Probabilistic Range Research Articles

The occurrence of sequential oviposition in fig wasps and the implications for interpreting sex ratio data

Abstract Pollinating fig wasps are believed to adjust their sex ratios according to standard local mate competition (LMC) theory. Standard LMC theory assumes that all mothers ovipositing in a patch or fig does so simultaneously. However, it has been shown that fig wasps can oviposit sequentially. We counted the number of figs containing dead and living mothers in figs where mothers entered naturally to estimate the incidence of sequential and simultaneous oviposition. Single mothers were the norm in two wasp species, and multiple mothers the norm in the other two. However, contrary to LMC theory, in all four species, when multiple mothers occurred, sequential oviposition seems to occur more frequently than simultaneous oviposition. The sex allocation problem fig wasp mothers face is thus more complicated than the widely assumed simultaneous ovipositing situation, and it leads to several expectations. Single mother's sex ratios should increase as the probability of additional mothers increases. Naturally founded multi‐mother figs should have more female‐biased sex ratios than the standard LMC model predicts for the final number of mothers. This is because early‐arriving mothers underestimate the number of mothers and lay more daughters than the final number of mothers would require and later‐arriving mothers can lay fewer sons to be competitive against the first mothers' too female‐biased clutches. Mothers must produce sex ratios that are optimised across a probabilistic range of foundress densities they experience.

Revealing the impact of renewable uncertainty on grid-assisted power-to-X: A data-driven reliability-based design optimization approach

Designing a reliable power-to-X (PtX) process that uses renewable energy and grid electricity as backup power is challenging due to the uncertain nature of renewables and potential limits on grid usage. To address this challenge, this paper proposes a data-driven, reliability-based optimization approach for designing a grid-assisted PtX process and understanding how the uncertainty of time-series renewable profile affects its design and optimal sizing. This approach uses generative adversarial networks (GANs) to capture the complex patterns of renewables in temporal and spatial dimensions, rather than simply treating uncertainty as a probabilistic range as in conventional methods. The optimization considers both the mean and variance of production cost as objectives, while also taking into account the level of grid energy penetration as a probabilistic constraint through the reliability analysis. A power allocation and performance evaluation model is developed and used in conjunction with the optimization to assess uncertainty propagation through the PtX process and evaluate the impact of renewable uncertainty on process performance. A case study involving the production of methanol on Jeju Island in South Korea demonstrates how the mean and variance of production cost and other performance indicators can be balanced in the reliable design of PtX processes under renewable energy uncertainty, providing insights for decision-making in such situations.

Random analysis of dynamic wheel–rail interactions in the switch panel of a turnout with geometric tolerances based on the probability density evolution method

During train operation, the geometric tolerance in a turnout can be very random, posing a challenge for traditional deterministic vehicle-turnout coupling studies and for daily maintenance. In light of the above situation, a coupled vehicle-switch panel model with random geometric tolerances is proposed. These tolerances result from the discrepancy in relative height difference of rail tops between the straight switch rail and the curved stock rail, and from the gap between the straight switch rail and the curved stock rail in the closure part. The model takes rail flexibility into consideration. The probability density evolution method (PDEM) is applied to capture the resultant probability density function (PDF) of dynamic wheel–rail interactions. The Monte Carlo method is employed for verification of results obtained from the PDEM. The results show that stronger randomness of the vertical wheel force is induced compared with that of the lateral wheel force. The probabilistic range of the vertical wheel force is found to be around 50 kN on the straight switch rail side. Given the largest maximum mean value of rail displacement, it is found that the part of the straight switch rail with a top width of 40 mm requires significant attention.

Electrochemical Oxidation of Ammonia-Laden Wastewater in the Mining Industry

Ammonia is a common contaminant in municipalities where human waste causes nitrification of local water bodies. In mining, ammonia contamination occurs as a byproduct of biological water treatment, from the use of ammonium nitrate fuel oil in explosives, and from the exposure of ammonia rich soils during the excavation process. In particular, gold mine effluent represents a significant source of ammonia and nitrogen-based contaminants. Current biological and abiotic treatment processes are difficult to employ at the scale required at mine sites due to the high operating costs, or are limited in effectiveness due to a lack of natural resources required to facilitate the treatment. This article evaluates the use of electrooxidation as a cost effective alternative to treating ammonia-laden wastewater in mining applications. Two mixed metal oxide electrodes are assessed in this article: IrO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>/Ti and RuO<inline-formula><tex-math notation="LaTeX">$_{2}$</tex-math></inline-formula>/Ti anodes. A Monte Carlo simulation is performed to determine a probabilistic range of capital and operating expenditure for a mining operation deploying an electrooxidation wastewater treatment system. The lowest capital cost of operating the electrochemical treatment occurs at a current density of 200 A/m<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>, where the number of cells required for treatment is minimized.

