Range Of Estimates Research Articles

Impact of jittering on raster- and distance-based geostatistical analyses of DHS data

Fine-scale covariate rasters are routinely used in geostatistical models for mapping demographic and health indicators based on household surveys from the Demographic and Health Surveys (DHS) program. However, the geostatistical analyses ignore the fact that GPS coordinates in DHS surveys are jittered for privacy purposes. We demonstrate the need to account for this jittering, and we propose a computationally efficient approach that can be routinely applied. We use the new method to analyse the prevalence of completion of secondary education for 20-49 year old women in Nigeria in 2018 based on the 2018 DHS survey. The analysis demonstrates substantial changes in the estimates of spatial range and fixed effects compared to when we ignore jittering. Through a simulation study that mimics the dataset, we demonstrate that accounting for jittering reduces attenuation in the estimated coefficients for covariates and improves predictions. The results also show that the common approach of averaging covariate values in windows around the observed locations does not lead to the same improvements as accounting for jittering.

Tectonically driven climate change and the spread of temperate biomes: Insights from dragon pseudoscorpions (Pseudotyrannochthoniidae), a globally distributed arachnid lineage

AbstractAimUnderstanding the historical biogeography of the Earth's oldest terrestrial lineages provides insights into lineage diversification in relation to plate tectonics, climate change and biome shifts at maximum timescales. We investigate the biogeography of an ancient arachnid family, dragon pseudoscorpions, which are found today in mesic (mostly temperate) forests on all continents except Antarctica and Europe, have potential origins on Pangea and comprise species with extremely limited dispersal capacities. We evaluate the respective role of continental vicariance (abiotic) and biome shifts (biotic) deep in time and unravel the evolutionary history of this ancient group.LocationGlobal.TaxonPseudotyrannochthoniidae (Arachnida: Pseudoscorpiones).MethodsFive loci were sequenced for 75 Pseudotyrannochthoniidae samples collected across the globe. A matrix was compiled comprising 106 terminals and ~8800 bp, and phylogenetic analyses were performed to uncover relationships. Divergence time and ancestral range estimation analyses were used to reconstruct historical biogeography.ResultsPseudotyrannochthoniidae was monophyletic with high support but relationships among genera did not reflect current taxonomy and instead showed geographical structuring. Pseudotyrannochthoniidae originated in East Asia during the Middle Triassic and began diversifying in the Early Cretaceous.Main ConclusionsDiversity and distributional patterns of dragon pseudoscorpions can be explained by the interplay of continental vicariance through Pangaean breakup, and biome shifts via the spread of temperate habitats in the Cretaceous and their ongoing distributional fluctuations. Pseudotyrannochthoniidae diversification began in the Early Cretaceous as they dispersed with temperate forests across the Northern Hemisphere. Cretaceous vicariance, followed by widespread Holarctic extinction in the Late Tertiary–Quaternary, created their disjunct distribution at northern latitudes. Northern and Southern Hemisphere lineages diverged as Gondwana and Laurasia rifted, and Gondwanan breakup resulted in Afrotropical/Indomalayan and Austral clades. Austral lineages spread with temperate forests, however, cooling caused their disappearance from Antarctica and led to disjunct distributions at low latitudes.

Relative navigation with Bayesian range estimation

The applications of multiple unmanned aerial vehicles (UAVs) require high-precision and reliable relative navigation for flight formation, collision avoidance, and mission performance. Providing high-precision relative navigation is possible by fusing the observations of inertial measurement units, global navigation satellite system receivers, and radio-ranging modules. However, radio-ranging is vulnerable to various types of interference, including additive noise, nonlinear propagation, and multi-user interference. At the same time, UAVs strict energy constraints force us to look for ways to increase interference immunity without increasing the transmitting power of radio-ranging modules. This work is about improving the noise immunity of relative navigation by using a scheme tightly coupled with radio ranging and Bayesian range estimation. We describe the proposed scheme and also carry out a comparative simulation of tightly coupled and conventional, loosely coupled schemes. According to the simulation, a tightly coupled scheme has a quarter smaller RMS error compared to a loosely coupled one at an SNR below -7 dB. The obtained results demonstrate the prospects for further development of the proposed scheme.

