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The gas-phase reaction Cl + NH3 → HCl + NH2 is a prototypical hydrogen abstraction reaction, whose minimum energy path involves several intermediate complexes. In this work, a full-dimensional, spin–orbit corrected potential energy surface (SOC PES) is constructed for the ground electronic state of the Cl + NH3 reaction. About 52 000 energy points are sampled and calculated at the UCCSD(T)-F12a/aug-cc-pVTZ level, in which the data points located in the entrance channel are spin–orbit corrected. The spin–orbit corrections are predicted by a fitted three-dimensional energy surface from about 7520 energy points in the entrance channel at the level of CASSCF (15e, 11o)/aug-cc-pVTZ. The fundamental-invariant neural network method is utilized to fit the SOC PES, resulting in a total root mean square error of 0.12 kcal mol−1. The calculated thermal rate constants of the Cl + NH3 → HCl + NH2 reaction on the SOC PES with the soft-zero-point energy constraint agree reasonably well with the available experimental values. [ABSTRACT FROM AUTHOR]

A number of internal rotation codes can deal with the combination of one or two internal rotors with one 14N quadrupole nucleus, but once it comes to two 14N nuclei, no such code is available even for the case of one internal rotor. We present here the extension of our internal rotor program called BELGI-2N using the rho-axis method global approach to deal with compounds containing one methyl rotor and two weakly coupling 14N nuclei. To test our new code, we applied it to the microwave data recorded for N-methylimidazole, 2-methylimidazole, 4-methylimidazole, and 5-methylimidazole using a chirped-pulse Fourier transform microwave spectrometer in the 7.0–18.5 GHz frequency range. Compared to the previously published study, BELGI-2N was able to (i) significantly increase the number of assigned and fitted lines, (ii) fit the complete datasets considering both the internal rotation and the 14N nuclear quadrupole coupling effects simultaneously, and (iii) achieve standard deviations within the measurement accuracy for all methylimidazole isomers. [ABSTRACT FROM AUTHOR]

The lattice thermal conductivity stands as a pivotal thermos-physical parameter of high-entropy alloys; nonetheless, achieving precise predictions of the lattice thermal conductivity for high-entropy alloys poses a formidable challenge due to their complex composition and structure. In this study, machine learning models were built to predict the lattice thermal conductivity of AlCoCrNiFe high-entropy alloy based on molecular dynamic simulations. Our model shows high accuracy with R2, mean absolute percentage error, and root mean square error of the test set is 0.91, 0.031, and 1.128 W m−1 k−1, respectively. In addition, a high-entropy alloy with low a lattice thermal conductivity of 2.06 W m−1 k−1 (Al8Cr30Co19Ni20Fe23) and with a high lattice thermal conductivity of 5.29 W m−1 k−1 (Al0.5Cr28.5Co25Ni25.5Fe20.5) was successfully predicted, which shows good agreement with the results from molecular dynamics simulations. The mechanisms of the thermal conductivity divergence are further explained through their phonon density of states and elastic modulus. The established model provides a powerful tool for developing high-entropy alloys with the desired properties. [ABSTRACT FROM AUTHOR]

Background: The Perceived Physical Literacy Instrument (PPLI) has been the most adopted physical literacy instrument designed for adults to date, having been already translated to 5 languages. Still, despite its popularity, there is limited evidence of its psychometric properties. The aim of this study was to translate and adapt cross-culturally the PPLI to the Portuguese language and to analyze the psychometric properties of the Portuguese version in a sample of Portuguese-speaking adults. Methods: The study sample was composed of 434 participants (70% females) with a median age of 25 (18–39) years. Confirmatory factor analysis was used to assess psychometric properties of the scale. In addition, instrument assumptions between sexes through invariance testing were examined. Finally, concurrent validity, test–retest reliability, and test–retest agreement analyses were performed to gather additional evidence. Results: The results of the present study generally supported the measurement model. The 9-item, 3-factor model presented a good fit to the data and adequate construct validity (χ2 = 82.859 [24], P <.001, comparative-fit index =.948, Tucker–Lewis index =.922, standardized root mean square residual =.049, root mean square error of approximation =.075). In addition, the scale presented concurrent validity. Some issues arose with measurement invariance and overtime consistency. Conclusions: Considering the lack of validated instruments, we suggest a fair use of Portuguese version of PPLI to access physical literacy in adults. This study contributes to the validation of the PPLI by providing evidence of its psychometric properties in the Portuguese-speaking adult population and facilitates comparative analysis with previous and future studies utilizing the PPLI instrument. [ABSTRACT FROM AUTHOR]

A novel mode-localized acceleration sensor employing an electrostatically coupled resonator and integrating a lever with proof mass is micromachined using standard silicon on insulator (SOI) technology. In order to determine the linear dynamic range of the sensor, a reduced order model is developed while assuming that the resonators vibrate below the critical amplitude. Then, open-loop and closed-loop testing platforms are established to measure the performance of the linearly operating accelerometer in a vacuum environment (less than 5 Pa). Moreover, the corresponding amplifier circuit based on the capacitive detection principle is designed in order to extract and amplify the current signal from the resonators. The obtained results show that the accelerometer sensitivity can be increased by three orders of magnitude when using the relative shift of amplitude ratio as the output metric instead of the relative shift of frequency, and the experimental measurements are consistent with the theoretical predictions. Remarkably, the Allan standard deviation of the mode-localized acceleration sensor obtained from the closed-loop testing circuit is around 5.03 μg. [ABSTRACT FROM AUTHOR]

Professional sport facility sustainability initiatives offer sport organizations an opportunity to demonstrate congruence with societal concern for the environment, an effort that also affects stadia revenue generation. Guided by diffusion of innovations theory, this study harnessed diffusion modeling and logistic regression to determine how quickly renewable energy source adoption is diffusing across 175 professional sport stadia in the United States and Canada and the factors catalyzing early renewable energy source adoption. Results revealed 86 (49%) facilities adopted at least one type of renewable energy source, with solar emerging as the predominant technology adopted (68 total adoptions). Full diffusion for renewable source adoption was predicted for 2061 (p =.0094, q = 0.1404, root mean square error = 3.25, mean absolute error = 2.51), while not all renewable energy sources were predicted to fully diffuse (wind; p =.0117, q = −0.0710, root mean square error =.853, mean absolute error = 0.675). New stadia construction during the time of adoption, facility type, and geographical social systems emerged as significant factors catalyzing adoption in the early majority. [ABSTRACT FROM AUTHOR]