Cross-sensor periocular biometrics in a global pandemic: Comparative benchmark and novel multialgorithmic approach

The massive availability of cameras and personal devices results in a wide variability between imaging conditions, producing large intra-class variations and a significant performance drop if images from heterogeneous environments are compared for person recognition purposes. However, as biometric solutions are extensively deployed, it will be common to replace acquisition hardware as it is damaged or newer designs appear or to exchange information between agencies or applications operating in different environments. Furthermore, variations in imaging spectral bands can also occur. For example, face images are typically acquired in the visible (VIS) spectrum, while iris images are usually captured in the near-infrared (NIR) spectrum. However, cross-spectrum comparison may be needed if, for example, a face image obtained from a surveillance camera needs to be compared against a legacy database of iris imagery. Here, we propose a multialgorithmic approach to cope with periocular images captured with different sensors. With face masks in the front line to fight against the COVID-19 pandemic, periocular recognition is regaining popularity since it is the only region of the face that remains visible. As a solution to the mentioned cross-sensor issues, we integrate different biometric comparators using a score fusion scheme based on linear logistic regression This approach is trained to improve the discriminating ability and, at the same time, to encourage that fused scores are represented by log-likelihood ratios. This allows easy interpretation of output scores and the use of Bayes thresholds for optimal decision-making since scores from different comparators are in the same probabilistic range. We evaluate our approach in the context of the 1st Cross-Spectral Iris/Periocular Competition, whose aim was to compare person recognition approaches when periocular data from visible and near-infrared images is matched. The proposed fusion approach achieves reductions in the error rates of up to 30%–40% in cross-spectral NIR–VIS comparisons with respect to the best individual system, leading to an EER of 0.2% and a FRR of just 0.47% at FAR = 0.01%. It also represents the best overall approach of the mentioned competition. Experiments are also reported with a database of VIS images from two different smartphones as well, achieving even bigger relative improvements and similar performance numbers. We also discuss the proposed approach from the point of view of template size and computation times, with the most computationally heavy comparator playing an important role in the results. Lastly, the proposed method is shown to outperform other popular fusion approaches in multibiometrics, such as the average of scores, Support Vector Machines, or Random Forest.

Multivariate Probabilistic Range Queries for Scalable Interactive 3D Visualization.

Large-scale scientific data, such as weather and climate simulations, often comprise a large number of attributes for each data sample, like temperature, pressure, humidity, and many more. Interactive visualization and analysis require filtering according to any desired combination of attributes, in particular logical AND operations, which is challenging for large data and many attributes. Many general data structures for this problem are built for and scale with a fixed number of attributes, and scalability of joint queries with arbitrary attribute subsets remains a significant problem. We propose a flexible probabilistic framework for multivariate range queries that decouples all attribute dimensions via projection, allowing any subset of attributes to be queried with full efficiency. Moreover, our approach is output-sensitive, mainly scaling with the cardinality of the query result rather than with the input data size. This is particularly important for joint attribute queries, where the query output is usually much smaller than the whole data set. Additionally, our approach can split query evaluation between user interaction and rendering, achieving much better scalability for interactive visualization than the previous state of the art. Furthermore, even when a multi-resolution strategy is used for visualization, queries are jointly evaluated at the finest data granularity, because our framework does not limit query accuracy to a fixed spatial subdivision.

Measuring the Degree of Overlap and Segregation among Multiple Probabilistic Home Ranges: A New Index with Illustrative Application to the Lesser Kestrel Falco naumanni.

