Estimation Of Capacity Research Articles

Hand fatigability, self-reported fatigue, and functionality in individuals with multiple sclerosis: A cross-sectional study

BackgroundThe individual experience of fatigue and fatigability in individuals with Multiple Sclerosis (MS) can vary greatly, beyond the high prevalence of fatigue in MS. Although fatigue is known as a common symptom that affects and potentially limits individuals with MS, it has recently been determined that fatigability also causes consequences that limit individuals’ daily lives. The purpose of this study was to compare the associations between self-reported levels of fatigue, measured fatigability, and estimates of functional capacity in people with MS and sex- and age-matched healthy individuals. MethodsTwenty-three individuals with Relapsing-Remitting MS (RRMS) diagnosis and 23 age- and sex-matched healthy individuals were included in the study. To examine the fatigability level Dynamic and Static Fatigue Index were used for gross and pinch-grip, and manual dexterity and functionality levels the Scale for the Assessment and Rating of Ataxia (SARA), Nine Hole Peg Test (NHPT), and Dexterity Questionnaire-24 (DextQ-24) were used. While the Fatigue Severity Scale (FSS) and Fatigue Impact Scale (FIS) were used to examine self-reported fatigue, the Beck Depression Inventory (BDI) was used to assess emotional status. ResultsThere was no difference between RRMS and healthy individuals in terms of fatigability levels (p > 0.05). While the relationship between Static and Dynamic Fatigue Index gross grip fatigability and FSS and FIS was not found to be statistically significant, the relationship between non-dominant side pinch grip Static Fatigue Index and FSS and FIS was significant. In addition, the relationship between the non-dominant side gross grip Static Fatigue Index and the DextQ-24 dressing and daily activities subsections, and the dominant side pinch grip Dynamic Fatigue Index and the television/compact disk/digital video disk subsection of DextQ-24 was significant (p < 0.05). ConclusionFatigability was related to daily life upper limb use for gross grip and self-reported fatigue for pinch grip in individuals with RRMS. It was concluded that future studies focusing on hand fatigability could also consider manual dexterity and self-reported fatigue in individuals with MS from the early-stage. Clarifying the relationship of between fatigability and self-reported fatigue to functioning will allow clinicians to plan more appropriate and directed treatment approaches for individuals with RRMS. Trial registrationClinicalTrials.gov NCT05880745.

Total capacity and LOS estimation of innovative and conventional roundabouts through macroscopic fundamental diagrams

Traffic congestion remains a significant problem, especially in urban areas. To avoid congestion phenomena, several measures can be adopted, including the construction of roundabouts and the control of the measures of effectiveness (MOEs) at intersections. This article compares different types of roundabouts by estimating the deterministic fundamental diagram (MFD) and using microscopic traffic simulations. The deduction of MFD for different roundabout types (single and double-lane, flower and turbo-roundabout) is based on reasonably simplified traffic and boundary conditions. MFDs are estimated through several OD traffic matrices, considering as outputs the values of the macroscopic traffic variable deduced in time intervals of 5 min and 1 h. The findings from this research, in terms of total capacity, travel times, and level of service (LOS) estimation, may be useful for traffic and highway engineers in many realistic applications to partially face congestion phenomena in urban areas. MFDs allow us to determine the best roundabout type based on the traffic demand and represent a parsimonious method for monitoring and controlling traditional and unconventional or smart roundabouts, even in the presence of connected and automated vehicles (CAVs).