The majority of warehouse expenses is driven by labour cost. Therefore, efficient management of labour resources is required. To do so, workforce planning is used to match the workforce capacity with the incoming workload. While doing so, it is often wrongly assumed that each worker has the same and constant capacity or performance. Addressing this, we build a model to predict the employee-based productivity of newly hired warehouse personnel that will support workforce planning by incorporating multiple data sources. To this end,wedevelop a framework to identify relevant variables in four categories: warehouse, operator, shift and product. We demonstrate that Extreme Gradient Boosting, using these variables may reduce the root mean squared error of the prediction by more than 50%. A comprehensive scenario analysis shows that improving productivity predictions translates into substantial cost savings. Furthermore, a sensitivity analysis identifies which variable categories should be favoured in the data collection process to achieve the best prediction results. [ABSTRACT FROM AUTHOR]

The largest ever set, RotGT-2023, of statistically validated experimental components of rotational g tensors and respective vibrational corrections, has been created. The experimental values were collected from literature data. The vibrational corrections for obtaining equilibrium molecular g values have been calculated at the highest affordable coupled cluster level of theory. The set comprises 278 parameters from 129 molecules, including their isotopologues. Statistical assessment of the data has been performed to exclude unreliable parameters. A benchmarking of two theoretical approximations, based on coupled cluster and density functional theories, has been performed. The determined weighted mean and weighted standard deviations of the relative errors in calculated equilibrium g values are 1.09% and 2.07% for the ae-CCSD(T)/x2c-TZVPPall-s//ae-CCSD(T)/cc-pwCVTZ level. The obtained results can be used for predicting the rotational Zeeman effect, correcting rotational constants in spectroscopic studies and in molecular structure refinements. The latter has been demonstrated on the refinement of the molecular structure of silane SiH4, which resulted in r e se (Si–H) = 1.473 323 1(27) Å. The other tested theoretical protocol, PBE0/x2c-TZVPPall-s//PBE0-D3BJ/def2-QZVPP, showed considerably worse statistical properties. The RotGT-2023 data set and the developed in this work statistical model are recommended for benchmarking of theoretical approximations for calculations of molecular magnetic properties. [ABSTRACT FROM AUTHOR]

Machine learning (ML) is effective to establish the complicated trilateral relationship among structures, properties, and photovoltaic performance, which is fundamental issue in developing novel materials for improving power conversion efficiency (PCE) of organic solar cells (OSCs). Herein, we constructed the database of 397 donor–acceptor pairs of OSCs with photovoltaic parameters and descriptor sets, which include donor–acceptor weight ratio within the active layer of the OSCs, root mean square of roughness, and 1024-bit Morgan molecular fingerprint for donor (Fp-D) and acceptor (Fp-A). The ML models random forest (RF), adaptive boosting (AdaBoost), extra trees regression, and gradient boosting regression trees were trained based on the descriptor set. The metrics determination coefficient (R2), Pearson correlation coefficient (r), root mean square error, and mean absolute error were selected to evaluate ML model performances. The results showed that the RF model exhibits the highest accuracy and stability for PCE prediction among these four ML models. Moreover, based on the decomposition of non-fullerene acceptors L8-BO, BTP-ec9, AQx-2, and IEICO, 20 acceptor molecules with symmetric A–D–A and A–π–D–π–A architectures were designed. The photovoltaic parameters of the designed acceptors were predicted using the trained RF model, and the virtual screening of designed acceptors was conducted based on the predicted PCE. The results indicate that six designed acceptors can reach the predicted PCE higher than 12% when P3HT was adopted as a donor. While PM6 was applied as a donor, five designed acceptors can achieve the predicted PCE higher than 16%. [ABSTRACT FROM AUTHOR]

A hybrid stochastic simulation method is developed to study H2–O2 auto-ignition at the microscale. Simulation results show that the discrete and stochastic characteristics of reaction collisions have notable impacts on the ignition process, particularly in the early stages when only a few radicals exist. The statistical properties of ignition delay time, which reflect the accumulated stochasticity during ignition, are obtained and analyzed for different initial temperatures and total molecular numbers. It is found that the average and standard deviation of ignition delay time increase as the total molecular number decreases, with this phenomenon being particularly pronounced near the crossover temperature. When the total molecular number is sufficiently small, the chain initiation reaction becomes crucial to the stochastic properties, as its average firing time exhibits an inverse proportionality to the total molecular number. As the total molecular number increases, the influence of other chain reactions intensifies, causing the power law relation between standard deviation and total molecular number to shift from −1 power to −0.5 power. Owing to different chain reaction paths for high- and low-temperature auto-ignition, the strongest relative fluctuation occurs near the crossover temperature. A theoretical equation for the standard deviation of ignition delay time is obtained based on dimensional analysis, giving excellent agreement with the simulation results in both high- and low-temperature modes. [ABSTRACT FROM AUTHOR]

Machine learning potentials (MLPs) are poised to combine the accuracy of ab initio predictions with the computational efficiency of classical molecular dynamics (MD) simulation. While great progress has been made over the last two decades in developing MLPs, there is still much to be done to evaluate their model transferability and facilitate their development. In this work, we construct two deep potential (DP) models for liquid water near graphene surfaces, Model S and Model F, with the latter having more training data. A concurrent learning algorithm (DP-GEN) is adopted to explore the configurational space beyond the scope of conventional ab initio MD simulation. By examining the performance of Model S, we find that an accurate prediction of atomic force does not imply an accurate prediction of system energy. The deviation from the relative atomic force alone is insufficient to assess the accuracy of the DP models. Based on the performance of Model F, we propose that the relative magnitude of the model deviation and the corresponding root-mean-square error of the original test dataset, including energy and atomic force, can serve as an indicator for evaluating the accuracy of the model prediction for a given structure, which is particularly applicable for large systems where density functional theory calculations are infeasible. In addition to the prediction accuracy of the model described above, we also briefly discuss simulation stability and its relationship to the former. Both are important aspects in assessing the transferability of the MLP model. [ABSTRACT FROM AUTHOR]

We present a local and transferable machine-learning approach capable of predicting the real-space density response of both molecules and periodic systems to homogeneous electric fields. The new method, Symmetry-Adapted Learning of Three-dimensional Electron Responses (SALTER), builds on the symmetry-adapted Gaussian process regression symmetry-adapted learning of three-dimensional electron densities framework. SALTER requires only a small, but necessary, modification to the descriptors used to represent the atomic environments. We present the performance of the method on isolated water molecules, bulk water, and a naphthalene crystal. Root mean square errors of the predicted density response lie at or below 10% with barely more than 100 training structures. Derived polarizability tensors and even Raman spectra further derived from these tensors show good agreement with those calculated directly from quantum mechanical methods. Therefore, SALTER shows excellent performance when predicting derived quantities, while retaining all of the information contained in the full electronic response. Thus, this method is capable of predicting vector fields in a chemical context and serves as a landmark for further developments. [ABSTRACT FROM AUTHOR]

