Good Estimates Research Articles

Response of stomatal conductance to plant water stress in buffalograss seed production: Observation with UAV thermal infrared imagery

Stomatal conductance (gs) is an indicator that allows for direct evaluation of plant water status, but it is challenging to achieve rapid monitoring in large-scale fields due to limitations in observation methods. Here this study was conducted to identify the thresholds of gs with different target yields and develop a gs-based water stress diagnostic model for buffalograss (Buchloe dactyloides (Nutt.) Engelm.) using UAV thermal infrared imagery for buffalograss in 2022 and 2023. The results of the field experiment demonstrated that the gs rapidly response to changes in the water stress status of buffalograss. The thresholds of gs were 403 and 385 mmol m−2 s−1 for the vegetative and reproductive growth stages, respectively, with the target seed yield of 1224 kg ha−1. The gs values were classified into three levels for the vegetative growth and four levels for the in reproductive growth stage of buffalograss, respectively. The canopy temperature depression response to water stress is consistent with the gs. Based on this relationship, this study developed a gs-based diagnostic model with a random forest algorithm for buffalograss. Furthermore, a spital map of gs was created using UAV thermal infrared imagery. The modification test results indicated that the model made a good estimation of gs were good with normalized root mean square errors of 15% in the vegetative stage and 11% in the reproductive stage, respectively. Therefore, it is feasible to use thermal infrared imagery for monitoring gs and evaluating the water stress of plants in buffalograss fields.

An Adaptive Neural Network Regression Method for Structure Identification

This article reports a study on a flexible neural network regression method within the functional analysis of variance framework that aims to adapt to the underlying structure of the target function. We develop a novel penalization scheme where a concept of node impurity is introduced in the neural network framework. The node impurity in neural networks represents the homogeneity of the effects of the inputs on the node. We first define the effect of individual input on node and in turn, measure the node impurity based on the effects of inputs on node. We adopt the sum of node impurities as a penalty function whose usage makes the connections from inputs to nodes sparse, which improves estimation accuracy by reducing unnecessary complexity and enables data-adaptive structure identification. Our method takes into account of a large parameter space of the networks ranging from a fully-connected structure to sparsely connected structures. Among possible node connection structures, an optimal model is selected based purely on observed data. Numerical studies based on simulated and real datasets show that the proposed method performs well in identifying the inherent structure of the regression function and produces good estimation accuracy. Supplementary materials for this article are available online.

Utilizing gas flux from automated head chamber systems to estimate dietary energy values for beef cattle fed a finishing diet.

Dietary net energy for maintenance (NEm) and gain (NEg) can be estimated using calculations based on live performance or adjusted-final body weight, which is calculated based on carcass characteristics. These values are commonly referred to as performance-adjusted (pa) NEm (paNEm) and NEg (paNEg). The NEm and NEg of a diet can also be estimated by adding recovered energy (RE) with heat production (HP) derived from an automated head chamber system (AHCS), which we will term gas-adjusted (ga) NEm (gaNEm) and NEg (gaNEg). Furthermore, HP from the Brouwer equation requires an estimate of urinarynitrogen (UN) excretion, which can be calculated based on N intake, blood urea N, UN concentration, and urine creatinine, or it could be zeroed. Alternatively, HP can be calculated using an alternative equation based on the respiratory quotient. Demonstrating agreement between pa and ga derived dietary energy values provides an opportunity to validate using the AHCS for energetic experiments and this comparison has not been conducted previously. Accordingly, the objective of this experiment was to assess the agreement between live and carcass paNEm and paNEg with gaNEm and gaNEg, where HP was calculated using 4 different approaches. Estimates of HP were not different (P = 0.99) between the 4 approaches employed, indicating that all options investigated are appropriate. Live paNEm and paNEg had a higher agreement (Lin's concordance correlation coefficient [CCC] = 0.91) with gaNEm and gaNEg than carcass values (CCC ≤ 0.84). These results suggest that researchers can implement the AHCS to provide good estimates of dietary energy values in finishing beef cattle that are unrestrained.

