Range Of Estimates Research Articles

Examining Whole Blood, Total and Free Plasma Tacrolimus in Elderly Kidney Transplant Recipients.

Therapeutic monitoring is routinely performed to ensure tacrolimus whole-blood concentrations fall within a predefined target. Despite this, patients still experience inefficacy and toxicity that could be related to variability in free (unbound) tacrolimus exposure. Therefore, the aim of this study was to compare tacrolimus-free plasma (Cu), total plasma (Cp), and whole-blood (Cwb) concentrations in adult kidney transplant recipients and to characterize tacrolimus disposition across different matrices. Twelve-hour concentration-time profiling was performed in 15 recipients, allowing simultaneous measurement of Cu, Cp, and Cwb. Pharmacokinetic parameters were estimated using noncompartmental analysis. The relationship between Cwb and Cp were examined using a capacity-limited binding model, incorporating the hematocrit fraction (fHCT) to estimate maximum binding concentration (Bmax) and dissociation constant (Kd). The relationship between Cp and Cu was evaluated using a linear binding model to estimate the nonspecific binding parameter (Nplasma). Nonlinear regression analysis was used to obtain estimates of Bmax, Kd, and Nplasma. A total of 195 paired Cwb, Cp, and Cu values were collected. The median ratios of Cwb:Cp, Cp:Cu, and Cwb:Cu were 9:1, 20:1, and 138:1, respectively. Variability in free plasma exposure was large; free trough values ranged from 8 to 51 ng/L and free area-under-the-concentration-time-curve values ranged from 424 to 7160 ng·h/L. Median (range) estimates of Bmax, Kd, and Nplasma were 90.4 µg/L (22.4-752.5 µg/L), 2.36 µg/L (0-69.2 µg/L), and 0.05 (0.035-0.085), respectively. The interindividual variability (CV%) in binding parameters was considerable (Bmax 117.2%; Nplasma 32.5%). Large variability was observed in tacrolimus-free plasma exposure and binding parameters. Future research to characterize the relationship between tacrolimus Cu and patient outcomes may be of benefit.

Joint range and velocity super-resolution estimation with Doppler effects for innovative OFDM-based RFA RadCom system

Conventional radar and communication signals face challenges when integrating for accurate sensing in dense electromagnetic environments, especially in scenarios involving high-velocity targets estimation. To address this issue, we propose the random frequency agile-orthogonal frequency division multiplexing-based radar and communication (RFA-OFDM-based RadCom) signal, a novel framework that combines RFA hopping radar signal and OFDM signal. This framework effectively handles high-velocity Doppler scenarios, enhancing electronic countermeasure capabilities. In high-velocity scenarios, achieving accurate range and velocity estimation is crucial. We introduce a comprehensive received signal model that considers intrapulse and intersubcarrier Doppler effects, often overlooked in traditional high-velocity contexts. The proposed two-phase hierarchical perceptual methodology enables joint super-resolution estimation using the shared signal. We transform the shared signal echo model into a uniform linear array-like model and employ the matrix decomposition algorithm based on bidirectional weighted frequency smoothing (BWFS-MD) for decoherence processing. Subsequently, the estimation of signal parameters via rotational invariance techniques (ESPRIT)-complementary integrated subspace fitting (E-CISF) algorithm accurately estimates joint range and velocity. Meanwhile, the contrastive analysis of the mutual impacts between radar and communication functions is conducted. Theoretical analysis and simulation results robustly validate the superior performance of the proposed BWFS-MD algorithm. Furthermore, considering the precision of joint range-velocity estimation, real-time constraints, and super-resolution capability (which is emphasized), the E-CSIF algorithm demonstrates the best overall performance from a comprehensive perspective.

