Distribution Of Values Research Articles

Klimapolitisches Engagement auf kommunaler Ebene in Deutschland – Entwicklung eines Index zu Bewusstsein, Konzeption und Durchführung von Klimaschutz und Klimawandelanpassung

DThis paper presents an additive index on climate policy engagement at the municipal level, based on the three dimensions awareness, conception and implementation of climate change mitigation and adaptation. The distortion of the index is based on the self-perception and personal assessment of municipal decision-makers, which were collected and evaluated as part of a primary survey (postal survey). The index shows how strongly a municipality is committed to climate policy. Thus, it represents an extension of previous indices that either only consider climate mitigation or adaptation or are only based exclusively on secondary statistics. The second part of the paper describes the application of the index in three counties or 51 municipalities in Saxony, Saxony-Anhalt and Thuringia. The interpretation takes place in the context of structural characteristics of the municipalities concerned (number of inhabitants, share of votes of different parties, geographical location). The distribution of the index values shows a similar awareness in climate protection and climate change adaptation, but an increasing discrepancy in the implementation of adaptation-specific measures.

Revisiting the hybrid approach of anomaly detection and extreme value theory for estimating pedestrian crashes using traffic conflicts obtained from artificial intelligence-based video analytics

Pedestrians represent a group of vulnerable road users who are at a higher risk of sustaining severe injuries than other road users. As such, proactively assessing pedestrian crash risks is of paramount importance. Recently, extreme value theory models have been employed for proactively assessing crash risks from traffic conflicts, whereby the underpinning of these models are two sampling approaches, namely block maxima and peak over threshold. Earlier studies reported poor accuracy and large uncertainty of these models, which has been largely attributed to limited sample size. Another fundamental reason for such poor performance could be the improper selection of traffic conflict extremes due to the lack of an efficient sampling mechanism. To test this hypothesis and demonstrate the effect of sampling technique on extreme value theory models, this study aims to develop hybrid models whereby unconventional sampling techniques were used to select the extreme vehicle–pedestrian conflicts that were then modelled using extreme value distributions to estimate the crash risk. Unconventional sampling techniques refer to unsupervised machine learning-based anomaly detection techniques. In particular, Isolation forest and minimum covariance determinant techniques were used to identify extreme vehicle–pedestrian conflicts characterised by post encroachment time as the traffic conflict measure. Video data was collected for four weekdays (6 am–6 pm) from three four-legged intersections in Brisbane, Australia and processed using artificial intelligence-based video analytics. Results indicate that mean crash estimates of hybrid models were much closer to observed crashes with narrower confidence intervals as compared with traditional extreme value models. The findings of this study demonstrate the suitability of machine learning-based anomaly detection techniques to augment the performance of existing extreme value models for estimating pedestrian crashes from traffic conflicts. These findings are envisaged to further explore the possibility of utilising more advanced machine learning models for traffic conflict techniques.

A maximum likelihood and regenerative bootstrap approach for estimation and forecasting of INAR(p) processes with zero-inflated innovations

In this work, we study a class of p-order non-negative integer-valued autoregressive (INAR(p)) processes, with innovations following zero-inflated (ZI) distributions called ZI-INAR(p) processes. Based on the EM algorithm, we present an estimation procedure of parameters model. We also develop a regenerative bootstrap method to construct confidence intervals for the parameters as well as to estimate the forecasting distributions for future values. We discuss asymptotic properties of the regenerative bootstrap method. The performance of the proposed methods is evaluated considering the analysis of two simulation studies and a real dataset.

Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion

Discrete Event Systems Theory for Fast Stochastic Simulation via Tree Expansion

Consistency is key: influence of skull density ratio distribution on the formation of clinically effective lesions and long-term tremor suppression following treatment with MR-guided focused ultrasound.