Estimating relative importance of criteria by post-processing dominance-based rough set approach’s outputs

The dominance-based rough set approach (DRSA) is one of the rare multicriteria sorting methods that does not require a priori values for the relative importance of criteria. This method therefore is cognitively less demanding. However, the decision-maker may lack some valuable information that is needed to explain and justify final decisions to stakeholders. The relative importance of criteria can instead be estimated based on the characteristics of the outputs of the DRSA, which are mainly decision rules and attribute reducts. The estimated values can then be used to understand the role played by each criterion and justify the final decisions to stakeholders. This paper first reviews and improves the decision rule attractiveness measure and the relative importance measures that are based on decision rules and attribute reducts, which were all proposed in the authors’ previous work. It also introduces new measures that rely on the marginal contributions and entropy of the criteria. This paper also extends these measures to estimate the relative importance of criteria with respect to specific unions of classes. The proposed measures have been applied to Brexit referendum and bankruptcy risk assessment datasets. Both applications showed that the proposed measures generally lead to different results. This can be explained by the diversity of the knowledge on which these measures rely and also by the multiplicity of the analytical perspectives associated with them. The results also showed that the marginal contribution-based measure is a good estimator of both Shapley and Banzhaf values.

Fuzzy Levenberg-Marquardt damping factor update strategy and Tikhonov regularization applied to a coupled conduction-radiation inverse heat transfer problem

The recently proposed Fuzzy Levenberg-Marquardt Damping Factor Updating Strategy (FLM) is used along with Tikhonov Regularization (TR) in order to solve a coupled conduction-radiation function estimation inverse problem. FLM consists of updating the Levenberg-Marquardt (LM) damping factor with the assist of Fuzzy Logic. To test the FLM algorithm combined with TR, a conduction-radiation problem is addressed, and it serves as test bed to the numerical experiments, where the objective is to obtain estimatives of parameters, such as the spatially variable scattering albedo, the thermal conductivity and the optical thickness, simultaneously, which is often performed separately. Two main test cases are presented, one considering only the radiation problem, used as benchmark, in order to investigate noise levels and hyperparameters. The second main test case deals with the coupled conduction-radiation heat transfer problem for different functional forms of the spatially variable scattering albedo. For all presented numerical experiments, good estimates were achieved for the sought functions and parameters. These results demonstrate that the methodology presented in this work, through the combination of the Fuzzy LM dampting factor updating strategy and the Tikhonov regularization scheme, may provide valuable tools for inverse heat transfer problems.

One-step sparse ridge estimation with folded concave penalty

The local linear approximation algorithm is an effective algorithm for computing a global solution of the folded concave penalization problem. However, the effectiveness of this method is highly dependent on a reasonably good initial estimator. It will lose efficacy when the correlation among predictors is high. In this paper, we propose a new local linear approximation ridge algorithm designed to deal with highly correlated predictors. The ridge estimator is chosen as an initial estimator, the local linear approximation ridge algorithm is stable and effective. Simulation studies and a real data analysis show that the proposed algorithm has better performance than the local linear approximation algorithm in the presence of highly correlated predictors.

State of Health Prediction of Lithium-Ion Batteries Using Combined Machine Learning Model Based on Nonlinear Constraint Optimization

Accurate State of Health (SOH) estimation of battery systems is critical to vehicle operation safety. However, it’s difficult to guarantee the performance of a single model due to the unstable quality of raw data obtained from lithium-ion battery aging and the complexity of operating conditions in actual vehicle operation. Therefore, this paper combines a long short-term memory (LSTM) network with strong temporality, and support vector regression (SVR) with nonlinear mapping and small sample learning. A novel LSTM-SVR combined model with strong input features, less computational burden and multiple advantage combinations is proposed for accurate and robust SOH estimation. The nonlinear constraint optimization is used to assign weights to individual models in terms of minimizing the sum of squared errors of the combined models, which can combine strengths while compensating for weaknesses. Furthermore, voltage, current and temperature change curves during the battery charging were analyzed, and indirect health features (IHFs) with a strong correlation with capacity decline were extracted as model inputs using correlation analysis and principal component analysis (PCA). The NASA dataset was used for validation, and the results show that the LSTM-SVR combined model has good SOH estimation performance, with MAE and RMSE all less than 0.75% and 0.97%.

