Magnitude Distribution Research Articles

Characteristic Extraction and Assessment Methods for Transformers DC Bias Caused by Metro Stray Currents.

Metro stray currents flowing into transformer-neutral points cause the high neutral DC and a transformer to operate in the DC bias state.Because neutral DC caused by stray current varies with time, the neutral DC value cannot be used as the only characteristic indicator to evaluate the DC bias risk level. Thus, unified characteristic extraction and assessment methods are proposed to evaluate the DC bias risk of a transformer caused by stray current, considering the signals of transformer-neutral DC and vibration. In the characteristic extraction method, the primary characteristics are obtained by comparing the magnitude and frequency distributions of transformer-neutral DC and vibration with and without metro stray current invasion. By analyzing the correlation coefficients, the final characteristics are obtained by clustering the primary characteristics with high correlation. Then, the magnitude and frequency characteristics are extracted and used as indicators to evaluate the DC bias risk. Moreover, to avoid the influence of manual experience on indicator weights, the entropy weight method (EWM) is used to establish the assessment model. Finally, the proposed methods are applied based on the neutral DC and vibration test data of a certain transformer. The results show that the characteristic indicators can be extracted, and the transformer DC bias risk can be evaluated by using the proposed methods.

A Bayesian Merging of Earthquake Magnitudes Determined by Multiple Seismic Networks

Abstract We introduce a Bayesian algorithm designed to integrate earthquake magnitudes of the same type reported by various seismic networks, aiming to create unified and standardized catalogs suitable for widespread use. The fundamental concept underpinning this algorithm is the utilization of the inherent consistency within each individual network’s magnitude determination process. Assuming that the magnitudes for an earthquake measured by all networks conform to a Gaussian distribution, with a linear function of the unknown true magnitude serving as its mean, we derive the posterior probability distribution of the true magnitude under four different assumptions for the prior distribution: the uninformative uniform distribution, the unbounded Gutenberg–Richter (GR) magnitude–frequency law, the GR magnitude–frequency relationship restricted by the detection rate, and the truncated GR law as priors. We assess the robustness of the method by a test on several synthetic catalogs and then use it to merge the catalogs compiled by five seismic networks in Italy. The results demonstrate that our proposed magnitude-merging algorithm effectively combines the catalogs, resulting in robust and unified data sets that are suitable for seismic hazard assessment and seismicity analysis.

Detection of the irrotational boundary using machine learning methods

Four machine learning methods, i.e., self-organizing map (SOM), Gaussian mixture model (GMM), eXtreme gradient boosting (XGBoost), and contrastive learning (CL), are used to detect the irrotational boundary (IB), which represents the outer edge of the turbulent and non-turbulent interface layer. To accurately evaluate the detection methods, high-resolution databases from direct numerical simulations of a temporally evolving turbulent plane jet are used. It is found that except for the SOM method, the general contour of the IB appears to be effectively captured using the GMM, XGBoost, and CL methods, which indicate the turbulent and non-turbulent regions can be roughly recognized. Furthermore, the intrinsic features of the detected IB using the GMM, XGBoost, and the CL methods are quantitatively evaluated. Unlike the conventional vorticity norm method, the three machine learning methods do not rely on a single threshold of vorticity magnitude to separate the turbulent and non-turbulent regions. A small part of the detected IB using the three machine learning methods is characterized by the rotational motions, which are expected to be only found inside the turbulent sublayer and turbulent core region. Compared to the vorticity norm and XGBoost methods, the fractal dimensions of the IB detected by the GMM and CL methods are relatively small, which are related to the missing detection of some highly contorted elements. With the three machine learning methods, a large part of the detected IB is characterized by a convex shape, similarly as with the vorticity norm. However, the probability density function profiles of the local curvature of the detected IB differ greatly between the three machine learning methods and the vorticity norm. A mild variation of the mean conditional distributions of the vorticity magnitude can be observed across the detected IB by the three machine learning methods. This study first implies that using the machine learning methods the turbulent and non-turbulent regions can be roughly distinguished, but it is still challenging to obtain the intrinsic features of the detected IB.