Потенциальные возможности узкополосной радиолокации с пространственной модуляцией сигналов

The methods of radar with narrow-band radiation by switching the antenna pattern around the equi-signal zone or in orthogonal planes are considered, which significantly (by tens of dB) reduce the power of the transmitter by narrowing the receiver bandwidth. The range to the target is determined by calculating the phase difference of the received signal at the switching frequency of the reference signal of this frequency by a stabilized quartz oscillator. It is shown that it is possible to increase the accuracy of measuring the range by introducing two scales: coarse, determined by the switching frequency, the wavelength of which corresponds to a double range to the target, and accurate, with a higher switching frequency.Since the required radiation power of the transmitter in the narrowband case is determined by the noise immunity of phase measurements, then at a given range of the system, by increasing the power of the transmitter, it is possible to reduce the error of measuring the range. As is known, in the pulsed radar method, the accuracy of the range estimation is limited by the duration of the emitted pulse and practically does not depend on the power of the transmitter. For this reason, the radars used do not allow for the accuracy of estimating their coordinates better than tens of meters. A possible solution to such a problem is the use of continuous signals with partial modulation, but requiring a wide bandwidth of receivers determined by frequency deviation, which significantly reduces the noise immunity of the system. The disadvantage of the narrowband radar method described below with spatial signal modulation is the inability to distinguish targets located at the same azimuth. However, in a number of tasks, for example, when used in altimeters, over-the-horizon radar, high-precision radio geodesic measurements, etc., the radar options described in the article make it possible to increase the accuracy of measuring the range not achieved by other methods. The main advantage of the new method is: stealth; high noise immunity; minimum radiation power of the transmitter; the ability to quickly change the carrier frequency, independence from the speed of movement of the target, since the measurement of the signal phase is determined only by the switching frequency.

Purposeful co-design of OFDM signals for ranging and communications

This paper analyzes the fundamental trade-offs that occur in the co-design of pilot resource allocations in orthogonal frequency-division multiplexing signals for both ranging (via time-of-arrival estimation) and communications. These trade-offs are quantified through the Shannon capacity bound, probability of outage, and the Ziv–Zakai bound on range estimation variance. Bounds are derived for signals experiencing frequency-selective Rayleigh block fading, accounting for the impact of limited channel knowledge and multi-antenna reception. Uncompensated carrier frequency offset and phase errors are also factored into the capacity bounds. Analysis based on the derived bounds demonstrates how Pareto-optimal design choices can be made to optimize the communication throughput, probability of outage, and ranging variance. Different pilot resource allocation strategies are then analyzed, showing how Pareto-optimal design choices change depending on the channel.

Acoustic estimates of sperm whale abundance in the Mediterranean Sea as part of the ACCOBAMS Survey Initiative

Acoustic surveys for sperm whales (Physeter macrocephalus) were conducted in the Mediterranean Sea in summer 2018 as part of the vessel-based component of the ACCOBAMS Survey Initiative (ASI). Equal-spaced zigzag transects provided uniform coverage of key sperm whale habitats and were surveyed using a towed hydrophone array deployed from a research vessel at speeds of 5-8 knots. A total of 14,039 km of tracklines were surveyed in the western basin, Hellenic Trench and Libyan waters, with an acoustic coverage of 10% realised for sperm whales. During these surveys, 254 individual sperm whales were detected on the trackline, with an additional 66 individuals off-track. Sperm whales were only seen ten times on-track, with an additional 16 off-track sightings. Estimates of slant range to echolocating whales were used to derive density estimates through both design- and model-based distance sampling methodologies. An acoustic availability of 0.912 (sd = 0.036) was derived from via published models. When correcting for availability bias, a design-based abundance estimates of 2,673 individuals (95% CI 1,739-4,105; CV = 0.21) was derived for the surveyed blocks, which incorporated most known sperm whale habitat in the Mediterranean Sea. The equivalent model-based estimate was 2,825 whales (2,053-3,888; CV = 0.16). Over 97% of detected whales were in the western basin, with highest densities in the Algerian and Liguro-Provencal Basins between Algeria and Spain/France. In the eastern basin, detections were sparse and concentrated along the Hellenic Trench. A density surface modelling (DSM) exercise identified location and benthic aspect as being the most instructive covariates for predicting whale abundance. Distance sampling results were used in a power analysis to quantify the survey effort required to identify population trends. In the most extreme scenario modelled (10% per annum decline with decennial surveys), the population could have dropped by 90% before the decline was identified with high statistical power. Increasing the regularity of surveys would allow population trends to be detected more expediently. Mediterranean sperm whales are listed as Endangered on the IUCN’s Red List and the need for urgent conservation measures to reduce injury and mortality remains paramount for this unique sub-population.

