a vulnerable region in concrete, particularly in high-volume fly ash (HVFA) concrete where a significant portion of fly ash particles remain unreacted. In order to improve the ITZ of HVFA concrete, this work introduces a novel approach by targeted employing slag in the ITZ. Slag-modified cement paste was used to coat coarse aggregate, which was further mixed with other materials to cast HVFA concrete. Various parameters during preparing the coated coarse aggregate, such as the water-to-cementitious material ratio of the cement paste, the slag content in cementitious materials, the mass ratio of coarse aggregate to cement paste, the mixing time of coarse aggregate and cement paste, and the drying time after mixing, were investigated to uncover their influence on the porosity of the ITZ in HVFA concrete. By using the optimized values of these variables of 0.35, 3.8%, 3.2, 10 min and 1 h, respectively, it is feasible to achieve ITZ in HVFA concrete characterized by a porosity of 23.6% and refined pore structure, alongside enhanced compressive strength of the concrete.

Urban Traffic Flow Prediction Based on Bayesian Deep Learning Considering Optimal Aggregation Time Interval

Predicting short-term urban traffic flow is a fundamental and cost-effective strategy in traffic signal control systems. However, due to the interrupted, periodic, and stochastic characteristics of urban traffic flow influenced by signal control, there are still unresolved issues related to the selection of the optimal aggregation time interval and the quantifiable uncertainties in prediction. To tackle these challenges, this research introduces a method for predicting urban interrupted traffic flow, which is based on Bayesian deep learning and considers the optimal aggregation time interval. Specifically, this method utilizes the cross-validation mean square error (CVMSE) method to obtain the optimal aggregation time interval and to establish the relationship between the optimal aggregation time interval and the signal cycle. A Bayesian LSTM-CNN prediction model, which extends the LSTM-CNN model under the Bayesian framework to a probabilistic model to better capture the stochasticity and variation in the data, is proposed. Experimental results derived from real-world data demonstrate gathering traffic flow data based on the optimal aggregation time interval significantly enhances the prediction accuracy of the urban interrupted traffic flow model. The optimal aggregation time interval for urban interrupted traffic flow data corresponds to a multiple of the traffic signal control cycle. Comparative experiments indicate that the Bayesian LSTM-CNN prediction model outperforms the state-of-the-art prediction models.

Rheological properties of blood in multiple myeloma patients.

Multiple myeloma (MM) is considered to be one of the hematological malignancies formed by excessive and abnormal proliferation of plasmocytes. Among other parameters, several blood tests are used to diagnose multiple myeloma. The hemorheological profile in multiple myeloma is not widely studied. Hemorheology includes the study of measuring the deformability and aggregation of erythrocytes, blood viscosity, and sedimentation rate. The degree of deformability of blood cells is necessary to maintain proper vital functions. Proper deformability of red blood cells ensures proper blood circulation, tissue oxidation and carbon dioxide uptake. The aim of the study was to compare morphology and blood rheology parameters in patients with MM and healthy individuals. The study included 33 patients with MM, and 33 healthy subjects of the same age. The hematological blood parameters were evaluated using ABX MICROS 60 hematology analyzer. The LORCA Analyzer to study erythrocyte aggregation and deformability. Patients with MM had lower red blood cells count (RBC) (9.11%) (p < 0.001) and half time of total aggregation (T1/2) (94.29%) (p < 0.001) values and higher mean corpuscular volume (MCV) (5.50%) (p < 0.001), aggregation index (AI) (68.60%) (p < 0.001), total extent of aggregation (AMP) (87.92%) (p < 0.001) values than the healthy control group. Aggregation in patients with MM is different compared to healthy individuals. It was observed that the percentage of cell aggregation is almost 50% higher than in the control group. The study of morphology, aggregation and deformability of erythrocytes in patients with suspected MM may be helpful in making clinical decisions.

Systematic review for the development of a core outcome set for monofocal intraocular lenses for cataract surgery.