In this work, the dynamics of a prototypical heavy–light–heavy abstract reaction, Cl(2P) + HCl → HCl + Cl(2P), is investigated both by constructing a new potential energy surface (PES) and by rate coefficient calculations. Both the permutation invariant polynomial neural network method and the embedded atom neural network (EANN) method, based on ab initio MRCI-F12+Q/AVTZ level points, are used for obtaining globally accurate full-dimensional ground state PES, with the corresponding total root mean square error being only 0.043 and 0.056 kcal/mol, respectively. In addition, this is also the first application of the EANN in a gas-phase bimolecular reaction. The saddle point of this reaction system is confirmed to be nonlinear. In comparison with both the energetics and rate coefficients obtained on both PESs, we find that the EANN is reliable in dynamic calculations. A full-dimensional approximate quantum mechanical method, ring-polymer molecular dynamics with a Cayley propagator, is employed to obtain the thermal rate coefficients and kinetic isotopic effects of the title reaction Cl(2P) + XCl→ XCl + Cl(2P) (H, D, Mu) on both new PESs, and the kinetic isotope effect (KIE) is also obtained. The rate coefficients reproduce the experimental results at high temperatures perfectly but with moderate accuracy at lower temperatures, but the KIE is with high accuracy. The similar kinetic behavior is supported by quantum dynamics using wave packet calculations as well. [ABSTRACT FROM AUTHOR]

In various types of many-particle systems, bidispersity is frequently used to avoid spontaneous ordering in particle configurations. In this study, the relation between bidispersity and disorder degree of particle configurations is investigated. By using magnetic dipole–dipole interaction, magnet particles are dispersed in a two-dimensional cell without any physical contact between them. In this magnetic system, bidispersity is introduced by mixing large and small magnets. Then, the particle system is compressed to produce a uniform particle configuration. The compressed particle configuration is analyzed by using Voronoi tessellation for evaluating the disorder degree, which strongly depends on bidispersity. Specifically, the standard deviation and skewness of the Voronoi cell area distribution are measured. As a result, we find that the peak of standard deviation is observed when the numbers of large and small particles are almost identical. Although the skewness shows a non-monotonic behavior, a zero skewness state (symmetric distribution) can be achieved when the numbers of large and small particles are identical. In this ideally random (disordered) state, the ratio between pentagonal, hexagonal, and heptagonal Voronoi cells becomes roughly identical, while hexagons are dominant under monodisperse (ordered) conditions. The relation between Voronoi cell analysis and the global bond orientational order parameter is also discussed. [ABSTRACT FROM AUTHOR]

BACKGROUND: The rapid and accurate detection of moisture content is important to ensure maize quality. However, existing technologies for rapidly detecting moisture content often suffer from the use of costly equipment, stringent environmental requirements, or limited accuracy. This study proposes a simple and effective method for detecting the moisture content of single maize kernels based on viscoelastic properties. RESULTS: Two types of viscoelastic experiments were conducted involving three different parameters: relaxation tests (initial loads: 60, 80, 100 N) and frequency‐sweep tests (frequencies: 0.6, 0.8, 1 Hz). These experiments generated corresponding force‐time graphs and viscoelastic parameters were extracted based on the four‐element Maxwell model. Then, viscoelastic parameters and data of force‐time graphs were employed as input variables to explore the relationships with moisture content separately. The impact of different preprocessing methods and feature time variables on model accuracy was explored based on force‐time graphs. The results indicate that models utilizing the force‐time data were more accurate than those utilizing viscoelastic parameters. The best model was established by partial least squares regression based on S‐G smoothing data from relaxation tests conducted with initial force of 100 N. The correlation coefficient and the root mean square error of the calibration set were 0.954 and 0.021, respectively. The corresponding values of the prediction set were 0.905 and 0.029, respectively. CONCLUSIONS: This study confirms the potential for accurate and fast detection of moisture content in single maize kernels using viscoelastic properties, which provides a novel approach for the detection of various components in cereals. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Ammonia emerges as a promising substitute for traditional fuels, offering a potential reduction in fossil fuel consumption and the associated emissions. Given its weak reactivity, an effective strategy to harness the potential of ammonia involves cofiring it with methane, to establish a self-sustaining combustion process. The understanding of heat release rate (HRR) in the cofiring process of ammonia and methane is crucial for burner design. This study introduces novel HRR indicators through a machine learning-based approach, focusing on species significantly contributing to HRR. Kinetic analysis is carried out utilizing a one-dimensional freely propagating flame model, applying a mechanism including 59 species and 356 reactions. Domain knowledge-based feature selection narrows down the search space, enhancing computational efficiency during HRR indicator identification. A random forest model is then employed to identify radical/radical combinations and their respective reaction orders, representing HRR optimally based on mean decrease impurity. The proposed HRR markers are [CH 4 ]0·76[OH]1.08, [HCO]0·65[NO]0.24, [CH 3 ]0.65[O]0.63, [NH 2 ][O]0.59, and [NH 3 ]0·93[OH]0.96. The root mean square error for each marker was extracted and compared with literature data, demonstrating higher reliability of the proposed indicators over those previously suggested. Additionally, the paper concludes with a discussion of the feasibility of measuring these markers from an experimental perspective. [Display omitted] • Thorough kinetic analysis of focusing on key HRR contributors. • Pioneering ML for crucial HRR indicator identification in ammonia-methane cofiring. • Domain-based feature selection streamlines HRR marker identification for efficiency. • Proposed 5 HRR markers that are superior in accuracy compared to existing markers. [ABSTRACT FROM AUTHOR]

