Search

An improved global potential energy surface (PES) for the electronic ground state of the HeLiH+ system is reported. The data points are calculated at the full configuration-interaction level of theory and extrapolated to the complete basis set limit. The fitting procedure implements a combination of neural network and Aguado–Paniagua functional forms to fit the ab initio data points. The fitted surface reproduces the ab initio data points accurately in short as well as long ranges and has an overall root mean square error of 1.76 × 10−3 eV (14.21 cm−1) in energy space < 10 and 9.28 × 10−4 eV (7.48 cm−1) upto 2 eV. The optimized global minimum is also accurately reproduced using the fitted surface. To establish the accuracy of the new PES, dynamics investigation of the He + LiH+(v = 0, j = 0) → LiHe+ + H reaction is performed using the Coriolis coupled quantum mechanical and quasi-classical trajectory methods. The results, such as integral cross sections and rate constants, show the effect of the opening of the collision-induced dissociation (CID) channel at low collision energy and are significantly different from the earlier study of Tacconi et al. [Phys. Chem. Chem. Phys. 14, 637–645 (2012)]. These discrepancies appear to be a result of the treatment of the CID channel in the dynamics calculations, which is excluded from the reactive channel in the current work. [ABSTRACT FROM AUTHOR]

Diffusion-controlled deactivation of excited phenanthrene and oxidation of triplet aryl-nitrene by molecular oxygen were used to determine the energetics of oxygen jump rates in the set of glassy polymers: poly(methyl methacrylate), poly(n-butyl methacrylate), polycarbonate, polystyrene, and polysulfone. To interpret experimental results, a simple model based on the transition state theory of diffusion jump has been used. The kinetic Monte Carlo simulations of phenanthrene deactivation and nitrene oxidation were carried out in a cubic lattice that modeled a polymer matrix. The bonds of the lattice were assigned to be activation barriers for the diffusion jumps of oxygen molecules from one site of the lattice to another. The standard deviation, σ‡, and spatial correlation length, rc, of the free energy of diffusion jump have been determined. It is shown that the spatial correlation of oxygen jump rates on a nanometer scale and the entropic nature of the dynamic heterogeneity are common features of all the studied polymers. [ABSTRACT FROM AUTHOR]

Electromagnetic applications of composites often impose constraints on the internal electric fields, such as an upper limit on the field strength to prevent local heating or dielectric breakthrough. However, owing to heterogeneity, the local fields in a composite differ from those in a homogeneous material. Moreover, they are accessible neither by experiment nor by effective medium theories, at least for arbitrary microstructures. In this work, we use numerical simulations to evaluate the electric field distribution and the effective permittivity for 3D systems of monodisperse impenetrable spheres dispersed in a continuous matrix phase. We restrict ourselves to loss-free dielectric materials and to a random spatial distribution of particles. Samples are placed in a parallel plate waveguide and exposed to a transverse electromagnetic wave. The local field amplitudes are calculated via the finite element method and are normalized to those of a homogeneous sample exhibiting the same effective permittivity and geometry. We analyze the distribution of the local electric field strength in both constituents, namely, particles and matrix. Thus, we evaluate mean values and standard deviations as well as the field strengths characterizing the highest and lowest percentiles. Increasing particle concentration or permittivity enhances heterogeneity, and so the local electric field strength in some domains can become much higher than its average value. The methods we apply here can also be used in further investigations of more complex systems, including lossy materials and agglomerating particles. [ABSTRACT FROM AUTHOR]

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]

An ultrasonic-assisted dispersive liquid–liquid microextraction based on deep eutectic solvent was utilised for simultaneous preconcentration and determination of illegal dyes Sudan orange G (SOG) and Sudan red 7B (SR7B) using second derivative ultraviolet spectrophotometry. Under optimised conditions, the limit of detection (0.014 and 0.011 μg mL−1) and linear dynamic range (0.036–0.900 and 0.030–1.000 μg mL−1) with high determination coefficient of 0.9916 and 0.9937 were obtained for SOG and SR7B, respectively. The relative standard deviations of method (n = 3) for intra- and inter-day were obtained as 3.5% and 10.5% for SOG and 5.0% and 9.0% for SR7B. The preconcentration factor for both dyes was calculated as 75 for all experiments. The applicability of the proposed method was investigated in different real samples, including in chilli powder, lipstick, river water and also in well water. A relative recovery percentage of 88–114% was obtained for these samples. Effect of interferences for the model solutions was also examined wherein satisfactory results confirm reasonable selectivity of the proposed method. [ABSTRACT FROM AUTHOR]