The aim of the study was to define a core outcome set (COS) to be measured following cataract surgery for the postoperative evaluation of monofocal intraocular lenses (IOLs). Compared to current COSs, the present work provides updates considering the advances in the technology due to the development of new generation monofocal IOLs, which are characterized by a safety profile comparable to standard monofocal IOLs but with an extended range of intermediate vision. Healthcare professionals (ophthalmologist surgeons) and patients were involved in the selection of outcomes to be included in the COS, starting from a list of indicators retrieved from a systematic literature search. The search considered observational studies with both a retrospective or prospective design, case studies and classic randomized controlled trials (RCTs). A mixed methodology integrating a Delphi-driven and an expert panel approach was adopted to reach an agreement among clinicians, while patients were involved in the completion of a questionnaire. The final COS included 15 outcomes. Eleven outcomes, all clinical, were considered for inclusion after a joint discussion among ophthalmologists; seven outcomes were linked to visual acuity, while the remaining to contrast sensitivity, refractive errors, aberrations and adverse events. Measurement metrics, method of aggregation and measurement time point of these outcomes were specified. The most important aspects for the patients were (1) quality of life after cataract surgery, (2) the capacity to perform activities requiring good near vision (e.g., reading), (3) spectacle independence, and (4) safety of movements without fear of getting hurt or falling (intermediate vision). In a context with limited healthcare resources, it is important to optimize their use considering also the preferences of end-users, namely patients. The proposed COS, developed involving both ophthalmologists and patients, provides an instrument for the postoperative evaluation of different technologies in the context of monofocal IOLs, which can be used not only in clinical trials but also in clinical practice to increase the body of real-world evidence.

Feasibility and comparability of different platelet function tests in acute stroke with or without prior antiplatelet therapy.

The clinical course of ischemic and hemorrhagic strokes can be influenced by the coagulation status of individual patients. The prior use of antiplatelet therapy (APT) such as acetylsalicylic acid (ASA) or P2Y12-antagonists has been inconsistently described as possibly increasing the risk of hemorrhagic transformation or expansion. Since clinical studies describing prior use of antiplatelet medication are overwhelmingly lacking specific functional tests, we aimed to implement testing in routine stroke care. We used fluorescence-activated cell sorting (FACS) with antibodies against CD61 for thrombocyte identification and CD62p or platelet activation complex-1 (PAC-1) to determine platelet activation. Aggregometry and automated platelet functioning analyzer (PFA-200) were employed to test thrombocyte reactivity. FACS and aggregometry samples were stimulated in vitro with arachidonic acid (AA) and adenosine diphosphate (ADP) to measure increase in CD62p-/PAC-1-expression or aggregation, respectively. Between February and July 2023, 20 blood samples (n = 11 ischemic strokes; n = 7 hemorrhagic strokes; n = 2 controls) were acquired and analyzed within 24 h of symptom onset. N = 11 patients had taken ASA, n = 8 patients no APT and n = 1 ASA+clopidogrel. ASA intake compared to no APT was associated with lower CD62p expression after stimulation with AA on FACS analysis (median 15.8% [interquartile range {IQR} 12.6-37.2%] vs. 40.1% [IQR 20.3-56.3%]; p = 0.020), lower platelet aggregation (9.0% [IQR 7.0-12.0%] vs. 88.5% [IQR 11.8-92.0%]; p = 0.015) and longer time to plug formation with PFA-200 (248.0 s [IQR 157.0-297] vs. 121.5 s [IQR 99.8-174.3]; p = 0.027). Significant correlations were noted between AA-induced CD62p expression and aggregometry analysis (n = 18; ρ = 0.714; p < 0.001) as well as a negative correlation between CD62p increase and PFA clot formation time (n = 18; ρ = -0.613; p = 0.007). Sensitivity for ASA intake was highest for PFA (81.8% for values ≥155.5 s). The combination of ASA + clopidogrel also affected ADP-induced CD62p and PAC-1 expression. In the clinical setting it is feasible to use differentiated platelet analytics to determine alterations caused by antiplatelet therapy. Among the tests under investigation, PFA-200 showed the highest sensitivity for the intake of ASA in stroke patients. FACS analysis on the other hand might be able to provide a more nuanced approach to altered platelet reactivity.

