Spatial Probability Research Articles

Enabling Predictive Redirection Reset Based on Virtual-Real Spatial Probability Density Distributions.

Redirected walking (RDW) allows users to explore vast virtual spaces by walking in confined real spaces, yet suffers from frequent boundary collisions due to physical constraints. The major solution is to use the reset strategy to steer users away from boundaries. However, most reset methods guide users to fixed spots or follow constant patterns, neglecting spatial features and users' movement trends. In this paper, we propose an innovative predictive reset method based on spatial probability density distribution to jointly involve impacts of spatial feature and walking intention for forecasting the user's possible positional distribution, and thereby determines the optimal reset direction by maximizing walking expectation. Given a space, we calculate the stationary layout energy to indicate traveling difficulties of all positions. Meanwhile, we exploit a novel intention inference model to anticipate the probability distribution of the user's presence across adjacent positions. Furthermore, we incorporate the obstacle energy attenuation to predict the obstacle avoidance behaviors. All aforementioned factors are amalgamated into a potential region energy map, and then we integrate energy maps of virtual and real spaces into a fusion energy map to enable the prediction considering both spaces simultaneously. Thus, the optimal reset direction is derived by maximizing the fusion energy. Simulation and user studies are conducted on a broad dataset containing plentiful virtual and real spaces. The results demonstrate that our method effectively reduces the physical collisions and increase the continuous walking distance compared to prevalent reset methods, while exhibiting superior applicability when combined with various RDW controllers. The source code and dataset are available at https://github.com/huiyuroy/Return2MaxPotentialEnergy.

A lattice model based on percolation theory for cold atmospheric DBD plasma decontamination kinetics

The tailing phenomenon, where the survival curve of bacteria shows a slow tailing period after a rapid decline, is a ubiquitous inactivation kinetics process in the advanced plasma sterilization field. While classical models suggest that bacterial resistance dispersion causes the tailing phenomenon, experiments suggest that the non-uniform spatial distribution of spores (clustered structure) is the cause. However, no existing inactivation kinetics model can accurately describe spatial heterogeneity. In this paper, we propose a lattice model based on percolation theory to explain the inactivation kinetics by considering the non-uniform spatial distribution of spores and plasma. Our model divides spores into non-clustered and clustered types and distinguishes between short-tailing and long-tailing compositions and their formation mechanisms. By systematically studying the effects of different spore and plasma parameters on the tailing phenomenon, we provide a reasonable explanation for the kinetic law of the plasma sterilization survival curve and the mechanism of the tailing phenomenon in various cases. As an example, our model accurately explains the 80-second kinetics of atmospheric pressure plasma inactivation of spores, a process that previous models struggled to understand due to its multi-stage and long-tail phenomena. Our model predicts that increasing the spatial distribution probability of plasma can shorten the complete killing time under the same total energy, and we validate this prediction through experiments. Our model successfully explains the seemingly irregular plasma sterilization survival curve and deepens our understanding of the tailing phenomenon in plasma sterilization. This study offers valuable insights for the sterilization of food surfaces using plasma technology, and could serve as a guide for practical applications.

Temporal and Spatial Changes and Trend Predictions of Forest Carbon Sequestration Efficiency in China Based on the Carbon Neutrality Goal