Holistic Sensitivity Analysis for Long-Term Energy Demand Prediction of Battery Electric Vehicles

AbstractAccurate and robust range estimation algorithms for battery electric vehicles have the potential to reduce range anxiety, increase the acceptance of lower-range vehicles, and improve the overall driving experience. However, developing such algorithms faces challenges due to the complexity of the driver-vehicle-environment system and the multitude of factors influencing a vehicle's energy demand. To address these challenges, this paper introduces a sensitivity analysis focused on driver- and environment-related factors, which are notably difficult to predict. Employing a global sensitivity analysis for factor prioritization, this study delineates and assesses the parameters and their value distributions using a validated vehicle simulation model. The co-simulation of a powertrain and an auxiliaries model enables the parameter-specific investigation of parameters related to the thermal system. The results are scenario-individual parameter rankings that show the importance of the considered factors in prediction algorithms and guide the strategy for the development of these algorithms. The acceleration behavior of the driver, often emphasized in literature, is shown to be of secondary importance to energy consumption. Moreover, factors such as air density and wind speed are identified as crucial in highway driving scenarios, whereas outside temperature and the probability of stopping at traffic lights are critical in urban settings. For validation purposes, the resulting rankings of the sensitivity study are validated by means of a convergence analysis.

A comparison of home range estimates using the time local convex hull (T‐LoCoH) and minimum convex polygon (MCP) methods for African savannah elephants in a semi‐arid protected area

A comparison of home range estimates using the time local convex hull (T‐LoCoH) and minimum convex polygon (MCP) methods for African savannah elephants in a semi‐arid protected area

An High Efficiency Angle and Range Estimation Method for Three-Dimensional Near-Field Sources Based on Higher-Order Cumulants

An High Efficiency Angle and Range Estimation Method for Three-Dimensional Near-Field Sources Based on Higher-Order Cumulants

Heisenberg-Limited Quantum Lidar for Joint Range and Velocity Estimation.

We propose a quantum lidar protocol to jointly estimate the range and velocity of a target by illuminating it with a single beam of pulsed displaced squeezed light. In the lossless scenario, we show that the mean-squared errors of both range and velocity estimations are inversely proportional to the squared number of signal photons, simultaneously attaining the Heisenberg limit. This is achieved by engineering the multiphoton squeezed state of the temporal modes and adopting standard homodyne detection. To assess the robustness of the quantum protocol, we incorporate photon losses and detuning of the homodyne receiver. Our findings reveal a quantum advantage over the best-known classical strategy across a wide range of round-trip transmissivities. Particularly, the quantum advantage is substantial for sufficiently small losses, even when compared to the optimal-potentially unattainable-classical performance limit. The quantum advantage also extends to the practical case where quantum engineering is done on top of a strong classical coherent state with watts of power. This, together with the robustness against losses and the feasibility of the measurement with state-of-the-art technology, make the protocol highly promising for near-term implementation.

Source Range Estimation Using Linear Frequency-Difference Matched Field Processing in a Shallow Water Waveguide

Matched field processing (MFP) is an established technique for source localization in known multipath acoustic environments. Unfortunately, in many situations, imperfect knowledge of the actual propagation environment and sidelobes due to modal interference prevent accurate propagation modeling and source localization via MFP. To suppress the sidelobes and improve the method’s robustness, a linear frequency-difference matched field processing (LFDMFP) method for estimating the source range is proposed. A two-neighbor-frequency high-order cross-spectrum between the measurement and the replica of each hydrophone of the vertical line array is first computed. The cost function can then be derived from the dual summation or double integral of the high-order cross-spectrum with respect to the depth of the hydrophones and the candidate sources of the replicas, where the range that corresponds to the minimum is the optimal estimation. Because of the larger modal interference distances, LFDMFP can efficiently provide only one optimal range within the same range search interval rather than some conventional matched field processing. The efficiency of the presented method was verified using simulations and experiments. The LFDMFP unambiguously estimated the source range in two experimental datasets with average relative errors of 2.2 and 1.9%.

Estimation of Source Range and Location Using Ship-Radiated Noise Measured by Two Vertical Line Arrays with a Feed-Forward Neural Network

Machine learning-based source range estimation is a promising method for enhancing the performance of tracking both the dynamic and static positions of targets in the underwater acoustic environment using extensive training data. This study constructed a machine learning model for source range estimation using ship-radiated noise recorded by two vertical line arrays (VLAs) during the Shallow-water Acoustic Variability Experiment (SAVEX-15), employing the Sample Covariance Matrix (SCM) and the Generalized Cross Correlation (GCC) as input features. A feed-forward neural network (FNN) was used to train the model on the acoustic characteristics of the source at various distances, and the range estimation results indicated that the SCM outperformed the GCC with lower error rates. Additionally, array tilt correction using the array invariant-based method improved range estimation accuracy. The impact of the training data composition corresponding to the bottom depth variation between the source and receivers on range estimation performance was also discussed. Furthermore, the estimated ranges from the two VLA locations were applied to localization using trilateration. Our results confirm that the SCM is the more appropriate feature for the FNN-based source range estimation model compared with the GCC and imply that ocean environment variability should be considered in developing a general-purpose machine learning model for underwater acoustics.