Enhancing capacity estimation of retired electric vehicle lithium-ion batteries through transfer learning from electrochemical impedance spectroscopy

The low economic feasibility caused by inefficient testing and inaccurate performance estimation is one of the main bottlenecks in the echelon utilization of large-scale retired batteries. This study proposes a fast and accurate capacity estimation method for retired batteries based on electrochemical impedance spectroscopy (EIS). Firstly, the EIS of the batteries that experience multi-condition aging in the laboratory are collected. EIS characteristic parameter sequences highly related to battery performance, including real part and magnitude, are directly extracted to establish a base bi-directional long short-term memory model. Secondly, a transfer learning method based on feature matching is designed, which applies a linear transformation layer to map the features between the source and target domains. The proposed transfer learning method has been effectively validated on laboratory battery data measured at different temperatures and retired battery datasets of different material types. The improvements are especially notable for retired batteries. The detection time has been reduced, with each cell requiring only 1.67 min. And using only a small amount of data as input for transfer learning can achieve an accuracy improvement of over 90 %, indicating an effective transfer channel from the base model established on laboratory small-capacity battery aging data to large-capacity retired battery data is successfully established for the first time. For retired nickel-cobalt-manganese batteries, the mean absolute percentage error (MAPE) and the root mean square percentage error (RMSPE) are 2.33 % and 2.75 %, respectively, while for retired lithium-iron-phosphate batteries, the MAPE and RMSPE reached 4.12 % and 5.04 %, respectively. The results demonstrate the proposed method reduces the cost of repeated testing, modeling, and training for specific retired batteries while maintaining the accuracy of capacity estimation. This advancement helps to improve the efficiency of large-scale retired battery grading, and injects new momentum into facilitating more effective decision-making processes.

Towards real-world state of health estimation: Part 2, system level method using electric vehicle field data

Accurate battery health estimation is pivotal for ensuring the safety and performance of electric vehicles (EVs). While predominant research has centered on laboratory-level single cells, the accurate estimation of battery system capacity using real-world data remains a challenge, due to the vast diversity in battery types, operating conditions, data recordings, etc. To this end, we release three large-scale field datasets of 464 EVs from three manufacturers, comprising over 1.2 million charging snippets. The EVs’ capacity and State of Health (SOH) are effectively labeled using K-means to cluster and concatenate charging snippets. A robust data-driven framework integrating a Gated Convolutional Neural Network (GCNN) for estimating battery capacity is proposed, and the results outperform other machine learning models. In addition, a fine-tuning technique is employed to further enhance model efficacy on new datasets and with limited training data. This research not only advances battery health estimations but also paves the way for broader applications in battery management systems (BMSs), offering a scalable solution to real-world challenges in battery technology.

Machine-learning assisted analysis on the seismic performance of steel reinforced concrete composite columns

This paper presents the data-driven analysis on the seismic performance of steel reinforced concrete (SRC) composite columns by using machine learning (ML) algorithms. A total of 248 test data of SRC columns subjected to the combined axial compressive force and low reversed cyclic horizontal force was collected from the published literatures to form the database. Since the seismic action triggers the SRC columns to suffer from different failure mode and hence exhibit quite different load bearing mechanism, six ML algorithms were employed to facilitate the failure mode classification and bearing capacity prediction. It was found that the Random Forest (RF) model predicts the failure mode most accurately, while XGBoost model delivers the best estimation of bearing capacity. To further evaluate the feasibility of ML models and unveil the interaction between different variables, interpretability analysis of ML models was carried out. In comparison with the conventional empirical judgement and theoretical derivation, ML model delivers enhanced accuracy and robustness to predict the seismic performance of SRC columns, and hence it could be used as a promising alternative for the preliminary design and analysis of SRC columns.

Dynamic and empirical methods for field capacity estimation in fine textured soils with a coarse interlayer