Performance analysis of joint range and velocity estimation in OFDM-IM-based RadCom system

Modulation symbol domain (MSD) method is usually applied in range and velocity estimation for the radar-communication (RadCom) shared system based on orthogonal frequency-division multiplexing with index modulation (OFDM-IM). Nevertheless, the deterioration of sidelobe characteristics significantly impairs the efficacy of both range and velocity estimation. In this paper, we propose a modulation symbol domain-spatial sparsity-compressed sensing (MSD-SS-CS) method to realize super resolution joint range and velocity estimation for the RadCom shared system based on OFDM-IM. The compressed sensing theory is used in MSD method to solve the problem of detection targets that meet spatial sparsity application scenarios. Simulation outcomes illustrate that the bit error rate (BER) performance of the RadCom shared system, leveraging OFDM-IM, surpasses that of the RadCom shared system relying solely on OFDM. The proposed MSD-SS-CS method not only solves the degradation of sidelobe caused by blank sub-carriers in the RadCom shared system based on OFDM-IM, but also achieves good super resolution joint range and velocity estimation.

Non-stationary load measurement using six-component strain-gauge balances

A novel method using six-component strain-gauge balances has been proposed to solve an important mechanics problem in aircraft model wind tunnel testing – measuring the non-stationary force and moment vectors. A physical model is at the method’s core: the sensitive elements of each balance component are replaced by damped springs. This model measures arbitrary non-stationary loads over the entire frequency range of the balance, including the natural frequency. The problem is solved for the general case of a non-inertial balance coordinate system. The standard deviation of the proposed method for measuring non-stationary loads did not exceed 2.8%. The proposed method solves a second problem – measuring object mass and the positions of its centre of mass and moments of inertia with respect to three mutually perpendicular axes. The method is experimentally verified. An estimation of the frequency range of the strain-gauge balance for measuring non-stationary loads was made using the developed method.

Aggravation of global maize yield loss risk under various hot and dry scenarios using multiple types of prediction approaches

AbstractHigh temperature and drought are widely known to cause a reduction of crop yield, but the simultaneously occurring risks in major producing countries and the associated uncertainty across various climate change scenarios remain unclear at the global scale. Here, we evaluate global maize yield loss risk (i.e., the probability of yield reduction by over 10% relative to historical trend yield during 1981–2010) across 30 hot and dry scenarios using regression, machine learning and process‐based models. Besides examining yield loss risk in a single country, we predict the potential risks simultaneously occurring in the top two and top ten producing countries. The three approaches agree on the aggravation of yield loss risk under dry and hot scenarios, but show large discrepancy in the magnitude and sensitivities. Specifically, 2°C warming alone could lead to a global yield loss risk of 73%, 100% and 62% based on regression, long‐short term memory (LSTM) and process‐based models, respectively, and warming‐induced risks can be further aggravated by droughts especially in process models. Global yield loss by over 10% would even become the new norm (i.e., yield loss probability is 100%) when temperature increases by over 2°C in some models. Importantly, the probabilities of yield loss simultaneously occurring in the top two countries (i.e., United States and China) and top ten countries are unexpectedly high and could even become 100% under extreme hot and dry scenarios. Our results highlight the large risks that future climate change may bring to multiple exporting and importing countries simultaneously, thus threating global food market and security. We also emphasize the important value of using different types of prediction approaches for yield projection under hot and dry scenarios, which enables more realistic estimation of uncertainty range than a single type of model.

