Magnitude Threshold Research Articles

Effects of complete slip conditions on the peristaltic pumping of a Casson nanofluid with suction and injection in a vertical channel

This study investigates the effects of complete slip conditions on the peristaltic pumping of a Casson nanofluid with suction and injection in a vertical due to the crucial role that nano liquids play in a variety of technological and medical fields, particularly in peristalsis, a mechanism that transports liquids. The Casson fluid belongs to a class of non-Newtonian fluids that, through a particular stress threshold magnitude, exhibit elastic solid behavior before changing to liquid behavior. These fluids have several uses in engineering, food preparation, drilling and other fields. After establishing the governing conservation equations, the resulting flow model is effectively simulated using the realistic assumptions of a long wavelength and a low Reynolds number. The temperature distributions, velocity, pressure rate per wavelength and nanoparticle concentration of the resulting flow problem have been solved analytically. The effects of all physical factors on temperature, velocity, concentration fields, pressure rate, frictional force and pressure gradient are graphically examined using Wolfram MATHEMATICA software. There are a variety of biofluids that cannot be classified as liquids. For example, blood contains WBC, RBC and plasma. It is essential to model biofluids (blood) as nanofluids given the physical properties of these biofluids. According to reports, one of the finest yield stress models is the Casson model, and blood exhibits a similar behavior. We took these facts into consideration when thinking about Casson nanofluid flow in a vertical layer under peristalsis. Additionally, the suction and injection mechanisms can be used to represent the exchange of carbon dioxide in bold. In order to understand how blood flows through small blood vessels, this model must be examined. The obtained results show that the Newtonian case and those found in the literature have a very good agreement. Since the liquid moves faster and more effectively when the value is increased, it becomes clear that this increases the strength of the velocity. In other words, nanoperistaltic pumps can maintain a pressure differential that increases or decreases at all operating flow rates with an increasing thermophoresis effect. Furthermore, it is obvious that the pressure reduction in a Casson fluid is greater than in a Newtonian fluid.

Evaluating the incompleteness magnitude using an unbiased estimate of the b value

SUMMARY The evaluation of the b value of the Gutenberg–Richter (GR) law, for a sample composed of n earthquakes, presents a systematic positive bias δb which is proportional to 1/n . In this study, we show how to incorporate in δb the bias introduced by deviations from the GR law. More precisely we show that δb is proportional to the square of the variability coefficient CV, defined as the ratio between the standard deviation of the magnitude distribution and its mean value. When the magnitude distribution follows the GR law CV = 1 and this allows us to introduce a new graphical procedure, based on the dependence of b on n, which allows us to identify the incompleteness magnitude mc as the threshold magnitude leading to CV = 1. The method is tested on synthetic catalogues and it is applied to estimate mc in Southern California, Japan and New Zealand.

О возможности детектирования ионосферных возмущений, вызванных мощными подземными взрывами, методами GNSS-радиопросвечивания

Using dense networks of GNSS receivers, an analysis of the ionospheric responses to two underground explosions with a magnitude less than the existing threshold for detecting the ionospheric response to earthquakes was carried out. It is shown that when regestering these responses, it is necessary to take into account the possible anisotropy of the propagation of atmospheric disturbances at ionospheric altitudes due to both large-scale wind structures and the inclination of the geomagnetic field. Taking these factors into account makes it possible to reduce the magnitude threshold (compared to that established for earthquakes) of impulse lithospheric sources (explosions, volcanic explosions), the response to which can still be detected in the ionospheric total electron content variations.

Estimation of Uncertainty in the Average Rate of Earthquakes Exceeding a Magnitude Threshold

