Aggregate Time Research Articles

Effect of various parameters on carbonation treatment of recycled concrete aggregate using the design of experiment method

Recycled concrete aggregate (RCA) comprises old mortar adhered having several interfacial transition zones that induces inferior properties in the concrete. This study used pressurized carbonization to strengthen the performance of RCA, and the effects of aggregate size (5–10 and 10–20 mm), carbonization pressure (0.5, 1, and 2 Bar) and time (2 and 4 h) on the performance of RCA and prepared concrete were investigated. The physical properties of RCA, including water absorption, loose and bulk density, and aggregate crushing value were determined. Meanwhile, the workability and mechanical properties of concrete prepared by 100% RCA were investigated. The Design of the Experiment method, a statistical approach, was applied to identify the optimized combination parameters to obtain better carbonation and thus higher strength of concrete mixed with RCA. For RCA, the best optimum carbonation regime was 1 Bar and 2 h (pressure and duration), and further increased carbonization pressure and time was detrimental to further development of RCA performance. When a carbonization pressure of 1 Bar and a carbonization time of 2 h were applied, water absorption and aggregate crushing value values of carbonized RCAs were decreased by 48.8%–49.3% and 9.7%–12.8%, respectively than uncarbonized RCA. The compressive strengths of concrete prepared by 100% carbonized RCA with a size of 5–10 and 10–20 mm were increased by 27.63% and 27.5% and split tensile strengths were increased by 11.7% and 10.6%, respectively compared to the concrete prepared by uncarbonized RCA. The results of microstructure analysis also showed that using the appropriate carbonization regime was beneficial to improve the denseness of the RCA by generating carbonate. Therefore, by adjusting the RCA and carbonized parameters, it can effectively strengthen the performance of the concrete prepared by RCA, so as to provide technical guidance for actual engineering.

Increased reactive oxygen species lead to overactivation of platelets in essential thrombocythemia

IntroductionPlatelet function, rather than platelet count, plays a crucial role in thrombosis in essential thrombocythemia (ET). However, little is known about the abnormal function of platelets in ET. Here, we investigated the functional characteristics of platelets in ET hemostasis to explore the causes of ET platelet dysfunction and new therapeutic strategies for ET. Materials and methodsWe analyzed platelet aggregation, activation, apoptosis, and reactive oxygen species (ROS) in ET patients and JAK2V617F-positive ET-like mice. The effects of ROS on platelet function and the underlying mechanism were investigated by inhibiting ROS using N-acetylcysteine (NAC). ResultsPlatelet aggregation, activation, apoptosis, ROS, and clot retraction were elevated in ET. No significant differences were observed between ET patients with JAK2V617F or CALR mutations. Increased ROS activated the JAK-STAT pathway, which may further influence platelet function. Inhibition of platelet ROS by NAC reduced platelet aggregation, activation, and apoptosis, and prolonged bleeding time. Furthermore, NAC treatment reduced platelet count in ET-like mice by inhibiting platelet production from megakaryocytes. ConclusionsElevated ROS in ET platelets resulted in enhanced platelet activation, function and increased risk of thrombosis. NAC offers a potential therapeutic strategy for reducing platelet count.

Understanding the evolution of particle size dispersion with time in a fluidised bed granulation process

Minimisation of final size dispersion remains an important objective of fluidised bed granulation. Some of the factors that control the evolution of the granule size distribution with time in such a process are examined using a validated aggregation model. The aim is to understand the intrinsic behaviour of size dispersion versus time with an approach that is not specific to a particular system of fluidised granulation. Because relative size dispersion (size dispersion compared to the mean size) is also important, the study also examines the evolution of mean granule size versus time. The model includes an aggregation rate that is time and size dependent and a granule kinetic energy dissipation mechanism. The relative importance of these components of the model is quantified in terms of how they control the output and it is shown that the action of the energy absorption mechanism (implemented using the viscous Stokes criterion) makes the dominant contribution although the interplay with the other effects is also significant. Mean granule volume monotonically approaches a quasi-stable asymptotic value while standard deviation in granule volume passes through a peak value before falling to a lower, quasi-stable asymptotic value. The reasons underlying such behaviour are outlined. Two new temporal parameters, an equivalent aggregation time constant and a Stokes time constant, are developed and can be employed to predict and interpret system behaviour. Such knowledge can then be applied to suggest how dispersion in size can be controlled within certain intrinsic limits, which will be of interest to the industry.