Skull density ratio (SDR) influences the permeability of the skull to the ultrasound waves used in magnetic resonance-guided focused ultrasound (MRgFUS) for the treatment of tremor. SDR values vary across the skull and the mean value is known to be predictive of sonication thermal increase. The aim of this investigation was to explore the effects of the SDR distribution on clinical outcomes following treatment with MRgFUS. Data from 61 patients with essential or dystonic tremor treated with MRgFUS targeting the ventral intermediate nucleus (Vim) were retrospectively analyzed. Tremor suppression was assessed using the Clinical Rating Scale for Tremor (CRST) and hand tremor score (HTS). Vim ablation volume was measured on the T1-weighted MR image acquired both at 1 day and 12 months after treatment. The numerical distribution of SDR values measured for each element in the ultrasound transducer was quantified by calculating the mean, standard deviation, skewness, entropy, and kurtosis of the SDR histogram. The effect of the SDR metrics on change in CRST and HTS was examined using a linear mixed-effects model. Additionally, the effect of the regional distribution of SDR values was explored in an element-wise analysis between patients with above- and below-average tremor suppression. A significant positive effect was found between SDR kurtosis and improvement in CRST (β = 0.33, p = 0.004) and HTS (β = 0.38, p < 0.001). The effect was found to be significant at 1 month posttreatment (CRST: β = 0.415, p = 0.008; HTS: β = 0.369, p = 0.016), and at the most recent clinical follow-up (CRST: β = 0.395, p < 0.001; HTS: β = 0.386, p < 0.001). One hundred seventy-one significant elements were identified in the element-wise analysis. The mean percentage difference from the mean SDR in these elements was associated with improvement in CRST (β = 0.27, p < 0.008) and HTS (β = 0.27, p < 0.015). Higher SDR kurtosis was associated with increased lesion volume at 12 months (p = 0.040) and less reduction in volume relative to the day-1 lesion volume (p = 0.007). Greater SDR kurtosis was associated with larger, more stable lesions at 12 months posttreatment and increased tremor suppression at long-term follow-up. SDR kurtosis may provide a more meaningful prognostic factor than the mean SDR.

A Distributional Perspective on Multiagent Cooperation With Deep Reinforcement Learning.

Among various value decomposition-based multiagent reinforcement learning (MARL) algorithms, the overall performance of the multiagent system is represented by a scalar global Q value and optimized by minimizing the temporal difference (TD) error with respect to that global Q value. However, the global Q value cannot accurately model the distributed dynamics of the multiagent system, since it is only a simplified representation for different individual Q values of agents. To explicitly consider the correlations between different cooperative agents, in this article, we propose a distributional framework and construct a practical model called distributional multiagent cooperation (DMAC) from a novel distributional perspective. Specifically, in DMAC, we view the individual Q value for the executed action of a random agent as a value distribution, whose expectation can further represent the overall performance. Then, we employ distributional RL to minimize the difference between the estimated distribution and its target for the optimization. The advantage of DMAC is that the distributed dynamics of agents can be explicitly modeled, and this results in better performance. To verify the effectiveness of DMAC, we conduct extensive experiments under nine different scenarios of the StarCraft Multiagent Challenge (SMAC). Experimental results show that the DMAC can significantly outperform the baselines with respect to the average median test win rate.

Identification of shale gas existence based on 2-dimensional magnetotelluric data in the Kutai basin, East Kalimantan, Indonesia

Research on unconventional methods in the shale gas exploration process has begun to be developed to meet the demand for oil and natural gas sustainability. The research area is located in the Kutai Basin, East Kalimantan, Indonesia. The current study from the Geological Survey Center shows that the Kutai Basin has 46.79 TCF of shale gas potential. The magnetotelluric (MT) method was applied in this study to describe the distribution of subsurface resistivity values. The inversion schemes performed in the MT method are 1-dimensional and 2-dimensional inversions. In this study, there are 8 measurement stations in one line. The presence of low resistivity values or conductive zones identifies the potential for black shale layers. The potential for shale gas is suspected to be detected at station KT29, which is included in the Pamaluan Formation, and at stations KT12, KT31, KT13, and KT15, which are included in the Pulaubalang Formation, because it is associated with source rock from petroleum in the Kutai Basin system, as supported by previous research data.