AbstractWe propose a method to estimate the uncertainty of the average rate of earthquakes exceeding a magnitude threshold in a future period of given length based on observed variability of the earthquake process in an existing catalog. We estimate the ratio R of the variability to that of a stationary Poisson process. R is estimated from subsets of the catalog over a wide range of timescales. The method combines the epistemic uncertainty in estimating the rate from the catalog and the aleatory variability of the rate in future time periods. If R is stable over many timescales, there is a solid basis for estimating the uncertainty of earthquake rate estimates. In the 2022 revision of the New Zealand National Seismic Hazard Model (NZ NSHM), estimation of the total shallow earthquake rate over the next 100 yr and its uncertainty is an important element. Using a 70 yr New Zealand catalog with hypocentral depths ≤40 km and standardized magnitudes M ≥ 4.95, we find stable estimates of R for timescales from 3 days to 2.4 yr. This gives a standard error of 0.95 on the estimated annual rate of M ≥ 4.95, in the next 100 yr. R becomes unstable and has poor precision for longer subperiods. We investigate potential causes using synthetic catalogs with known inhomogeneities. Analysis of International Seismological Centre-Global Earthquake Model (ISC-GEM) catalog, to investigate the effect of higher magnitude thresholds, shows that R is lower for M ≥ 6.95 than for M ≥ 5.45. The ISC-GEM catalog restricted to New Zealand gives comparable stable estimates of R to the NZ NSHM 2022 catalog for M ≥ 5.45 and lower estimates than the NZ NSHM 2022 catalog for M ≥ 4.95. We also verify that magnitude standardization of the New Zealand GeoNet catalog has reduced the uncertainty of rate estimates by decreasing R throughout the entire range of timescales.

Mass movement hazard & global climate change: Physical-deterministic modelling study of the rest and be thankful pass, Scotland UK

Mass movements frequently affect infrastructure corridors, particularly so in mountainous areas. The A83 at the Rest and be Thankful pass (Scotland, UK) is a prime example of a road experiencing frequent closure due to precipitation-triggered landslides and debris flows. The closures result in long (>90 km) diversions and severe economic losses (>£60 million). To ensure persistence of transport services, future engineered interventions and maintenance schedules must anticipate the potential consequences of climate change that affects the frequency and magnitude threshold precipitation events that are capable of triggering mass movement. In turn this requires a better understanding of the hydrological implications of climate change on precipitation-triggered landslides and debris flows. The area is prone to mass movements due to steep topography, the glacial history, reworked deposits of mud and siltstone, and landslide-triggering precipitation. The objective here is to assess the severity of future events, i.e. the coming decades, was assessed by means of an event-based runout model for selected events that were identified using a present-day precipitation threshold applied to hydro-meteorological forecasts for this part of the UK. A convex hull method was applied to these forecasts to create a comprehensive selection of modelled events and a much reduced computational load. This selection is considered to be representative for the largest variation within the forecasts.Results show that 61 mm of daily precipitation will likely trigger mass movement. This threshold reduces by 0.15 mm for every millimetre accounted for in an antecedent precipitation index calculated based on 8 antecedent days. On the basis of the hydro-meteorological forecasts it can be concluded that the frequency of these trigger conditions gradually declines, as seven out of the eleven RCM (Regional Climate Model) configurations show longer recurrence intervals. This corresponds with the decline in the modelled future precipitation. However, it is observed that, for scenarios with lower average daily precipitation and antecedent precipitation indices, event severity is likely to increase as intense daily precipitation follows drier antecedent conditions. For scenarios with higher average daily precipitation and antecedent precipitation indices, persistent wetting appears to expand source areas and increases material entrainment. The simulations show that the main source areas, situated between 300 and 500 metre elevation, extend upslope when the antecedent precipitation regime has peak precipitation values in the latter stages of the 8 antecedent days.

Increased earthquake rate prior to mainshocks

According to the Omori-Utsu law, the rate of aftershocks after a mainshock decays as a power law with an exponent close to 1. This well-established law was intensively used in the past to study and model the statistical properties of earthquakes. Moreover, according to the so-called inverse Omori law, the rate of earthquakes should also increase prior to a mainshock—this law has received much less attention due to its large uncertainty. Here, we mainly study the inverse Omori law based on a highly detailed Southern California earthquake catalog, which is complete for magnitudes mc≥1 or even lower. First, we develop a technique to identify mainshocks, foreshocks, and aftershocks. We then find, based on a statistical procedure we developed, that the rate of earthquakes is higher a few days prior to a mainshock. We find that this increase is much smaller for (a) a catalog with a magnitude threshold of mc=2.5 and (b) for the Epidemic-Type Aftershocks Sequence (ETAS) model catalogs, even when used with a small magnitude threshold (i.e., mc=1). We also analyze the rate of aftershocks after mainshocks and find that the Omori-Utsu law does not hold for many individual mainshocks and that it may be valid only statistically when considering many mainshocks together. Yet, the analysis of the ETAS model based on the Omori-Utsu law exhibits similar behavior as that of the real catalogs, indicating the validity of this law.