How does an urban landscape influence spatiotemporal ecology of South American coatis (Nasua nasua)?

Abstract Increasing urbanization and consequent habitat fragmentation and loss call for studies of the influence of urbanization on native fauna. South American Coati (Procyonidae: Nasua nasua) live in groups and are common in urban areas of South America. Here, we aimed to understand how coatis occupy and select space, and whether they change their circadian activity in an urban environment. We estimated home range sizes, verified any overlap, and evaluated the effects of sex and weight on home range size. We also described coati activity patterns associated with habitat selection. Animals established their home ranges only within the study area, with few exploratory exits to surrounding areas. Furthermore, our home range estimates of 0.66 km2 are lower than previously found in natural habitats. We did not observe an effect of sex or weight on home range size. Coatis predominantly selected forested areas and explored houses only early in the morning. We suggest that this demonstrates the need for forested areas for survival of the species. Regarding activity patterns, coatis were active only during the day, as described for the species in its natural environment. Human barriers—in addition to human refuse that serves as food—could reduce the home range sizes. Our data demonstrate differences in home range size, but not circadian activity or habitat selection when compared to coatis in their natural environment.

Zero-emission Fueling Infrastructure for IWT: Optimizing the Connection between Upstream Energy Supply and Downstream Energy Demand

A key challenge in the energy transition for Inland Water Transport is the functional design of bunker networks and first-order dimensioning of individual bunker stations. A fundamental ingredient for this is an improved understanding of how upstream energy supply (‘well-to-bunker-station’) and downstream demand (‘bunker-station-to-tank’) may interconnect. In this paper we discuss an approach to the design of bunkering networks that takes logistic modelling to estimate network scale energy demand as a starting point. Depending on the vessels that use the network and the anticipated fuel mix for the overall fleet, logistical modelling may be used to estimate the magnitude of the energy demand along the network. Estimates of the operational range of vessels per energy carrier help to estimate maximum bunker station inter-distances. Insight into the potential supply chains that connect the source of each energy carrier to a physical bunker facility is needed to close the loop. Energy carriers may be needed on board in a gaseous or liquid form, or in the form of electrons. Transfer may take place in the form of loading (e.g., filling the fuel tank, charging the battery pack) or swapping (e.g., exchanging fuel containers, exchanging battery containers). Depending on the energy carrier, transfer method(s) and demand quantities, functional designs of bunker stations (in terms of required system elements and their order-of-magnitude dimensions) can be made. Depending on service level requirements both the dimensions of individual bunker stations and their spread over the network may be optimized. Key contribution of this work is a thorough overview of aspects that play a role in the design of bunker infrastructure for the decarbonisation of inland shipping. Based on this overview steps for further research are recommended.

Temporal variation of tritium concentration in monthly precipitation collected at a Difficult-to-Return Zone in Namie Town, Fukushima Prefecture, Japan.

This article discusses tritium concentrations in monthly precipitation in part of the Difficult-to-Return Zone in Namie Town during 2012-2021. The tritium concentrations, which were measured with a low background liquid scintillation counter after carrying out an enrichment procedure, fluctuated seasonally from 0.10 ± 0.02 to 0.85 ± 0.02Bq L-1. This range of concentrations is concluded to not be unusual based on comparisons with the concentrations at other sites and estimates of the past range of the concentrations. Moreover, no significant variations in observed tritium concentrations were observed due to decommissioning work at the Fukushima Dai-ichi Nuclear Power Plant. These results contribute to understanding the background level of tritium concentration in precipitation before the oceanic discharge of treated water from the Fukushima plant. In addition, this article evaluates the amount of tritium supplied to the ocean by terrestrial rainwater pouring into the Pacific Ocean via Ukedo River, which flows through Namie Town; this information will contribute to the discussion on the impact of the oceanic discharge of treated water.