Radial Evolution of the Near-Sun Solar Wind: Parker Solar Probe Observations

Abstract A statistical study of the radial evolution of the solar wind within 0.3 au is shown in this Letter based on Parker Solar Probe observations. We show the radial distribution of the main solar wind parameters, including the solar wind speed V sw, magnetic field ∣B∣, the number density of electrons N e , protons N p , and α particles N α , and the temperature of protons T p and α particles T α . The power-law fitting results of these parameters in the near-Sun solar wind are compared with previous radial models. We also show the radial distribution of the angle between the magnetic field B , solar wind V sw, and radial vector R . In the solar wind within 0.3 au, θ BV sw , and θ BR mainly concentrate around 135°, and θ RV sw almost concentrates in the region less than 20°. Furthermore, we also present the radial distribution of the relative values between the solar wind parameters, including the electric neutrality estimation ((2 × N α + N p )/N e ≃ 1 within 0.2 au), relative number density ratio (N α /N p ≃ 0.02 in slow solar wind and N α /N p ≃ 0.04 in faster solar wind), relative temperature ratio (T α /T p decreases with the increase of heliocentric distance and its decay rate is larger in faster solar wind), and differential speed (both V α − V p and (V α − V p )/V A are larger in the faster solar wind and decrease as heliocentric distance increases).

Compression AutoEncoder for High-Resolution Ocean Sound Speed Profile Data

High-resolution ocean sound speed profile (HROSSP) data is essential for ocean acoustic modeling and sonar performance evaluation. However, the large volume and storage requirements of this data severely restrict its practical application in ocean acoustics. In this paper, we propose a compression autoencoder specifically designed for managing HROSSP data (CAE-HROSSP) and investigate the optimal network structure. Experimental results demonstrate that by using the min-max normalization method for input data and the corresponding inverse normalization for output data, along with employing the LeakyReLU function as the final activation layer, the accuracy of decompressed data reconstruction can be significantly improved. To tackle the challenges of fitting the distribution of surface sound speed data caused by significant variations and noise, we propose two loss functions: slice mean square error and elemental mean square error. These loss functions are combined with mean squared error through weighted summation to enhance CAE-HROSSP’s ability to fit the distribution of surface sound speed values and minimize the reconstruction errors of compressed data. Performance evaluation experiments reveal that CAE-HROSSP outperforms two existing methods in compressing HROSSP data, achieving superior performance with smaller data reconstruction errors at higher compression ratios. Furthermore, transfer learning is utilized to enhance the training of CAE-HROSSP, employing HROSSP data from the area where the mesoscale eddy is situated, as well as at the convergence of cold and warm ocean currents. The compression performance of both the training set and the validation set is comparable in the sea, where the structure of the sound speed profile varies greatly. This indicates that CAE-HROSSP can compress highly variable sound speed profile data in more sea areas using transfer learning, and has the potential to be extended globally. The findings and insights obtained from this study provide guidance for future endeavors in utilizing autoencoders to compress HROSSP data.

Choice of molecular marker influences spatial patterns of phylogenetic diversity.

Spatial patterns of phylogenetic diversity (PD) are increasingly becoming relevant for conservation decisions. PD measures are based on phylogenies estimated from molecular data. This paper addresses the question of how different molecular markers impact PD spatial patterns. We first conducted a simple simulation to explore the effect of deep and shallow changes in topology (simulating variations in molecular markers), using ultrametric and non-ultrametric trees, and then used a dataset of Chilean flora with four sets of markers to assess potential differences in spatial patterns of PD ranks using different markers and types of trees. The simulation consistently showed that the difference in PD rank was lower for ultrametric trees than for phylograms. A similar trend was observed using the Chilean flora dataset, with among-markers variability in spatial patterns of the PD metrics lower for ultrametric than for non-ultrametric trees, depicted as top 2.5 and 5% hotspots. Frequency distribution of PD values differed among markers as well, with this variation less apparent for ultrametric trees. We conclude that the choice of markers impacts spatial patterns of PD, and these results vary more strongly for phylograms, suggesting that ultrametric trees are more robust to the choice of marker.