Incentive mechanism-based interval-valued q-rung orthopair fuzzy dynamic comprehensive evaluation method for measuring regional green development level

Interval-valued q-rung orthopair fuzzy number (IVq-ROFN) is a popular tool for modeling complex uncertain information and has gained successful applications in the field of comprehensive evaluation. However, most of the existing studies are based on the absolute values of evaluation data but fail to take incentive effects into account. Reasonable and appropriate incentive can guide the evaluated objects to better achieve the decision goals. Therefore, this study develops an incentive mechanism-based interval-valued q-rung orthopair fuzzy dynamic comprehensive evaluation method. Firstly, new interval-valued q-rung orthopair fuzzy measures including deviation measure and correlation coefficient are proposed for managing IVq-ROFNs data. To overcome the limitations of the existing aggregating operators that are not suitable for scenarios with need of many times of data aggregation, we introduce two new interval-valued q-rung orthopair fuzzy aggregating operators. Furthermore, a new interval-valued orthopair fuzzy CRITIC method is developed to objectively determine the importance of the evaluated criteria. More importantly, the horizontal incentive effects within a single period and the vertical incentive effects during multiple periods under IVq-ROFNs environments are proposed to reward (or punish) the evaluated objects in the evaluation process. The evaluated results are determined based on the full compensatory model and the multiplicative form model. The main advantage of the developed method is that the expectations of decision-makers and the dynamic characteristics during multiple periods are taken fully into account, which can make the evaluation results more reasonable and reliable. Finally, this developed comprehensive evaluation method is applied to evaluate the green development level of Jiangxi province within eleven cities from 2016 to 2020. We observe that the cities x2, x3, x4, x5, x7, x8 are rewarded within positive incentive values and the cities x1, x6, x9, x10, x11 are punished within negative incentive values. Especially, the positive incentive value for the city x3 is the biggest and the negative incentive value for the city x9 is the biggest. The best city in term of GDL is x3. The evaluated results with consideration of incentive effects are in line with the expectation of the decision-maker.

Modified squirrel search algorithm based data aggregation framework for improved network lifetime in wireless sensor network

WSNs (Wireless Sensor Networks) have various applications that include environments and traffic monitors, surveillance of battlefields, innovative agriculture because of their communication effectiveness. Individual sensor nodes in WSNs have restrictions in energy consumptions, packet deliveries, network longevities, and latencies. In previous work, an energy efficient data aggregation is achieved by developing an EADSLPBNC-DA framework for sensor network. Data aggregation performance is enhanced with minimum time through classifying the sensor nodes. But it has issue with data aggregation time, energy level of nodes will be changes periodically which is not concentrated in the previous work. The goal of this study is to address aforementioned difficulties and hence MSSA (Modified Squirrel Search Algorithm) which can improve data aggregations is proposed. Data aggregations based on CHs (cluster heads) improves energy efficiencies and network longevities. Aggregations of CHs related data is critical for minimizing sensor node energy consumptions in WSNs and their selections enhance WSNs lifespan. The MSSA-DA generates best fitness values using objective function which selects optimal sensor nodes as CHs to aggregate data optimally. The sensor nodes are chosen depending on their proximities, energies, and bandwidths. The suggested MSSA-DA system was simulated for assessing network lives, energy consumptions, accuracies, and data aggregation times. Experimental results suggested MSSA-DA model enhances data aggregation accuracies and network lives while consuming lesser energies and times.

Brownian translation and rotation from the ballistic to the diffusive limit and derivation of the physical properties of dust agglomerates.