Advancing spatial analysis of invasive species movement data to improve monitoring, control programs and decision making: feral cat home range as a case study

Context Many invasive animals are typically active across large areas, making monitoring and control programs expensive. To be efficacious, monitoring devices and control tools need to be strategically located to maximise the probability of encounter. This requires an understanding of how the target species uses the landscape, through identifying key habitat or landscape features that are preferred and used disproportionately more frequently by the species. Spatial analysis of animal movements can help identify high use areas. Aims The variability introduced by different range calculation methods can lead to uncertainty in subsequent habitat analyses. We aimed to determine which method is superior for accurate delineation of core areas for feral cats. Methods We analysed spatial data from 35 collared feral cats across four Australian study sites between 2016 and 2019, and compared the core areas generated using seven commonly used home range estimation methods. Key results We found that the α-hull method provided a higher precision of polygon placement, resulting in lower Type I and II errors and higher conformity to landscape features than other methods. The α-hull used a single default parameter and required no subjective input, making it a more objective, superior method. Conclusions We recommend that the α-hull method be used to define core activity areas for feral cats, enabling more robust habitat analysis, and identification of key habitat and landscape features to strategically target for monitoring and control programs. Implications This strategic approach could significantly improve cost efficiencies, particularly where existing management is widely dispersed, and core activity areas are clumped.

Factors shaping home ranges of Eurasian lynx (Lynx lynx) in the Western Carpathians

Understanding how large carnivores utilize space is crucial for management planning in human-dominated landscape and enhances the accuracy of population size estimates. However, Eurasian lynx display a large inter-population variation in the size of home ranges across their European range which makes extrapolation to broader areas of a species distribution problematic. This study evaluates variations in home range size for 35 Eurasian lynx in the Western Carpathians during 2011–2022 based on GPS telemetry and explains how intrinsic and environmental factors shape lynx spatial behaviour when facing anthropogenic pressure. The average annual home range size of lynx ranged from 283 (± 42 SE) to 360 (± 60 SE) km2 for males and from 148 (± 50 SE) to 190 (± 70 SE) km2 for females, depending on home range estimator (95% MCP, KDE and AKDE). Females with kittens had smaller annual and summer home ranges compared to non-reproducing females and subadults had smaller home ranges compared to adults. Lynx home range size was explained by availability of roe deer, except for summer, when alternative prey was likely available. We also found clear evidence of human-induced changes in lynx home range size, in particular, forest cover significantly decreased the home range size of male lynx during summer while road density led to an expansion of both annual and summer lynx home ranges. Lynx exhibited consistent fidelity to their home ranges throughout consecutive seasons, showing no seasonal variations. Strong territoriality was observed among competing males maintaining relatively low home range overlaps and considerable distances between centres of activity. The most pronounced tendency for association was observed between males and females, maintaining relatively close proximity year-round. The insights into lynx spatial requirements provided by our study will greatly enhance the accuracy of population size estimates and effectiveness of mitigation measures across the Western Carpathians.