An Efficient Multi-Party Secure Aggregation Method Based on Multi-Homomorphic Attributes

The federated learning on large-scale mobile terminals and Internet of Things (IoT) devices faces the issues of privacy leakage, resource limitation, and frequent user dropouts. This paper proposes an efficient secure aggregation method based on multi-homomorphic attributes to realize the privacy-preserving aggregation of local models while ensuring low overhead and tolerating user dropouts. First, based on EC-ElGamal, the homomorphic pseudorandom generator, and the Chinese remainder theorem, an efficient random mask secure aggregation method is proposed, which can efficiently aggregate random masks and protect the privacy of the masks while introducing secret sharing to achieve tolerance of user dropout. Then, an efficient federated learning secure aggregation method is proposed, which guarantees that the computation and communication overheads of users are only O(L); also, the method only performs two rounds of communication to complete the aggregation and allows user dropout, and the aggregation time does not increase with the dropout rate, so it is suitable for resource-limited devices. Finally, the correctness, security, and performance of the proposed method are analyzed and evaluated. The experimental results indicate that the aggregation time of the proposed method is linearly related to the number of users and the model size, and it decreases as the number of dropped out users increases. Compared to other schemes, the proposed method significantly improves the aggregation efficiency and has stronger dropout tolerance, and it improves the efficiency by about 24 times when the number of users is 500 and the dropout rate is 30%.

Temporal variations in soil aggregate re‐formation behaviors after disturbance by tillage

AbstractAggregate re‐formation after a disturbance is important for maintaining soil hydraulic status and carbon stabilization. A study investigating the re‐formation of aggregates after disturbance by spring tillage was conducted at a site located on a south‐facing (6%) slope with a silt‐loam soil at Arlington‐Wisconsin in 2018 and 2019. Treatments were conventional tillage (CT) and no‐tillage (NT) with winter application of solid manure (SM) and no manure in a complete randomized design. Soils under NT had a higher proportion of larger aggregates (&gt;1 mm), whereas the proportion of smaller (&lt;1 mm) aggregates was greater under CT. Soil organic carbon, total nitrogen, bulk density, soil water retention, and micropores of NT treatments were higher compared to CT systems at 0‐ to 5‐cm depth. However, the impacts of manure application on soil properties were not significant, except for those of soil organic carbon (SOC) and total nitrogen. Harvesting in 2018 decreased the relative proportion of aggregates smaller than 1 mm and hydraulic conductivity of saturated soil. These results indicate that the immediate effect of tillage is to decrease larger aggregates, SOC, and total porosity, whereas harvesting decreases the proportion of smaller aggregates. Comparatively, aggregates smaller than 1 mm were mainly influenced by the long‐term effects of the management operations. It appears that larger aggregates can recover on an annual basis, but aggregates smaller than 1 mm do not. In long‐term studies, there is a need to monitor aggregates, including their size distribution and pore structures, to identify aggregate turnover time and rate, which will augment our understanding of aggregate formation.

Over 18.8% Efficiency of Layer‐By‐Layer Organic Photovoltaics Enabled by Ameliorating Exciton Utilization in Acceptor Layer

AbstractThe layer‐by‐layer (LbL) organic photovoltaics (OPVs) are constructed with wide‐bandgap donor PM1 and narrow‐bandgap acceptor L8‐BO. The exciton utilization near cathode is still challenging considering restricted diffusion distance of excitons and inability for transferring energy from L8‐BO to PM1. Herein, donor incorporation into acceptor layer (DIA) strategy is employed to improve exciton utilization near cathode. The efficiency of LbL OPVs can be improved from 18.02% to 18.81% by incorporating 10 wt% PM1 into L8‐BO layer, which is closely associated with efficient exciton separation into L8‐BO layer originated from more adequate donor/acceptor interface for faster charge transfer, as evidenced by magneto‐photocurrent and transient absorption results. The in situ test and morphological characterization clarify that molecular packing property can be improved benefited from prolonged aggregation and nucleation time of acceptor layer assisted by DIA strategy, contributing to more efficient charge transport and inhibited charge recombination in active layers. The thickness insensitive property of LbL OPVs can be also improved induced by DIA strategy, indicated by PCE retention value (82.2% vs. 74.0%) for PM1/L8‐BO:PM1 and PM1/L8‐BO OPVs when acceptor layer thickness increased to ≈180 nm. This work demonstrates the effectiveness of DIA strategy in improving efficiency and thickness tolerance of LbL OPVs.

Studying Selective Autophagy of Protein Aggregates Using Particles Induced by Multimerization (PIMs).