Encapsulation of the fine powder of ammonium molybdophosphate (AMP) was carried out to produce AMP-Ca-alginate composite beads for removal of Cs+, U6+, Co2+ and Sr2+ ions from multicomponent media using a fixed-bed column. The results denote that the initial concentration (50, 100 and 200 mg/L from each ion), bed length (3, 6 and 8 cm) and flow rates (0.5, 1.5 and 2 mL/min) affect the sorption operation. The column performance was 40, 37.5, 32.34 and 31.7% for Cs+, U6+, Co2+ and Sr2+, respectively. Thomas and Yoon-Nelson models were utilised to forecast the breakthrough curves for adsorption. The two models are found as well-fitted models for investigated ions adsorption system except for the adsorption of Cs+ is more fitted for the Thomas model at a high flow rate. Marquardt's percent standard deviation error function was used and confirmed these results. Good separation of strontium and caesium with a purity of 100% from this multi-component solution is accomplished by elution using 4 M of NaNO3 and HNO3 solution. The desorption percent of the loaded ions was 98, 91, 76 and 37.5% for Cs+, Sr2+, U6+ and Co2+, respectively. Overall, AMP-Ca-alginate composite beads are an environmentally friendly potential sorbent for hazardous ions from multicomponent media. [ABSTRACT FROM AUTHOR]

Within the scope of this study, a simple, highly sensitive and rapid trapping method for the determination of mercury (Hg) was developed. In this method, the surface of the tungsten coil (W-coil) was coated with platinum using the electrolytic deposition technique. Scanning electron microscopy–energy-dispersive X-ray analyses were performed to examine the surface morphology of Pt-coated W-coil. During the experiment, firstly, Hg vapour generated by tetrahydroborate, used as a reducing agent, was efficiently trapped on the Pt-coated W-coil at collection temperature in hydrochloric acid medium. Subsequently, Hg trapped on the surface of the Pt-coated W-coil was revolatilised by the effect of releasing-stage gases at the optimised releasing temperatures and transported to quartz T-tube atomiser. The experimental conditions have been optimised. Under the optimised conditions, a limit of detection (LOD) of 8.8 ng L−1 was obtained using three times the standard deviation of blank values using Pt-coated W-coil trap cold vapour atomic absorption spectrometry (CVAAS) system. The precision of the measurements was evaluated by relative standard deviation of 4.4%, which is obtained by 11 consecutive measurements of reagent blanks for a collection volume of 6.83 mL corresponding to 90 s collection. The enhancement factors for both LOD (3 s) and characteristic concentration (Co) were found to be 5.09 and 5.02 when compared with the regular CVAAS system without trap. The accuracy of the proposed method was evaluated by analysing BCR 146 R (Sewage sludge), RTC (Sandy Loam 7), ERM-BD151 (skimmed milk powder) and NIST 1641e (mercury in water) certified reference materials (CRMs). There was a good agreement between certified and found values for all CRMs. The results of the application of the developed method in seawater showed the spike recovery ranges from 94.2% to 102.4%. [ABSTRACT FROM AUTHOR]

Management of oil reservoirs requires many high computational cost simulations for decision-making. This work aims to develop fast machine learning-based proxies based on production history data to replace the reservoir simulator. The proxy must accurately forecast reservoir responses by analyzing time series of fluid phase rates or bottom hole pressures, defined as a function of informed controls of the injector and producer wells. It is crucial to note that this is not a regular problem of forecasting the future of time series based on past trends but the prediction of reservoir response time series as a function of informed new controls. The proposed strategy is to use neural networks to learn reservoir dynamics based only on samples of production history data. Multi-head architectures based on recurrent neural networks (RNNs), convolutional neural networks (CNNs) configurations, and hybridization are considered. Parallel architectures involving separate RNNs are tested, using long short-term memory (LSTM) and CNN concatenated sub-networks. Eight different architectures are tested on two reservoir models of different sizes and heterogeneity complexities. Several training samples are generated to assess their impact on accuracy and precision. For the smaller example, the CNN architecture produced the most precise results with fewer trainable parameters. The proposed Parallel CNN-LSTM architecture is generally the most successful for the larger and more complex reservoir. In any case, the proposed techniques have been demonstrated to be very promising, with root mean square error in the order of 2.25%. [ABSTRACT FROM AUTHOR]

Integrating isothermal nucleic acid amplification strategies into immunoassays can significantly decrease analytical limits of detection (LODs). On the other hand, an amplification step adds time, complication, reagents, and costs to the assay format. To evaluate the pros and cons in the context of heterogeneous multistep immunoassays, we quantified prostate-specific antigen (PSA) with and without rolling circle amplification (RCA). In addition, we compared time-gated (TG) with continuous-wave (CW) photoluminescence (PL) detection using a terbium complex and a fluorescein dye, respectively. For both direct (non-amplified) and amplified assays, TG PL detection provided circa four- to eightfold lower LODs, illustrating the importance of autofluorescence background suppression even for multi-wash assay formats. Amplified assays required an approximately 2.4 h longer assay time but led to almost 100-fold lower LODs down to 1.3 pg/mL of PSA. Implementation of TG-FRET (using a Tb-Cy5.5 donor–acceptor pair) into the RCA immunoassay resulted in a slightly higher LOD (3.0 pg/mL), but the ratiometric detection format provided important benefits, such as higher reproducibility, lower standard deviations, and multiplexing capability. Overall, our direct comparison demonstrated the importance of biological background suppression even in heterogeneous assays and the potential of using isothermal RCA for strongly decreasing analytical LODs, making such assays viable alternatives to conventional enzyme-linked immunosorbent assays (ELISAs). [ABSTRACT FROM AUTHOR]