We analyzed the translational and rotational Brownian motion of aggregates of micrometer-sized silica spheres under microgravity conditions and in rarefied gas. The experimental data was collected in the form of high-speed recordings using a long-distance microscope as part of the ICAPS (Interactions in Cosmic and Atmospheric Particle Systems) experiment on board of the sounding rocket flight Texus-56. Our data analysis shows that the translational Brownian motion can be used to determine the mass and translational response time of each individual dust aggregate. The rotational Brownian motion additionally provides the moment of inertia and the rotational response time. A shallow positive correlation between mass and response time was found as predicted for aggregate structures with low fractal dimensions. Translational and rotational response times are roughly in agreement. Using the mass and the moment of inertia of each aggregate, we determined the fractal dimension of the aggregate ensemble. Slight deviations from the pure Gaussian one-dimensional displacement statistics were found in the ballistic limit for both the translational and rotational Brownian motion.

Research on smart city data encryption and communication efficiency improvement under federated learning framework

To improve the data communication processing capability of smart city informatization construction and defend against malicious joint attacks of internal communication participants, we studied the use of function encryption, blockchain, differential privacy and other technologies to defend against weight disclosure, participation in collusion attacks, single point failures and other issues in the federal learning process, and introduced edge computing and asynchronous communication in the classic federal learning framework, Improve the communication efficiency of the model while ensuring the accuracy. The research results show that the accuracy of FE-BDP algorithm can maintain above 95% for a long time, and the change is not significant with the increase of the number of users, indicating that the algorithm has strong stability. The loss value of FE-LDP model is significantly smaller than that of other models and can be stabilized below 0.05. The data aggregation time of blockchain encryption technology is less than 1.0s. The edge-asynchronous communication framework can be applied to multiple urban scenarios and achieve effective data communication, with the highest accuracy rate of 93.87%and the lowest communication cost of 1315.29s. The research results show that the security encryption fusion technology can effectively protect user data privacy, and the edge-asynchronous communication framework has obvious effect on improving communication efficiency, which has important application value for promoting the construction of smart city informatization.

Upcycled synthesis and extraction of carbon‐encapsulated iron carbide nanoparticles for gap Plasmon applications in perovskite solar cells

AbstractAn effective method for obtaining large amounts of metal nanoparticles (NPs) encapsulated by carbon layers through upcycling from floating‐catalyst aerosol chemical vapor‐deposited carbon nanotubes is demonstrated. NPs with diameters of less than 20 μm are selectively extracted from the synthesized carbon assortments through sonication, centrifugation, and filtration. The particles show an aggregation behavior owing to the π–π interaction between the graphitic carbon shells surrounding the iron carbides. By controlling the degree of the aggregation and arrangement, the light scattering by the gap‐surface plasmon effect in perovskite solar cells is maximized. Application of the NPs to the devices increased the power conversion efficiency from 19.71% to 21.15%. The short‐circuit current density (JSC) trend over the particle aggregation time accounts for the plasmonic effect. The devices show high stability analogue to the control devices, confirming that no metal‐ion migration took place thanks to the encapsulation.image

Three Level Recognition Based on the Average of the Phase Differences in Physical Wireless Parameter Conversion Sensor Networks and Its Effect to Localization with RSSI

In recent years, there have been increased demands for aggregating sensor information from several sensors owing to the spread of the Internet of Things (IoT). However, packet communication, which is a conventional multiple-access technology, is hindered by packet collisions owing to simultaneous access by sensors and waiting time to avoid packet collisions; this increases the aggregation time. The physical wireless parameter conversion sensor network (PhyC-SN) method, which transmits sensor information corresponding to the carrier wave frequency, facilitates the bulk collection of sensor information, thereby reducing the communication time and achieving a high aggregation success rate. However, when more than one sensor transmits the same frequency simultaneously, the estimation accuracy of the number of accessed sensors deteriorates significantly because of multipath fading. Thus, this study focuses on the phase fluctuation of the received signal caused by the frequency offset inherent to the sensor terminals. Consequently, a new feature for detecting collisions is proposed, which is a case in which two or more sensors transmit simultaneously. Furthermore, a method to identify the existence of 0, 1, 2, or more sensors is established. In addition, we demonstrate the effectiveness of PhyC-SNs in estimating the location of radio transmission sources by utilizing three patterns of 0, 1, and 2 or more transmitting sensors.

Stock price informativeness of risk disclosure: Does time orientation matter?