Forestry’s high-quality development is crucial for China’s sustainable ecological, economic, and social progress. To elevate the efficiency of carbon sequestration in forestry, continuously improve the increment of carbon sinks, and contribute to achieving carbon neutrality, it is crucial to accurately assess the level of carbon sequestration efficiency in China’s forestry and explore its long-term evolution trend. In this paper, a super-efficiency SBM model, which combines the SBM model with the super-efficiency method and considers the relaxation variables, was selected to evaluate the forestry carbon sequestration efficiency of 31 provinces in China; likewise, the temporal development features of the efficacy of Chinese forests in sequestering carbon were examined using the nuclear density estimation method. Secondly, the study constructed traditional and spatial Markov probability transfer matrices to further explore the spatiotemporal evolution of carbon sequestration efficiency within Chinese forestry. Finally, combined with the Markov chain infinite distribution matrix, the future trajectory of carbon sequestration efficiency in China’s forestry was scientifically forecasted. The findings indicate that: (1) The average carbon sequestration efficiency of forestry in China showed a stable increase with fluctuations and reached the optimal state in 2018. The carbon sequestration efficiency level of various forest regions was always portrayed as southwest forest region > southern forest region > northeast forest region > northern forest region. From 2003 to 2018, there were significant differences in forestry carbon sequestration efficiency among provinces. The distribution of forestry carbon sequestration efficiency exhibited a “three-pillar” distribution pattern with Xizang, Zhejiang, and Heilongjiang as the core, and the marginal regions continuously promoted the carbon sequestration efficiency to the inland. (2) The type of transfer of forestry carbon sequestration efficiency in China is stable, and it is difficult to achieve cross-stage transfer in the short term. Moreover, the forestry carbon sequestration efficiency of each province tended to converge to a high (low) level over time, showing a “bimodal distribution” of low efficiency and high efficiency, indicating the existence of the obvious “club convergence phenomenon”. (3) Forecasting from a long-term evolution trend perspective, the outlook for the future evolution of forestry carbon sequestration efficiency in China is optimistic, and the overall trend was concentrated in the high-value area. Therefore, future forestry development in China should contemplate both internal structure optimization and coordinated regional development. Attention should be placed on forestry carbon sequestration’s role while considering the distinctive endowments of each region and developing reasonable, differentiated, and collaborative forestry management strategies.

Development of Sports Tourism Resources Based on Spatial Analysis Perspective

Abstract This paper examines the reality of sports tourism resources by effectively integrating the universal symbiosis and spatial hierarchy of sports tourism resources. Spatial analysis is utilized to view each element of the near-neighboring related element environment to find the spatial distribution probability density value of the random distribution within the sports tourism resources. The overall data distribution can be inferred from the limited samples, and calculations are performed to recognize localized density changes in the distribution of point-like samples. The highest mean value of sports resources density is 0.827, the highest comprehensive value of tourism resources evaluation is 0.82 points, and the maximum value of tourism competitiveness is 0.389. The spatial analysis perspective greatly improves the sports tourism industry’s comprehensive strength and the economy’s sustainable development is realized.

Scaling high Rayleigh number natural convection boundary layer statistics: A vertical water tunnel experiment

A high Rayleigh number natural convection boundary layer adjacent to the vertical heated wall was investigated at a large-scale facility. The global Rayleigh number (Rax) measured by the temperature difference between the wall and ambient water and the distance from the bottom of the heated wall reached 1013. Experimental results confirm that the global Nusselt number Nux is scaled by power 1/3 of Rax, which is similar to the well-known asymptote of the previously achieved Rax. The velocity field obtained using particle image velocimetry (PIV) indicated that the buoyancy-dominant outer-layer scaling suggested by Wells and Worster [A geophysical-scale model of vertical natural convection boundary layers, J. Fluid Mech. 609, 111–137 (2008)] was not only applicable to scale the lower-order velocity statistics but was also valid as a reasonable measure of the spatial correlation, probability distribution, and quadrant contribution features in the outer layer. The dynamical behavior of fluid motion captured by PIV supported a robust momentum transfer to positive wall-normal direction, which was sustained by the Q1 and Q3 quadrants. In addition, merging the existing literature and current data suggested that near-wall function can be applied at a moderate Rax; the universality of this wall-function model was confirmed around z×∼0.7 (where z× represents the near-wall scaled wall-normal distance, Kiš and Herwig [The near wall physics and wall functions for turbulent natural convection, Int. J. Heat Mass Transfer 55, 2625–2635 (2012)]). At a larger buoyancy regime, it was expected to follow a canonical boundary layer flow.