The complexity of drilling carbon fiber reinforced polymers (CFRP) requires accurate predictive models. This study addresses the challenge using an ensemble machine learning (ML) approach with stacked generalization. The model captures the relationships between key input variables—such as graphene nanoplatelet (GNP) content, ultrasonic assistance, tool type, stacking sequence, and feed rate—and output parameters, specifically thrust force and delamination. A nested feature scoring (NFS) method was employed for importance analysis, revealing tooling type and feed rate as key features for minimizing delamination and reducing thrust force, respectively. The machinability results revealed that ultrasonic drilling lowered thrust force by improving chip evacuation and reducing fiber breakage. HSS tools with cobalt content, alongside symmetrical stacking sequence, helped to further minimize both thrust force and delamination. However, the inclusion of GNPs led to an increase in thrust force and delamination, attributed to the increased strength of the CFRP/GNP composite. The process involved meticulous training, resulting in four optimal-fit models serving as inputs for the stacked meta-model. Iterative enhancements fortified the ensemble robustness, with fine-tuning of hyperparameters through Bayesian optimization. The ensemble superiority over individual models manifested in a remarkable reduction of mean absolute error (MAE) and root mean squared error (RMSE) by up to 97% and 124% for delamination, and 205% and 154% for thrust force, compared to the best base learner. Visual and statistical assessments effectively illuminated the intricate interactions between variables in the drilling process. The methodology resulted in a highly adaptable predictive model with applications across diverse manufacturing contexts. [ABSTRACT FROM AUTHOR]

The prediction of food shelf life has become a vital tool for distributors and consumers, enabling them to determine storage and optimal edible time, thus avoiding unexpected food waste. Artificial neural network (ANN) have emerged as an effective, fast and accurate method for modeling, simulating and predicting shelf life in food. ANNs are capable of tackling nonlinear, complex and ill-defined problems between the variables without prior knowledge. ANN model exhibited excellent fit performance evidenced by low root mean squared error and high correlation coefficient. The low relative error between actual values and predicted values from the ANN model demonstrates its high accuracy. This paper describes the modeling of ANN in food quality prediction, encompassing commonly used ANN architectures, ANN simulation techniques, and criteria for evaluating ANN model performance. The review focuses on the application of ANN for modeling nonlinear food quality during storage, including dairy, meat, aquatic, fruits, and vegetables products. The future prospects of ANN development mainly focus on optimal models and learning algorithm selection, multiple model fusion, self-learning and self-correcting shelf-life prediction model development, and the potential utilization of deep learning techniques. HIGHLIGHTS: ANN-based food shelf life prediction methods are reviewed. This paper discusses application of ANN in the food storage process. BPNN is the mainstream ANN architecture used for the prediction of food quality. ANNs are useful for prediction of outputs with high accuracy. Future trends of ANN in the agri-supply chain are evaluated. [ABSTRACT FROM AUTHOR]

The dynamic performances of a sandwich beam with a shear thickening fluid (STF) core subjected to a random excitation are investigated. First, the STF is prepared and a new constitutive model is proposed based on the oscillatory rheological measurements. Next, a flexural vibration model of the sandwich beam with an STF core under random load is developed, and a parametric study is carried out. It is found that the response frequencies and damping ratios for the first three modes of the beam increase with the standard deviation of the external random excitation. The sandwich beam with STF provides better damping for larger broadband excitation. Additionally, the beam with an STF core will produce a response both inside and outside the load frequency domain. Furthermore, an optimal thickness ratio for the structure that maximizes the damping ratio of the structure is identified. It is important to note that this optimal thickness ratio of the structure varies with the order of vibration modes. This research provides valuable insights into the design of structures using STF and also contributes to the development of dynamic constitutive models for STF. [ABSTRACT FROM AUTHOR]

This study aims to accurately predict Cu(II) removal efficiencies from synthetic aqueous solutions with Artificial Neural Networks (ANNs) using normal and modified Abies bornmulleriana cone. The predicted values of Cu(II) removal efficiencies for normal and modified Abies bornmulleriana cone were determined using a single ANNs model structure. In the present investigation, the neural network's input parameters are biosorption time, biosorbate concentration, biosorbent amount, pH, particle size, agitation rate, and temperature. The removal efficiencies of the Cu (II) ions were selected as the experimental responses. The optimal network design is (7:7-9-2:2). Sensitivity analysis was employed to estimate the relative contribution of the input parameters to the ANN's performance. The most critical parameter affecting the removal efficiencies of the Cu(II) was defined as biosorption time. The ANNs model for the normal and the modified Abies bornmulleriana cone had determination coefficients of (0.95, 0.98), standard deviation ratios of (0.22, 0.14), root mean square errors of (27.89, 26.02), and mean absolute errors of (2.21, 2.06), respectively. The outcomes from the network signify that the ANNs model could predict high accuracy Cu (II) removal efficiencies for both biosorbents. [ABSTRACT FROM AUTHOR]