Inferring maximum magnitudes from the ordered sequence of large earthquakes.

The largest magnitude earthquake in a sequence is often used as a proxy for hazard estimates, as consequences are often predominately from this single event (in small seismic zones). In this article, the concept of order statistics is adapted to infer the maximum magnitude ([Formula: see text]) of an earthquake catalogue. A suite tools developed here can discern [Formula: see text] influences through hypothesis testing, quantify [Formula: see text] through maximum likelihood estimation (MLE) or select the best [Formula: see text] prediction amongst several models. The efficacy of these tools is benchmarked against synthetic and real-data tests, demonstrating their utility. Ultimately, 13 cases of induced seismicity spanning wastewater disposal, hydraulic fracturing and enhanced geothermal systems are tested for volume-based [Formula: see text]. I find that there is no evidence of volume-based processes influencing any of these cases. On the contrary, all these cases are adequately explained by an unbounded magnitude distribution. This is significant because it suggests that induced earthquake hazards should also be treated as unbounded. On the other hand, if bounded cases exist, then the tools developed here will be able to discern them, potentially changing how an operator mitigates these hazards. Overall, this suite of tools will be important for better-understanding earthquakes and managing their risks. This article is part of the theme issue 'Induced seismicity in coupled subsurface systems'.

Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea

The use of observation-dependent methods for crop productivity and food security assessment is challenging in data-sparse regions. This study presents a transferable framework and applies it to North Korea (NK) to assess rice productivity based on climate similarity, transferable machine-learning techniques, and extendable multi-source data. We initially divided the primary phenological stages of rice in the study region and extracted dynamic rice distributions based on Moderate Resolution Imaging Spectroradiometer products and phenological observations. We compared the performances of four representative environmentally driven models (Linear Regression, back-propagation Neural Network, Support Vector Machine, and Random Forest) in simulating rice productivity using an extensive dataset that included multi-angle vegetation monitoring, climate variables, and planting distribution information. The framework integrated an optimal environmentally driven model with agricultural management practices for transferability to predict rice productivity in NK over multiple years. Additionally, two crop growth scenarios (whole growth period (WGP) and seeding–heading period (SHP)) were compared to assess pre-harvest forecasting capabilities and identify dominant factors. Finally, independent datasets from the Food and Agriculture Organization, World Food Program, and Global Gridded Crop Models were used to validate the magnitude and spatial distribution of the predicted results. The results showed that phenological identification based on remote sensing can accurately capture rice growth characteristics and map rice distribution. Random Forest outperformed other models in simulating rice productivity variation, with r-squares of 0.87 and 0.83 in the WGP and SHP, respectively. The solar-induced chlorophyll fluorescence, maximum temperature, and evapotranspiration collectively determined approximately 40 % of the variation in yield simulated using Random Forest. Conversely, planting areas contributed over 42 % of the variation in rice production. Compared to Food and Agriculture Organization statistics, the environmentally driven framework explained 78.72 % and 76.89 % of the production variation and 69.42 % and 71.15 % of the yield variation in NK under the WGP and SHP, respectively. Moreover, the environmental management-driven framework captured over 90 % of the yield variation. The predicted spatial pattern of rice productivity exhibited significant concordance with the World Food Program and Global Gridded Crop Model reports. In summary, the proposed transferable framework for crop productivity assessment contributes to early warnings of production reduction and has the potential for scalability across various crops and data-sparse regions.