Phylogenetic analyses show the Select Agent Coniothyrium glycines represents a single species that has significant morphological and genetic variation

ABSTRACT Soybean red leaf blotch (RLB), caused by the fungus Coniothyrium glycines, represents a foliar disease of soybean that is thus far restricted to Africa. The fungus is listed as a Select Agent by the Federal Select Agent Program because it could pose a severe threat to plant health were it to establish in the United States. Previous work uncovered tremendous molecular diversity at the internal transcribed spacer region, suggesting that there may be multiple species causing RLB. To determine whether multiple species cause RLB, we reconstructed the phylogeny of C. glycines and taxonomic allies using sequence data from four genes. We included 33 C. glycines isolates collected from six African countries and determined that all isolates form a well-supported, monophyletic lineage. Within this lineage there are at least six well-supported clades that largely correspond to geography, with one clade exclusively composed of isolates from Ethiopia, another exclusively composed of isolates from Uganda, and four composed of isolates from southern Africa. However, we did not detect any concordance for these clades between the four genes, indicating that all isolates included in this analysis are representative of a single species. Isolates in the Ethiopia clade are morphologically distinct from isolates in the other clades, as they produce larger sclerotia and smaller pycnida and more sclerotia in planta. Additionally, ancestral range estimations suggest that the C. glycines lineage emerged in southern Africa. These results show that there is significantly more genetic and morphological diversity than was initially suspected with this high-consequence fungal plant pathogen.

High Agreement Between 3D Motion Capture and 2D Video Analysis Systems and Raters for Knee Flexion Range of Motion During A Single Leg Drop Landing Task

OBJECTIVE: To determine the agreement between a 2-dimensional (2D) and 3-dimensional (3D) video capture system and between multiple raters during a single leg drop landing task. DESIGN: Cross-sectional observational study METHODS: Single leg drop landing trials of twenty-three males (n=12) and females (n=11) were recorded using the 2D and 3D systems. Reliability and agreement were quantified through intraclass correlation coefficients (ICC2, k), standard error of the measurement (SEM), and Bland-Altman plots. Analysis of variance (ANOVA) assessed systematic error between the 2D raters and 3D system. RESULTS: A one-way ANOVA on 35 drop-landing trials across raters detected a difference across the 3 estimates of knee flexion range of motion (p<0.001) where rater 1 did not differ (mean difference ± standard error: 1.15±0.48°, p=0.54) from the 3D system, rater 2 > 3D system (3.30±0.76°, p<0.001), and rater 1 and 2 differed (2.14±0.82°, p=0.04). There was high reliability between the two 2D raters (ICC2, k=0.89; SEM=3.44°). Rater 1 completed analyses on 87 additional trials (total n=117) and was comparable to the 3D system (0.87±0.47°, p=0.06; ICC2, k=0.90; SEM=3.52°). CONCLUSION: 2D video analysis can be used in a clinical setting where 3D capture of movement mechanics is not feasible.

Comparison and application of the far-field identification algorithms for multiple sound sources based on microphone array

Based on the conventional beamforming (CBF), some algorithms for far-field sound source identification have been proposed in the past few decades. Typically, the functional beamforming (FBF) and the deconvolution methods, such as CLEAN, CLEAN with source coherence (CLEAN-SC), CLEAN-SC with compressed grids (CLEAN-SC-CG), CLEAN with cross spectral matrix function (CLEAN-CSM), High-resolution CLEAN-SC (HR-CLEAN-SC), are frequently mentioned and their advantages are widely discussed in the previous literatures. To assess their efficacy and suitability in engineering applications, with a focus on spatial resolution, computational efficiency, and dynamic range, a comparative study by locating two types of sound sources is carried out. The first scenario represents the case where the distance between two sound sources is smaller than the Rayleigh limit, while the second scenario represents the situation involving multiple sound sources, such as four or more complex sound sources. The analysis demonstrates that CBF, CLEAN, and CLEAN-SC cannot surpass the Rayleigh limit. However, FBF, CLEAN-SC-CG, CLEAN-CSM, and HR-CLEAN-SC have the potential to overcome it. In FBF, the grid mismatch results in a compromise between its dynamic range and source strength estimation. Meanwhile, HR-CLEAN-SC requires prior knowledge of the number of sound sources, which is challenging in applications. Because of superiority in the fundamental acoustic image and searching strategy, CLEAN-CSM and CLEAN-SC-CG exhibits superior features compared to the others. By compressing the number of grids, the CLEAN-SC-CG can improve the computational efficiency up to at least 46%. By constructing the cross spectral matrix function related to the real source, CLEAN-CSM uses the power function to simultaneously enhance the spatial resolution, dynamic range and source strength estimation. The conclusions are further validated through sound-source identification experiments involving two loudspeakers and an engine. The findings presented in this paper serve to guide the selection of suitable approaches for multi-sound source identification in engineering applications.