Domain-Specific Self-Efficacy Scales for Elementary and Middle School Students.

Self-efficacy refers to students' perceived confidence in their ability to tackle learning tasks. Research shows that self-efficacy serves as an important predictor of academic achievement and relates to students' academic success, self-regulated learning, and motivation. It is therefore important to understand how self-efficacy develops and manifests itself in Russian schoolchildren and relates to their academic achievement. To establish evidence of the validity and reliability of domain-specific self-efficacy scales developed for elementary and middle school students. Messick's unified framework was used to establish validity. The surveys were administered to elementary and middle school students in two regions of Russia. The pilot testing of the self-efficacy scales for elementary school, using exploratory (n = 972) and confirmatory (n = 972) factor analyses, resulted in a four-factor model, which was later confirmed with a different sample of elementary students (n = 1,392) with good reliability estimates (α = 0.75-0.82). The pilot testing of self-efficacy scales for middle school, using exploratory (n = 583) and confirmatory (n = 584) factor analyses, resulted in a three-factor model, showing excellent reliability estimates (α = 0.88-0.93). The evidence of construct validity suggests that the domain-specific self-efficacy scales for elementary and middle school students can be recommended for use by researchers and practitioners. The article presents ideas for additional validation studies and future research using domain-specific self-efficacy scales.

Posture Estimation of Curve Running Motion Using Nano-Biosensor and Machine Learning

Curve running is a common form of training and competition. Conducting research on posture estimation during curve running can provide more accurate training and competition data for athletes. However, due to the unique nature of curve running, traditional posture estimation methods neglect the temporal changes in athlete posture, resulting in a decrease in estimation accuracy. Therefore, a posture estimation method for curve running motion using nano-biosensor and machine learning is proposed. First, the motion parameters of humans are collected by nano-biosensor, and the posture coordinates are obtained preliminarily. Second, the posture coordinates are established according to the human motion parameters, and the curve running posture data is obtained and filtered to obtain more accurate data. Finally, the Bayesian network in machine learning is used to continuously track the posture, and a nonlinear equation is established to fuse the posture angle obtained by the sensor and the posture tracked by the Bayesian network, to realize the posture estimation of curve running motion. The results show that the proposed estimation method has a good motion posture estimation effect, and the hip joint estimation error, knee joint estimation error and ankle joint estimation error are all less than 5°, and the endpoint displacement estimation offset rate is less than 2%. It can realize accurate motion posture estimation of curve running motion, and has important application value in the field of track training.

An extension of Thwaites’ method for turbulent boundary layers

Thwaites (Aeronaut. Q., vol. 1, 1949, pp. 245–280) developed an approximate method for determining the evolution of laminar boundary layers. The approximation follows from an assumption that the growth of a laminar boundary layer in the presence of pressure gradients could be parameterized solely as a function of the Holstein–Bohlen flow parameter, thus reducing the von Kármán momentum integral to a first-order ordinary differential equation. This method is useful for the analysis of laminar flows, and in computational potential flow solvers to account for the viscous effects. In this work, an approximate method for determining the momentum thickness of a two-dimensional, turbulent boundary layer is proposed following Thwaites’ work. It is shown that the method provides good estimates of the momentum thickness for multiple boundary layers, including both favourable and adverse pressure gradient effects, up to the point of separation. In the limit of high Reynolds numbers, it is possible to derive a criterion for the onset of separation from the proposed model, which is shown to be in agreement with prior empirical observations (Alber, 9th Aerospace Sciences Meeting, 1971). The sensitivity of the separation location with respect to upstream perturbations is also analysed through this model for the NASA/Boeing speed bump and the transonic Bachalo–Johnson bump.