Simple SummaryIntraspecific and interspecific interactions regulate the extent and spatial patterns of animal home ranges. If we are able to estimate the home range overlap for a large number of individuals, populations and/or species, then we can readily identify important ecological properties, such as social network structure, competition during the breeding season, contact rates with implications for disease transmission, change in space use over time, interactions among different age classes and site fidelity for a particular individual, population or species. We can also evaluate the robustness of probabilistic home range assessment through the degree of overlap of several estimators. Accordingly, in this study, we first solve the issue of measuring the degree of overlap/segregation among a large number of probabilistic animal home ranges and provide a demonstrative case study.Home range overlap/segregation has several important applications to wildlife conservation and management. In this work, we first address the issue of measuring the degree of overlap/segregation among an arbitrarily large number (i.e., n ≥ 2) of probabilistic animal home ranges (i.e., utilization distributions). This subject matter has recently been solved for home ranges measured as polygons (e.g., percent minimum convex polygons and multinuclear cores) but not yet for probabilistic ones. Accordingly, we introduce a novel index named the PGOI (probabilistic general overlap index), and its complement, the PGSI (probabilistic general segregation index), an index for computation of probabilistic home range overlap/segregation at individual, population and species levels. Whatever the number of probabilistic home ranges, the PGOI returns a single score ranging in the [0, 100] interval. We applied the PGOI to five lesser kestrels (Falco naumanni) at Santeramo in Colle (Apulia region; Southern Italy) as a case study. Our new index can be applied to any animal species and to home ranges derived from any type of probabilistic home range estimator.

E&amp;P Notes (February 2021)

E&amp;P Notes (February 2021)

Uncertain probabilistic range queries on multidimensional data

Probabilistic Range Queries (PRQ) retrieve objects which, according to imprecise object properties, are (with a given probability) inside a precise range. When the query range is based on some imprecise object properties, which makes the query range imprecise as well, then Uncertain Probabilistic Range Queries (UPRQ) arise. Unfortunately, in the literature UPRQs ranges are constrained to be balls, i.e., the range is defined by providing a certain radius around an imprecise object property. Moreover, another important issue is the efficiency of answering UPRQs due to the necessary numerical operations to calculate probabilities.In this work we give a novel definition for UPRQs with query ranges of any shape; in addition we prove that any UPRQ can be reduced to a PRQ. Concerning the efficiency of UPRQs, we adopt and improve the usual way to address this family of queries (i.e., constructing indexes to prune/validate which objects belong to the answer, avoiding unnecessary numerical calculations) presenting: (1) a method to improve the filtering capabilities of the indexes when dealing with uniform distributions over rectangles or balls; and (2) a new index (eUD-Index), which enhances the state of the art, for any type of probability distribution. Our experiments show the feasibility of the proposals.

A Framework to Facilitate Firebrand Characterization

Generation of firebrands from various fuels has been well studied in the past decade. Limited details have been released about the methodology for characterizing firebrands such as the proper sample size and the measurement process. This study focuses on (1) finding the minimum required sample size to represents the characteristics of the population, and (2) proposes a framework to facilitate the tedious measurement process. To achieve these goals, several firebrand generation tests were conducted at a boundary layer wind tunnel with realistic gusty wind traces. Firebrands were generated from burning structural fuels and collected in 46 strategically located water pans. The statistical analysis showed that the minimum required sample size based on the chosen statistical parameters (standard deviation, confidence interval, and margin of error) is 1400 for each test. To facilitate characterizing such a large sample of firebrands, an automated image processing algorithm to measure the projected area of the firebrands was developed, which can automatically detect the edges of the background sheet, rotate the photo if its tilted before cropping, detect edges of firebrands, remove erroneous particles (e.g. ash) and finally measures the projected area. To facilitate the weighing process, a Gaussian process regression was performed to predict the mass based on projected area, traveling distance and wind speed. The model can predict the firebrand mass within 5% error compared to the measurement. This framework and model can provide a probabilistic range of firebrand characteristics over the continuous range of the collection region.

Evaluating the Effect of Financing Costs on PV Grid Parity by Applying a Probabilistic Methodology

This paper presents a study that analyses the effect of financing costs on grid parity in photovoltaic (PV) installations by applying a probabilistic methodology. Three different case studies, located in Spain, have been considered, with 500 kW, 50 kW and 5 kW grid-connected PV generators. The technical and economic calculations were performed, considering the interest rate, yield across the Spanish geography, and PV module cost as parameters. The Monte Carlo method was applied to consider the full probabilistic range of values given to the different variables. The goal of this study was to determine, for the studied cases, the levelised cost of energy (LCOE) and the internal rate of return by considering realistic values of the variables. A success rate parameter was calculated, which determined the likelihood of the number of times that the LCOE was below the retail cost of electricity. All the cases were evaluated by applying 10,000 iterations, considering the standard deviations and means defined.