Field capacity (FC) is an important soil hydraulic concept in soil science and irrigation management. It is generally determined from soil water content in a soil layer when soil profile reaches a steady pressure head or negligible drainage flux from an initially saturated soil. However, the proposed criteria are mainly tested for uniform soils and vary with soil textures. To quantify FC in layered soils, a Richards equation-based model was used to describe water flow in fine-textured soils with a coarse interlayer. With calibrated soil hydraulic parameters for loam and sand from infiltration measurements, drainage from saturation was simulated in the loam with a sand interlayer. A relative drainage rate (δ) was defined as a function of water storage and drainage flux to analyze soil water status at FC. Soil water content in the upper loam layer of layered profiles was improved compared with that in the uniform loam, which was negatively correlated with buried depth but positively correlated with thickness of the sand layer for a specified δ. Under different buried depths and thicknesses, soil water content decreased with the decline of δ and decreased rapidly as δ reduced to 1 % d−1. The drainage flux at δ = 1 % d−1 changed within a range of 0.056–0.26 cm d−1, and soil water content reached to 0.278–0.346 cm3 cm−3, which accounted for 70–87 % of the saturated water content of loam. Although the FC in the upper fine-textured soil layer varied for different buried depths and thicknesses of coarse interlayer, the proposed dynamic method is reliable and universal to estimate the FC in the above layered soils at δ = 1 % d−1. An empirical equation was also developed to calculate the FC in fine-textured soils with different buried depths and thicknesses of a coarse interlayer based on the critical δ value.

Effect of soil spatial variability on the stability of pipelines under horizontal loading

Submarine pipeline has been utilized more and more widely in offshore oil and gas transportation engineering. Given that these pipelines frequently subject horizontal loading during operation, thus precise predicting their horizontal bearing capacity is essential. However, most previous studies employ deterministic analysis without considering soil spatialvariability. This article introduces a two-dimensional (2D) random finite element analysis to study the horizontal pullout behaviour and failure mechanisms of a pipeline in a spatial variability soil. It is observed that the effect of soil spatial variability on the horizontal bearing capacity factor and failure mechanisms is remarkably substantial, with the distribution of the former showing a close approximation to a log-normal distribution. Furthermore, the average horizontal bearing capacity typically falls below the corresponding deterministic value. Consequently, the deterministic estimation of the horizontal bearing capacity tends to be inflated. Eventually, a reliability-based approach is formulated, where a factor of safety is incorporated to account for the uncertainties arising from spatial variability and ensure a sufficient safety margin. This methodology enables engineers to determine appropriate factor of safety tailored to the specific requirements of each project.

Efficiency of shear connectors under combined shear and torsion

The connection of steel elements to concrete is typically achieved through embedded shear connectors (anchors) placed at the interface of concrete and steel. These shear connectors come in various types, such as headed studs, welded or coupled rebars, shear lugs, or a combination of these, designed to handle large shear forces. Shear connectors primarily resist shear forces and bending loads. However, when the load is not applied along the centroid of the shear connector, it generates a torsional moment in addition to shear- and flexure-induced moments, impacting the performance of shear connectors. To address this phenomenon, this experimental study assesses the behavior of shear connectors, including studs, shear lugs, and rebars, both individually and in combination, under three different load eccentricities relative to anchors spacing connectors (0, 0.25, 0.50, and 0.75). The results are then compared with the control specimen (without eccentricity). The findings revealed that vertical shear lugs can significantly enhance the shear capacity, and ACI 318-19 is conservative in this regard by 41.5% for a relative load eccentricity of 0.75. Nonetheless, it gives good estimations of the shear capacity of connectors without vertical shear lugs. HIGHLIGHTS Contributing to the very limited data on the performance of anchors under the combined action of shear and torsion. ACI 318-19 [1] provides reliable estimations of shear strength for studs/rebars, both with and without eccentricity. However, it tends to be conservative by 41.5% (for a relative eccentricity of 0.75) when horizontal and vertical shear lugs are utilized.

How battery capacities are correctly estimated considering latent short-circuit faults

Capacity is a key factor in assessing battery health. Traditional capacity estimation methods assume by default the battery is in a normal state. When there is a latent short-circuit fault, the measured current deviates from the actual current flowing into or out of the battery unit, leading to errors in capacity estimation. To address this challenge, we have constructed an end-cloud collaborative framework to accurately estimate the module's capacity considering the latent short-circuit faults. The core scheme is to estimate the module's charging capacity and discharging capacity simultaneously, obtain the module's operating mode based on the historical relationship between the two, conduct quantitative diagnosis for the short-circuit fault, and feedback the accurate capacity value. This framework addresses the coupled issue of erroneous capacity estimation in the presence of latent short-circuit faults, and the inability to diagnose module external short-circuit faults due to the lack of a comparison. This has profound significance for achieving more reliable battery safety management.