Selective autophagy of protein aggregates, called aggrephagy, is vital for maintaining cellular homeostasis. Classically, studying aggrephagy has been challenging due to the infrequent occurrence of autophagic events and the lack of control over the specificity and timing of protein aggregation. We previously reported two variants of a PIM (particles induced by multimerization) assay that enable the formation of chemically induced, fluorescently labeled protein aggregates in cells. PIMs are recognized by the selective autophagy machinery and are subsequently degraded in the lysosome. By making use of pH-sensitive fluorescent proteins, such as GFP or mKeima, the PIM assay allows for direct visualization of aggregate clearance in cells. Here, we describe a protocol for the use of the PIM assay to study aggrephagy in live and fixed cells.

Addition of Bioactive Glass Decreases Setting Time and Improves Antibacterial Properties of Mineral Trioxide Aggregate.

This study aimed to assess the effect of addition of bioactive glass (BG) on the setting time and antibacterial activity of mineral trioxide aggregate (MTA) against Enterococcus faecalis (E. faecalis). In this in vitro study, BG was synthesized by the sol-gel technique and added to MTA powder in certain ratios. Three groups of specimens were fabricated from pure MTA, MTA mixed with 10wt% BG, and MTA mixed with 20wt% BG. The setting time of specimens was measured according to ISO9917-2007. Direct contact test was used to assess the antimicrobial activity of the three groups against E. faecalis. Data were analyzed by repeated measures ANOVA (alpha = 0.05). Addition of BG (in both concentrations) to MTA decreased its setting time and improved its antibacterial activity against E. faecalis (p < 0.05). By an increase in concentration of BG (20%), the antimicrobial activity further improved (p < 0.05). Addition of BG to MTA in 10wt% and 20wt% concentrations decreased its setting time and improved its antibacterial activity against E. faecalis.

The Analysis of Wildlife Trade Based on Aggregate Analysis Model and Time Series Prediction

The Analysis of Wildlife Trade Based on Aggregate Analysis Model and Time Series Prediction

Unveiling the effect of CaCl2 on amyloid β aggregation via supercritical angle Raman and fluorescence spectroscopy and microscopy.

Amyloid β aggregation is an important factor in Alzheimer's disease. Since calcium homeostasis plays an important role in amyloid β aggregation, it is crucial to study the interaction between calcium ions and amyloid β directly at the surface of the lipid membrane. With supercritical angle techniques, the signal of interest at the surface is easily separated from the bulk solution, making them a powerful tool for aggregation study. In this work, the influence of calcium ions on amyloid β aggregation over different aggregation time periods is investigated with supercritical angle Raman and fluorescence spectroscopy and microscopy. Note that calcium ions have a larger influence on amyloid β1-42 than on the 40 amino acid variant. We found that a small layer of calcium ions significantly protects the lipid membrane against the protein insertion process.

AutoFL: A Bayesian Game Approach for Autonomous Client Participation in Federated Edge Learning

Given that devices (i.e., clients) participating in federated edge learning (FEL) are autonomous and resource-constrained in nature, it is critical to design effective incentive mechanisms to encourage client participation so as to improve the performance of FEL. In this paper, we aim to boost the FEL training efficiency by answering how much compute resource should clients autonomously contribute to maximize their utilities. To this end, we develop AutoFL, an autonomous client participation decision framework for federated learning at the network edge without assuming that each client possesses complete information. We first model the problem of autonomous client participation as a Bayesian game with incomplete information, where each player in the game is associated with a set of types according to network conditions. We optimize an individual client's decision based on the dynamics of the population estimated following the Bayes rule. We prove that AutoFL can converge to a unique Bayesian Nash equilibrium point. Empirical results on three real datasets show that AutoFL achieves a higher model accuracy with only 15.5-24.5% model aggregation time per global training round, and its energy cost saving on mobile devices is 82.2-86.8% compared to the state-of-the-art algorithms. Moreover, we can achieve a 2.75-3.2x long-term fairness compared to classical solutions.