Cluster consensus for nonlinear multi‐agent systems restricted with input saturation by event triggered control

AbstractThis article studies the cluster consensus problem for the nonlinear multi‐agent systems subject to input saturation under the context of event triggered control. Taking ordinary consensus object as a special case, this article considers the problem of cluster consensus. The event triggered mechanism is designed and the Zeno phenomenon is ruled out. The sufficient conditions of control protocol and range estimation of domain of attraction are formulated by the linear matrix inequalities for the constraints of input saturation and nonlinear terms existed in the system dynamics. Finally, the proposed results are validated by one example in the background of spring damping system.

Contribution of environmental DNA toward fungal Red Listing

In navigating the biodiversity crisis, a major uncertainty is the conservation status of inconspicuous, yet megadiverse and functionally crucial, soil organisms. Massive datasets on soil biota are accumulating through molecular sampling approaches, but to date these datasets have provided only limited input into conservation planning and management. We investigated how environmental DNA (eDNA) data of soil macrofungi contribute to regional Red List assessments, which are currently based on fruiting bodies (hereafter, fruit‐bodies). In our test region of Estonia (northern Europe), which contained ~15,000 fruit‐body records for 1583 assessed species, an average soil sample increased the range estimates of Threatened and Near Threatened fungal species by 0.18%. Five hundred soil samples almost doubled their known localities and added 19% previously unrecorded species. However, even after accumulating >1000 soil samples, about half of the Threatened and Near Threatened species known by fruit‐bodies remained undetected through eDNA techniques. Effective conservation assessment of soil fungi thus requires both fruit‐body and eDNA data; therefore, special efforts are needed to make these data available to conservationists.

Bayesian inference of the spatial distribution of steel corrosion in reinforced concrete structures using corrosion-induced crack width

Observations of corrosion-induced crack widths offer crucial information about the corrosion states of steel reinforcements in reinforced concrete (RC) structures, enabling a cost-effective method for inferring corrosion states through inverse analysis. However, the uncertainty associated with the relationship between corrosion-induced cracking and steel weight loss necessitates a probabilistic inference method, especially when considering the spatial distributions of steel weight loss, which provides important information to estimate the load-bearing capacity loss of corroded RC structures. This paper proposes a Bayesian framework to infer the steel weight loss distribution in RC structures based on the observed corrosion-induced crack width. To reduce the dimensions of the Bayesian inference, a Karhunen-Loève transform is applied to extract the principal distribution features of the steel weight loss. The forward model of the Bayesian inference adopts a data-driven sequence-to-sequence transduction approach to predict corrosion-induced crack width from steel weight loss. This model incorporates a novel nonlinear convolution kernel for input encoding and a sparse polynomial chaos expansion for decoding, which proves more accurate and efficient than finite element simulations. The Hamiltonian Markov chain Monte Carlo (HMCMC) sampler is used to efficiently sample from the posterior distribution of the Bayesian inference. The case study of the proposed method demonstrated that Bayesian inference provides robust range estimation for the steel weight loss distribution, with its 95% confidence interval encompassing most observations. Additionally, the method efficiently inferred high-dimensional steel weight loss sequences up to 61 dimensions, taking advantage of the dimension reduction technique and the gradient-informed HMCMC sampler.

Thermodynamic analysis of lined rock caverns for initial inflation and cyclic operational conditions in compressed air energy storage