Analytical Estimation of Hydrogen Storage Capacity in Depleted Gas Reservoirs: A Comprehensive Material Balance Approach

Analytical Estimation of Hydrogen Storage Capacity in Depleted Gas Reservoirs: A Comprehensive Material Balance Approach

A fast fish swimming protocol that provides similar insights to critical swimming speed.

Performance measures are an important tool to assess the impact of environmental change on animals. In fish, performance is often measured as critical swimming speed (Ucrit), which reflects individual maximal physiological capacities. A drawback of Ucrit is that trials are relatively long (∼30-75 min). Ucrit may therefore not be suitable for several repeated measurements because of the potential for training effects, long recovery periods, and low throughput. Here we test a shorter (∼4-5 min) protocol, "Ucrit fast" (UCfast) in zebrafish (Danio rerio). We show that UCfast and Ucrit have similar, significant repeatabilities within individuals. Unlike Ucrit, repeated UCfast trials did not elicit a training effect. Both UCfast and Ucrit provide the same insights into thermal acclimation, and both provide similar estimates of individual acclimation capacity in doubly acclimated fish. We propose that UCfast is a valid substitute for Ucrit particularly when higher throughput and several repeated measures are necessary.

A new prediction model for residual deformation capacity of corroded steel bars

Corrosion of reinforcing steel bars (rebar) is one of the primary factors leading to the performance degradation of reinforced concrete (RC) structures. It is essential to take into consideration the deteriorating effects of corrosion when it comes to assessing the performance of existing RC structures. However, due to the uncertainties and randomness associated with the degree and distribution of corrosion, accurately assessing the residual deformation capacity of corroded rebar is a significant challenge. This paper presents a comprehensive theoretical model for the prediction of the residual deformation capacity of corroded rebar. Taking into account the mechanical characteristics of the steel material, three classes of non-uniform corrosion in rebar are firstly defined on a quantitative basis to characterize the deformation distribution patterns under tension. Based on the concept of deformation accumulation, a simplified theoretical model is proposed for evaluating the residual deformation capacity of corroded rebar, incorporating both the non-uniformity of corrosion and the sample (or gauge) length; thus, a length scale is built into the nominal strain measure to accommodate the use of different sample or gauge lengths. Numerical tensile tests on 500 randomly generated samples of rebar with varying corrosion levels are conducted, and the parameters of the theoretical model for the three corrosion classes are determined based on a regression analysis of the tensile test results. Comparisons with actual experimental results and existing degradation models demonstrate that the proposed theoretical model provides a more accurate estimation for the deformation capacity of corroded rebar. The model offers a reliable reference for accurately assessing the tensile performance of corroded rebar in subsequent engineering applications.

Verification of Screening Conditions Related to Reuse of Used Lithium-Ion Batteries