This paper investigates the extent to which firms’ stock prices may incorporate aggregate risk disclosure levels differently depending on its time orientation, whether forward-looking or non-forward-looking. Using a sample period of ten years for the UK FTSE all-share non-financial firms, this paper used price synchronicity to capture stock informativeness and utilized textual analysis of the narrative sections of annual reports to capture aggregate risk disclosure levels and time orientation. Our empirical results suggest that, whereas the UK market does not incorporate aggregate risk disclosure, it does react significantly to the time orientation of the content of risk disclosure. Specifically, we find that non-forward-looking risk disclosure is significantly and negatively associated with stock price synchronicity, suggesting that this disclosure is firm-specific and it is difficult to replace with market-wide factors. However, we also find that forward-looking risk disclosure, is significantly and positively associated with stock price synchronicity, indicating that such disclosure is generic, and it can be replaced by market-wide factors. These results are likely to have theoretical and practical implications because they testify to the importance of considering the content (time orientation) rather than the level of aggregate risk information when observing the informativeness of the narrative sections of annual reports.

Influence of key cations and anions on phosphate removal by Fe(0) electrocoagulation

The electrocoagulation technology is efficient for the removal of phosphate. The removal efficiency of phosphate in the secondary effluent is higher than that of in the synthetic wastewater. In order to identify the key factors for promoting the removal of phosphate in Fe(0) electrocoagulation (EC), this study investigated typical coexisted substances in the secondary effluent, including Ca2+, Mg2+, NH4+, HCO3−, SO42−. The results indicated that Ca2+ and Mg2+ were the key factors promoting the removal of phosphate by Fe(0) EC both under normal (5–6 mg/L) and low dissolved oxygen (DO) (≤ 1 mg/L) conditions. Under low and normal DO conditions, the phosphate removal efficiency increased from 28 % to 98 % ± 2 % (in 5 min) at 10 mA/cm2 with the addition of Ca2+ (0.2–20 mM) or Mg2+ (0.2–2 mM) but decreased to approximately 70 % with the addition of a Mg2+ of 20 mM. Under low and normal DO conditions, the aggregation time of iron oxide flocs decreased from 10 min to 5 min when the concentration of Ca2+ increased from 0 to 20 mM and Mg2+ increased from 0 to 2 mM. The aggregation stability of flocs was not sufficient when the Mg2+ concentration was 20 mM at 5 mA/cm2. Our results confirmed that Fe(0) EC was suitable for the removal of phosphate in the secondary effluent, especially containing moderate amounts of Ca2+ and Mg2+ regardless of the concentration of DO.

Elastic-Wave Characteristics from Crack Initiation and Propagation of High-Strength Steel Immersed in Acetic-Acid Solution

In this study, elastic waves were detected when different bending stresses were applied to cracked specimens of high-strength steel (SKD11: HV550) immersed in a 0.057 M solution of acetic acid (CH3COOH), and frequency characteristics were analyzed using time-frequency analysis. The dominant frequency obtained using the tensile test was approximately 103 kHz, and those in the acetic-acid solution without stress were approximately 32 and 101 kHz. The dominant frequencies of the crack specimens in which cracks propagated were approximately 30–40 (F1), 60–85 (F2), and 100–110 (F3) kHz. An elastic wave was obtained by corrosion, pitting, crack initiation, and propagation but not during the hydrogen aggregation time. The dominant frequencies of the crack specimens without crack propagation were approximately 28–33 (F1) and 94–109 (F3) kHz. These were the same as the dominant frequency in the acetic-acid solution under nonstress conditions. The fractured surface showed many traces of pitting and corrosion regardless of the applied stress, resulting in microcracks in the Cr carbide. -----------------------------------------------------------------------------------------------------------

Determining the impact of gold nanoparticles on amyloid aggregation with 2D IR spectroscopy.