Machine learning-based surrogates for eVTOL performance prediction and design optimization

<p>Predicting the performance of different electric vertical take-off and landing (eVTOL) vehicle designs is paramount to vehicle manufacturers and hobbyists. These vehicles' maximum flight time (endurance) and maximum flight distance (range) depend on design and operational parameters relating to their structure, propulsion system, payload, and mission profile. In recent years, sophisticated physics-based models have been developed to estimate and optimize their aerodynamic, propulsion, and electrical performance. Integrating and simulating those models can closely estimate a vehicle's endurance and range. However, this demands advanced knowledge of different subsystems utilized and extensive computational resources limiting the wide-scale utilization of such models. This paper showcases the development and implementation of a framework to train simpler machine learning-based surrogates. The surrogate models are trained on a limited number of eVTOL performance estimates generated by physics-based models and can mimic them accurately. Forty-seven thousand eVTOL vehicle designs were simulated to generate the training data for various machine-learning models. These include several decision tree models, K-nearest neighbor models, linear regression models, and a multi-perceptron neural network model. Vehicle design and operational parameters such as propeller size, payload mass, drag coefficient, velocity, and motor and battery parameters are used as features, and vehicle endurance and range estimates are used as targets. Compared to the alternative approaches, these surrogate models are computationally very efficient and easy to understand and use. Testing on hold-out datasets shows excellent performance, with multiple models having a mean average percentage error of less than 2% in estimating vehicle endurance and range.</p>

Near-Field Beam Training: Joint Angle and Range Estimation with DFT Codebook

Near-Field Beam Training: Joint Angle and Range Estimation with DFT Codebook

Joint Estimation of DOA and Range for Near-Field Sources in the Presence of Far-Field Sources and Alpha-Stable Noise

Joint Estimation of DOA and Range for Near-Field Sources in the Presence of Far-Field Sources and Alpha-Stable Noise

Improving Efficiency of Iso-Surface Extraction on Implicit Neural Representations Using Uncertainty Propagation.

Implicit Neural representations (INRs) are widely used for scientific data reduction and visualization by modeling the function that maps a spatial location to a data value. Without any prior knowledge about the spatial distribution of values, we are forced to sample densely from INRs to perform visualization tasks like iso-surface extraction which can be very computationally expensive. Recently, range analysis has shown promising results in improving the efficiency of geometric queries, such as ray casting and hierarchical mesh extraction, on INRs for 3D geometries by using arithmetic rules to bound the output range of the network within a spatial region. However, the analysis bounds are often too conservative for complex scientific data. In this paper, we present an improved technique for range analysis by revisiting the arithmetic rules and analyzing the probability distribution of the network output within a spatial region. We model this distribution efficiently as a Gaussian distribution by applying the central limit theorem. Excluding low probability values, we are able to tighten the output bounds, resulting in a more accurate estimation of the value range, and hence more accurate identification of iso-surface cells and more efficient iso-surface extraction on INRs. Our approach demonstrates superior performance in terms of the iso-surface extraction time on four datasets compared to the original range analysis method and can also be generalized to other geometric query tasks.

Reliability and Validity of Visual Estimation in Determining Thorax Rotation Mobility using the Quadruped Lumbar-Locked Position.

Thoracic rotation mobility is crucial for athletes in rotational sports such as baseball, golf, and swimming to maintain the proper biomechanics associated with the sport. Accurate differentiation between normal mobility and active and passive physiological deficits in the thoracic region is critical for identifying the need for intervention to the thorax. To establish the reliability and discriminant validity of visual estimation of thorax rotation range of motion across clinicians of differing experience levels in determining normal mobility and active or passive physiological deficits when utilizing the quadruped lumbar-locked position. Cross-sectional. Thirty-eight subjects (21 female, 17 male) with a mean age of 27 years ± 6.67 were assessed with the quadruped lumbar-locked thorax rotation test by three examiners with various clinical experience in real-time and again one week later. Bilateral active and passive lumbar-locked thorax rotation mobility was assessed by all raters and categorized as "Unrestricted" (≥50°) or "Restricted" (<50°) while a research assistant simultaneously measured the motion with a digital inclinometer. All raters were blinded to the results. All results were analyzed for intra-rater reliability and agreement. Test-retest intra-rater reliability ranged from 0.55-0.72 and percent absolute agreement ranged from 0.82-0.89. Inter-rater reliability ranged from 0.45-0.59 while percent absolute agreement between raters ranged from 0.74-0.84. There was a significant difference in range of motion between "Unrestricted" and "Restricted" categories for both active (Unrestricted=54.6-58.9; Restricted=40.4-44.4; p<0.001) and passive motion (Unrestricted=61.3-63.5; Restricted=39.2-39.7; p<0.001). The only interaction effect was for passive left rotation [Rater A Restricted x ® =34.3(30.4-38.2); Rater C Restricted (x ) ®=43.8(41.3-46.4); p=.000]. The quadruped lumbar-locked thorax rotation test demonstrates moderate to substantial test-retest intra-rater and inter-rater reliability regardless of clinical experience. The quadruped lumbar-locked thorax rotation test can accurately discriminate between individuals with active and passive physiological deficits regardless of rater experience using visual estimation. 3b.