The huge integration of photovoltaic (PV) systems into power systems due to fossil fuel scarcity and environmental considerations leads to serious issues in the operation of electrical grids. The reliability and accuracy of PV module models allow the optimization of PV systems. The PV cells are described by a nonlinear current–voltage characteristic, and the parameters not specified by the manufacturer's sheet are generally estimated. Despite the significant effort provided by the PV parameter extraction problem, the latter is still an arduous task. This work suggests a new modified coot optimization algorithm with local search (MCOOT-LS) for effectively extracting the unknown parameters of PV modules. More to the point, the conventional COOT algorithm profits from chaotic dynamic-opposite learning, a new exploration strategy, and a local search to reach a high balance between exploitation and exploration for complex problems. In this work, we employed two PV modules composed of silicon monocrystalline and polycrystalline cells, like the LSM 20 and STE 4/100 photovoltaic panels. Previous works in the domain of PV parameter estimation overlooked the importance of anti-noise ability. However, with a view to revealing the efficacy of the proposed MCOOT-LS, this manuscript examined the solution accuracy, convergence speed, and anti-noise properties of all the algorithms used. To assess the effect of noise on the obtained results, two levels of noise (5% and 10%) have been considered, and the objective function called a Root Mean Square Error (RMSE) is adopted. The results prove that the proposed MCOOT-LS algorithm outperformed all the comparative algorithms regarding convergence feature and solution reliability for both noise-free and noisy results. • A modified COOT optimization algorithm for PV parameter estimation is proposed. • A simulation model of PV modules is developed. • Experimental validation for the used PV modules is implemented. • Statistical analysis is conducted for all used algorithms. • The effects of noise measurement on all used algorithms is investigated. [ABSTRACT FROM AUTHOR]

Due to various unavoidable reasons or gross error elimination, missing data inevitably exist in global navigation satellite system (GNSS) position time series, which may result in many analysis methods not being applicable. Typically, interpolating the missing data is a crucial preprocessing step before analyzing the time series. The conventional methods for filling missing data do not consider the influence of adjacent stations. In this work, an improved Gaussian process (GP) approach is developed to fill the missing data of GNSS time series, in which the time series of adjacent stations are applied to construct impact factors, together with a comparison of the conventional GP and the commonly used cubic spline methods. For the simulation experiments, the root mean square error (RMSE), mean absolute error (MAE) and correlation coefficient (R) are adopted to evaluate the performance of the improved GP. The results show that the filled missing data of the improved GP are closer to the true values than those of the conventional GP and cubic spline methods, regardless of the missing percentages ranging from 5 to 30%, with an interval of 5%. Specifically, the mean relative RMSE and MAE improvements for the improved GP with respect to the conventional GP are 21.2%, 21.3% and 8.3% and 12.7%, 16.2% and 11.01% for the North (N), East (E) and Up (U) components, respectively. In the real experiment, eight GNSS stations are analyzed using improved GP, together with conventional GP and a cubic spline. The results indicate that the first three principal components (PCs) of the improved GP can perverse 98.3%, 99.8% and 77.0% of the total variance for the N, E and U components, respectively. This value is obviously higher than those of the conventional GP and cubic spline. Therefore, we can conclude that the improved GP can better fill in the missing data in GNSS position time series than the conventional GP and cubic spline because of the impacts of adjacent stations. [ABSTRACT FROM AUTHOR]

Literature concerning operant behavioral economics shows a strong preference for the coefficient of determination (R2) metric to (a) describe how well an applied model accounts for variance and (b) depict the quality of collected data. Yet R2 is incompatible with nonlinear modeling. In this report, we provide an updated discussion of the concerns with R2. We first review recent articles that have been published in the Journal of the Experimental Analysis of Behavior that employ nonlinear models, noting recent trends in goodness‐of‐fit reporting, including the continued reliance on R2. We then examine the tendency for these metrics to bias against linear‐like patterns via a positive correlation between goodness of fit and the primary outputs of behavioral‐economic modeling. Mathematically, R2 is systematically more stringent for lower values for discounting parameters (e.g., k) in discounting studies and lower values for the elasticity parameter (α) in demand analysis. The study results suggest there may be heterogeneity in how this bias emerges in data sets of varied composition and origin. There are limitations when using any goodness‐of‐fit measure to assess the systematic nature of data in behavioral‐economic studies, and to address those we recommend the use of algorithms that test fundamental expectations of the data. [ABSTRACT FROM AUTHOR]

A transient pipeline flow model with gas composition tracking is solved for studying the operation of a natural gas pipeline under nonisothermal flow conditions in a hydrogen injection scenario. Two approaches to high-resolution pipeline flow modeling based on the WENO scheme are presented and compared with the implicit finite difference method. The high-resolution models are capable of capturing fast fluid transients and tracking the step changes in the composition of the transported mixture. The implicit method assumes the decoupling of the flow model components in order to enhance calculation efficiency. The validation of the composition tracking results against actual gas transmission pipeline indicates that both models exhibit good prediction performance, with normalized root mean square errors of 0.406% and 1.48%, respectively. Under nonisothermal flow conditions, the prediction response of the reduced model against a high-resolution flow model, with respect to the mass and energy linepack, is at most 3.20%. [Display omitted] • Two approaches to high-resolution pipeline flow modeling based on the WENO scheme are presented. • The solutions are capable of capturing fast fluid transients and tracking natural gas-hydrogen mixture composition. • High-resolution solutions are compared to the solution with the implicit finite difference method. • The effect of hydrogen injection on pipeline mass and energy linepack is studied in detail. [ABSTRACT FROM AUTHOR]

Partial least squares regression (PLSR) and support vector regression (SVR) models were optimized for the quantification of U(VI) (10-320 g L-1) and HNO3 (0.6-6 M) by Raman spectroscopy with optimized calibration sets chosen by optimal design of experiments. The designed approach effectively minimized the number of samples in the calibration set for PLSR and SVR by selecting sample concentrations with a quadratic process model, despite complex confounding and covarying spectral features in the spectra. The top PLS2 model resulted in percent root mean square errors of prediction for U(VI), HNO3, and NO3 - of 3.7%, 3.6%, and 2.9%, respectively. PLS1models performed similarly despitemodeling an analyte with a majority linear response (i.e., uranyl symmetric stretch) and another with more covarying vibrational modes (i.e., HNO3). Partial least squares (PLS) model loadings and regression coefficients were evaluated to better understand the relationship between weaker Raman bands and covarying spectral features. Support vector machine models outperformed PLS1 models, resulting in percent root mean square error of prediction values for U(VI) and HNO3 of 1.5% and 3.1%, respectively. The optimal nonlinear SVR model was trained using a similar number of samples (11) compared with the PLSR model, even though PLS is a linear modeling approach. The generic D-optimal design presented in this work provides a robust statistical framework for selecting training set samples in disparate two-factor systems. This approach reinforces Raman spectroscopy for the quantification of species relevant to the nuclear fuel cycle and provides a robust chemometric modeling approach to bolster online monitoring in challenging process environments. [ABSTRACT FROM AUTHOR]