Impact of the Uncertainty in the Parameters of the Earthquake Occurrence Model on Loss Estimates of Urban Building Portfolios

Abstract Understanding the potential socioeconomic losses due to natural hazards, such as earthquakes, is of foremost importance in the field of catastrophe risk management. The construction of a probabilistic seismic risk model is complex and requires the tuning of several parameters essential to represent the seismic hazard of the region, the definition of the exposed inventory characteristics, and its vulnerability to ground motion. Because significant uncertainties could be associated with each model component, the loss estimates are often highly volatile. Nevertheless, to reduce the conceptual complexity and the computational burden, in many real-life applications these uncertainties are either not adequately treated or neglected altogether. The false high fidelity of the ensuing loss estimates can mislead decision-making strategies. Hence, it is useful to assess the influence that the variability in the estimated values of the model input parameters may exert on the final risk results and their relevant contributions. To this purpose, we have performed a sensitivity analysis of the results of an urban seismic risk assessment for Isfahan (Iran). Systematic variations have been applied to the values of the parameters that control the earthquake occurrence in the probabilistic seismic hazard model. Curves of input–output relative variations were built for different risk metrics with the goal of identifying the parameters most sensitive to input uncertainty. Our findings can be useful to support risk managers and practitioners in the process of building seismic hazard and risk models. We found that the Gutenberg–Richter a and b values, the maximum magnitude, and the threshold magnitude are large contributors to the variability of important risk measures, such as the 475 yr and the average annual loss, with the more frequent losses being, in general, most sensitive.

A multifault earthquake threat for the Seattle metropolitan region revealed by mass tree mortality.

Compound earthquakes involving simultaneous ruptures along multiple faults often define a region's upper threshold of maximum magnitude. Yet, the potential for linked faulting remains poorly understood given the infrequency of these events in the historic era. Geological records provide longer perspectives, although temporal uncertainties are too broad to clearly pinpoint single multifault events. Here, we use dendrochronological dating and a cosmogenic radiation pulse to constrain the death dates of earthquake-killed trees along two adjacent fault zones near Seattle, Washington to within a 6-month period between the 923 and 924 CE growing seasons. Our narrow constraints conclusively show linked rupturing that occurred either as a single composite earthquake of estimated magnitude 7.8 or as a closely spaced double earthquake sequence with estimated magnitudes of 7.5 and 7.3. These scenarios, which are not recognized in current hazard models, increase the maximum earthquake size needed for seismic preparedness and engineering design within the Puget Sound region of >4 million residents.

Use of peak over threshold data for flood frequency estimation: An application at the UK national scale

This study investigates choices of statistical distributions to represent the threshold exceedance frequency and magnitude of peaks-over-threshold (POT) series from a national dataset of extreme hydrological events from 842 gauging stations in the UK. From the initial POT series, two new series were created, POT1 and POT3, representing POT series with an average of, respectively, one and three exceedances per year. Using a χ2 goodness-of-fit test, the choice of distributions for both the annual exceedance counts and the magnitude of threshold exceedances were explored for both the POT1 and POT3 datasets. The results show that the negative binomial and geometrical distributions provide a better fit to the annual exceedance count than the Poisson distribution typically assumed. These results are particularly pronounced when considering the POT3 dataset and in the South-East of the UK where many river flow series are dominated by slow responding groundwater dominated catchments. Finally, estimates of design floods with a return period of 2 and 100 years were obtained for each POT series and compared to the equivalent estimates obtained from direct at-site analysis of the annual maximum series. The results show a good alignment between the magnitude of the design floods estimated by the two methods, but a generally lower standard deviation of estimates obtained from the POT data, as quantified using a bootstrap procedure. The results presented here show that the POT series could beneficially replace the current operational guidelines based on annual maximum series for design flood estimation in the UK.