Insight into melting point differences of dinitroimidazoles and dinitropyrazoles from the perspective of intermolecular interactions.

Dinitroimidazole (DNI) and dinitropyrazole (DNP), along with their congeners, possess similar molecular structures but exhibit distinct melting points. To analyse and elucidate the fundamental reasons for property differences from the perspective of intermolecular interactions, we proposed a simplified approach named binding energy in clusters (BEC) in computing weak interactions within complex crystal systems. Based on the results of the symmetry-adapted perturbation theory (SAPT) calculations, an approximate estimation of the melting point range can be derived by taking into account the cumulative effect (energy of electrostatic, dispersion and induction terms) and repulsive effect (energy of exchange term) values. We have also proposed a formula for calculating the specific melting point, which indicates that stronger intermolecular interactions have a major impact on the melting point, while the distribution of weak interactions also affects the melting point. This work would provide an effective reference for molecular design and structure-performance analysis.

Dual attention-guided self-adaptive aware cascade network for multi-view stereo and stereo matching

Extensive studies have been conducted on multi-view stereo and stereo matching for 3D reconstruction, whereas relatively few methods have been proposed for a large-scale environment. The difficulty of producing high-resolution depth/disparity maps is one of the main reasons. In this paper, we propose a dual attention-guided self-adaptive aware cascade network (DAscNet) that achieves state-of-the-art results for generating high-resolution depth/disparity maps of complex scenes by introducing a cascade inference strategy using a set of input views. A pyramid cost volume fusion and a self-adaptive cost volume cascade are built upon a dual attention-guided context multi-scale feature extraction encoding geometric, spatial and contextual information at gradually finer scales to achieve robust structural representation for predictions. The dual attention-guided context multi-scale feature extraction is made up of two distinct modules that are both based on the attention mechanism. In the pyramid cost volume fusion, an inter-cost attention aggregation module fuses multiple low-resolution dense cost volumes to achieve a robust structural representation for initial predictions. In the self-adaptive cost volume cascade, a changeable depth/disparity range estimation module is employed to alter the depth/disparity searching range interval of following stage based on the prediction information from the previous stage. This module can drive the network to gradually deal with complicated matching ambiguities and make better the accuracy of depth/disparity searching range interval prediction. Experiments on two publicly available datasets, the Tanks and Temples dataset and the DTU dataset, show that DAscNet outperforms prior work. The effectiveness of our proposed method is also supported by statistics on the accuracy, runtime, and GPU memory of other representative methods.

Energy Efficiency Analysis and Research of Pure Electric Vehicles under Actual Working Conditions

Pure electric vehicles are a hot topic in today’s social development. From beginning to end, the energy density of power batteries in pure electric vehicles is generally low, resulting in short range, inaccurate range estimation, and driver anxiety. This is also one of the reasons that hinder the promotion of pure electric vehicles. This article discusses the energy consumption of pure electric vehicles based on actual road tests. By comparing the battery energy efficiency of various roads (including urban roads, suburban roads, and highways) using electric vehicles from Guangzhou to Qingyuan, the energy consumption patterns of pure electric vehicles are analyzed. Experiments have shown that continuously improving the energy density of power batteries is the key technology to solve the problem of insufficient range for electric vehicles. Based on the existing power battery technology, this study investigates the relationship between energy consumption and various factors affecting electric vehicles, which can provide better reference for drivers to predict energy consumption and range.