Over- and underweighting of extreme values in decisions from sequential samples.

People routinely make decisions based on samples of numerical values. A common conclusion from the literature in psychophysics and behavioral economics is that observers subjectively compress magnitudes, such that extreme values have less sway over people's decisions than prescribed by a normative model (underweighting). However, recent studies have reported evidence for anti-compression, that is, the relative overweighting of extreme values. Here, we investigate potential reasons for this discrepancy in findings and propose that it might reflect adaptive responses to different task requirements. We performed a large-scale study (n = 586) of sequential numerical integration, manipulating (a) the task requirement (averaging a single stream or comparing two interleaved streams of numbers), (b) the distribution of sample values (uniform or Gaussian), and (c) their range (1-9 or 100-900). The data showed compression of subjective values in the averaging task, but anticompression in the comparison task. This pattern held for both distribution types and for both ranges. In model simulations, we show that either compression or anticompression can be beneficial for noisy observers, depending on the sample-level processing demands imposed by the task. This suggests that the empirically observed patterns of over- and underweighting might reflect adaptive responses. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Drought‐Induced Vertical Displacements and Water Loss in the Po River Basin (Northern Italy) From GNSS Measurements

AbstractWe study vertical ground displacement time series from Global Navigation Satellite System (GNSS) stations to measure deformation associated with hydrological drought in the Po river basin. Focusing on interannual trend changes, rather than seasonal (annual) components, we found a clear spatially correlated deformation signal that is temporally (anti)correlated with changes in the Po river level and the SPEI‐12 drought index, with stations moving upward during periods of river/index level decrease and vice versa. In the 2021–2022 time span, which culminated in the most severe drought of the last two centuries, we estimate the amount and spatial distribution of water loss in the basin and its surroundings. Excluding the seasonal signals, between January 2021 and August 2022, the GNSS stations underwent uplift, up to 7 mm, which corresponds to ∼70 Gtons of water loss. Compared to Global Land Data Assimilation System and Gravity Recovery and Climate Experiment estimates, GNSS results show a similar temporal evolution of water content but a more heterogeneous distribution of values. We show that continuous GNSS networks provide an effective way to monitor multiannual trend changes in water storage even in small water basins and serve as a reliable indicator of drought severity.

Understanding the Impact of Precipitation Bias‐Correction and Statistical Downscaling Methods on Projected Changes in Flood Extremes

AbstractThis study evaluates five bias correction and statistical downscaling (BCSD) techniques for daily precipitation and examines their impacts on the projected changes in flood extremes (i.e., 1%, 0.5%, and 0.2% floods). We use climate model outputs from the Coupled Model Intercomparison Project Phase 6 (CMIP6) to conduct hydrologic simulations across watersheds in Iowa and determine historical and future flood extreme estimates based on generalized extreme value distribution fitting. Projected changes in these extremes are examined with respect to four Shared Socioeconomic Pathways (SSPs) alongside five BCSD techniques. We find the magnitude of the estimates of future annual exceedance probabilities (AEPs) are expected to increase under all SSPs, especially for the emission scenarios with higher greenhouse gases concentrations (i.e., SSP370 and SSP585). Our results also suggest the choice of BCSD impacts the magnitude of the projected changes, with the SSPs that play a more limited role compared to the choice of downscaling method. The variability in projected flood changes across Iowa is similar across the downscaling technique but increases as the AEP increases. Our findings provide insights into the impact of downscaling techniques on flood extremes' projections and useful information for climate planning across the state.