Transport and localization properties of excitations in one-dimensional lattices with diagonal disordered mosaic modulations

We present a numerical study of the transport and localization properties of excitations in one-dimensional lattices with diagonal disordered mosaic modulations. The model is characterized by the modulation period κ and the disorder strength W. We calculate the disorder averages , , and , where T is the transmittance and P is the participation ratio, as a function of energy E and system size L, for different values of κ and W. For excitations at quasiresonance energies determined by κ, we find power-law scaling behaviors of the form , , and , as L increases to a large value. In the strong disorder limit, the exponents are seen to saturate at the values , , and , regardless of the quasiresonance energy value. This behavior is in contrast to the exponential localization behavior occurring at all other energies. The appearance of sharp peaks in the participation ratio spectrum at quasiresonance energies provides additional evidence for the existence of an anomalous power-law localization phenomenon. The corresponding eigenstates demonstrate multifractal behavior and exhibit unique node structures. In addition, we investigate the time-dependent wave packet dynamics and calculate the mean square displacement , spatial probability distribution, participation number, and return probability. When the wave packet’s initial momentum satisfies the quasiresonance condition, we observe a subdiffusive spreading of the wave packet, characterized by where η is always less than 1. We also note the occurrence of partial localization at quasiresonance energies, as indicated by the saturation of the participation number and a nonzero value for the return probability at long times.

Dynamic and Pareto-improving swapping of vehicles to enhance integrated and modular mobility services

Efficiency of many mobility services depends on effective matching of service vehicles and customers over time and space. New technologies, such as those related to service integrators and modularized autonomous vehicle platforms (i.e., chassis with built-in power and control systems), further allow pooling of vehicles from traditionally separate service operators to flexibly serve multiple types of customers (e.g., passengers and freight). Dynamic matching systems, if not carefully managed, suffer from the so-called wild goose chase (WGC) phenomenon, which describes an inefficient system equilibrium with a large number of vehicles trapped in unproductive deadheading. This paper proposes a new dynamic vehicle swap strategy that can be used to enhance system efficiency by reducing the expected waiting/deadheading time. This strategy can not only mitigate the WGC and reduce the needed fleet size, but also ensure that the vehicle swap achieves a Pareto improvement for each involved participant. Approximate analytic formulas are derived from a series of differential equations and spatial probability models to estimate the expected system performance in the steady state. This system of nonlinear equations can be solved numerically to yield the equilibrium points and the associated system performance metrics. Agent-based simulations are used to verify the accuracy of the derived formulas, and to demonstrate the effectiveness of the proposed dynamic and Pareto-improving swap strategy in a variety of application contexts (e.g., taxi, service integrator, or pooled modular chassis).

A 3D Geological Modeling Method for Grain-size Lithofacies of Tight Sandstone Gas Reservoirs: A Case Study of Xujiahe Formation Gas Reservoir in Sichuan Basin, China

Tight sandstone gas reservoir has strong heterogeneity, and it is difficult to control the change of internal storage and permeability properties by simple sedimentary facies. It is necessary to explore a geological modeling method that can more accurately characterize the distribution of high-quality reservoirs. Taking the Xujiahe Formation gas reservoir in Xinchang area, Sichuan Basin, China as an example, this paper introduces the modeling method of three orders: sand-mudstone facies, sedimentary facies and grain-size lithofacies, so as to realize the spatial characterization of high-quality tight sandstone reservoirs with strong heterogeneity. In this technical process, the sand-mudstone model is first established. Based on the sand and mudstone model, four sedimentary types (or sedimentary facies) of distributary channel, channel edge, interdistributary bay and mouth bar are further divided. The sedimentary facies model is constructed by multi-point geostatistical modeling method. The quantitative relationship between sedimentary facies and grain-size lithofacies is linked by using argillaceous content as a “bridge”. The spatial distribution probability of grain-size lithofacies is constrained by the neural network clustering of argillaceous content and natural gamma-ray data in three-dimensional space. By controlling the types, ratios, and boundaries of grain-size lithofacies through sedimentary facies, and combining probabilistic bodies to cooperate with constraints, precise simulation of lithofacies can be achieved. The grain-size lithology lithofacies model established by this method follows the depositional law in space and has more reasonable contact relations between various sand bodies. The anastomosis rate of the model reached 85% with the new drilling test. This paper provides a new modeling idea for quantitative characterization and prediction of high-quality tight sandstone gas reservoirs, and provides a more accurate model basis.