In this study, a simple and efficient eco-friendly procedure for preconcentration of trace amounts of Y, Sm, Eu, Gd, Er and Yb was developed prior to determination by inductively coupled plasma optical emission spectrometry (ICP-OES). The extraction process involved two sequential steps. The first is based on ultrasonic-assisted emulsification solidified floating organic drop microextraction using morin and 1-undecanol as complexing and extraction agents, respectively. The analyte in the solidified floated organic drop was then back-extracted into aqueous solution of HNO3 to prevent the relapsing effect of the organic matrix on the performance of ICP-OES. The effect of various experimental parameters on the extraction efficiency was studied and optimised. The limits of detection were in the range of 0.053 (Y) – 0.086 (Sm) µg L−1 with linear dynamic analytical range of 0.3–1000 µg L−1 at the optimal conditions. The relative standard deviations (C = 20 µg L−1, n = 10) ranged from 1.7% (Eu) to 2.5% (Yb). Finally, the approach represented high efficiency in determining the rare earth elements in certified reference material and phosphite rock samples. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

[Display omitted] • The non-linear evolutions of HSI spectra caused by water and oil were confirmed. • Autoencoder methods were compared based on their performance on spectral unmixing. • Endmember content maps were predicted for solid waste affected by impurities. • HU shows promise in promoting waste management by quantifying pollutants or purity. Rapid characterization of solid waste using near-infrared hyperspectral imaging (HSI) coupled with machine learning models has been increasingly investigated to replace the traditional time- and labor-intensive methods. However, contamination by waste-derived leachates or other fractions etc., can cause the spectra evolutions and significantly influences the identification performance, which has not been investigated before. The first attempt was made by using hyperspectral unmixing (HU) to extract the endmember components and demonstrate their contributions (abundance) to solid waste, taking the non-linear reflectance changes due to the O–H vibration of water and unclear variation associated with oil and leachates as an example. The HSI spectra of various solid waste components influenced by pure water, oil and three kinds of leachates were acquired. A novel method based on HU models, including multivariate curve resolution with alternating least squares and state-of-the-art autoencoder architectures (deep learning models), was developed to estimate the spectra of endmembers as well as their abundances in individual pixel. Their spatial distribution overview in solid waste was then yielded. The selected models were validated via an independent test data set, with lower spectral angle distance, 12.3° ± 6.5°, indicating the similarity of the predicted endmembers with real components. And the lowest root of mean square error on endmember distribution maps was 0.17. The non-linear liquid's effects by water and oil on spectra variations of solid waste were clearly illuminated. Additionally, the proposed method can extract information from mixed spectroscopic images and generate reconstructed spectra. [ABSTRACT FROM AUTHOR]

Among all the factors affecting the destructiveness of debris flow, the mean velocity is one of the most important characteristics. In this paper, we aim to apply a particle swarm optimization (PSO) based on the relevance vector machine (RVM) to predict the mean velocity. The PSO is used to optimize kernel parameters inside the RVM, whereas the RVM is responsible for completing the prediction task. Through sample training, a nonlinear relationship can be obtained, enabling a rapid prediction of the mean velocity for new samples. The debris flow dataset of Jiangjia Gully is used to evaluate the performance of PSO‐RVM in this study. Besides, we further compare the prediction results of PSO‐RVM with other prominent approaches, for example, the support vector machine (SVM), BP neural network (BP), and the RVM. The results show that the mean relative error (MRE) of PSO‐RVM is only 0.69%. In addition, BP yields the highest MRE (27.61%), and the MRE (2.75%) corresponding to the RVM is lower than that (5.98%) yielded by the SVM. For the root mean square error (RMSE) and Theil's inequality coefficient (TIC), the PSO‐RVM method still generates much lower RMSE (6.48%) and TIC (0.179%) values than the other three methods. Overall, compared with current debris flow prediction models, the PSO‐RVM achieves high prediction accuracy, fewer optimization parameters, and low computational complexity. Finally, a sensitivity analysis is conducted to explore the dominative factors of debris flow. [ABSTRACT FROM AUTHOR]