ASTERIA—Asteroid Thermal Inertia Analyzer

Thermal inertia estimates are available for a limited number of a few hundred objects, and the results are practically solely based on thermophysical modeling (TPM). We present a novel thermal inertia estimation method, the Asteroid Thermal Inertia Analyzer (ASTERIA). The core of the ASTERIA model is the Monte Carlo approach, based on the Yarkovsky drift detection. We validate our model on asteroid Bennu plus 10 well-characterized near-Earth asteroids (NEAs) for which a good estimation of the thermal inertia from TPM exists. The tests show that ASTERIA provides reliable results consistent with the literature values. The new method is independent of TPM, allowing an independent verification of the results. As the Yarkovsky effect is more pronounced in small asteroids, the noteworthy advantage of ASTERIA compared to TPM is the ability to work with smaller asteroids, for which TPM typically lacks input data. We used ASTERIA to estimate the thermal inertia of 38 NEAs, with 31 of them being sub-kilometer-sized asteroids. Twenty-nine objects in our sample are characterized as potentially hazardous asteroids. On the limitation side, ASTERIA is somewhat less accurate than TPM. The applicability of our model is limited to NEAs, as the Yarkovsky effect is yet to be detected in main-belt asteroids. However, we can expect a significant increase in high-quality measurements of the input parameters relevant to ASTERIA with upcoming surveys. This will surely increase the reliability of the results generated by ASTERIA and widen the model’s applicability.

Study of subclinical chronotropic response to acute exercise in children of type 2 diabetic parents

Background: Type 2 diabetes mellitus (T2DM) has multiple pathogenesis. Hereditary propagation marks the genetic role as a very certain factor in the causation of T2DM. Reports have suggested that individuals with a family history (FH) of T2DM exhibit an altered chronotropic response even before the manifestations of the disease. Physical exercise is a proven way to extract any of the underlying cardiovascular abnormalities. These abnormalities may not be present during rest and identifying them can be useful as a good estimate of the individual’s cardiac functional sufficiency. After the stoppage of exercise, heart rate recovery (HRR) gives a thought toward the consideration of the coordinated equilibrium of the parasympathetic and sympathetic neural pathways. The withdrawal of the sympathetic influence and simultaneous parasympathetic reactivation make HRR as a measure of the underlying autonomic dysfunction. The early outcomes of cardiac effects in healthy children of T2DM parents were studied. Any evidence of changes observed will suggest that such individuals can avert and retard the genetically propagated diseases by following an appropriate healthy lifestyle. Aims and Objectives: (1) To assess subclinical cardiac autonomic dysfunction by way of an episode of acute aerobic exercise and study heart rate (HR), HRR, and blood pressure (BP) parameters in children of T2DM. (2). To compare above assessment parameters among the 2 groups - children of T2DM and children of non-diabetic parents. Materials and Methods: The present study is a cross-sectional observational study comprising 120 subjects in the age group of 18–40 years, 60 Cases – children of T2DM parents and 60 age-matched Controls – children of non-diabetic parents. HR and BP were measured at rest, during exercise, immediately after (Peak HR and BP), 10 min, 20 min, and 30 min after completion of exercise. An automated multipara monitor (RMS-Phoebus P513) was used to record these parameters. An informed written consent and ethics committee approval from the institution was taken. All data were summarized and statistically analyzed. Values for statistical analysis are expressed as Mean ± standard deviation (SD) and for statistical significance, P < 0.05 was considered. Results: Basic attributes, anthropometry of both cases and controls were comparable. Blood glucose parameters despite being in normal range, were higher in cases (P > 0.05). HR, BP parameters at rest, and their response (behavior) to exercise were similar in both groups. Cases had an increased HR and BP with an increase in exercise duration, at peak exercise and during the early phase of recovery but returned to baseline levels following a total recovery period (30 min). Conclusion: Young euglycemic, non-diabetic children of T2DM parents have an altered chronotropic response to exercise. There is an increased HR and BP response during peak exercise, beginning and early phase of the recovery period. Such a dysregulation is probably suggestive of higher cardiovascular risk. This emphasizes a healthy lifestyle and early detection.

Cochlear dysfunction as an early biomarker of cognitive decline in normal hearing and mild hearing loss.