With the irreversible trend towards cleaner and lower carbon energy alternatives on a global scale, the Lined Rock Cavern (LRC) compressed air energy storage technology emerges as a promising solution, offering vast prospects for large scale energy storage. This study explores the thermodynamic behaviors that arise from complex factors under high-frequency charging and discharging conditions in compressed air energy storage. It comprehensively considers transient gas flow, heat transfer, and real air properties, employing Computational Fluid Dynamics (CFD) methods. The simulations and analyses focus on the temperature and pressure variations inside the cavern during the initial inflation and cyclic operational conditions, along with the heat transfer characteristics of the sealing layer steel plate, concrete lining, and surrounding rock. As revealed by the research results, the inlet temperature and the air mass flow rate are two key factors affecting the average temperature inside the cavern during the initial inflation. Specifically, the inlet temperature shows a linear relationship with the average temperature while the air mass flow rate exhibits a nonlinear relationship. By fitting the simulation results, expressions that enable effective control of the average temperature are obtained. Under cyclic operational conditions, the pressure inside the cavern fluctuates within the range of 6 MPa to 10 MPa, displaying regular oscillations. With an increase in the number of cycles, the average temperature inside the cavern exhibits a decreasing trend, eventually stabilizing within a fixed range of fluctuations, indicating that the cavern has reached a steady state. The temperature of the steel plate and concrete lining undergoes significant changes throughout the entire operational cycle, influenced by both convective heat transfer and thermal conduction. With an increase in the number of cycles, their temperature variations align with the changes in the average temperature inside the cavern. In contrast, the temperature of the surrounding rock fluctuates within a narrow range throughout the process. The research findings of this study enable a more accurate estimation of the temperature variation range inside the cavern, facilitating the optimization of cavern design and providing crucial guidance for the application of lined rock caverns compressed air energy storage systems.

Marine Fish Movement: home range sizes for commercially relevant species

Estimates of home range sizes for marine fishes are essential for designing and assessing the effects of spatial wildlife conservation policies and management interventions. However, in situ studies of marine species movement are challenging and often expensive, resulting in a paucity of data on the home range size of the vast majority of marine fishes. Here, we develop a set of new datasets, which we have collectively named Marine Fish Movement, that synthesises published empirically evaluated home ranges reported for adult marine fishes that interact with fisheries and leverage these data to estimate home range sizes for unstudied species. The empirical data contain estimated home range sizes (km2) for 193 species across 63 family groups from 179 studies published between 1971 and 2022. We use a random forest regression model to estimate home range sizes (km2) for 664 fished marine species currently lacking home range estimates. Marine Fish Movement can inform spatial interventions including the design and management of marine protected areas and dynamic fisheries management to meet sustainability goals.

Performance Improvement Approach to Naval Gun Fire Control System Based on Linear Target Tracking Filter with Radar Line-of-sight Measurements

This paper addresses a novel approach to performance enhancement of the naval gun fire control system(FCS) by using the projectile tracking filter without any distortion of radar measurements. Under the assumption that the maneuvering between the projectile and the ship equipped with the radar is not quite large, this method is based on the concept of polar-coordinate target tracking, which separates the range estimation filter and the direction cosine estimation filter. Note that using polar-coordinates allows tracking to be performed in the same coordinate system from which the radar line-of-sight(LOS) measurements are obtained, unlike the conventional tracking process in Cartesian. Also, it is easy to implement in real-time and guarantees consistent estimates due to its linear filter structure. With the help of the above method, therefore, the proposed filter is able to improve the overall performance of FCS which requires stability of projectile estimates within a short engagement time. The effectiveness of the presented scheme is validated through computer simulations.

Ocean weather, biological rates, and unexplained global ecological patterns.

As on land, oceans exhibit high temporal and spatial temperature variation. This "ocean weather" contributes to the physiological and ecological processes that ultimately determine the patterns of species distribution and abundance, yet is often unrecognized, especially in tropical oceans. Here, we tested the paradigm of temperature stability in shallow waters (<12.5 m) across different zones of latitude. We collated hundreds of in situ, high temporal-frequency ocean temperature time series globally to produce an intuitive measure of temperature variability, ranging in scale from quarter-diurnal to annual time spans. To estimate organismal sensitivity of ectotherms (i.e. microbes, algae, and animals whose body temperatures depend upon ocean temperature), we computed the corresponding range of biological rates (such as metabolic rate or photosynthesis) for each time span, assuming an exponential relationship. We found that subtropical regions had the broadest temperature ranges at time spans equal to or shorter than a month, while temperate and tropical systems both exhibited narrow (i.e. stable) short-term temperature range estimates. However, temperature-dependent biological rates in tropical regions displayed greater ranges than in temperate systems. Hence, our results suggest that tropical ectotherms may be relatively more sensitive to short-term thermal variability. We also highlight previously unexplained macroecological patterns that may be underpinned by short-term temperature variability.