A new method for selecting optimum levels in asymmetric Cascaded H‐Bridge‐Multilevel Inveter with variable DC sources

SummaryIn general, cascaded H‐bridge multilevel inverters (CHB‐MLI) are typically operated with either symmetrical or asymmetrical input DC sources, set at predefined specific ratios such as binary (1:2) or trinary (1:3) in the case of asymmetry, to achieve the desired output voltage waveform. However, if any DC source fails to provide the predefined voltage magnitude, or CHB‐MLIs with unspecified DC source ratios are utilized, the output voltage waveform may exhibit unequal magnitudes between consecutive levels, thereby causing a significant increase in total harmonic distortion (THD). Conventionally, to mitigate this effect, the corresponding H‐bridge is bypassed through zero voltage switching, which leads to an additional burden on the remaining H‐bridges to serve the same load. To reduce the burden on the remaining cells and improve the THD profile of the inverter, this article proposes a novel method for CHB‐MLI with varying DC magnitudes. It aims to enhance the quality of the output voltage waveform by strategically selecting optimum voltage levels rather than utilizing all available levels when CHB‐MLI has unspecified or variable DC sources. This approach can achieve a more balanced distribution of voltage magnitudes across successive levels by eliminating redundant states. Moreover, the proposed technique can reduce switch losses and enhance the converter's efficiency. The proposed method is validated through MATLAB/Simulink software simulations, followed by experimental verification.

Seismotectonics-Driven Estimation of b-Value: Implications for Seismic Hazard Assessment

Abstract Taking full advantage of good-quality historical earthquake and seismogenic source data available for Italy (Catalogo Parametrico dei Terremoti Italiani [CPTI15] and Database of Individual Seismogenic Sources [DISS] databases), we tested the regional variability of the b-value of the Gutenberg–Richter law, focusing on the dominantly extensional, nearly 1200-km-long seismogenic corridor straddling the Apennines chain; an 18,200 km2 area generating about 45% of the seismic moment released countrywide. We carefully chose and tested the most appropriate completeness interval and completeness magnitude (Mc), and used the Lilliefors method, the Utsu test, and a bootstrap technique to verify the quality of our results and inferences. The 0.65 b-value we obtained is substantially lower than b-values used in Italian seismic hazard models, often close to 1.0; yet it predicts more accurately the observed Apennines earthquake record, suggesting that most currently adopted magnitude-frequency distributions underpredict events in the Mw range 6.0–7.1 and overpredict those in the range 5.2–5.5. We also highlight that the time, space, and magnitude distribution of the largest Italian earthquakes hardly follows the Gutenberg–Richter law: it rather favors a characteristic behavior. We contend that the b-value is too critical of a parameter for being calculated using statistically weak data sets, such as those resulting from overly detailed area-source models: one may end up characterizing low-hazard areas more accurately than high-hazard areas. Conversely, we advocate the use of fewer, larger area sources that fully exploit the current understanding of regional seismotectonics, as a way of obtaining more realistic regional hazard models.

Precise relative magnitude measurement improves fracture characterization during hydraulic fracturing

SUMMARY Microseismic monitoring is an important technique to obtain detailed knowledge of in-situ fracture size and orientation during stimulation to maximize fluid flow throughout the rock volume and optimize production. Furthermore, considering that the frequency of earthquake magnitudes empirically follows a power law (i.e. Gutenberg–Richter), the accuracy of microseismic event magnitude distributions is potentially crucial for seismic risk management. In this study, we analyse microseismicity observed during four hydraulic fracture treatments of the legacy Cotton Valley experiment in 1997 at the Carthage gas field of East Texas, where fractures were activated at the base of the sand-shale Upper Cotton Valley formation. We perform waveform cross-correlation to detect similar event clusters, measure relative amplitude from aligned waveform pairs with a principal component analysis, then measure precise relative magnitudes. The new magnitudes significantly reduce the deviations between magnitude differences and relative amplitudes of event pairs. This subsequently reduces the magnitude differences between clusters located at different depths. Reduction in magnitude differences between clusters suggests that some attenuation-related biases could be effectively mitigated with relative magnitude measurements. The maximum likelihood method is applied to understand the magnitude frequency distributions and quantify the seismogenic index of the clusters. Statistical analyses with new magnitudes suggest that fractures that are more favourably oriented for shear failure have lower b-value and higher seismogenic index, suggesting higher potential for relatively larger earthquakes, rather than fractures subparallel to maximum horizontal principal stress orientation.