Age-related hearing loss is an important risk factor for cognitive decline. However, audiogram thresholds are not good estimators of dementia risk in subjects with normal hearing or mild hearing loss. Here we propose to use distortion product otoacoustic emissions (DPOAEs) as an objective and sensitive tool to estimate the risk of cognitive decline in older adults with normal hearing or mild hearing loss. We assessed neuropsychological, brain magnetic resonance imaging, and auditory analyses on 94 subjects >64 years of age. We found that cochlear dysfunction, measured by DPOAEs-and not by conventional audiometry-was associated with Clinical Dementia Rating Sum of Boxes (CDR-SoB) classification and brain atrophy in the group with mild hearing loss (25 to 40dB) and normal hearing (<25dB). Our findings suggest that DPOAEs may be a non-invasive tool for detecting neurodegeneration and cognitive decline in the older adults, potentially allowing for early intervention.

An empirical and a dynamic-empirical model for the estimation of maize seedbed temperature

Soil temperature is the main factor in determining the germination of maize seeds and the emergence time of the crop. It controls the rate of phenological development while the meristem is underground, which is until the V6 (six leaf collar) stage of maize. The research performed by the authors aimed to model maize seedbed temperatures at sowing depth (soil temperature at 5 cm depth) during the sowing-emergence-early development period. The research is based on measurements in ploughed plots of the maize experiments at the Látókép Experiment Site of the University of Debrecen (Eastern Hungary) in two growing seasons of 2021–2022. Two types of empirical models were established, a multilinear regression model (M1) and a dynamic-empirical model (M2), where the daily increase and decrease of soil temperature are determined by multilinear regression. Candidates for input variables for both models were various, easily available daily meteorological parameters. M2 model performed better than M1 when applied to an independent database of 2022. This is particularly valid for the maximum and minimum soil temperatures. It was found that both M1 and M2 can be used to predict the soil temperature of the maize seedbed before shading by the plants. For daily mean temperature, M1 and M2 give a similarly good estimation, while the dynamic-empirical model has to be preferred for the maximum and minimum temperatures. M2, which is based on daily temperature, global radiation and wind speed data, predicts the daily mean (RMSE = 1.4 °C), maximum (RMSE = 2.2 °C), and minimum (RMSE = 1.6 °C) of seedbed temperature not worse than many earlier soil temperature models do, even hybrid or mechanistic ones with a large number of parameters.

Priestley Chao Estimator in Nonparametric Multivariable Kernel Regression in Estimating The Value of Indonesia’s Balance Trade

Several researchers have speculated that the model for the Indonesian Trade Balance Value uses a parametric model, but this model has not provided accurate results in determining the actual Indonesian Trade Balance Value. The estimation used is a parametric approach which assumes the data follows a certain pattern. This can result in big mistakes. We propose a nonparametric approach using Kernel functions for data that does not follow a particular pattern and has outliers. The Kernel function used for multivariables is the Gaussian Kernel function with the Priestley-Chao estimator. Analysis of Indonesia’s Trade Balance Data for 2019-2020 using the available data on Indonesia’s Trade Balance Rate, shows that this model is able to estimate with a very small Mean Square Error (MSE) of 0.98 at optimal bandwidth value are h1 =8.72 and h2 = 0.39. Optimum bandwidth selection uses minimum Generalized Cross Validation (GCV). With this bandwidth value, it gives very good estimation results. This model can be used to predict Indonesia’s Trace Balance Accurately on data that does not have a specific pattern and there are outlier data.

Loopy Orthogonal Signal Correction Scatter Correction in Non-Invasive Blood Glucose

Spectroscopy is the study of matter based on light, sound, or particles emitted, absorbed, or reflected as well as the study of methods for generating and analyzing spectra. The spectrum has systematic diversity, namely the presence of light scattering and differences in the size of objects. The spectroscopic output allows for scattering shifts, because the same object measured several times does not exactly produce the same spectrum. Problems found in the spectrum can be overcome by pre-processing the data, namely the scatter correction method. Scatter correction is used to reduce the physical properties in the spectrum so that the information obtained is relatively the same for each spectrum, produces good estimates, and can be interpreted well. One of the spectroscopic tools that utilize infrared light is a non-invasive blood glucose level measuring device. The output of the tool is the time domain and intensity spectrum. Each object from the resulting spectrum still has noise, so scatter correction can be applied to this data. The purpose of this study was to perform a loopy Orthogonal Signal Correction (OSC) scatter correction method on time domain spectrum data on intensity on a non-invasive blood glucose level measuring device. The OSC method uses the concept of orthogonality to the mean by drawing the intensity value, weighting it, calculating the vector loading and then making corrections to the initial intensity. Based on the analysis, the loopy OSC method is better than OSC because the convergence is more accurate, the mean difference is smaller, the variance is smaller and the value converges on all the values tested. Based on exploration and the average difference, the loopy OSC method is better able to form the same pattern for each replication. This also shows that an object that is measured repeatedly has been able to be identified as the same object.