Introduction: The precise forecasting of Significant wave height(SWH) is vital to ensure the safety and efficiency of aquatic activities such as ocean engineering, shipping, and fishing. Methods: This paper proposes a deep learning model named SAC-ConvLSTM to perform 24-hour prediction with the SWH in the South China Sea. The long-term prediction capability of the model is enhanced by using the attention mechanism and context vectors. The prediction ability of the model is evaluated by mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and Pearson correlation coefficient (PCC). Results: The experimental results show that the optimal input sequence length for the model is 12. Starting from 12 hours, the SAC-ConvLSTM model consistently outperforms other models in predictive performance. For the 24-hour prediction, this model achieves RMSE, MAE, and PCC values of 0.2117 m, 0.1083 m, and 0.9630, respectively. In addition, the introduction of wind can improve the accuracy of wave prediction. The SAC-ConvLSTM model also has good prediction performance compared to the ConvLSTM model during extreme weather, especially in coastal areas. Discussion: This paper presents a 24-hour prediction of SWH in the South China Sea. Through comparative validation, the SAC-ConvLSTM model outperforms other models. The inclusion of wind data enhances the model's predictive capability. This model also performs well under extreme weather conditions. In physical oceanography, variables related to SWH include not only wind but also other factors such as mean wave period and sea surface air pressure. In the future, additional variables can be incorporated to further improve the model's predictive performance. [ABSTRACT FROM AUTHOR]

Introduction: Traditional statistical methods cannot find quantitative relationship from environmental data. Methods: We selected gene expression programming (GEP) to study the relationship between pollutant gas and PM2.5 (PM10). They were used to construct the relationship between pollutant gas and PM2.5 (PM10) with environmental monitoring data of Xi'an, China. GEP could construct a formula to express the relationship between pollutant gas and PM2.5 (PM10), which is more explainable. Back Propagation neural networks (BPNN) was used as the baseline method. Relevant data from January 1st 2021 to April 26th 2021 were used to train and validate the performance of the models from GEP and BPNN. Results: After the models of GEP and BPNN constructed, coefficient of determination and RMSE (Root Mean Squared Error) are used to evaluate the fitting degree and measure the effect power of pollutant gas on PM2.5 (PM10). GEP achieved RMSE of [8.7365-14.6438] for PM2.5; RMSE of [13.2739-45.8769] for PM10, and BP neural networks achieved average RMSE of [13.8741-34.7682] for PM2.5; RMSE of [29.7327-52.8653] for PM10. Additionally, experimental results show that the influence power of pollutant gas on PM2.5 (PM10) situates between -0.0704 and 0.6359 (between -0.3231 and 0.2242), and the formulas are obtained with GEP so that further analysis become possible. Then linear regression was employed to study which pollutant gas is more relevant to PM2.5 (PM10), the result demonstrates CO (SO2, NO2) are more related to PM2.5 (PM10). Discussion: The formulas produced by GEP can also provide a direct relationship between pollutant gas and PM2.5 (PM10). Besides, GEP could model the trend of PM2.5 and PM10 (increase and decrease). All results show that GEP can be applied smoothly in environmental modelling. [ABSTRACT FROM AUTHOR]

In response to the pressing need for innovative educational approaches, this research investigates the implementation of SIMULE to evaluate its impact on the conceptual understanding and procedural fluency of Grade 7 students. This study focused on utilizing SIMULE as an instructional resource in improving the conceptual understanding and procedural fluency of Grade 7 students at Lambayong National High School, School Year 2023 – 2024. A total of 89 students were the respondents of the study. Respondents were selected through purposive sampling and grouped using simple random sampling. This study employed a quasi-experimental design. The data were gathered using the researcher-made pretest and posttest. Mean and standard deviation determined the student’s concept fluency level and procedural understanding. T-test was used to determine the significant difference between the two groups' pretest and posttest scores and the mean gain scores. The study revealed that both groups had little mastery of the topics before the conduct of the study. In the posttest, both groups improved their conceptual understanding and procedural fluency. It revealed that using SIMULE and conventional teaching can help students improve their conceptual understanding and procedural fluency. However, students exposed to SIMULE performed better than those exposed to traditional teaching. Further, the learning gains were significantly higher in favor of the students exposed to SIMULE. Thus, the study recommended using SIMULE as an intervention material to improve students’ conceptual understanding and procedural fluency. [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "The ANGPTL4-HIF-1α loop: a critical regulator of renal interstitial fibrosis" published in the Journal of Translational Medicine. The correction states that Figure 2 in the original article was published incorrectly and provides the correct version of the graph. The article has been updated to reflect this correction. The publisher, Springer Nature, remains neutral regarding jurisdictional claims and institutional affiliations. The authors of the article are Yan Li, Shuang Chen, Qian Yang, Xiao Liu, Weiming Zhou, Ting Kang, Weihua Wu, and Santao Ou. [Extracted from the article]

Single-domain antibodies (sdAbs) or nanobodies have received widespread attention due to their small size (~ 15 kDa) and diverse applications in bio-derived therapeutics. As many modern biotechnology breakthroughs are applied to antibody engineering and design, nanobody thermostability or melting temperature (Tm) is crucial for their successful utilization. In this study, we present TEMPRO which is a predictive modeling approach for estimating the Tm of nanobodies using computational methods. Our methodology integrates various nanobody biophysical features to include Evolutionary Scale Modeling (ESM) embeddings, NetSurfP3 structural predictions, pLDDT scores per sdAb region from AlphaFold2, and each sequence's physicochemical characteristics. This approach is validated with our combined dataset containing 567 unique sequences with corresponding experimental Tm values from a manually curated internal data and a recently published nanobody database, NbThermo. Our results indicate the efficacy of protein embeddings in reliably predicting the Tm of sdAbs with mean absolute error (MAE) of 4.03 °C and root mean squared error (RMSE) of 5.66 °C, thus offering a valuable tool for the optimization of nanobodies for various biomedical and therapeutic applications. Moreover, we have validated the models' performance using experimentally determined Tms from nanobodies not found in NbThermo. This predictive model not only enhances nanobody thermostability prediction, but also provides a useful perspective of using embeddings as a tool for facilitating a broader applicability of downstream protein analyses. [ABSTRACT FROM AUTHOR]