In this study, the variance seismic attribute was used to track fault geomorphology, which interpreted in time. To convert these fault surfaces and isochron maps to depth, the velocity model was prepared. Therefore, 14 velocity models for depth conversion were built by using different equations in East Baghdad Field, central Iraq to depth conversion assessment and select a precise model from many candidates of velocity models to generate an accurate depth map. All the velocity models are divided into two main velocity layers. The upper-velocity layer represents the tertiary sequences, while the lower-velocity layer represents the Upper Cretaceous sequences. Three criteria are made in this study (mis-tie value, standard deviation analysis, and blind well test) to select the best depth map. The results reveal the velocity model-7 is better to use because the depth map of the Khasib Reservoir, which produced from model-7 shows less mis-tie between estimated and real depth, less standard deviation values, and lower errors at EB 24 and 26 well locations. The corrected depth map of Khasib Reservoir shows three structure noses. Two of them opened toward the NW direction, while the southern structural nose opened toward the SW direction with a minor enclosure. [ABSTRACT FROM AUTHOR]

Deep learning-based models have emerged as promising tools for multivariate long-term time series forecasting. These models are finely structured to perform feature extraction from time series, greatly improving the accuracy of multivariate long-term time series forecasting. However, to the best of our knowledge, few scholars have focused their research on preprocessing time series, such as analyzing their periodic distributions or analyzing their values and volatility at the global level. In fact, properly preprocessing time series can often significantly improve the accuracy of multivariate long-term time series forecasting. In this paper, using the cross-variable transformer as a basis, we introduce a statistical characteristics space fusion module to preprocess the time series, this module takes the mean and standard deviation values of the time series during different periods as part of the model's inputs and greatly improves the model's performance. The Statistical Characteristics Space Fusion Module consists of a statistical characteristics space, which represents the mean and standard deviation values of a time series under different periods, and a convolutional neural network, which is used to fuse the original time series with the corresponding mean and standard deviation values. Moreover, to extract the linear dependencies of the time series variables more efficiently, we introduce three different linear projection layers at different nodes of the model, which we call the Multi-level Linear Projection Module. This new methodology, called the SCSformer, includes three innovations. First, we propose a Statistical Characteristics Space Fusion Module, which is capable of calculating the statistical characteristics space of the time series and fusing the original time series with a specific element of the statistical characteristics space as inputs of the model. Second, we introduce a Multi-level Linear Projection Module to capture linear dependencies of time series from different stages of the model. Third, we combine the Statistical Characteristics Space Fusion Module, the Multi-level Linear Projection Module, the Reversible Instance Normalization and the Cross-variable Transformer proposed in Client in a certain order to generate the SCSformer. We test this combination on nine real-world time series datasets and achieve optimal results on eight of them. Our code is publicly available at https://github.com/qiuyueli123/SCSformer. [ABSTRACT FROM AUTHOR]

Copyright of Retos: Nuevas Perspectivas de Educación Física, Deporte y Recreación is the property of Federacion Espanola de Asociaciones de Docentes de Educacion Fisica and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study aimed to enhance the reading comprehension level of Grade 10 students using the 4R Strategy. Framed after Richard Sagor's Four -Stage Model, this action research is designed to determine if there is an improvement in students' reading comprehension after implementing the 4R Strategy. Through purposive sampling method, a total of forty students who belonged to frustration level readers participated in the study. They were then randomly assigned into control group and experimental group. 4R Strategy (Read, Retell, React & Reflect) was employed, and a reading instrument from PHIL-IRI was used to collect the data. There was a Pre Test done before implementing the intervention and a Post Test was conducted after the 15-day session. Mean and Standard Deviation were used to determine the students' comprehension level during Pre-Test and Post Test and to determine how dispersed the scores were. Paired T-Test was also employed to determine the significant difference between the Pre-Test and Post Test Scores. Findings revealed that the 4R Strategy helped improve the reading comprehension level of students as proven by the results of their pre-test and post test scores. This study found out that the 4R strategy was an effective approach in enhancing students' comprehension. Thus, this study recommends adopting 4R Strategy to improve the reading comprehension skills of students. [ABSTRACT FROM AUTHOR]

This study examines the effectiveness of Project PAGE in improving the reading comprehension skills of sixth-grade students at Clementa F. Royo Elementary School is investigated. The study aimed to address the high number of students experiencing difficulties understanding what they read, as seen in national and local educational settings. An action research approach was used in the study, which involved selecting 30 students purposefully and categorizing them into instructional and frustration groups based on their pretest scores. The intervention included using the Alabama Reading and Mathematics Test (ARMT) to evaluate reading comprehension before and after implementing Project PAGE. The methodology consisted of conducting a pretest, followed by a two-week intervention period, and concluding with a post-test. Data analysis involved calculating the mean and standard deviation and conducting a paired sample t-test to assess the significance of score improvements. The study's ultimate finding is that Project PAGE is a successful intervention for enhancing reading comprehension, suggesting that similar programs could be advantageous for other students facing challenges. [ABSTRACT FROM AUTHOR]