Statistical learning of target and distractor spatial probability shape a common attentional priority computation

Converging evidence recently put forward the notion that dedicated neurocognitive mechanisms do exist for the suppression of salient, but irrelevant distractors. Along this line, it is plausible to hypothesize that, in appropriate contexts, experience-dependent forms of attentional learning might selectively induce plastic changes within this dedicated circuitry, thus allowing an independent shaping of priorities at the service of attentional filtering. Conversely, previous work suggested that statistical learning (SL) of both target and distractor spatial probability distributions converge in adjusting only the overall attentional priority of locations: in fact, in the presence of an independent manipulation, either related to the target or to the distractor only, SL induces indirect effects (e.g., changes in filtering efficiency due to an uneven distribution of targets), suggesting that SL-induced plastic changes affect a shared neural substrate. Here we tested whether, when (conflicting) target- and distractor-related manipulations are concurrently applied to the very same locations, dedicated mechanisms might support the selective encoding of spatial priority in relation to the specific attentional operation involved. In three related experiments, human healthy participants discriminated the direction of a target arrow, while ignoring a salient distractor, if present; both target and distractor spatial probability distributions were concurrently manipulated in relation to each single location. Critically, the selection bias produced by the target-related SL was marginally reduced by an adverse distractor contingency, and the suppression bias generated by the distractor-related SL was erased, or even reversed, by an adverse target contingency. Our results suggest that even conflicting target- and distractor-related SL manipulations result in the adjustment of a unique spatial priority computation, likely because the process directly relies on direct plastic alterations of shared spatial priority map(s).

An electric vehicle charging load prediction model for different functional areas based on multithreaded acceleration

This paper proposes an electric vehicle (EV) charging load prediction model for different functional areas based on multithreaded technology. This model comprehensively incorporates the preference characteristics of EV users in charging behavior and mode, as well as accounting for diverse travel purposes. It is worth emphasizing that a comprehensive analysis of the spatiotemporal travel characteristics exhibited by users during different seasons and on working days versus rest days is provided. In terms of temporal scale, refined probability models are employed to accurately fit the user's travel time variables. In terms of the spatial scale, the initial position and spatial transfer probability of users are analyzed. Subsequently, leveraging graph theory, a regional transportation network model is established. On this basis, a novel improved time optimal path decision-making algorithm is proposed by holistically considering factors such as road grade, saturation, traffic conditions, one-way/two-way streets, and delay at signalized intersections. Additionally, the impact of temperature on the upper limit capacity of batteries is analyzed, as well as driving/air conditioning energy consumption models are established. It is noteworthy that a two-stage charging power variation model is introduced, enhancing the precision of charging power calculation. Finally, the effectiveness of the method is validated through case analysis. The results demonstrate a significant disparity in load demand among various functional areas, and the spatiotemporal distribution of load demand is significantly influenced by seasons and working days versus rest days. Furthermore, when employing 4 threads, the proposed method achieves a speedup ratio exceeding 2.5 compared to conventional serial methods.

Classifying a Highly Polymorphic Tree Species across Landscapes Using Airborne Imaging Spectroscopy

Vegetation classifications on large geographic scales are necessary to inform conservation decisions and monitor keystone, invasive, and endangered species. These classifications are often effectively achieved by applying models to imaging spectroscopy, a type of remote sensing data, but such undertakings are often limited in spatial extent. Here we provide accurate, high-resolution spatial data on the keystone species Metrosideros polymorpha, a highly polymorphic tree species distributed across bioclimatic zones and environmental gradients on Hawai’i Island using airborne imaging spectroscopy and LiDAR. We compare two tree species classification techniques, the support vector machine (SVM) and spectral mixture analysis (SMA), to assess their ability to map M. polymorpha over 28,000 square kilometers where differences in topography, background vegetation, sun angle relative to the aircraft, and day of data collection, among others, challenge accurate classification. To capture spatial variability in model performance, we applied Gaussian process classification (GPC) to estimate the spatial probability density of M. polymorpha occurrence using only training sample locations. We found that while SVM and SMA models exhibit similar raw score accuracy over the test set (96.0% and 93.4%, respectively), SVM better reproduces the spatial distribution of M. polymorpha than SMA. We developed a final 2 m × 2 m M. polymorpha presence dataset and a 30 m × 30 m M. polymorpha density dataset using SVM classifications that have been made publicly available for use in conservation applications. Accurate, large-scale species classifications are achievable, but metrics for model performance assessments must account for spatial variation of model accuracy.