Suitability of 2D depth-averaged simulations to support predictions of cyprinids passage through Vertical Slot Fishways

Fishways are a key measure to recover longitudinal connectivity. Among them, Vertical Slot Fishways (VSFs) have undergone extensive studies. However, very few numerical studies have included coupled hydrodynamics and ecological simulations. We investigated the impact of using three-dimensional (3D RANS) and two-dimensional (2D SWE) flow simulations in a Eulerian Lagrangian Agent-based Model (ELAM) for VSFs. Spatial distribution of turbulent kinetic energy ( k ), velocity magnitude ( | u i | ) , and velocity strain rate ( S ij ) were evaluated. While the | u i | was consistent across simulations, k was overestimated by the 2D SWE simulations. The frequency of S ij highest values was different between 3D RANS and 2D SWE. Also, the influence of these variables on the agent’s cognitive response was inconsistent. 2D SWE displayed an influence of around 0%, 35%, and 25% for k , | u i | and S ij , respectively. 3D RANS resulted in percentages of around 80% of k , 10–20% of | u i | and 0–10% of S ij . All cases were validated, demonstrating that both approaches are suitable to reproduce experimental observations. We demonstrated that the 2D SWE might serve as a good input to simulate fish movement in VSF. Nonetheless, hydrodynamic variables’ influence on agents’ decision making might be biased from real fish experience.

Spatial auto-correlation and endemicity pattern analysis of crimes against children in Tamil Nadu from 2017 to 2021.

A child is a nation's supreme asset and future. India homes 444 million children, aged between 0 and 18 years, contributing to 19% of the world's children. Crime against children is detrimental to their mental and physical health and affects their growth and development. The National Crime Record Bureau recently reported that a crime targeting children happens every 4 minutes. There is a paucity of literature regarding the burden of crime against children. To understand the magnitude and spatial distribution of crime against children, a retrospective surveillance study was conducted in the state of Tamil Nadu, India, from 2017 to 2021. This is a cross-sectional analytical type of study conducted in KIMSRC, Chengalpattu, Tamil Nadu. The data from the yearly crime review bulletin of Tamil Nadu from 2017 to 2021 were cleaned, transformed, and analyzed using Python v3.8 and subjected to geospatial auto-correlation and hotspot analysis using the Getis-Ord Gi* in ArcGIS Pro v3.1. The endemicity pattern was studied through cluster analysis with Hierarchical Density Based Scanning in Python and visualization in ArcGIS pro v3.1 in the study area. In Tamil Nadu, only one hotspot district in 2017 [Tiruppattur (95% confidence, P < 0.05)] and one hotspot in 2020 [Villupuram (90% confidence, P < 0.1)] were identified, with others being insignificant. The districts which show very high prevalence of crimes against children are Chennai, Ranipet, Chengalpattu, Viluppuram, Tiruvannamalai, Vellore, Tiruppattur, Krishnagiri, Dharmapuri, Salem, Cuddalore, Thanjavur, Tiruchirappalli, Karur, Tiruppur, Coimbatore, Dindigul, Pudukkottai, Sivaganga, Tenkasi, Thoothukkudi, Tirunelveli, and Kanniyakumari. This study identifies key areas within the state of Tamil Nadu which have a high prevalence of crimes against children and also areas that are hotspots for such crimes. Greater resources and measures can now be targeted toward these areas by stakeholders, which can help in the reduction of crimes against children.