A new method for measuring fast aggregation rate based on the growth of the colloidal aggregate size

We propose a novel technique to determine the absolute fast aggregation rate directly from the growth curve of the aggregate size. Our approach is convenient and effective as it eliminates the need for calculations of optical properties and does not require a precise zero point of aggregation time, which are both essential in traditional light-scattering methods. Through comparisons with conventional turbidity measurements, the reliability and precision of this new approach are verified for both homogeneous and heterogeneous aggregation.

Epidemiological characteristics and spatio-temporal aggregation of severe fever with thrombocytopenia syndrome in Jinan City, China, 2018-2022.

Severe fever with thrombocytopenia syndrome (SFTS) has become a significant public health issue in Jinan City. However, the analysis of epidemiological characteristics and spatio-temporal clustering of SFTS in Jinan has not been studied yet. SFTS data from 2018-2022 in Jinan City were obtained from the China Information System for Disease Control and Prevention. Global spatial autocorrelation and local spatial autocorrelation analyses were performed using ArcGIS 10.2 software, and spatiotemporal hotspot area detection was carried out using SatScan 9.6 software. Between 2018 and 2022, 680 SFTS cases were reported in Jinan City, resulting in 53 deaths and an average case fatality rate of 7.8%. 99.0% of cases occurred between April and October, 91.9% individuals were over 50 years old, and 87.79% were primarily farmers. A positive spatial correlation of SFTS in Jinan was observed (Moran's I value between 0.135-0.197, P<0.001), indicating spatial aggregation, primarily in Licheng, Zhangqiu, Laiwu, and Gangcheng districts in southeastern Jinan. Spatiotemporal scanning detected one class I and two class II aggregation areas, with the class I aggregation area (RR = 5.66, LLR = 192.547, P<0.001) locating in southeastern Jinan City, comprising 31 towns/streets, and an aggregation time from 13 May 2020 to 13 October 2022. Spatial and temporal aggregation of SFTS is evident in Jinan. Based on the spatial and temporal distribution and epidemiological characteristics, prevention and control measures such as public education, monitoring, and training should target key populations in high-incidence epidemic areas.

A categorized information fusion model for reliable services delivery in smart cities

Wireless Sensor Network serves as the core source of data in the Internet of Things (IoT) environment. The WSN nodes sense, aggregate, and relay sensed data for the different services in the IoT. The reliability of the aggregated and transmitted information is crucial for the effectiveness of IoT services. The data from multiple sensors at different timestamps need to be fused for efficient data transmission, reducing complexity and improving the reliability of the data being transmitted, improving the quality of IoT services. Therefore, data fusion is an essential aspect of WSN, where multiple sensors collect and combine data to produce a single, more comprehensive data for transmission. This data fusion reduces the amount of data transmission and network energy consumption and increases the accuracy of the result. This paper introduces a Categorised Information Fusion Model (CIFM) to minimise the replication in handling the aggregation of multiple instances of sensor data by categorising the information. The proposed CIFM employs federated learning for distributed verification of different aggregation time frames. This learning identifies the replication of sensor data from different sensors based on time frames and aggregation instances. In the multi-timed aggregation level, the sensed information's interfering distribution is diminished by controlling the relaying instance. The recurrent learning instances diminish the multi-timed information based on the occurrence factor. This recurrent learning improves delivery precision by controlling computation complexity and fusion time. The CIFM model also reduces the communication overhead by reducing the amount of replicated information that needs to be transmitted and processed and providing a consistent delivery ratio. The use of federated learning also allows for privacy-preserving data processing as the raw data is only processed locally, and aggregated results are shared. The performance of the proposed CIFM model is evaluated through simulations and compared with other existing fusion methods. The results show that the proposed model outperforms existing accuracy and communication efficiency methods. This approach helps to simplify the process of aggregating and relaying the data in the WSN to improve the performance and efficiency of IoT services. The goal of CIFM is to provide a more effective way to process and utilise the information from the sensors to deliver accurate and reliable data to the IoT platform. The CIFM improves the fusion rate by 7.77 % and minimize the fusion time up to 13.03 % compared to other methods.