An improved semi-empirical formula for cluster radioactivity half-lives is proposed by incorporating the influences of reduced mass, the blocking effect of unpaired nucleons and mass asymmetry on the basis of the original Tavares formula [Eur. Phys. J. A 49 (2013) 6]. Using this improved semi-empirical formula, the calculated results of the cluster radioactivity half-lives for the daughter nuclei around 208Pb or its neighbors can reproduce the experimental data well with a corresponding root-mean-square deviation σ= 0.570, which proves the feasibility of this formula to calculate cluster radioactivity half-lives in trans-lead nuclei. The accuracy of the improved semi-empirical formula is improved by approximately 37% compared to its predecessor whose the root-mean-square deviation is 0.902. Meanwhile, the influence of deformation effect for cluster radioactivity is also briefly discussed. In addition, we extend this improved semi-empirical formula to predict the half-lives of 51 possible cluster radioactive candidates whose cluster radioactivity are energetically allowed or observed but not yet quantified in NUBASE2020. For comparison, some empirical and/or semi-empirical formulae are also used. [ABSTRACT FROM AUTHOR]

For years, researchers have searched for novel antibiotics to combat pathogenic infections. However, antibiotics lack specificity, harm beneficial microbes, and cause the emergence of antibiotic‐resistant strains. This study proposes an innovative approach to selectively eradicate pathogenic bacteria with a minimal effect on non‐pathogenic ones by discovering selectively antimicrobial nanoparticles. To achieve this, a comprehensive database is compiled to characterize nanoparticles and their antibacterial activity. Then, CatBoost regression models are trained for predicting minimal concentration (MC) and zone of inhibition (ZOI). The models achieve a ten‐fold cross‐validation (CV) R2 score of 0.82 and 0.84 with root mean square error (RMSE) of 0.46 and 2.41, respectively. Finally, a machine learning (ML) reinforced genetic algorithm (GA) is developed to identify the best‐performing selective antibacterial NPs. As a proof of concept, a selectively antibacterial nanoparticle, CuO, is identified for targeted eradication of a pathogenic bacteria, Staphylococcus aureus. A difference in minimal bactericidal concentration (MBC) of 392.85 µg mL−1 is achieved when compared to non‐pathogenic bacteria, Bacillus subtilis. These findings significantly contribute to the emerging research domain of selectively toxic (SelTox) nanoparticles and open the door for future exploration of synergetic interactions of SelTox nanoparticles with drugs. [ABSTRACT FROM AUTHOR]

In this study, we evaluated the performance of machine learning (ML) models (XGBoost) in predicting low‐cloud fraction (LCF), compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. ML models show a substantial enhancement in predicting LCF regarding root mean squared errors and correlation coefficients. The good performance is consistent across the full spectrums of atmospheric stability and large‐scale vertical velocity. Employing an explainable ML approach, we revealed the importance of including the amount of available moisture in ML models for representing spatiotemporal variations in LCF in the midlatitudes. Also, ML models demonstrated marked improvement in capturing the LCF variations during the stratocumulus‐to‐cumulus transition (SCT). This study suggests ML models' great potential to address the longstanding issues of "too few" low clouds and "too rapid" SCT in global climate models. Plain Language Summary: Low clouds impose a strong radiative cooling effect on Earth's climate. Predicting low‐cloud fraction (LCF) is, however, challenging in global climate models (GCMs), partly due to some deficiencies in cloud parameterization schemes. Machine learning (ML) models might fill this gap as it is recognized as an efficient, economical, and accurate method to make predictions. In this study, we find that ML models (XGBoost) exhibit superior proficiency in predicting LCF regarding root mean squared errors and correlation coefficients compared to two generations of the community atmospheric model (CAM5 and CAM6) and ERA5 reanalysis data, each having a different cloud scheme. This improvement helps address one important issue of "too few" low clouds in GCMs. Furthermore, ML models demonstrate marked improvement in representing LCF variations when stratocumulus clouds transition to cumulus clouds, as opposed to too rapid decreases in LCF simulated by two CAMs and ERA5. Such findings testify to the unique role of ML models in refining the parameterization of LCF within GCMs. Key Points: Machine learning (ML) models substantially improve "too few" low‐cloud problems in the subtropics compared to traditional cloud schemesThey also show marked improvement in representing low‐cloud fraction (LCF) variations during the stratocumulus‐to‐cumulus transitionIncluding the effect of moisture source in ML models is crucial to representing spatiotemporal variations in LCF in the midlatitudes [ABSTRACT FROM AUTHOR]

This study investigates the effects of sea spray on extreme precipitation forecast in Beijing of China between 28 July and 2 August 2023 as a case test. In this case, fully coupled model increased upward moisture in the Bohai and Yellow Seas and increased accumulated rainfall by 21% in North China. For the extreme precipitation events with the 5‐day accumulated precipitation exceeding 500 mm, the atmosphere‐only model did not forecast the events; the coupled model without sea spray performed well with the 0.29 threat score (TS) and 88 mm root mean square error (RMSE); in the fully coupled model, the effects of sea spray increased atmospheric instability, which increased the precipitation around Beijing and yielded a more accurate forecast with the 0.37 TS and 65 mm RMSE. This paper suggests a scientific clue to improve numerical simulation for extreme rainfall events, however, more cases are still needed for statistical evaluation. Plain Language Summary: Although meteorological forecasting ability has been improved considerably during the past decades, precipitation during extreme events remains typically underestimated. Improving model accuracy for heavy rainfall events is, indeed, a ground challenge. In this study, we conducted three numerical experiments under different conditions to evaluate the effects of sea spray on extreme precipitation forecasts for the Beijing extreme rainfall event between 28 July and 2 August 2023. Our results indicated that for this 5‐day forecasting case, including sea spray in the simulations enhanced precipitation in North China by transporting more moisture from the Bohai and Yellow Seas upward into the atmosphere. The moister and warmer air caused by sea spray effects was transported to Beijing through northwestward wind and lifted by the local terrain, then caused a more unstable atmosphere and higher intensity of precipitation around Beijing. By using statistical indicators and setting different precipitation thresholds for this extreme rainfall case, we determined that in this case, the fully coupled model including sea spray yielded more accurate forecast. To statistically evaluate the effects of sea spray in extreme precipitation forecasts, more cases are still needed. Key Points: Fully coupled model led more upward moisture in the Bohai and Yellow Seas and increased 5‐day accumulated rainfall by 21% in North ChinaFully coupled model improved the forecasting threat score from 0 to 0.37 with the 5‐day accumulated precipitation exceeding 500 mmSea spray led to more unstable atmosphere for the Beijing extreme rainfall event, yielding a more accurate forecast [ABSTRACT FROM AUTHOR]