The dynamic effects of solar eclipses of October 25, 2022, and October 14, 2023, on GPS-derived total electron content

This study analyzes total electron content (TEC) variations during two solar eclipse events that occurred on October 25, 2022, and October 14, 2023. The solar eclipse of October 25, 2022, was a partial solar eclipse, while the eclipse of October 14, 2023, was an annular solar eclipse. For this study, the data of eight International GNSS Service (IGS) stations from different eclipse coverage zones are used to analyze TEC variations. It is found that the stations located in the maximum eclipse cover zone exhibited notable decreases in TEC values. The minimum variation of about 11.76% in TEC values is observed at the station situating in about 20 percent eclipse cover zone, while it varies from 22% to 38% at the stations falling in about 60 percent eclipse cover zone. The highest variation in TEC values of about 44% is found at the stations in about 80 percent eclipse cover zone. The time of occurrence of maximum depletion in TEC values at each station is in line with their longitudinal sequence. Atmospheric gravity waves (AGWs) are also observed by performing wavelet analysis on TEC data. The global TEC maps visualize and confirm observed TEC variations, providing spatial and temporal insights into the ionospheric response. This analysis highlights the influence of station location and eclipse coverage on the magnitude and spatial distribution of TEC variations.

Identification of a transition from stochastic to secular star formation around z = 9 with JWST

Star formation histories (SFHs) of early galaxies (6 &lt; z &lt; 12) have been found to be highly stochastic in both simulations and observations, while at z≲6 the presence of a main sequence (MS) of star-forming galaxies implies secular processes at play. In this work we characterise the SFH variability of early galaxies as a function of their stellar mass and redshift. We used the JADES public catalogue and derived the physical properties of the galaxies as well as their SFHs using the spectral energy distribution modelling code CIGALE. To this end, we implemented a non-parametric SFH with a flat prior allowing for as much stochasticity as possible. We used the star formation rate (SFR) gradient, an indicator of the movement of galaxies on the SFR–M* plane, linked to the recent SFH of galaxies. This dynamical approach of the relation between the SFR and stellar mass allows us to show that, at z &gt; 9, 87% of massive galaxies (log(M*/M⊙)≳9) have SFR gradients consistent with a stochastic star formation activity during the last 100 Myr, while this fraction drops to 15% at z &lt; 7. On the other hand, we see an increasing fraction of galaxies with a star formation activity following a common stream on the SFR–M* plane with cosmic time, indicating that a secular mode of star formation is emerging. We place our results in the context of the observed excess of UV emission as probed by the UV luminosity function at z ≳ 10 by estimating σUV, the dispersion of the UV absolute magnitude distribution, to be of the order of 1.2 mag, and compare it with predictions from the literature. In conclusion, we find a transition of star formation mode happening around z ∼ 9: Galaxies with stochastic SFHs dominate at z ≳ 9, although this level of stochasticity is too low to reach those invoked by recent models to reproduce the observed UV luminosity function.

Microwave plasma interaction in a printed transmission line for a power limiting application: from surface-wave-sustained to leaky-wave-sustained discharge

The coupling between a microwave signal and a plasma discharge in a suspended microstrip transmission line is analytically studied. Maxwell’s equations are solved in a 2D approximation to get the expressions of the electromagnetic field. The wave propagation in the guiding structure is first explored without plasma, and for several modes and frequencies. A unified characterization of the three different modes that can propagate at the interface between two dielectric media, namely the leaky waves, the pseudo-surface wave and the pure surface wave, is given in terms of of both wave vectors and electromagnetic field magnitude distribution. This analyze allow to conclude that the fundamental mode in this case is a pseudo-surface wave. Thereafter, we focus on the microwave propagation with a uniform plasma inside the guiding structure. In the non collisional limit, it appears that the plasma discharge is sustained by the so-called pure surface wave, whereas in the collisional limit, a leaky wave propagates along the plasma column. Finally, a non-uniform density profile is taken into account in the calculation. The numerical results obtained from the self-consistent simulation of the microwave-plasma coupling, in a previous work, are thus analyzed with the aid of the analytical formulas to identify the microwave coupling involved in our plasma-based microwave power limiter. The computed propagation constant from numerical data confirmed the type of coupling exhibited for a uniform electron density. Furthermore, we highlight the role of the dielectric slab, from which electromagnetic power transfer occurs into the plasma discharge.