（Introduction, Background/Research objectives）Lithium-ion batteries (hereinafter LIB) are becoming explosively popular around the world, and demand for electric vehicles (hereinafter EV) is particularly remarkable. However, lithium and rare metals are finite and their prices are soaring worldwide, which is hindering LIB market growth. On the other hand, technology for recycling and reusing used LIBs (EVs) is attracting attention. If safety, performance, and economic efficiency are ensured, a new market will be created and it will also contribute to the realization of a recycling-oriented society.However, there are many challenges to actually reusing LIBs, so we will consider screening methods that enable LIB reuse in an optimal, safe, and low-cost manner.This time, we will perform deterioration diagnosis and capacity estimation of reused LIBs whose usage history is unknown, and construct assembled batteries. The battery is actually installed in an EV, driven, and the battery status monitored. We will devise combinations of assembled batteries and verify the accuracy required for screening in order to achieve both performance and economy.Keywordsreused LIB, BMS, LIB screening, EVProposed approachUp until now, we have been able to obtain satisfactory results with battery packs that have taken time to ensure that battery deterioration and capacity are as consistent as possible. However, this time, we purposely created an assembled battery by adding batteries with different deterioration conditions and different capacities, and monitored what kind of behavior and performance changes occur when installed and operated in an EV. We will verify these results and provide feedback to the screening method.Materials and methodsFirst, we screened 100 LIB modules taken from an EV that had traveled 100,000 km. The screening method involves estimating capacity using a charger/discharger manufactured in-house and diagnosing deterioration by measuring internal resistance. These LIBs will form a 12-module assembled battery in three patterns and will be installed in a converted EV manufactured in-house. The status of the onboard LIB will be remotely monitored and logged using our own BMS with IoT functionality, and statistical verification will be performed.The following three patterns are used for the 12-module assembled battery configuration.condition A: Match all capacitance and internal resistance.condition B: 1 module capacity is low.condition C: High internal resistance of 1 module.The capacity of the entire battery pack was taken as the average capacity of the installed batteries. 100% SOC was defined as an average voltage of 4.2V, and 0% was defined as an average voltage of 3.2V. Further, the lower limit voltage of each cell is set to 3.2V, and if the OCV of even one cell falls below the lower limit voltage, it is assumed that the battery is used up and the running test is terminated.ResultsThe pack voltage, SOC, and standard deviation when running from SOC 100 to 90% to 5 to 0% are shown in Fig. 1 to 3. The standard deviation was used as an index for observing voltage variations among the 24 cells. Only in the case of condition A, the battery capacity could be used up until the SOC reached 0%, but in the cases of B and C, the battery capacity could not be used up because some cells had a voltage lower than the lower limit voltage when the SOC was 4%. Under all conditions, the variation in each cell voltage tends to increase during acceleration/deceleration (charging/discharging) and at low SOC (20% or less), but we observed a large difference in the magnitude of the variation and the focusing value.Discussion and conclusionIn the case of condition A, the fluctuation and dispersion of the pack voltage are small, and the focusing time and focusing value when the pack voltage varies are also short and small. In the case of condition B, although the pack voltage fluctuation was large, the variation was slightly increased compared to condition A, and the convergence time and convergence value were also slightly increased. Condition C had large fluctuations and dispersions in the pack voltage, and the convergence time when the dispersion occurred was also long and large. From these results, it can be said that the important condition to consider during screening is to match the capacity of the batteries. This result not only improves the accuracy of screening, but also has the potential to increase the reuse rate of used batteries. Figure 1

Automatic topology and capacity generation framework for urban drainage systems with deep learning-based land use segmentation and hydrological characterization

This paper proposed a comprehensive and automated framework that integrates the entire urban drainage design process, from refined land use segmentation, to catchment subdivision and characterization, to network topology generation, and finally to hydraulic estimation of pipeline capacity, based on multiple open-source datasets. The method incorporated deep learning-based semantic segmentation, GIS-based hydrological characterization, topology generation and hydraulic calculation algorithms. The performance and applicability of the method were validated through case studies of two cities. The results showed that the deep learning model with prior knowledge could accurately segment urban land use to obtain the detailed shape and boundary description, and achieved higher detection accuracy for water bodies, buildings, and roads. The stormwater drainage topology and conveyance capacity were automatically generated based on subcatchment division and hydrological characterization under a set of topological parameter conditions. In the optimal Critical Success Index (CSI) scenario, only 8% and 21% of the generated pipelines were inconsistent with the expert-designed networks. Compared to the traditional manual design process, the automated approach can save significant time and resources and reduce the reliance on field surveys and empirical/expert work. At the same time, the tool can be employed for system layout and capacity optimization, providing important support for the automation, design, and rehabilitation of stormwater drainage systems.