Modeling multivariate extremes

AbstractThe Central Limit Theorem justifies using a normal distribution when looking at sums of many terms. In a parallel way, extreme value distributions arise in the study of maxima of many terms. The goal of this paper is to briefly review the univariate theory of extremes based on the Fisher‐Tippet‐Gnedenko Theorem. We then state the basics of the multivariate theory, which is significantly more complicated because it requires a measure to define the distribution. Some properties of these laws are explored, including a description of the support, an expression for the density when it exists, and some examples that illustrate possible joint dependence structures.This article is categorized under: Statistical and Graphical Methods of Data Analysis &gt; Multivariate Analysis Applications of Computational Statistics &gt; Computational Finance

Quantal Response Equilibrium and Rationalizability: Inside the Black Box

This paper aims to connect epistemic and behavioral game theory by examining the epistemic foundations of quantal response equilibrium (QRE) in static games. We focus on how much information agents possess about the probability distributions of idiosyncratic payoff shocks, in addition to the standard assumptions of rationality and common belief in rationality. When these distributions are transparent, we obtain a solution concept called Δp-rationalizability, which includes action distributions derived from QRE; we also give a condition under which this relationship holds true in reverse. When agents only have common belief in the monotonicity of these distributions (for example, extreme value distributions), we obtain another solution concept called ΔM-rationalizability, which includes action distributions derived from rank-dependent choice equilibrium, a parameter-free variant of QRE. Our solution concepts also provide insights for interpreting experimental and empirical data.

Gumbel’s Extreme Value Distribution for Flood Frequency Analyses of Timis River

One of the major problems in the engineering design of water resource is the estimation of peak flood flows. In probability theory and statistics, flood frequency analysis is used to obtain the distribution of flood probabilities. Gumbel distribution represents distributions of extreme values ​​used in hydrological studies to predict flood peak, maximum rainfall, etc. This paper aims to analyses the frequency of floods, Gumbel's frequency distribution method, based on the maximum annual flows in the Timis River for the period of 30 years (1993 – 2022). For this analysis the return period (T) used is 5 years, 10 years, 50 years, 100 years, 150 years.

Identification of Rock Characteristics Using the Microtremor Inversion Method at Air Putih Geothermal Field, Lebong Regency

Research has been carried out to identify rock characteristics based on Vs values using the microtremor method in the Air Putih geothermal field, Lebong Regency, which aims to determine the geothermal prospect area on rock characteristics using Vs values based on the microtremor method. The data obtained in this study were 20 measurement points obtained from data analysis by obtaining the H/V curve through Geopsy software and HVSR inversion to obtain the Vs value of the inverse. The results of this calculation indicate that at a depth of 0-4 meters the homogeneous Vs value 200 m/s, at a depth of 4 meters the Vs value 200-440 m/s, at a depth of 20 meters the value of Vs 440 -880 m/s and a depth of 20 meters homogeneous Vs value of 880 m/s and for the distribution of Vp values, at a depth of 0-5 meters the Vp values vary between 2000-2900 m/s, at depths of 6-20 meters in general the value of the Vp wave velocity is the same in the range of 3100-3800 m/s and at depth 21-100 meters the Vp value ranges from 3900-5100 m/s. This shows that the greater the depth, the denser the rock.