The Low-mass Stellar Initial Mass Function in Nearby Ultrafaint Dwarf Galaxies

The stellar initial mass function (IMF) describes the distribution of stellar masses that form in a given star formation event. The long main-sequence lifetimes of low-mass stars mean that the IMF in this regime (below ∼ 1 M ⊙) can be investigated through star counts. Ultrafaint dwarf galaxies are low-luminosity systems with ancient, metal-poor stellar populations. We investigate the low-mass IMF in four such systems (Reticulum II, Ursa Major II, Triangulum II, and Segue 1), using Hubble Space Telescope imaging data that reaches to ≲ 0.2 M ⊙ in each galaxy. The analysis techniques that we adopt depend on the number of low-mass stars in each sample. We use Kolmogorov–Smirnov tests for all four galaxies to determine whether their observed apparent magnitude distributions can reject a given combination of IMF parameters and binary fraction for the underlying population. We forward model 1000 synthetic populations for each combination of parameters, and reject those parameters only if each of the 1000 realizations reject the null hypothesis. We find that all four galaxies reject a variety of IMFs, and the IMFs that they cannot reject include those that are identical, or similar, to that of the stellar populations of the Milky Way. We determine the best-fit parameter values for the IMF in Reticulum II and Ursa Major II and find that the IMF in Reticulum II is generally consistent with that of the Milky Way, while the IMF in Ursa Major II is more bottom heavy. The interpretation of the results for Ursa Major II is complicated by possible contamination from two known background galaxy clusters.

Different earthquake nucleation conditions revealed by stress drop and b-value mapping in the northern Chilean subduction zone

Stress drop is an earthquake property indicative for the characteristic relation of slip to fault dimension. It is furthermore affected by fault strength, fault topography, the presence of fluids, rupture size, slip, and velocity. In this article, the stress drop image of an entire subduction zone, namely for the seismically highly active northernmost part of Chile, is combined with mapped b-values and their corresponding magnitude distribution in order to better constrain the conditions under which earthquakes of different provenances may nucleate. The underlying recent earthquake catalog contains over 180,000 events, covering 15 years of seismicity, from which more than 50,000 stress drop estimates were computed. Their spatial average segments the subduction zone into different parts, i.e., average stress drop between seismotectonic areas is different, although this difference is small compared to the natural scatter of stress drop values. By considering stress drop variations, b-value map, magnitude distribution, and thermal models, candidate earthquake nucleation mechanisms are identified which can explain the observed distributions. This is done for two exemplary regions: (1) The plate interface, where principally lower stress drop events are found, while at the same time a high spatial heterogeneity of stress drop values is observed. This indicates relatively smooth or lubricated rupture surfaces, and locally it suggests the existence of alternating regions controlled by strong asperities, weaker material, or creep. (2) The highly active intermediate depth (ID) seismicity region, where the variation of stress drop and b-value point to a gradual change of nucleation mechanism from dehydration embrittlement at the top of the ID cloud, over dehydration driven stress transfer in its central part, to thermal runaway shear mechanisms at its bottom. In both cases, the combination of stress drop and b-value distribution helps to better understand the origin and the differences of the observed seismicity.