Traditional matric potential thresholds underestimate soil moisture at field capacity across Oklahoma

AbstractField capacity is a dubious soil physical property, but its use continues because of its perceived value for representing a soil's capacity to store water. Appropriate field capacity estimates can be useful for interpreting data from soil moisture sensors, including those in large‐scale monitoring networks, but suitable methods for defining field capacity in this context are unclear. Motivated by the desire to determine optimal field capacity values for the Oklahoma Mesonet, our objectives were (1) to develop and apply an automated time series analysis algorithm to estimate volumetric soil water content at field capacity and corresponding matric potential and (2) to compare the resulting water contents to those calculated from traditional matric potential thresholds (−33 and −10 kPa). Across 118 Oklahoma Mesonet sites and three soil depths (5, 25, and 60 cm), a matric potential threshold of −10 kPa underestimated field capacity water content by 0.010–0.014 cm cm−3 (3–4%) on average, and a threshold of −33 kPa underestimated it for every site and depth by 0.055–0.078 cm cm−3 (16%−22%) on average. Median matric potentials corresponding to field capacity were −7.6 kPa at the 5‐cm depth, −7.2 kPa at the 25‐cm depth, and −7.3 kPa at the 60‐cm depth. The algorithm developed here can be used to estimate field capacity wherever adequate data are available, and for sites where soil water retention properties are known, matric potentials at field capacity can also be estimated. Using a matric potential of −33 kPa as a standard threshold to represent field capacity is not scientifically justified and should be discontinued.

Enhancing Quality Management of PHC Piles through Improved Driving Formulas: A Comprehensive Review and Analysis

In construction projects, conducting dynamic load tests on all piles proves impractical. Selective testing estimates bearing capacity, while the remaining piles rely on penetration depth for management. This approach, however, faces reliability issues due to varying conditions among piles. Technological advancements, such as non-contact hammers and sensors, have enhanced the accuracy of penetration depth measurements during final driving. Nonetheless, relying solely on penetration depth for construction and quality management remains problematic. This study, therefore, focuses on enhancing the use of driving formulas to improve pile quality management, particularly for the widely used pre-stressed high-strength concrete (PHC) piles. To improve pile quality management, existing driving formulas underwent review and refinement. Utilizing 258 dynamic load test data from various sites, the Hiley, Gates, and Danish formulas underwent validation through statistical analysis and graphical comparison. Enhancements to the Gates formula, achieved through curve fitting with actual data and the application of segment-based coefficients, demonstrated increased accuracy in bearing capacity estimation. These improvements offer a more reliable approach to pile quality management in construction projects.

Pharmacogenetic Variants and Plasma Concentrations of Antiseizure Drugs

Precise estimation of a patient's drug metabolism capacity is important for antiseizure dose personalization. To quantify the differences in plasma concentrations for antiseizure drugs associated with variants of genes encoding drug metabolizing enzymes. PubMed, Clinicaltrialsregister.eu, ClinicalTrials.gov, International Clinical Trials Registry Platform, and CENTRAL databases were screened for studies from January 1, 1990, to September 30, 2023, without language restrictions. Two reviewers performed independent study screening and assessed the following inclusion criteria: appropriate genotyping was performed, genotype-based categorization into subgroups was possible, and each subgroup contained at least 3 participants. The Meta-analysis of Observational Studies in Epidemiology (MOOSE) guidelines were followed for data extraction and subsequent quality, validity, and risk-of-bias assessments. The results from the included studies were pooled with random-effect meta-analysis. Plasma concentrations of antiseizure drugs were quantified with the dose-normalized area under the concentration-time curve, the dose-normalized steady state concentration, or the concentrations after a single dose at standardized dose and sampling time. The ratio of the means was calculated by dividing the mean drug plasma concentrations of carriers and noncarriers of the pharmacogenetic variant. Data from 98 studies involving 12 543 adult participants treated with phenytoin, valproate, lamotrigine, or carbamazepine were analyzed. Studies were mainly conducted within East Asian (69 studies) or White or European (15 studies) cohorts. Significant increases of plasma concentrations compared with the reference subgroup were observed for phenytoin, by 46% (95% CI, 33%-61%) in CYP2C9 intermediate metabolizers, 20% (95% CI, 17%-30%) in CYP2C19 intermediate metabolizers, and 39% (95% CI, 24%-56%) in CYP2C19 poor metabolizers; for valproate, by 12% (95% CI, 4%-20%) in CYP2C9 intermediate metabolizers, 12% (95% CI, 2%-24%) in CYP2C19 intermediate metabolizers, and 20% (95% CI, 2%-41%) in CYP2C19 poor metabolizers; and for carbamazepine, by 12% (95% CI, 3%-22%) in CYP3A5 poor metabolizers. This systematic review and meta-analysis found that CYP2C9 and CYP2C19 genotypes encoding low enzymatic capacity were associated with a clinically relevant increase in phenytoin plasma concentrations, several pharmacogenetic variants were associated with statistically significant but only marginally clinically relevant changes in valproate and carbamazepine plasma concentrations, and numerous pharmacogenetic variants were not associated with statistically significant differences in plasma concentrations of antiseizure drugs.