SOF-RRT*: An improved path planning algorithm using spatial offset sampling

Recently, sampling-based algorithms have demonstrated advantages in complex and high-dimensional environments. The RRT* algorithm, as the best variant of RRT, provides progressive optimality. It is shown that the RRT* algorithm has a slow convergence speed and a high initial path cost that result in the low efficiency of the algorithm, due to the low probability of adequate sampling caused by the invalid expansion of the tree and redundant sampling. To overcome these limitations, the SOF-RRT* algorithm is proposed to generate better initial solutions with higher stability and faster convergence. The SOF-RRT* algorithm is an improvement on the F-RRT* algorithm. The SOF-RRT* algorithm introduces a spatial probability weight sampling strategy into sampling, which makes the sampling probability higher in the area with a large feasible area. It reduces redundant sampling and increases the effective sampling rate. In the aspect of tree expansion, the strategy of target bias and obstacle tangential bias is introduced to improve the tree expansion efficiency, which makes tree expansion away from obstacles and bias to the target point. The proposed algorithm improves the expansion efficiency of sampling and tree. Finally, the initial path generation and convergence speed are simulated and compared, which proves that the algorithm has been greatly optimized in terms of the number of iterations, path quality, and convergence speed.

A random error estimation method for satellites being launched into orbit based on ellipsoid theory

Accurate and fast random error estimation is essential to improve satellite control. In this paper, the random error of the satellite being launched into orbit is explored. According to the uncertainty matrix of six satellite orbit parameters associated with the project, the uncertainty of the orbit position in the three axes of the geocentric inertial frame of reference is calculated via the propagation rate of the covariance matrix. Then, the random error probability density of the satellite position is constructed for normal distribution. The surface of the error with the same probability density is approximated as an ellipsoid, the size is deduced along with the ellipsoid's shape and direction, and the satellite's spatial position probability is obtained. Monte Carlo method and STK software were employed to simulate low-earth orbit micro-nano satellite launching into the same orbital position. It is confirmed that the results obtained via the theoretical model are in line with practice. Therefore, the ellipsoid theory proposed in this paper can be used to describe the satellite random position error.

An Integration of Deep Learning and Transfer Learning for Earthquake-Risk Assessment in the Eurasian Region

The problem of estimating earthquake risk is one of the primary themes for researchers and investigators in the field of geosciences. The combined assessment of spatial probability and the determination of earthquake risk at large scales is challenging. To the best of the authors’ knowledge, there no updated earthquake-hazard-and-risk assessments for the Eurasia region have been published since 1999. Considering that Eurasia is characterized by a seismically active Alpine–Himalayan fault zone and the Pacific Ring of Fire, which are frequently affected by devastating events, a continental-scale risk assessment for Eurasia is necessary to check the global applicability of developed methods and to update the earthquake-hazard, -vulnerability, and -risk maps. The current study proposes an integrated deep-transfer-learning approach called the gated recurrent unit–simple recurrent unit (GRU–SRU) to estimate earthquake risk in Eurasia. In this regard, the GRU model estimates the spatial probability, while the SRU model evaluates the vulnerability. To this end, spatial probability assessment (SPA), and earthquake-vulnerability assessment (EVA) results were integrated to generate risk A, while the earthquake-hazard assessment (EHA) and EVA were considered to generate risk B. This research concludes that in the case of earthquake-risk assessment (ERA), the results obtained for Risk B were better than those for risk A. Using this approach, we also evaluated the stability of the factors and interpreted the interaction values to form a spatial prediction. The accuracy of our proposed integrated approach was examined by means of a comparison between the obtained deep learning (DL)-based results and the maps generated by the Global Earthquake Model (GEM). The accuracy of the SPA was 93.17%, while that of the EVA was 89.33%.