This study focused on the utilization of Quizizz-Assisted Instructional Materials for Mathematics for the Grade 8 students at San Jose National High School, City Schools Division Office of Antipolo, School Year 2022 - 2023. The topics that were developed into Quizizz-Assisted Instructional Materials based on the school quarterly test result for two consecutive years' school year 2020 - 2020 and 2021 - 2022 were the topics under the second quarter on which gained least mean percentage score. The evaluation of Math experts and Math teacher respondents on the developed instructional materials in terms of content, organization and presentation, ease of use, usefulness, impact was interpreted as very highly acceptable, with a grand weighted mean of 3.89 and 3.92, respectively which also showed no significant difference. Meanwhile, the level of performance of the control group and the experimental group based on the pretest revealed that the performance of the control and experimental groups has mean scores of 7.87 and 7.67, respectively, and standard deviations of 2.97 and 3.10, with both interpreted as Not Proficient, On the other hand, the posttest performance of two groups of students, the control group has the mean score of 16.83 and standard deviation of 5.14 with verbal interpretations of Nearly Proficient, while the experimental group has the mean score of 23.20 and standard deviation of 4.73 with verbal interpretation of Proficient. Also, there was a significant difference between the pretest and posttest mean scores of the experimental groups. Comments and suggestions were given by the respondents to further improve the instructional material. [ABSTRACT FROM AUTHOR]

High-power pulse generators are widely used in civil and military fields. The main switch directly determines the output characteristics of the high-power pulse generators, such as the voltage front time (t f). Pseudospark switches (PSS) show a promising future for middle voltage, high repetitive frequency pulse power applications. However, how to further improve the breakdown behavior without reducing its advantages is a challenging task. In this paper, the influence of operating parameters (anode voltage U A and gas pressure p) and structural parameter (number of cathode holes) on the breakdown behavior are investigated, the related mechanism are explained, and specific improvement schemes are proposed. It is found that the t f of the single channel PSS (SCPSS) decreased significantly with increasing p, but hardly varied with U A under moderate p. However, it is not a sound solution to increase the p excessively to reduce t f. Besides, increasing the number of cathode holes can obtain a shorter t f at low pressures (which implies superior repetition frequency performance). However, at 25 Pa, the jitter (which is defined as the standard deviation of t f in multiple tests) of the 2-channel PSS is larger than that of the SCPSS. And the jitter of the 4-channel and 8-channel PSS is also greater than 6 ns and 2 ns, respectively. Through experimental and simulation analyses, it can be explained as the stepwise penetration of the virtual anode and the non-simultaneous ignition of the channels. A scheme to increase the trigger energy (ϵ) has been adopted to improve the simultaneous ignition probability, while shortening t f and reducing jitter. After optimization, the good ignition probability of the 4-channel PSS has been improved to 82% and the jitter has been reduced to less than 1 ns at 25 Pa and 14.7 mJ. [ABSTRACT FROM AUTHOR]

Since the coronavirus disease 2019 (COVID-19) pandemic, some patients with COVID-19 have experienced abnormal heart rates, posing potential health risks. In this study, we develop a noncontact method for measuring heart rate (HR) and heart rate variability (HRV) to effectively reduce the risk of infection and assist healthcare professionals in achieving accurate diagnosis and treatment. In this research, we collected data from 20 experimental testers based on palm images captured from photoplethysmography signals and measuring HR and HRV data by combining intelligent algorithms, namely, separation methods by particle swarm optimization and independent component analysis signal. The proposed method’s new contactless measurement performance can effectively eliminate infection concerns and obtain HR and HRV rapidly and handily. Moreover we provide higher accuracies for physiological parameters, namely, root mean square error (2.00 bpm), mean absolute percentage error (1.5%), and measurement time (8 s), than those in recently published literature. [ABSTRACT FROM AUTHOR]

This study aimed to assess the reading performance of Grade 1 learners and their impact on academic performance. Employing a descriptive-correlational research design, the investigation focused on describing the demographic profile and its correlation with English performance among Grade 1 learners. The study was set at St. Therese de Avila Learning Center, a well-regarded private school in Iligan City, during the school year 2023-2024. The research targeted 43 Grade 1 learners, comprising 21 males and 22 females. Utilizing statistical tools such as frequency, percentage, mean, standard deviation, and regression analysis, the researchers aimed to identify and analyze the reading performance levels of learners, exploring how these aspects influence academic performance in the English subject. This research provided an opportunity to gain insights into the relationship between reading performance and academic achievement, offering valuable information for educators and policymakers to enhance language education strategies for young learners. Findings revealed a diverse distribution of respondents based on age and gender, with a majority falling into the 6-year age group and a balanced representation of males and females. Learners exhibited notable proficiency in reading performance, with a majority achieving outstanding scores. Academic performance in English also showcased remarkable achievement, with a significant proportion attaining outstanding grades. Regression Analyses underscored critical role of reading performance in predicting academic success in English, informing the Comprehensive English Proficiency Initiative aimed at enhancing learners' English proficiency through targeted strategies and programs. [ABSTRACT FROM AUTHOR]