Global change has substantially shifted vegetation phenology, with important implications in the carbon and water cycles of terrestrial ecosystems. Various vegetation phenology datasets have been developed using remote sensing data; however, the significant uncertainties in these datasets limit our understanding of ecosystem dynamics in terms of phenology. It is therefore crucial to generate a reliable large-scale vegetation phenology dataset, by fusing various existing vegetation phenology datasets, to provide comprehensive and accurate estimation of vegetation phenology with fine spatiotemporal resolution. In this study, we merged four widely used vegetation phenology datasets to generate a new dataset using the Reliability Ensemble Averaging fusion method. The spatial resolution of the new dataset is 0.05° and its temporal scale spans 1982–2022. The evaluation using the ground-based PhenoCam dataset from 280 sites indicated that the accuracy of the newly merged dataset was improved substantially. The start of growing season and the end of growing season in the newly merged dataset had the largest correlation (0.84 and 0.71, respectively) and accuracy in terms of the root mean square error (12 and 17 d, respectively). Using the new dataset, we found that the start of growing season exhibits a significant (p < 0.01) advanced trend with a rate of approximately 0.24 d yr−1, and that the end of growing season exhibits a significant (p < 0.01) delayed trend with a rate of 0.16 d yr−1 over the period 1982–2022. This dataset offers a unique and novel source of vegetation phenology data for global ecology studies. [ABSTRACT FROM AUTHOR]

Background: Tanzania has made significant progress in improving access to HIV care and treatment. However, virologic suppression among people living with HIV (PLHIV) has not been fully realized. In March 2019, Tanzania introduced a World Health Organization (WHO)—recommended dolutegravir-based regimen as the default first-line regimen. Eighteen months later we investigated the HIV viral suppression rates and the factors associated with lack of viral suppression among PLHIV (children and adults) in Tanzania. Methodology: A cross-sectional survey was conducted from September to December 2020 among PLHIV on antiretroviral therapy (ART) in Tanzania. Whole blood samples, demographic data and clinical information were obtained from eligible adults (≥15 years) and children (< 15 years) attending thirty-six HIV care and treatment centres located in 22 regions of Tanzania mainland. A whole blood sample from each participant was processed into plasma and HIV viral load was estimated using real-time PCR. HIV viral suppression was defined at a cut-off of < 50 copies/mL as recommended by WHO. Analyses were conducted using descriptive statistics to establish the national representative prevalence of viral suppression, and logistic regression analyses to determine independent factors associated with non-suppression. Results: A total of 2,039 PLHIV on ART were recruited; of these, adults and children were 57.5% (n = 1173) and 42.5% (n = 866), respectively. Among the adult population, the mean age and standard deviation (SD) was 42.1 ± 12.4 years, with 64.7% being female. Among children, the mean age and SD were 9.6 ± 3 years, and 53.2% were female. Overall viral suppression at < 50 copies/mL (undetectable) was achieved in 87.8% of adults and 74.4% of children. Adults and children on dolutegravir-based regimen recorded viral suppression rates of 89.7% and 85.1% respectively. Factors independently associated with lack of viral suppression status in the adult population were age and ART adherence while in the children population, the factors were sex, ART adherence, and current ART regimen (p<0.05). Conclusion: Dolutegravir-based regimens are promising to help attain epidemic control in Tanzania. More efforts especially on ART adherence are needed to attain optimal treatment outcomes for children and adults PLHIV in Tanzania. [ABSTRACT FROM AUTHOR]

Successful performance in competitive swimming requires a swimmer to maximise propulsion and minimise drag, which can be assessed using instantaneous swimming velocity. Many systems exist to quantify velocity, and therefore, it is important to understand the agreement between systems. This study examined the agreement between an automated video-based system and a tethered system to measure instantaneous velocity. Twenty-two competitive swimmers (state level or higher) completed 25 m of each stroke at maximal intensity. The tethered speedometer was attached to the swimmer’s waist, while videos of each trial were recorded. The swimmer’s head was then automatically tracked using proprietary software, and instantaneous velocity was determined from each system. Bland-Altman plots showed good agreement between the two systems in backstroke (95% Limits of Agreement (LOA): −0.24–0.26 m.s−1) and freestyle (95% LOA: −0.36–0.38 m.s−1) but poorer agreement in butterfly (95% LOA: −0.51–0.53 m.s−1) and breaststroke (95% LOA: −0.88–0.92 m.s−1). The root mean square error was higher in butterfly (0.27 m.s−1) and breaststroke (0.46 m.s−1) compared to backstroke (0.13 m.s−1) and freestyle (0.19 m.s−1). Results demonstrated that the two systems are comparable for measuring instantaneous swimming velocity; however, larger discrepancies are evident for butterfly and breaststroke. [ABSTRACT FROM AUTHOR]

Purpose Methods Results Conclusion Level of Evidence To evaluate the burden experienced by primary informal caregivers of patients who have undergone hip arthroscopy and to identify factors that predict increased caregiver burden.A cross‐sectional study was conducted at a single academic hospital centre, enroling caregivers of patients who underwent hip arthroscopy between November 2018 and November 2023. Caregiver burden was assessed using the Caregiver Burden Inventory (CBI) survey. Multivariable linear regression models were used to identify predictors of caregiver burden, with the global CBI score serving as the primary outcome measure. Secondarily, open‐ended survey questions were analyzed qualitatively to elucidate specific challenges and facilitators of caregiving, as reported by the caregivers themselves.The study involved 99 eligible caregivers (mean [standard deviation] age; 47 [11] years), 58% were female, and 85% were relatives of the patient. The median global CBI score was 13.0 (interquartile range: 8.0–22.4), indicating a moderate burden. Regression analyses demonstrated that younger caregiver age and a higher number of caregiving tasks were significant predictors of increased global burden. Additionally, nonweightbearing status of patients, female gender of caregivers and working full‐time statistically significantly increased specific dimensions of caregiver burden.This study highlights the meaningful burden faced by caregivers of patients undergoing hip arthroscopy, despite its minimally invasive nature and outpatient setting. Identified risk factors such as younger caregiver age, female gender of the caregiver, nonweight‐bearing status and increased caregiving tasks suggest targeted areas for intervention. The qualitative analysis revealed that caregivers struggle with time management and physical and emotional strain, yet better communication and practical support from healthcare teams could help to alleviate these challenges.Level IV, prognostic study. [ABSTRACT FROM AUTHOR]