A novel approach to traffic modelling based on road parameters, weather conditions and GPS data using feedforward neural networks

This article presents the development of a estimation model using an artificial neural network to estimate the traffic factor parameter, which reflects changes in travel time for a single transport connection. The modeling of traffic is highly challenging due to the complex and non-linear nature of road systems, the interactions between diverse road users, and the ripple effects of congestion. The research problem addressed is the need to consider a wide range of factors that contribute to congestion, as identified in the literature. To address this challenge, a universal Feedforward Neural Network (FNN) was designed, specifically the Multilayer Perceptron (MLP) 38–10-1 model. The input layer neurons of the MLP receive a vector of input values representing various factors such as road types, technical properties, time of day, days of the week, incidents, weather conditions, and population density. The model was trained using a dataset consisting of 41,945 records of fully completed input variables, extracted from a larger dataset of 300,000 travel time measurements collected from a real transport network mapped on a macro scale (Mazovia, Poland). During the research, the model achieved a satisfactory level of Mean Absolute Percentage Error (MAPE) for the test set (9.12%) and a Normalized Root Mean Squared Error (NRMSE) below 0.02, indicating good estimation performance. Furthermore, this work proposes specialized models with the structure of MLP 34–10-1 for individual types of roads, which also demonstrated satisfactory estimation abilities. These models have practical applications in traffic management and planning. Overall, this research addresses the research problem of modeling factors for predicting travel time changes using an artificial neural network approach. The developed models provide accurate estimations and offer potential applications in transportation systems.

Application of AI for Short-Term PV Generation Forecast.

The efficient use of the photovoltaic power requires a good estimation of the PV generation. That is why the use of good techniques for forecast is necessary. In this research paper, Long Short-Term Memory, Bidirectional Long Short-Term Memory and the Temporal convolutional network are studied in depth to forecast the photovoltaic power, voltage and efficiency of a 1320 Wp amorphous plant installed in the Technology Support Centre in the University Rey Juan Carlos, Madrid (Spain). The accuracy of these techniques are compared using experimental data along one year, applying 1 timestep or 15 min and 96 step times or 24 h, showing that TCN exhibits outstanding performance, compared with the two other techniques. For instance, it presents better results in all forecast variables and both forecast horizons, achieving an overall Mean Squared Error (MSE) of 0.0024 for 15 min forecasts and 0.0058 for 24 h forecasts. In addition, the sensitivity analyses for the TCN technique is performed and shows that the accuracy is reduced as the forecast horizon increases and that the 6 months of dataset is sufficient to obtain an adequate result with an MSE value of 0.0080 and a coefficient of determination of 0.90 in the worst scenarios (24 h of forecast).

On the Importance of MPL Structural Characteristics for PEM Fuel Cell Performance