Research on the Level of Agricultural Green Development, Regional Disparities, and Dynamic Distribution Evolution in China from the Perspective of Sustainable Development

Green development is a concept of sustainable development, aiming to protect the environment and ecosystems while meeting economic development needs. In the field of agriculture, green development has emerged as a crucial pathway for reconciling the conflicts between agricultural development and ecological conservation. To investigate the level of green development in Chinese agriculture, regional variations, and the evolutionary patterns, this paper is based on the framework of sustainable development theory. This study establishes a comprehensive evaluation system for agricultural green development and applies methods such as entropy-weighted TOPSIS, Dagum’s Gini coefficient, kernel density estimation, Moran’s I index, and Markov chains to analyze the level of agricultural green development, regional disparities, and dynamic evolution in China. The findings of this study reveal that: (1) The overall level of agricultural green development in China is steadily improving, with notable differences in the level of agricultural green development among different regions and provinces. There are significant disparities in agricultural green development between regions, and the overall disparities exhibit a fluctuating downward trend characterized by periods of increase followed by decrease. The regional disparities are identified as the primary cause of the overall disparities in agricultural green development in China. (2) The eight major economic regions in China are experiencing steady development in agricultural green practices, but there are varying degrees of polarization due to different development speeds. (3) This study also highlights a clear spatial positive correlation in the level of agricultural green development in China, with most provinces showing clustering in the first and third quadrants, indicating a “high–high” (H-H) and “low–low” (L-L) agglomeration pattern. (4) The study reveals that the level of agricultural green development in China exhibits a certain degree of stability. Over time, the probability of transitioning from lower-level regions to neighboring higher-level regions increases, and the agricultural green development level in neighboring regions can influence the spatial transfer probability within a given region. Therefore, agricultural green development demonstrates significant spatial dependence.

Data-driven statistical reduced-order modeling and quantification of polycrystal mechanics leading to porosity-based ductile damage

Predicting the process of porosity-based ductile damage in polycrystalline metallic materials is an essential practical topic. Ductile damage and its precursors are represented by extreme values in stress and material state quantities, the spatial probability density function (PDF) of which are highly non-Gaussian with strong fat tails. Traditional deterministic forecasts utilizing sophisticated continuum-based physical models generally lack in representing the statistics of structural evolution during material deformation. Computational tools which do represent complex structural evolution are typically expensive. The inevitable model error and the lack of uncertainty quantification may also induce significant forecast biases, especially in predicting the extreme events associated with ductile damage. In this paper, a data-driven statistical reduced-order modeling framework is developed to provide a probabilistic forecast of the deformation process of a polycrystal aggregate leading to porosity-based ductile damage with uncertainty quantification. The framework starts with computing the time evolution of the leading few moments of specific state variables from the spatiotemporal solution of full-field polycrystal simulations. Then a sparse model identification algorithm based on causation entropy, including essential physical constraints, is utilized to discover the governing equations of these moments. An approximate solution of the time evolution of the PDF is obtained from the predicted moments exploiting the maximum entropy principle. Numerical experiments based on polycrystal realizations of a representative body-centered cubic (BCC) tantalum illustrate a skillful reduced-order model in characterizing the time evolution of the non-Gaussian PDF of the von Mises stress and quantifying the probability of extreme events. The learning process also reveals that the mean stress is not simply an additive forcing to drive the higher-order moments and extreme events. Instead, it interacts with the latter in a strongly nonlinear and multiplicative fashion. In addition, the calibrated moment equations provide a reasonably accurate forecast when applied to the realizations outside the training data set, indicating the robustness of the model and the skill for extrapolation. Finally, an information-based measurement is employed to quantitatively justify that the leading four moments are sufficient to characterize the crucial highly non-Gaussian features throughout the entire deformation history considered.

Impact-based probabilistic modeling of hydro-morphological processes in China (1985–2015)

Hydro-morphological processes (HMP, any natural phenomenon contained within the spectrum defined between debris flows and flash floods) pose a relevant threat to infrastructure, urban and rural settlements and to lives in general. This has been widely observed in recent years and will likely become worse as climate change will influence the spatio-temporal pattern of precipitation events. The modelling of where HMP-driven hazards may occur can help define the appropriate course of actions before and during a crisis, reducing the potential losses that HMPs cause in their wake. However, the probabilistic information on locations prone to experience a given hazard is not sufficient to depict the risk our society may incur. To cover this aspect, modeling the loss information could open up to better territorial management strategies. In this work, we made use of the HMP catalogue of China from 1985 to 2015. Specifically, we implemented the Light Gradient Boosting (LGB) classifier to model the impact level that locations across China have suffered from HMPs over the thirty-year record. We obtained six impact levels as a combination of financial and life losses, whose classes we used as separate target variables for our LGB. In doing so, we estimated spatial probabilities of certain HMP impact, something that has yet to be tested in the natural hazard community, especially over such a large spatial domain. The results we obtained are encouraging, with each of the six impact categories being separately classified with excellent to outstanding performance (the worst case corresponds to a mean AUC = 0.862, whereas the best case corresponds to a mean AUC of 0.915). The good predictive performance our model produced suggest that the cartographic output could be useful to inform authorities of locations prone to human and infrastructural losses of specific magnitudes.