Wave impact pressure and pressure impulse on a square column with an overhanging deck in regular waves

As the main component of semi-submersible platforms, the deck-column structure is inevitably subjected to severe wave impacts in extreme ocean environments. The wave impact pressure and its time integral, impact pressure impulse, play an important role in the structural design, especially for the analysis of dynamic response. To investigate the characteristics of impact pressure and pressure impulse, wave impact tests on a square column with overhanging deck were carried out under a series of regular waves. Wavelet denoising technique and empirical mode decomposition (EMD) method were employed to remove the noise interference and the dynamic amplification effect from raw data. Considering the stochastic feature, statistical analysis of pressure impulse was performed based on generalized extreme value (GEV) and two-parameter Weibull distribution models. The results show the strong variability of the wave pressure impulse and the necessity of statistical analysis even in regular waves. The peak impact pressure is more sensitive to the variation of initial air gap and column incline angle than the pressure impulse. The probability distribution of extreme pressure impulse agrees well with the GEV distribution and is mainly distinguished by the shape parameter. Furthermore, the probability distribution of extreme pressure impulse is significantly affected by the initial air gap, and the effect is also related to wave parameters. The present research reveals the probability distribution of wave pressure impulse in regular waves and lays a foundation for the statistical analysis of pressure impulse in irregular waves.

Climate change effects on rainfall extreme value distribution: the role of skewness

Quantifying the potential effects of Climate Change (CC) on hydrological scales is a topic with a more and more increasing interest for the scientific community, given the CC impact on agriculture, industry, economy, human health, ecosystems, among the others. In this context, the paper focuses on the sub-daily Annual Maxima (AM) of rainfall height time series, and specifically on the crucial role played by initial skewness, i.e. the skewness of the observed rainfall series used to evaluate potential parametric trends. Here a quick and user-friendly methodology is proposed, that is aimed at quantifying plausible future changes in terms of probability distributions assumed at rain gauge scale, from projections of any climatic model. In detail, the Generalized Extreme Value (GEV) and the Two-Component Extreme Value (TCEV) distributions are adopted as probability functions suitable for modelling observed rainfall AM series, which could increase their frequency and magnitude into future horizons under CC. EURO-CORDEX projections for Europe are considered, under the hypothesis that the values of the change factor, i.e. the ratio between the values of a specific quantile at two specific time horizons, are invariant when moving from an areally-averaged scale (typical for any climate model) to a point rain gauge scale, which induces that future changes are provided (in terms of frequency and magnitude of extreme events) without the need of any spatial downscaling from the assumed projections. The proposed methodology can contribute to hazard quantification associated to potential climate changes, and thus it can play a crucial role in the assessment of hydraulic structures resilience; the obtained results, specific for the study area of Italy, but easily extendable on a global scale, showed that larger increases in frequency of future heavy events are expected for time series with “EV1 alike” values of initial skewness.

Study on anisotropy orientation due to well-ordered fibrous biological microstructures.

Most biological fibrous tissues have anisotropic optical characteristics, which originate from scattering by their fibrous microstructures and birefringence of biological macromolecules. The orientation-related anisotropic interpretation is of great value in biological tissue characterization and pathological diagnosis. We focus on intrinsic birefringence and form birefringence in biological tissue samples. By observing and comparing the forward Mueller matrix of typical samples, we can understand the interpretation ability of orientation-related polarization parameters and further distinguish the sources and trends of anisotropy in tissues. For glass fiber, silk fiber, skeletal muscle, and tendon, we construct a forward measuring device to obtain the Mueller matrix image and calculate the anisotropic parameters related to orientation. The statistical analysis method based on polar coordinates can effectively analyze the difference in anisotropic parameters. For those birefringent fibers, the statistical distribution of fast-axis values derived from Mueller matrix polar decomposition was found to exhibit bimodal characteristics, which is a key point in distinguishing the single-layer birefringent fiber sample from a layered, multioriented fibrous sample. The application conditions and interference factors of anisotropic orientation parameters are analyzed. Based on the parameters extracted from the orientation bimodal distribution, we can evaluate the relative change trend of intrinsic birefringence and form birefringence in anisotropic samples. The cross-vertical bimodal distribution of the fast axis of anisotropic fibers is beneficial to accurately analyze the anisotropic changes in biological tissues. The results imply the potential of anisotropic orientation analysis for applications in pathological diagnosis.