The Impact of Fractal Dimension, Stress Tensors, and Earthquake Probabilities on Seismotectonic Characterisation in the Red Sea

The frequency–magnitude statistics of 6527 earthquakes with 1.0 ≤ ml ≤ 5.7 and focal depths between 0 and 49 km in the Red Sea region between 1980 and 2021 show that the threshold magnitude, above which most of the Red Sea earthquakes are precisely located, is 1.5. The b-value, which identifies regional stress situations and associated energy release modalities, has a value of 0.75, less than in historical data, and averages between 0.4 and 0.85 as it varies over time, indicating modest stress accumulation. We utilised these instrumental data to examine dynamic stress patterns in the Red Sea region, shedding light on the region’s geodynamics and providing a foundation for estimating the region’s seismic hazard. The computed fractal dimension (Dc) has a relatively high value of 2.3, which is significant for the Red Sea’s geological complexity and structural diversity. This result indicates the regular distribution of Red Sea earthquakes, which occur in clusters or along fault lines. The low b-value and comparatively high Dc were most likely due to major earthquakes in the past and the greater stress they caused. The focal mechanisms of the big earthquakes, predominantly normal solutions, are consistent with the movement and extensional regime. The pressure and tension (P-T) axes show a compression axis trending NW-SE and a tension axis trending NE-SW. According to the stress inversion results, the maximum principal stress (σ1) is oriented vertically, the minimum stress axis (σ3) is subhorizontal and strikes in the NE-SW direction, and the intermediate principal stress (σ2) is trending in the NE-SW direction. The variance in the region that characterises the homogeneity of stress directions within the range is 0.19. The stress ratio (R), which identifies the faulting type, is 0.76, suggesting a normal faulting pattern for the region. The hazard parameters are expressed by the probability of exceedance for 1-, 10-, 50-, and 100-year return periods. The highest probability that an earthquake will occur within a 50-year period is thought to be around 6.0. The largest observed catalogue and instrumental magnitudes in the area, 5.7 and 6.7, respectively, show average recurrence intervals of 36 and 142 years.

A transition point: Assistance magnitude is a critical parameter when providing assistance during walking with an energy-removing exoskeleton or biomechanical energy harvester.

Researchers and engineers have developed exoskeletons capable of reducing the energetic cost of walking by decreasing the force their users' muscles are required to produce while contracting. The metabolic effect of assisting concentric and isometric muscle contractions depends, in part, on assistance magnitude. We conducted human treadmill experiments to explore the effects of assistance magnitude on the biomechanics and energetics of walking with an energy-removing exoskeleton designed to assist eccentric muscle contractions. Our results demonstrate that the assistance magnitude of an energy-removing device significantly affects the energetics, muscle activity, and biomechanics of walking. Under the moderate assistance magnitude condition, our device reduced the metabolic cost of walking below that of normal walking by 3.4% while simultaneously producing 0.29 W of electricity. This reduction in the energetic cost of walking was also associated with an 8.9% decrease in hamstring activity. Furthermore, we determined that there is an assistance magnitude threshold that, when crossed, results in the device transitioning from assisting to hindering its user. This transition is marked by significant increases in muscle activity and the metabolic cost of walking. These results could aid in the future design of exoskeletons and biomechanical energy harvesters, as well as adaptive control systems, that identify user-specific control parameters associated with minimum energy expenditure.

Effect on signal magnitude thresholding on detecting student engagement through EEG in various screen size environment

In this study, a new method was developed to detect student involvement in the online learning process. This method is based on convolutional neural network (CNN) as a classifier with an emphasis on the preprocessing process combined with a new feature in the form of signal magnitude area (SMA) thresholding. In this study, the data used as training data is a public dataset that emphasizes the decomposition of electroencephalography (EEG) signals into individual signal processing. Twenty subjects were taken to be used as test data, with each subject watching online learning lectures in the field of computer science on three different devices, either with a flat screen, a curved screen or a smartphone screen that is smaller than two standard computer monitors. Based on the study's results, it is known that the change in screen size is inversely proportional to the level of student attention, the smaller the screen, the lower the student's attention. For classification results, the model equipped with SMA thresholding outperformed the standard classifier by 8.33% with a test set of 20 people.