Cost Optimization in Cloud Computing: Capacity Reservation for Intermittent Random Demand Surges

The adoption of cloud computing has been accelerating over the last decade, while enterprise cloud users (“firms”) are struggling to manage their growing cloud expenditures in the face of intermittent digital demand surges caused by planned or random events. To deal with such challenges, a firm can employ reserved instances with the standard contract length (e.g., one year) to meet the stationary base demand, which we refer to as the base contracts, complemented by additional reserved instances with either standard or shorter contract lengths, which we refer to as the supplementary contracts, to cope with the demand surges. We first analyze a model whereby the surge and inter-surge durations are deterministic, demand magnitude is random, and cancellation of the reservations is allowed. We develop a capacity management plan for the firm including not only the optimal capacity levels, which follow a newsvendor-type solution, but also the optimal policy for managing the purchase, renewal, cancellation, or expiration of the supplementary contracts, which can be characterized as a two-threshold policy. Due to the complexity of the structure of the optimal policy, we also construct an effective heuristic policy by excluding the renewal option from the action space, which can be applied to a more general setting where the surge and inter-surge durations are random. We examine two model extensions: (1) when trades of reserved instances are allowed in a secondary marketplace; (2) when the firm does not have exact information about the distributions of the surge magnitude and duration while it can adjust the capacity levels as data unveils. Our analysis shows that the optimal policy for managing the supplementary contracts depends on the relative magnitude of the surge and inter-surge durations in relationship to the cancellation fee. Moreover, our numerical results show that cloud platforms that offer a secondary marketplace are more attractive to firms from a cost standpoint than those that allow cancellation only. The latter, without the secondary marketplace, however, can achieve parity with the former by offering a deeper discount rate for the reserved instances, thereby bypassing the cost of administering the secondary marketplace.

#2795 In-silico assessment of arteriovenous fistula maturation with patient-specific computational hemodynamic models

Abstract Background and Aims Native arteriovenous fistula (nAVF) is the preferred access for haemodialysis (HD) treatment. Both the quality and complications of the haemodialysis technique are closely related to vascular access. nAVF maturation process includes several morphological changes affecting haemodynamics, with previous studies suggesting that it is highly related to AVF failure. Computational Fluid Dynamics (CFD) simulations by using idealized morphologies or the analysis of one patient-specific geometry have been proposed to understand the pathophysiology of maturation failure. However, it is difficult to correlate with clinical practice and therefore limits its use. The aim is to develop a patient-specific modelling time pipeline, incorporating the haemodynamics of the blood flow to real anatomical and morphological characteristics. Method Design: prospective computational simulation analysis of nAVF in 3 patients at 3 different stages: after fistula creation, (1 week), maturation (1 month) and post-maturation (6 months). Analysis: morphological and haemodynamic. Inclusion criteria: &amp;gt;18 years and end-stage chronic kidney disease. Variables and data: Once morphology was extracted from nAVF magnetic resonance imaging, generated meshes were refined and compiled to obtain a volumetric mesh. The boundary conditions of the model were defined and extracted using ultrasound nAFV data. Data were analyzed at the 3 stages. Models: A 3-element Windkessel model was used to simulate the pressure waveform to feed the vein of the system. In this model, clinical data were also considered to classify the patient as normotensive or hypertensive to estimate waveform parameters. The Windkessel model estimates wall shear stress (WSS), velocity and flow for all the points located in the mesh by making an analogy from an electric circuit resistance model. As validation, the flow obtained from the Windkessel model was compared with the one obtained from the ultrasound scan of the patient. Results Different temporal points were analyzed for each nAVF. Results from the simulations in Fig. 1, illustrating the evolution over time and development. The geometrical characteristics were found to significantly impact on flow dynamics. The area most affected by flow was the juxta-anastomotic section which showed higher magnitude distribution of WSS and velocity for all time-points. According to the analysis of the simulations, a perpendicular junction between the vein and the artery potentially resulted in less damage in this area (in WSS terms), being important to mitigate the AVF failure. At maturation stage (1 month) the analysis shows WSS distribution mainly located in the juxta-anastomotic section and decreased in magnitude regarding one week; therefore, this can be marked as the stabilization point for nAFV maturation. Conclusion