Kaon Femtoscopy with Lévy-Stable Sources from sNN=200 GeV Au+Au Collisions at RHIC

Femtoscopy has the capacity to probe the space-time geometry of the particle-emitting source in heavy-ion collisions. In particular, femtoscopy of like-sign kaon pairs may shed light on the origin of non-Gaussianity of the spatial emission probability density. The momentum correlations between like-sign kaon pairs are measured in data recorded by the STAR experiment, from sNN=200 GeV Au+Au collisions at RHIC, BNL. Preliminary results hint at the possible existence of non-Gaussian, Lévy-stable sources, with the likely presence of an anomalous diffusion process in the signal for the identically charged kaon pairs so produced. More statistically significant studies at lower centre-of-mass energies may contribute to the search for the critical end point of QCD.

Spatial patterns of exposure to sediment-laden flows on an experimental alluvial fan

Sediment-laden flows with significant amounts of large wood can impact alluvial fans and seriously damage infrastructure and property which makes a profound knowledge of exposure an essential requisite for risk mitigation. To investigate its spatial variability, we executed experiments on a simplified physical model and assessed the observed spatial process and exposure patterns by quantifying synthetic indexes and geostatistically analysing spatial probabilities. We systematically varied the loading conditions, i.e. total flow volume, solid fraction and the tank opening controlling water release, and repeated each experimental configuration eight times. Two alluvial fan layouts were considered, one equipped with a guiding channel and a bridge and one with a guiding channel only. First, we tested the hypothesis, that water released though the fully opened tank outflow valve induces a sediment-laden flow which is associated with higher exposure and lower spatial uncertainty of exposure if compared to flows generated by a half-opened tank outflow valve. Second, we tested whether a higher flow volume is associated with higher exposure on the alluvial fan and with lower spatial uncertainty. It turned out that neither of the aforementioned hypotheses is verified for the whole set of tested flow conditions. The first hypothesis is rejected in the majority of the considered conditions. The second hypothesis is prevailingly corroborated when the exposed areas due to both sediment and water were considered. Instead, when only the areas of sediment deposition are considered, this hypothesis is prevailingly corroborated on the alluvial fan featuring the presence of the bridge. We provided exposure probability maps for all experimental conditions and presented the variability of exposure by standard deviation ellipses. Although solely indicative for the adopted alluvial fan layouts, a variation of the loading parameters led to remarkable changes in the patterns of exposure probability and the parameters of the standard deviation ellipses. Our results urge decision makers to fully acknowledge the potential variability of geomorphic responses on alluvial fans in their risk management practices as to avoid underestimating the impacts for the built environment.

Anomalous excess noise behavior in thick Al0.85Ga0.15As0.56Sb0.44 avalanche photodiodes

Al0.85Ga0.15As0.56Sb0.44 has recently attracted significant research interest as a material for 1550 nm low-noise short-wave infrared (SWIR) avalanche photodiodes (APDs) due to the very wide ratio between its electron and hole ionization coefficients. This work reports new experimental excess noise data for thick Al0.85Ga0.15As0.56Sb0.44 PIN and NIP structures, measuring low noise at significantly higher multiplication values than previously reported (F = 2.2 at M = 38). These results disagree with the classical McIntyre excess noise theory, which overestimates the expected noise based on the ionization coefficients reported for this alloy. Even the addition of ‘dead space’ effects cannot account for these discrepancies. The only way to explain the low excess noise observed is to conclude that the spatial probability distributions for impact ionization of electrons and holes in this material follows a Weibull–Fréchet distribution function even at relatively low electric-fields. Knowledge of the ionization coefficients alone is no longer sufficient to predict the excess noise properties of this material system and consequently the electric-field dependent electron and hole ionization probability distributions are extracted for this alloy.