Fresh state and strength performance evaluation of slag-based alkali-activated concrete using soft-computing methods

In this study, machine learning prediction models for the slump (SL) and compressive strength (CS) of alkali-activated concrete (AAC) were developed. Extreme gradient boosting (XGB) as an ensemble and support vector machine (SVM) as individual methods were chosen. To evaluate the performance of the models, the Taylor diagram, k-fold validation, and statistical tests were performed. Moreover, to determine the significance of features, a SHapley Additive exPlanations (SHAP) study was carried out. XGB outperformed SVM considerably in predicting the SL and CS of AAC. XGB outperformed SVM in terms of R2 (0.94 for SL and 0.97 for CS), which was 0.86 and 0.88, respectively. Precursor content had the greatest effect on the SL of AAC, followed by blast furnace slag ratio, test time, SiO2/Na2O, and quantities of NaOH, aggregate, and water, according to the results of the SHAP study. The SHAP investigation revealed that curing time had the greatest effect on the CS of AAC, followed by SiO2/Na2O, NaOH quantity, precursor content, aggregate quantity, blast furnace slag ratio, and water quantity. It was determined that curing time, SiO2/Na2O, and blast furnace slag had beneficial effects; precursor content and aggregate quantity had adverse effects, while water had both beneficial and deleterious effects.

Numerical Simulation of Oil-water Two-phase Flow Interface Tracking

The insulation performance of transformer oil has a great connection with the stable operation of power systems, and transformer oil will inevitably be mixed with water and other impurities in the actual operation. To explore the mechanism of the influence of suspended water droplets on the insulation performance of transformer oil, this paper uses the phase field method to simulate the interaction and phase transition process between oil and water phases in motion. They systematically analyse the influence of electric field and oil flow parameters on the deformation and coalescence of water droplets. The results indicate that droplets in the oil channel will deform along the direction of the electric field, and the degree of deformation gradually increases with the increase of the electric field strength. The electric field strength has an important impact on the degree of deformation of droplets in the oil channel. In addition, under the same electric field intensity, the radius of droplets is an important factor that leads to the length of aggregation time of droplets. Research has found that the aggregation time between droplets becomes shorter as the radius gradually increases.

Sensitivity of acoustofluidic particle manipulation to microchannel height in standing surface acoustic wave-based microfluidic devices

In this paper, the flow and particle trajectories, induced by standing surface acoustic waves (SSAWs) in a poly-dimethylsiloxane microchannel, are investigated by establishing a two-dimensional cross-sectional model with the finite element method and improved boundary conditions. Extensive parametric studies are conducted regarding the channel height, ranging from 0.2 to 4.0 times the spacing of the repetitive vertical interference pattern, to investigate its influences on the flow field and microparticle aggregation. The first-order flow field is found to be related to the channel height, exhibiting a periodic spatial distribution and oscillatory variation in its amplitude as the height changes. We theoretically analyze the propagation mechanism of the acoustic waves in the vertical direction and thus determine the periodicity of the wave interference pattern. Furthermore, we find that the speed of the particle aggregation is a function of the channel height, so the channel height can be optimized to maximize the strength of the first-order flow field and thus minimize the time of particle aggregation. The optimum heights can reduce the aggregation time by up to 76%. In addition, the acoustophoretic motions of microparticles exhibit a spatially dependent pattern when the channel height becomes larger than a quarter of the wavelength of the SAW, which can be explained by the change in the ratio between the radiation force and the streaming drag force from position to position. Our findings provide guidelines to the design and optimization of SSAW-based acoustofluidic devices.

Enhancement of the wet carbonation of artificial recycled concrete aggregates in seawater

This study aimed to improve the carbonation efficiency and reduce processing time of recycled concrete aggregates (RCAs). Different liquid mediums were used for wet carbonation and compared with dry carbonation. Reaction kinetics, phase assemblage, microstructure and performance of RCAs and recycled aggregate concrete (RAC) prepared with carbonated RCAs were evaluated. RCAs carbonated under seawater for 10 min achieved >9 % reduction in water absorption and 3 % increase in density, which were more efficient than dry carbonated samples. Compressive strength of RAC prepared with 1-h seawater carbonated RCAs was significantly improved. Formation of ettringite and calcite under wet carbonation increased density and strength by contributing to solid volume, also leading to improved bond strength in interfacial transition zone in RAC. Seawater presents several advantages as a medium for wet carbonation due to its abundant availability, CO2 capture capacity and accelerated hydration and carbonation, thereby enabling rapid improvement of RCA's and resulting concrete formulations' performance.