Variation in Zero Plane Displacement and Roughness Length for Momentum Revisited

Zero plane displacement height (d0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_0$$\\end{document}) and momentum roughness length (z0m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z_{0m}$$\\end{document}), describe the aerodynamic characteristics of a vegetated surface. Usually, d0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_0$$\\end{document} and z0m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z_{0m}$$\\end{document} are assumed to be constant functions of the physical characteristics of the surface. Prior evidence collected from the literature and our examination of flux tower data show that d0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_0$$\\end{document} and z0m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z_{0m}$$\\end{document} vary in time at sites with tree and shrub canopies, but not grasslands. The conventional explanations of these variations are based on linear functions of wind velocity and friction velocity, with little theoretical basis. This study explains the variation in aerodynamic parameters by matching four analytical canopy velocity models to a logarithmic above-canopy velocity profile at canopy height. d0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_0$$\\end{document} and z0m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z_{0m}$$\\end{document} come out as functions of 2 non-dimensional terms, the canopy momentum absorption capacity (parameter) and a (measurable) Péclet number. To test the theories of variation, we analysed the velocity profiles from Ozflux and Ameriflux sites. None of the theories could recreate d0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_0$$\\end{document} and z0m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z_{0m}$$\\end{document} at half-hourly intervals. However, the canopy velocity models were able better to recreate the distribution of the variations in d0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$d_0$$\\end{document} and z0m\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$z_{0m}$$\\end{document}. Additionally, the estimates of canopy momentum absorption capacity varied consistently with phenological changes in the canopies, whereas, the fitting parameters of the linear regression of using wind speed and friction velocity did not exhibit physically interpretable variations. The canopy velocity models may offer better predictions with an accurate estimation of the canopy height, a horizontally homogeneous and rigid canopy, and incorporation of the roughness sublayer.

Capacity Estimation for Lithium-Ion Batteries Based on Impedance Spectral Dynamics and Deep Gaussian Process

Capacity Estimation for Lithium-Ion Batteries Based on Impedance Spectral Dynamics and Deep Gaussian Process

A stacking machine learning model for predicting pullout capacity of small ground anchors

Small ground anchors are widely used to fix securing tents in disaster relief efforts. Given the urgent nature of rescue operations, it is crucial to obtain prompt and accurate estimations of their pullout capacity. In this study, a stacking machine learning (ML) model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents, leveraging cone penetration data. The proposed stacking model incorporates three ML algorithms as the base regression models: K-nearest neighbors (KNN), support vector regression (SVR), and extreme gradient boosting (XGBoost). A dataset comprising 119 in-situ anchor pullout tests, where the cone penetration data were measured, is utilized to train and assess the stacking model performance. Three metrics, i.e., coefficient of determination (R2), mean absolute error (MAE), and root mean square error (RMSE), are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach. The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R2 and lower MAE and RMSE and more predicted data points falling within 20% error line. Thus, the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.