The whiskbroom scanner is a critical component in remote sensing payloads, such as the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Joint Polar Satellite System (JPSS) and the Medium Resolution Spectral Imager (MERSI) on FengYun-3. However, panoramic distortion in whiskbroom scanner images increases overlap from the nadir to the edges between adjacent scans. These distortions present significant challenges for improving geolocation accuracy, particularly when errors occur in sensors and platforms. This manuscript derives analytic expressions for all potential error sources, including sensors, platforms, and elevation, using homogeneous coordinates in the focal plane. This derivation demonstrates that geolocation errors vary with view angles and detector positions. To further investigate these error properties, a gradient-aware least-squares matching method was developed to extract highly accurate and dense ground control points (GCPs) with approximately 100,000 points in a single scene. A three-step geometric calibration method was then introduced, which includes boresight misalignment correction, parametric geometric calibration, and non-uniform scanning compensation. Given the varying spatial resolution of the GCPs, the weight of the GCPs was dynamically updated for least-squares estimation. This method effectively demonstrated the complex geolocation errors in MERSI on FY-3D, a system that was not meticulously calibrated in the laboratory. The initial root mean square errors (RMSEs) were 3.354 and 12.441 instantaneous field of view (IFoV) for the designed parameters. The proposed geometric calibration method successfully corrected view-angle and detector position-related geolocation errors, reducing them to 0.211 and 0.225 IFoV in the scan and track directions, respectively. The geolocation validation software and experiment results were provided https://github.com/hongbop/whiskgeovalidation.git. [ABSTRACT FROM AUTHOR]

Mine excavation systems are usually dozens of kilometers long with varying geometry on a small scale (roughness and shape of the walls) and on a large scale (varying widths of the tunnels, turns, and crossings). In this article, the authors address the problem of analyzing laser scanning data from large mining structures that can be used for various purposes, with focus on ventilation simulations. Together with the quality of the measurement data (diverse point-cloud density, missing samples, holes induced by obstructions in the field of view, measurement noise), this creates problems that require multi-stage processing of the obtained data. The authors propose a robust methodology to process a single segmented section of the mining system. The presented approach focuses on obtaining a point cloud ready for application in the computational fluid dynamics (CFD) analysis of airflow with minimal need for additional manual corrections on the generated mesh model. This requires the point cloud to have evenly distributed points and reduced noise (together with removal of objects inside) while keeping the unique geometrical properties and shape of the scanned tunnels. Proposed methodology uses trajectory of the excavation either obtained during the measurements or by skeletonization process explained in the article. Cross-sections obtained on planes perpendicular to the trajectory are processed towards the equalization of point distribution, removing measurement noise, holes in the point cloud and objects inside the excavation. The effects of the proposed algorithm are validated by comparing the processed cloud with the original cloud and testing within the CFD environment. The algorithm proved high effectiveness in improving skewness rate of the obtained mesh and geometry mapping accuracy (standard deviation below 5 centimeters in cloud-to-mesh comparison). • Real scan data can be used for ventilation simulations of mining excavations. • Volumetric mesh from raw point cloud is of poor quality. • Improved process involves an automatic approach to generate regularized point clouds. • Uniform point distribution cuts mesh skewness from unsuitable to good quality. • The algorithm addresses noise and unnecessary objects in the tunnel section. [ABSTRACT FROM AUTHOR]

Remotely sensed evapotranspiration (ET) at a high spatial resolution (30 m) has wide-ranging applications in agriculture, hydrology and meteorology. The original optical trapezoid model for ET (O_OPTRAM-ET), which does not require thermal remote sensing, shows potential for high-resolution ET estimation. However, the non-automated O_OPTRAM-ET heavily depends on visual interpretation or optimization with in situ measurements, limiting its practical utility. In this study, a SpatioTemporal Aggregated Regression algorithm (STAR) is proposed to develop an automatic trapezoid model for ET (A_OPTRAM-ET), implemented within the Google Earth Engine environment, and evaluated globally at both moderate and high resolutions (500 m and 30 m, respectively). Through the integration of an aggregation algorithm across multiple dimensions to automatically determine its parameters, A_OPTRAM-ET can operate efficiently without the need for ground-based measurements as input. Evaluation against in situ ET demonstrates that the proposed A_OPTRAM-ET model effectively estimates ET across various land cover types and satellite platforms. The overall root mean square error (RMSE), mean absolute error (MAE), and correlation coefficient (CC) when compared with in situ latent heat flux (LE) measurements are 35.5 W·m−2 (41.3 W·m−2, 40.0 W·m−2, 36.1 W·m−2,), 26.3 W·m−2 (28.9 W·m−2, 28.7 W·m−2, 25.8 W·m−2,), and 0.78 (0.73, 0.70, 0.72) for Sentinel-2 (Landsat-8, Landsat-5, MOD09GA), respectively. The A_OPTRAM-ET model exhibits a stable accuracy over long time periods (approximately 10 years). When compared with other published ET datasets, ET estimated by the A_OPTRAM-ET model is better with the land cover types of cropland and shrubland. Additionally, global ET derived from the A_OPTRAM-ET model shows trends consistent with other published ET datasets over the period 2001–2020, while offering enhanced spatial details. Therefore, the proposed A_OPTRAM-ET model provides an efficient, high-resolution, and globally applicable method for ET estimation, with significant practical values for agriculture, hydrology, and related fields. [ABSTRACT FROM AUTHOR]