As nanomaterials become more prevalent in both industry and medicine, it is crucial to fully understand their health risks. One area of concern is the interaction of nanoparticles with proteins, including their ability to modulate the uncontrolled aggregation of amyloid proteins associated with diseases, such as Alzheimer's disease and type II diabetes, and potentially extend the lifetime of cytotoxic soluble oligomers. This work demonstrates that two-dimensional infrared spectroscopy and 13C18O isotope labeling can be used to follow the aggregation of human islet amyloid polypeptide (hIAPP) in the presence of gold nanoparticles (AuNPs) with single-residue structural resolution. 60nm AuNPs were found to inhibit hIAPP, tripling the aggregation time. Furthermore, calculating the actual transition dipole strength of the backbone amide I' mode reveals that hIAPP forms a more ordered aggregate structure in the presence of AuNPs. Ultimately, such studies can provide insight into how mechanisms of amyloid aggregation are altered in the presence of nanoparticles, furthering our understanding of protein-nanoparticle interactions.

The Relationship between Premature Adrenarche and Platelet Aggregation.

Premature adrenarche has been associated with an increase in adrenal androgens, and the hyperandrogenic hormonal environment is known to lead to increased platelet aggregation. Here, we evaluated the effects of premature adrenarche (PA) on platelet aggregation in platelet-rich plasma samples from female patients. The study included 40 female patients diagnosed with PA between February 2014 and June 2018 and 30 healthy female individuals in the control group. Adenosine diphosphate (ADP) and collagen-induced platelet aggregation were studied with the photometric aggregometry method. There was no significant difference in the platelet count or volume values between the participants with PA and the control group. Additionally, the ADP-induced maximum aggregation time, value, and slope values did not significantly differ between the patient and control groups (p>0.05). However, the collagen-induced maximum aggregation time, value, and slope values were significantly higher in the patient group (p < 0.001). Increased collagen-induced platelet aggregation was detected in female patients with PA. As PA is associated with a higher risk of cardiovascular events later in life, close follow-up of PA in this respect may be beneficial.

Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.

Tuberculosis (TB) is a public health problem worldwide, and the influence of meteorological and air pollutants on the incidence of tuberculosis have been attracting interest from researchers. It is of great importance to use machine learning to build a prediction model of tuberculosis incidence influenced by meteorological and air pollutants for timely and applicable measures of both prevention and control. The data of daily TB notifications, meteorological factors and air pollutants in Changde City, Hunan Province ranging from 2010 to 2021 were collected. Spearman rank correlation analysis was conducted to analyze the correlation between the daily TB notifications and the meteorological factors or air pollutants. Based on the correlation analysis results, machine learning methods, including support vector regression, random forest regression and a BP neural network model, were utilized to construct the incidence prediction model of tuberculosis. RMSE, MAE and MAPE were performed to evaluate the constructed model for selecting the best prediction model. (1) From the year 2010 to 2021, the overall incidence of tuberculosis in Changde City showed a downward trend. (2) The daily TB notifications was positively correlated with average temperature (r = 0.231), maximum temperature (r = 0.194), minimum temperature (r = 0.165), sunshine duration (r = 0.329), PM2.5 (r = 0.097), PM10 (r = 0.215) and O3 (r = 0.084) (p < 0.05). However, there was a significant negative correlation between the daily TB notifications and mean air pressure (r = -0.119), precipitation (r = -0.063), relative humidity (r = -0.084), CO (r = -0.038) and SO2 (r = -0.034) (p < 0.05). (3) The random forest regression model had the best fitting effect, while the BP neural network model exhibited the best prediction. (4) The validation set of the BP neural network model, including average daily temperature, sunshine hours and PM10, showed the lowest root mean square error, mean absolute error and mean absolute percentage error, followed by support vector regression. The prediction trend of the BP neural network model, including average daily temperature, sunshine hours and PM10, successfully mimics the actual incidence, and the peak incidence highly coincides with the actual aggregation time, with a high accuracy and a minimum error. Taken together, these data suggest that the BP neural network model can predict the incidence trend of tuberculosis in Changde City.