An Australian first initiative to re-develop the first commercial onshore oilfield into a CO2 miscible-EOR project

The Moonie onshore oil field discovered in 1961, was the first commercial oil discovery in Australia. The field was purchased by Bridgeport Energy Limited (BEL) from Santos in late 2015. An Australian first initiative by BEL is to enhance oil production from the field using tertiary recovery CO2 miscible flood to maximise field oil recovery. The process involves an evaluation of well injection strategies for a miscible displacement process using reservoir simulation modelling. In addition, the project jointly addresses community concerns regarding the rise in greenhouse gas emissions by sourcing 60000–120000 tonnes/annum of CO2 from a nearby power station and/or an ethanol plant. Justified by laboratory experiments and reservoir compositional simulations, BEL’s project timeline to implement a CO2-enhanced oil recovery (EOR) pilot could start from 2020 followed by a 2–3-year full field oil production acceleration project if additional CO2 can be sourced. Based on incremental recovery and operational consideration, an injection well in the southern end of the field surrounded by six existing producers has been selected as a pilot flood. Positive indicative economics are achieved by the efficient displacement with CO2 of 8000 scf/bbl of incremental oil. Full field dynamic modelling predicts a further 8% oil recovery factor by injecting 60 Bcf of CO2 over five years, which could store in excess of three million tonnes of CO2. For the pilot, more than 90% of the injected CO2 will remain in the Precipice sandstone reservoir. However, the efficiency and viability of a CO2-EOR project is subject to successful implementation of the miscibility modelling, logistics and injection strategy and uncertainty quantification. To propel the project into the execution phase a fast-multiphase reservoir simulator has been implemented to complete a probabilistic range of results in optimal time.

A Probabilistic Range Query of Moving Objects in Road Network

The range query of moving objects in a road network is widely used in battlefield environment, intelligent transportation systems, and mobile phone location. It has gradually become a research hotspot in the field of spatiotemporal data management. Most of the existing research on range query of moving objects in the road network is based on the premise of dense sampling of the device and complete storage of data. It ignores the uncertainty of discrete location data caused by a blind area of actual devices and limited memory, which cannot guarantee the accuracy of the range query. In this paper, the processing framework of probabilistic range query of moving objects in the road network is constructed. A spatiotemporal index structure is proposed based on the framework. This index can effectively represent time weights and the relationship between road sections. The probabilistic range query algorithm of moving objects in the road network with an uncertain trajectory caused by sampling frequency is designed and implemented. The experiments verify that the method proposed in this paper can improve query efficiency and ensure query accuracy.

The Volume of Gas Hydrate‐Bound Gas in the Northern Gulf of Mexico

AbstractThe northern Gulf of Mexico is known to host gas hydrate in submarine sediments. Estimating the amount of gas hydrate for both carbon cycle and resource interest has been ongoing for more than three decades. A large range of estimates (from 0.2 to 680 trillion cubic meters (TCM)) for hydrate‐bound natural gas at standard temperature and pressure exists. We bring a new perspective to resource estimates by using ~800 publicly available petroleum industry well logs assessed for natural gas hydrate. Our resulting probabilistic range of the gas hydrate‐bound natural gas in the northern Gulf of Mexico ranges from 37 TCM (10th percentile estimate) to 78 TCM (90th percentile estimate) of gas hydrate‐bound natural gas, with a mean estimate of 56 TCM. This suggests that the gas hydrate resource density of the northern Gulf of Mexico is 4 times lower than that previously estimated by the Bureau of Ocean Energy Management. Our results also indicate that over half of the gas hydrate in the area is strategically hosted in sand reservoirs, which is significantly higher than that previously estimated.

Optimizing Winter Wheat Resilience to Climate Change in Rain Fed Crop Systems of Turkey and Iran.

Erratic weather patterns associated with increased temperatures and decreasing rainfall pose unique challenges for wheat breeders playing a key part in the fight to ensure global food security. Within rain fed winter wheat areas of Turkey and Iran, unusual weather patterns may prevent attaining maximum potential increases in winter wheat genetic gains. This is primarily related to the fact that the yield ranking of tested genotypes may change from one year to the next. Changing weather patterns may interfere with the decisions breeders make about the ideotype(s) they should aim for during selection. To inform breeding decisions, this study aimed to optimize major traits by modeling different combinations of environments (locations and years) and by defining a probabilistic range of trait variations [phenology and plant height (PH)] that maximized grain yields (GYs; one wheat line with optimal heading and height is suggested for use as a testing line to aid selection calibration decisions). Research revealed that optimal phenology was highly related to the temperature and to rainfall at which winter wheat genotypes were exposed around heading time (20 days before and after heading). Specifically, later winter wheat genotypes were exposed to higher temperatures both before and after heading, increased rainfall at the vegetative stage, and reduced rainfall during grain filling compared to early genotypes. These variations in exposure to weather conditions resulted in shorter grain filling duration and lower GYs in long-duration genotypes. This research tested if diversity within species may increase resilience to erratic weather patterns. For the study, calculated production of a selection of five high yielding genotypes (if grown in five plots) was tested against monoculture (if only a single genotype grown in the same area) and revealed that a set of diverse genotypes with different phenologies and PHs was not beneficial. New strategies of progeny selection are discussed: narrow range of variation for phenology in families may facilitate the discovery and selection of new drought-resistant and avoidant wheat lines targeting specific locations.