Effect on signal magnitude thresholding on detecting student engagement through EEG in various screen size environment

In this study, a new method was developed to detect student involvement in the online learning process. This method is based on convolutional neural network (CNN) as a classifier with an emphasis on the preprocessing process combined with a new feature in the form of signal magnitude area (SMA) thresholding. In this study, the data used as training data is a public dataset that emphasizes the decomposition of electroencephalography (EEG) signals into individual signal processing. Twenty subjects were taken to be used as test data, with each subject watching online learning lectures in the field of computer science on three different devices, either with a flat screen, a curved screen or a smartphone screen that is smaller than two standard computer monitors. Based on the study's results, it is known that the change in screen size is inversely proportional to the level of student attention, the smaller the screen, the lower the student's attention. For classification results, the model equipped with SMA thresholding outperformed the standard classifier by 8.33% with a test set of 20 people.

Combination of direct boiling and glass beads increases the purity and accuracy of bacterial DNA extraction.

Extraction of DNA is a key step in molecular biology experiments and important for counting tiny microbial individuals. Direct boiling and mechanical cell lysis like glass beads are two independent physical extraction methods, thus crossing the barriers of thresholds of magnitude in popular reagent kits or traditional spread plate method when non-equilibrium phenomenon is ongoing. The two approaches above were combined to generate a new one. In three typical microbial species, direct boiling with glass beads significantly increased the purity of DNA solution compared with some other methods (p<0.05). The qPCR results of them were closer to direct microscopy counting than some other methods. Therefore, it provides a new choice in extracting bacterial DNA for specific circumstances.

Optimal uniform guide star catalog using a geneticalgorithm.

To achieve optimal and reliable star sensors and overcome some onboard hardware and software limitations, this study aimed to make an optimal uniform guide star catalog. For this purpose, the objective function was defined by the field of view (FOV) and magnitude threshold, and then design variables were optimized. The optimal uniform guide star catalog was obtained by a genetic algorithm alongside the Latinized stratified sampling method and by a novel, to the best of our knowledge, spherical density determination algorithm based on the minimum number of stars required for a star identification algorithm. Finally, Monte Carlo simulation was used to validate the results, which indicate a dramatic improvement, including a reduction in the number of stars in the uniform catalog and an increase in the probability of observing the minimum required stars for the star identification algorithm (at least 5 stars) in 98.34% of all possible optimal FOVs (about 12°).

Agglomerative Clustering of 2022 Earthquakes in North Sulawesi, Indonesia

This paper presents a cluster analysis of earthquake data in the surrounding region of North Sulawesi, Indonesia. The dataset comprises seismic data recorded throughout the year 2022, obtained from the BMKG earthquake repository. A total of 211 earthquakes were included in the analysis, with a minimum magnitude threshold of 2.5 and a maximum depth of 300 km. The agglomerative clustering technique, combined with the elbow method, was employed to determine the optimal and distinct number of clusters. As a result, four unique clusters were identified. Cluster 1 exhibited high magnitudes, with an average magnitude of 4.4, and shallow depths, averaging at 20 km. Cluster 2 also had high magnitudes, averaging at 4.4, but deeper depths, with an average of 199 km. Cluster 3 consisted of earthquakes with low magnitudes, averaging at 3.4, and shallow depths, averaging at 21 km. Lastly, Cluster 4 comprised earthquakes with low magnitudes, averaging at 3.4, but deeper depths, with an average of 136 km. Among the 211 earthquakes, 29 were assigned to Cluster 1, 39 to Cluster 2, 100 to Cluster 3, which had the highest population, and 43 to Cluster 4. This study provides valuable insights into the clustering patterns and characteristics of earthquakes in the region, contributing to a better understanding of seismic activity in North Sulawesi, Indonesia

LI-DWT- and PD-FC-MSPCNN-Based Small-Target Localization Method for Floating Garbage on Water Surfaces

Typically, the process of visual tracking and position prediction of floating garbage on water surfaces is significantly affected by illumination, water waves, or complex backgrounds, consequently lowering the localization accuracy of small targets. Herein, we propose a small-target localization method based on the neurobiological phenomenon of lateral inhibition (LI), discrete wavelet transform (DWT), and a parameter-designed fire-controlled modified simplified pulse-coupled neural network (PD-FC-MSPCNN) to track water-floating garbage floating. First, a network simulating LI is fused with the DWT to derive a denoising preprocessing algorithm that effectively reduces the interference of image noise and enhances target edge features. Subsequently, a new PD-FC-MSPCNN network is developed to improve the image segmentation accuracy, and an adaptive fine-tuned dynamic threshold magnitude parameter V and auxiliary parameter P are newly designed, while eliminating the link strength parameter. Finally, a multiscale morphological filtering postprocessing algorithm is developed to connect the edge contour breakpoints of segmented targets, smoothen the segmentation results, and improve the localization accuracy. An effective computer vision technology approach is adopted for the accurate localization and intelligent monitoring of water-floating garbage. The experimental results demonstrate that the proposed method outperforms other methods in terms of the overall comprehensive evaluation indexes, suggesting higher accuracy and reliability.