Modulus watch: In situ determination of the gel modulus by timing the fluorescence color change

The gel modulus, a key parameter for gel materials, is traditionally determined by cumbersome rheometer. Recently, probe technologies occur to meet the requirements of in situ determination. Till now, in situ and quantitatively testing of gel materials with unabridged structure informations still remains a challenge. Here, we provide a facile, in situ approach to determine the gel modulus, by timing the aggregation of a doped fluorescence probe. The probe shows green emission during aggregation and shifts to blue once it forms aggregates. The higher modulus of the gel, the longer probe’s aggregation time. Furthermore, a quantitative correlation of gel modulus with the aggregation time is established. The in situ method not only facilitates the scientific researches in the field of gels, but also provides a new approach for spatiotemporal materials.

Combined electrical and fluorescence recordings of single molecules in bilayers formed on MECA-opto chips.

Molecular processes within lipid bilayers, such as ion channel insertion and gating, can be followed using electrophysiology techniques. With fluorescently labeled molecules, additional features such as protein aggregation and spatial distribution can also be followed. However, correlated electrical and optical measurements, especially at the single-molecule level, have been historically challenging. Factors such as bilayer stability, the timing of protein aggregation and insertion, and the photostability of fluorescent dyes, all work against successful collection of simultaneous electrical and fluorescence records. Here, we describe approaches for assessing lateral diffusion, single-molecule aggregation, and ion channel insertion in bilayers formed on commercially available micro-electrode cavity arrays (MECA-opto chips). These array chips have been specifically designed to enhance the collection of simultaneous electrical and optical records. We compare lipid and ion-channel diffusion results from both fluorescence correlation spectroscopy and wide-field single-molecule tracking experiments. We also use both approaches to assess protein aggregation, spatial distribution, and correlation with insertion currents in the bilayer.

Efficient Tree Aggregation and Processing Time for Wireless Sensor Networks

Efficient Tree Aggregation and Processing Time for Wireless Sensor Networks

Effect of nanoparticles on the aggregation and amyloid formation of amyloid-beta peptide.

Nanoparticles are becoming more and more important in medicine and biotechnology. Moreover, due to environmental pollution, the human body is exposed to nanoparticles in the everyday life. Nanomaterials can also affect the aggregation processes of proteins. The Aβ peptides are known for forming amyloid aggregates in the brain in Alzheimer's disease. We used in-house expressed recombinant Aβ(1-42) to study the structure, aggregation kinetics and cytotoxicity of the peptide. We characterized nanoparticles of different materials and studied their effects on the structure and aggregation of Aβ. We followed amyloid formation by monitoring Thioflavin T fluorescence in the presence of nanoparticles. We observed that nanoparticles typically reduce the lag time of aggregation. We found that polystyrene particles bind monomers to a certain saturation point and allow only excess monomers to form fibrils. Calcium fluoride (CaF2) nanoparticles do not bind monomers in a similar fashion, but do influence the secondary structure composition of the resulting amyloid fibrils. We used CD spectroscopy and the BeStSel method to determine the secondary structure composition of the different forms of Aβ. While the main characteristics of Aβ fibrils formed alone is parallel β-sheet, in the presence of CaF2, Aβ forms significant amount of antiparallel β-structure as well. We also conducted cytotoxicity experiments to investigate if nanoparticles alter the toxic effect of Aβ on mouse hippocampal cells. Our preliminary findings suggest that studied particles might reduce cytotoxicity of Aβ if applied in very low concentrations.

Technical note: A procedure to clean, decompose, and aggregate time series

Abstract. Errors, gaps, and outliers complicate and sometimes invalidate the analysis of time series. While most fields have developed their own strategy to clean the raw data, no generic procedure has been promoted to standardize the pre-processing. This lack of harmonization makes the inter-comparison of studies difficult, and leads to screening methods that can be arbitrary or case-specific. This study provides a generic pre-processing procedure implemented in R (ctbi for cyclic/trend decomposition using bin interpolation) dedicated to univariate time series. Ctbi is based on data binning and decomposes the time series into a long-term trend and a cyclic component (quantified by a new metric, the Stacked Cycles Index) to finally aggregate the data. Outliers are flagged with an enhanced box plot rule called Logbox that corrects biases due to the sample size and that is adapted to non-Gaussian residuals. Three different Earth science datasets (contaminated with gaps and outliers) are successfully cleaned and aggregated with ctbi. This illustrates the robustness of this procedure that can be valuable to any discipline.