Internal waves generated from asymmetric topographies

Previous experimental and theoretical research on tidally generated internal waves over oceanic topography has often used symmetric topographic profiles. However, due to the complex nature of real ocean topography, the effect of asymmetry cannot be overlooked. Studies have shown that topographic complexities, including asymmetry, can have a significant impact on internal wave generation, but topographic asymmetry has not yet been explored in a systematic manner. This work presents a comparison of tidally generated internal waves from nine different two-dimensional asymmetric topographies, consisting of a steeper Gaussian curve on one side, and a wider Gaussian curve on the other. The amplitude of the wider curve varies from 60% of the steeper curve to its equivalent. Two oscillation frequencies are tested. Kinetic energy density in tidally generated internal waves on each side of the topography is compared qualitatively and quantitatively, in both physical and Fourier space. When compared to similar symmetric topographies, waves generated by the asymmetric topographies varied distinctly in both magnitude and wavenumber distribution of kinetic energy density. Waves on the relatively steep side of the topography contain up to 50% more kinetic energy than on the wider side. However, there is very little kinetic energy at the higher wavenumbers associated with the steeper topography. Instead, the internal wavefield is dominated by lower wavenumbers, 50%–90% of the kinetic energy density is contained in wavelengths corresponding to the wider side of the topography. Decreasing the amplitude of the wider curve does not make an appreciable difference in the kinetic energy density spectrum. Thus, the differences quantified here are due solely to changing slope and show the effect which relatively slight asymmetry has on internal wave generation.

BRDF-Based Photometric Modeling of LEO Constellation Satellite from Massive Observations

BRDF-Based Photometric Modeling of LEO Constellation Satellite from Massive Observations

The estimation of b-value of the frequency–magnitude distribution and of its 1σ intervals from binned magnitude data

SUMMARY The estimation of the slope (b-value) of the frequency–magnitude distribution of earthquakes is based on a formula derived by Aki decades ago, assuming a continuous exponential distribution. However, as the magnitude is usually provided with a limited resolution, its distribution is not continuous but discrete. In the literature, this problem was initially solved by an empirical correction (due to Utsu) to the minimum magnitude, and later by providing an exact formula such as that by Tinti and Mulargia, based on the geometric distribution theory. A recent paper by van der Elst showed that the b-value can be estimated also by considering the magnitude differences (which are proven to follow an exponential discrete Laplace distribution) and that in this case the estimator is more resilient to the incompleteness of the magnitude data set. In this work, we provide the complete theoretical formulation including (i) the derivation of the means and standard deviations of the discrete exponential and Laplace distributions; (ii) the estimators of the decay parameter of the discrete exponential and trimmed Laplace distributions and (iii) the corresponding formulas for the parameter b. We deduce (iv) the standard 1σ intervals for the estimated b. Moreover, we are able (v) to quantify the error associated with the Utsu minimum-magnitude correction. Furthermore, we have discussed the formulas to produce statistically independent magnitude differences. We tested extensively the b-value estimators on simulated synthetic data sets including complete catalogues as well as catalogues affected by a strong incompleteness degree such as aftershock sequences where the incompleteness is made to vary from one event to the next. We have also analysed the real aftershock sequence of the 30/10/2016 Norcia (central Italy) to integrate the finding of the simulations. To judge the performance of the various estimators we have introduced an index p that can be seen as a non-parametric extension of the Student's t index. The main outcomes of this paper are that (1) the b-value estimators devised for continuous magnitude data are not adequate for binned magnitudes, (2) for complete data sets, estimators based on magnitudes and on magnitude differences provide substantially equivalent results, (3) for incomplete magnitude data sets, estimators based on magnitude differences provide better results and (4) for incomplete aftershock sequences there is no evidence that methods based on positive magnitude differences are superior than other methods using differences. This conclusion is further confirmed by our analysis of the above-mentioned Norcia seismic sequence. This last finding contrasts with the van der Elst's claim that the so called ${{b}_ + }$ method is the most adequate to treat real aftershock sequences.