NUMERICAL COMPUTATION AND PREDICTION OF ELECTRICITY CONSUMPTION IN TOBACCO INDUSTRY

Electricity is a key energy source in each country and an important condition for economic development. It is necessary to use modern methods and tools to predict energy consumption for different types of systems and weather conditions. In every industrial plant, electricity consumption presents one of the greatest operating costs. Monitoring and forecasting of this parameter provide the opportunity to rationalize the use of electricity and thus significantly reduce the costs. The paper proposes the prediction of energy consumption by a new time-series model. This involves time series models using a set of previously collected data to predict the future load. The most commonly used linear time series models are the AR (Autoregressive Model), MA (Moving Average) and ARMA (Autoregressive Moving Average Model). The AR model is used in this paper. Using the AR (Autoregressive Model) model, the Monte Carlo simulation method is utilized for predicting and analyzing the energy consumption change in the considered tobacco industrial plant. One of the main parts of the AR model is a seasonal pattern that takes into account the climatic conditions for a given geographical area. This part of the model was delineated by the Fourier transform and was used with the aim of avoiding the model complexity. As an example, the numerical results were performed for tobacco production in one industrial plant. A probabilistic range of input values is used to determine the future probabilistic level of energy consumption.

Indexing metric uncertain data for range queries and range joins

Range queries and range joins in metric spaces have applications in many areas, including GIS, computational biology, and data integration, where metric uncertain data exist in different forms, resulting from circumstances such as equipment limitations, high-throughput sequencing technologies, and privacy preservation. We represent metric uncertain data by using an object-level model and a bi-level model, respectively. Two novel indexes, the uncertain pivot B$$^{+}$$+-tree (UPB-tree) and the uncertain pivot B$$^{+}$$+-forest (UPB-forest), are proposed in order to support probabilistic range queries and range joins for a wide range of uncertain data types and similarity metrics. Both index structures use a small set of effective pivots chosen based on a newly defined criterion and employ the B$$^{+}$$+-tree(s) as the underlying index. In addition, we present efficient metric probabilistic range query and metric probabilistic range join algorithms, which utilize validation and pruning techniques based on derived probability lower and upper bounds. Extensive experiments with both real and synthetic data sets demonstrate that, compared against existing state-of-the-art indexes for metric uncertain data, the UPB-tree and the UPB-forest incur much lower construction costs, consume less storage space, and can support more efficient metric probabilistic range queries and metric probabilistic range joins.

Hadoop-based Probabilistic Range Queries of Moving Objects on Road Network

Hadoop-based Probabilistic Range Queries of Moving Objects on Road Network

S-MRST: a novel framework for indexing uncertain data

This paper studies the problem of probabilistic range query over uncertain data. Although existing solutions could support such query, it still has space for improvement. In this paper, we firstly propose a novel index called S-MRST for indexing uncertain data. For one thing, via using an irregular shape for bounding uncertain data, it has a stronger space pruning ability. For another, by taking the gradient of probability density function into consideration, S-MRST is also powerful in terms of probability pruning ability. More important, S-MRST is a general index which could support multiple types of probabilistic queries. Theoretical analysis and extensive experimental results demonstrate the effectiveness and efficiency of the proposed index.

The Quantitative Relationship Between ISO 15197 Accuracy Criteria and Mean Absolute Relative Difference (MARD) in the Evaluation of Analytical Performance of Self-Monitoring of Blood Glucose (SMBG) Systems.

The relationship between International Organization for Standardization (ISO) accuracy criteria and mean absolute relative difference (MARD), 2 methods for assessing the accuracy of blood glucose meters, is complex. While lower MARD values are generally better than higher MARD values, it is not possible to define a particular MARD value that ensures a blood glucose meter will satisfy the ISO accuracy criteria. The MARD value that ensures passing the ISO accuracy test can be described only as a probabilistic range. In this work, a Bayesian model is presented to represent the relationship between ISO accuracy criteria and MARD. Under the assumptions made in this work, there is nearly a 100% chance of satisfying ISO 15197:2013 accuracy requirements if the MARD value is between 3.25% and 5.25%.