P-band radar interferometry is a promising tool for digital terrain modelling in forested areas such as the Amazon due to the ability of P-band to penetrate densely forested areas. The quality of Digital Terrain Models (DTMs) is a primary requirement for many geoscience applications because DTM accuracy is an essential variable for characterizing landform shapes. This article presents a multicriteria evaluation of the overall quality of a digital terrain model produced using P-band SAR interferometric processing. The evaluation considers behaviours related to elevation and slope and analyses the impact of landscape properties such as slope and aspect. The multicriteria accuracy of the study showed promising results about the realism of landforms, according to applied geomorphological criteria, and also in the visual comparison between the produced DTM and the reference. The study provided an estimate of the accuracy of P-band radar DTM in terms of elevation with a standard deviation of 2.36 m and in terms of slope with a mean error of approximately −4°, a standard deviation of 4.74°, and a RMSE (Root Mean Square Error) slope error of 6.14°, using a reference DTM derived from an airborne LiDAR (Light Detection and Ranging) survey. The spatial behaviour of these errors and their sensitivity to slope and aspect were analysed. The investigation of hydrographic inconsistencies in the DTM was conducted based on various criteria, including identifying depressions along watercourses and adhering to Horton's law. Finally, the effects of the acquisition, processing, and resampling processes are revealed through indicators such as the rose histogram. This multicriteria study demonstrates the suitability of airborne P-band radar interferometry for digital elevation modelling in tropical rainforest environments. [ABSTRACT FROM AUTHOR]

Airborne laser scanning (ALS) datasets are used widely for estimating forest biometrics. The transferability of predictive models among ALS acquisitions is a topic of research due to differences in timing, flight parameters, equipment specifications, environmental conditions, and processing methods. The transferability of predictive models therefore is subject to uncertainty. This paper presents an evaluation of the transferability of models for the estimation of stem volume and diameter at breast height (DBH) based on individual tree crown size and competitive neighbourhood metrics derived for managed loblolly pine (Pinus taeda) and slash pine (Pinus elliottii) forest in the Southern USA. Two predictive models types were tested: multiple linear regression (MLR) and Rand Forest (RF). We also evaluated the inclusion of additional training data to model development. Models were able to be transferred to other locations with similar structural and management conditions as the original training dataset with little decrease in accuracy, specifically unthinned stands, despite different ALS acquisitions (Plot stem volume: R2 0.7–0.8; NRMSE 10–12%; mean DBH: R2 0.4–0.7; NRMSE 10–17%; plot basal area: R2 0.7–0.8; NRMSE 12%). Increases in structural differences between the training and test data, driven by age or thinning status, introduced unacceptable levels of uncertainty (Stem volume: R2 0.4–0.7; NRMSE 12–16%; mean DBH: R2 0.4–0.5; NRMSE 18–20%; plot basal area: R2 0.5–0.6; NRMSE 22–40%). Generally, RF models most accuracy estimated DBH, and MLR for stem volume. Improvements to estimate accuracy can be achieved through the addition of relatively small datasets, representing features which were not present in the original data. ALS's ability to provide accurate and near-complete inventories of forests hold a great deal of potential for forest management. The existence of a transferable model that can be used across different acquisitions represents a saving in terms of cost and time, we would argue that future research is therefore warranted. Novel LiDAR-based allometric models were tested using new LiDAR acquisitions; LiDAR predictions were based on tree crown size and immediate neighbourhood metrics; Model inputs were derived from drone and crewed aircraft LiDAR acquisitions; Model predictions were most accurate when applied to similar stand structures as the original model; Normalized root means square error for stem volume and diameter in pine stands with no thinning was <15%; Stem size estimates in thinned stands were consistently overestimated. [ABSTRACT FROM AUTHOR]

Potentially poisonous elements (PPEs) water pollution has become one of the key issues in the field of water treatment. In recent years, it has threatened ecosystems as well as human health. This study aims to remove PPEs such as chromium (Cr6+), copper (Cu2+), lead (Pb2+), and Zinc (Zn2+) from both vehicle wash wastewater and aqueous solutions through batch experiments utilising carbonaceous adsorbents. Carbonaceous adsorbents were prepared from waste polyethylene terephthalate (wPET) and waste polystyrene (wPS) by carbonisation under a N2 atmosphere followed by chemical activation with 1 M KOH and 1 M HCl. The carbonaceous adsorbents were characterised by Fourier transform infrared spectroscopy, surface area analysis, as well as scanning electron microscopy. The results show that, except for Cr6+, the adsorption of PPEs from aqueous solutions is highly dependent on pH, and the highest removal rate was recorded at pH 6.0. However, the largest Cr6+ adsorption was noted at pH 3.0. Using 0.2 g of adsorbent, the adsorption reached equilibrium after 90 min. Results demonstrated that highest removal of Pb2+, Cu2+, Cr6+, and Zn2+ from aqueous solution achieved over wPET-AC adsorbent was 98.40%, 97.60%, 96.80% and 99.90%, respectively, whereas that of wPS-AC adsorbent was 96.30%, 95.64%, 94.22% and 97.60%, respectively. Root mean square error (RMSE) was also calculated for the validation of data. Isotherms as well as kinetics models are used to evaluate the adsorption capacity of carbonaceous adsorbents for PPEs. The experimental results have shown that the pseudo-second-order as well as Langmuir models are the most suitable for the data. Thermodynamic studies have shown that the adsorption of PPEs is spontaneous as well as endothermic in nature. This study shows that the novel carbonaceous adsorbent wPET-AC and wPS-AC has great potential to remove PPEs from vehicle wash wastewater. [ABSTRACT FROM AUTHOR]

Several efforts have been made to understand the complex physical processes involved in a seismic process, but the findings are vague considering prediction capabilities. Nevertheless, recent seismo-ionosphere precursory research has enlightened new pathways toward building an earthquake (EQ) forecasting system. Previously, some conventional mathematical/statistical approaches have been proposed for detecting an anomalous value as a potential precursor. We propose a hybrid Bayesian-based Long Short-Term Memory (B-LSTM) Network model to forecast the Total Electron Content (TEC) data. We applied B-LSTM on different Vertical TEC (VTEC) datasets of two EQs (Mw7.7 Awaran EQ and Mw7.1 Van EQ) by forecasting VTECs with Normalised Root Mean Square Error (NRMSE) scores of 0.15 and 0.10, respectively. We calculated errors and estimated the 99 % confidence interval to extract the VTEC anomalies. The model detects VTEC anomalies successfully but these anomalies may still have some biases and may lead to a false alarm. In order to minimize possible false alarms, we calculated the intensity of each anomaly and found a strong anomaly that occurred 2–3 days before the EQs. To strengthen the relationship of the detected VTEC anomalies with the investigated earthquakes, we examined the state of space weather conditions during and before the event. Our analysis expands the use of deep learning methods in EQ prediction and VTEC forecasting that can be used for various applications e.g. space weather and navigation. [ABSTRACT FROM AUTHOR]