PIRET-A Platform for Treatment Planning in Electroporation-Based Therapies.

We developed PIRET, a platform to predict the treatment volume in electroporation-based therapies. The platform seamlessly integrates tools to build patient-specific models where the electric field is simulated to predict the treatment volume. Patient anatomy is segmented from medical images and 3D reconstruction aids in placing the electrodes and setting up treatment parameters. Four canine patients that had been treated with high-frequency irreversible electroporation were retrospectively planned with PIRET and with a workflow commonly used in previous studies, which uses different general-purpose segmentation (3D Slicer) and modeling software (3Matic and COMSOL Multiphysics). PIRET outperformed the other workflow by 65 minutes (× 1.7 faster), thanks to the improved user experience during treatment setup and model building. Both approaches computed similarly accurate electric field distributions, with average Dice scores higher than 0.93. A platform which integrates all the required tools for electroporation treatment planning is presented. Treatment plan can be performed rapidly with minimal user interaction in a stand-alone platform. This platform is, to the best of our knowledge, the most complete software for treatment planning of irreversible electroporation. It can potentially be used for other electroporation applications.

DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks

Load forecasting is a crucial topic in energy management systems (EMS) due to its vital role in optimizing energy scheduling and enabling more flexible and intelligent power grid systems. As a result, these systems allow power utility companies to respond promptly to demands in the electricity market. Deep learning (DL) models have been commonly employed in load forecasting problems supported by adaptation mechanisms to cope with the changing pattern of consumption by customers, known as concept drift. A drift magnitude threshold should be defined to design change detection methods to identify drifts. While the drift magnitude in load forecasting problems can vary significantly over time, existing literature often assumes a fixed drift magnitude threshold, which should be dynamically adjusted rather than fixed during system evolution. To address this gap, in this paper, we propose a dynamic drift-adaptive Long Short-Term Memory (DA-LSTM) framework that can improve the performance of load forecasting models without requiring a drift threshold setting. We integrate several strategies into the framework based on active and passive adaptation approaches. To evaluate DA-LSTM in real-life settings, we thoroughly analyze the proposed framework and deploy it in a real-world problem through a cloud-based environment. Efficiency is evaluated in terms of the prediction performance of each approach and computational cost. The experiments show performance improvements on multiple evaluation metrics achieved by our framework compared to baseline methods from the literature. Finally, we present a trade-off analysis between prediction performance and computational costs.

Development of magnitude correlation equations for the tsunamigenic zones of the Indian Ocean

There is a pressing need for a homogonous tsunami catalogue for the Indian Ocean as nearly 20% of tsunami events worldwide affect the region. Any study on tsunami hazard assessment necessitates a homogenous tsunamigenic earthquake catalogue. The existing records of strong tsunamigenic earthquakes have the magnitudes expressed in moment magnitude (MW), body wave magnitude (mb), local magnitude (ML), and surface wave magnitude (MS). This study deals with developing regional magnitude correlation equations for tsunamigenic earthquakes of the Indian Ocean. The present investigation estimates the threshold magnitude and focal depth for an earthquake to turn tsunamigenic. It is found that earthquakes above MW ≥ 5.9 and focal depth ≤ 80 km have the potential to generate a tsunami in the region. The moment magnitude is the most proper scale to characterize the size of large tsunamigenic earthquakes as it is more directly related to the released energy and does not suffer saturation. Hence, equations have been developed to convert surface wave magnitude (MS) to moment magnitude (MW) using three types of regression models viz. standard regression (SR), inverse standard regression (ISR), and orthogonal standard regression (OSR). The efficacy of these models has been compared in terms of R-squared and residual analysis. This study indicates that OSR is the best-suited regression model for developing magnitude correlation equations for the three zones of the Indian Ocean region under study. Also, a single unified conversion equation for the whole of the Indian Ocean has been derived with rational accuracy.