Gas diffusions layers (GDLs) constitute vital subcomponents of proton exchange membrane fuel cells (PEMFCs) since they participate in all relevant transport processes (gases, vapor, liquid water, electricity and heat).Meanwhile, state-of-the-art GDLs are produced on commercial scale using high volume reel-to-reel (R2R) processes. In almost any case, GDLs are designed as dual-layer structures with a macro-porous carbon fiber-based substrate (backing) and a micro-porous layer (MPL) consisting of carbon particulates bonded with polytetrafluoroethylene (PTFE) binder. A body of scientific work has verified that different aspects of the MPL microstructure and the MPL/macroporous substrate interface play a substantial role in the water management of PEMFCs [1-6].A systematic study was performed based on a singular backing using different MPL types and penetration levels.A simple method to estimate the micro-porous layer penetration in gas diffusion layers based on data obtained from force-deflection behavior of backings and MPL-coated GDLs is proposed. Comparison with SEM and previously published tomographic studies shows that a good estimate for the extent of MPL intrusion into the backing is obtained.MPL loading and penetration is shown to affect different ex-situ GDL properties (in-plane gas permeability, electrical and thermal conductivity) as well as the GDL single cell performance characteristics with respect to humification and high current density operation.Keywords: Gas diffusion layers, Microporous Layer, PEMFC fuel cells Weber, and J. Newman, “Effects of microporous layers in polymer electrolyte fuel cells”, J. Electrochem. Soc. 152, A677-A688 (2005).Lee, R. Yip, P. Antonacci, N. Ge, T. Kotaka, Y. Tabuchi, and A. Bazylak, Synchrotron investigation of microporous layer thickness on liquid water distribution in a PEM fuel cell, J. Electrochem. Soc. 162, F669-F676 (2015).Zhou, S. Shukla, A. Putz, and M. Secanell, “Analysis of the role of the microporous layer in improving polymer electrolyte fuel cell performance”, Electrochim. Acta. 268, 366-382 (2018).Cho, J. Park, H. Oh, K. Min, E. Lee, and J.Y. Jyoung, “Analysis of the transient response and durability characteristics of a proton exchange membrane fuel cell with different micro-porous layer penetration thicknesses”, Appl. Energy 111, 300-309 (2013).T. Gostick, M. A. Ioannidis, M. W. Fowler, and M. D. Pritzker, “On the role of the microporous layer in PEMFC operation.”, Electrochem. Comm. 11, 576-579 (2009).P. Ramasamy, E. C. Kumbur, M. M. Mench, W. Liu, D. Moore, and M. Murthy, “Investigation of macro- and micro-porous layer interaction in polymer electrolyte fuel cells”, Int. J. Hydrogen Energy 33, 3351-3367 (2008).

Hf-W isotope systematics of enstatite chondrites: Parent body chronology and origin of Hf-W fractionations among chondritic meteorites

Five enstatite chondrites, including two EH4 (Abee, Indarch) and three EL6 samples (Hvittis, Khairpur, Pillistfer), were investigated for their Hf-W isotope systematics to constrain the chronology and thermal evolution of their parent bodies and to assess the nature and extent of Hf-W fractionations among chondritic meteorites. The Hf-W ages range from ∼4.5 Ma to ∼10 Ma after CAI formation, where Pillistfer exhibits an older Hf-W age (∼4.5 Ma) than the other two EL6 chondrites (∼8.5–10 Ma), probably reflecting rapid cooling after impact excavation. The ∼8 Ma Hf-W age of the EH4 chondrite Indarch overlaps with those of the younger EL6 chondrites, indicating a similar cooling timescale despite the lower metamorphic grade. By contrast, Abee shows evidence for only partial resetting of the Hf-W system during metamorphism, and the 182W composition of Abee's metal still records the time of chondrule formation at ∼2 Ma after CAI formation. Thus, the thermal and cooling histories of enstatite chondrites do not appear to be a simple function of their metamorphic grade.Despite their distinct Fe-metal contents, EH and EL chondrites and their precursor material have uniform Hf/W ratios. This most likely reflects the early and proportional removal of refractory metal and silicate components from the enstatite chondrite formation region, which left the Hf/W ratio unchanged. As such, the Hf/W ratio of enstatite chondrites provides a good estimate for the average composition of primitive chondritic material from the inner solar system. All carbonaceous chondrites except CI chondrites have higher Hf/W ratios, reflecting admixture of CAIs or metal-silicate fractionation during chondrule formation. Using the Hf/W ratio of enstatite chondrites, rather than carbonaceous chondrites, in the calculation of Hf-W core formation model ages makes these ages up to ∼0.7 Ma younger, shifting the core formation ages for non-carbonaceous iron meteorites closer to those of carbonaceous iron meteorites.