Satellite-derived bathymetry (SDB) is a vital technique for the rapid and cost-effective measurement of shallow underwater terrain. However, it faces challenges of image noise, including clouds, bubble clouds, and sun glint. Consequently, the acquisition of no missing and accurate bathymetric maps is frequently challenging, particularly in cloudy, rainy, and large-scale regions. In this study, we propose a multi-temporal image weighted composition (MIWC) method. This method performs iterative segmentation and inverse distance weighted composition of multi-temporal images based only on the near-infrared (NIR) band information of multispectral images to obtain high-quality composite images. The method was applied to scenarios using Sentinel-2 imagery for bathymetry of four representative areas located in the South China Sea and the Atlantic Ocean. The results show that the root mean square error (RMSE) of bathymetry from the composite images using the log-transformed linear model (LLM) and the log-transformed ratio model (LRM) in the water depth range of 0–20 m are 0.67–1.22 m and 0.71–1.23 m, respectively. The RMSE of the bathymetry decreases with the number of images involved in the composition and tends to be relatively stable when the number of images reaches approximately 16. In addition, the composition images generated by the MIWC method generally exhibit not only superior visual quality, but also significant advantages in terms of bathymetric accuracy and robustness when compared to the best single images as well as the composition images generated by the median composition method and the maximum outlier removal method. The recommended value of the power parameter for inverse distance weighting in the MIWC method was experimentally determined to be 4, which typically does not require complex adjustments, making the method easy to apply or integrate. The MIWC method offers a reliable approach to improve the quality of remote sensing images, ensuring the completeness and accuracy of shallow water bathymetric maps. [ABSTRACT FROM AUTHOR]

Phase-change nanodroplets (PCNDs) have been used in ultrasound imaging, targeted drug delivery, blood-brain-barrier (BBB) opening, sonothrombolysis and histotripsy for over a decade. For these ultrasound applications, PCNDs provide higher in vivo lifetime than microbubbles (MBs), the potential for extravasation inside tumour and on demand activation, which is the transition of the liquid-core of nanodroplets to gaseous microbubbles through acoustic droplet vaporisation (ADV). Operating above the ADV threshold can offer repeatable activation of PCNDs and the subsequent oscillation of acoustically activated PCNDs, which is advantageous in imaging and therapeutic applications. Efficient and repeatable activation of PCNDs require a good understanding of ultrasound parameters and nanodroplet composition for different biomedical applications. Therefore, this article presents a meta-analysis of the effect of ultrasound activation parameters on ADV for various PCNDs in different biomedical applications. About 7,500 articles were considered for this study, but only 45 articles were chosen and evaluated in the meta-analysis based on the following criteria: 1): activation parameters, including ultrasound frequency, peak negative pressure, transmit pulse length or duration have been clearly mentioned, 2), droplets range in nanometre size (<1 µm), 3), experiments are performed at a temperature of 37°C and 4) ADV threshold has been clearly mentioned and observations are not due to inertial cavitation (IC). From selected publications, we recorded the activation frequency (0.06–16 MHz), ultrasound pressure (0.18–14.9 MPa), activation pulse length (µs-ms range) and nanodroplet size for different types of perfluorocarbon PCNDs (C3F8, C4F10, C5F12 and C6F14) and evaluated the relation of these parameters to each other. Finally, a Root Mean Square (RMS)-like power metric, which is a combination of ultrasound peak negative pressure and square root of ultrasound pulse length, is proposed for identifying the ADV threshold behaviour instead of using pressure or mechanical index values. [ABSTRACT FROM AUTHOR]

Objective: The present study aimed to determine the effectiveness of responsibility training based on choice theory on students' academic motivation and academic performance. Methods and Materials: The research design of the present study was applied in terms of purpose and quasi-experimental in terms of method, with a pre-test-post-test design, including an experimental group and a control group, along with a three-month follow-up phase. The statistical population of the present study included all students referred to counseling centers in a private specialized clinic for child and adolescent counseling and psychotherapy in District 10 of Tehran. Participants were randomly assigned to two groups: experimental (n = 15) and control (n = 15) through convenience sampling. For the experimental group, the responsibility training program based on choice theory was implemented according to the protocol developed by Mirsalar Bakhshi (2019). To collect data, research instruments including Vallerand's Academic Motivation Scale (1998) and students' grade point averages (GPA) were used. Data analysis was conducted in two sections: descriptive (mean and standard deviation) and inferential. In the descriptive section, frequency distribution tables were used, and in the inferential section, data analysis was performed using mixed ANOVA with repeated measures and Bonferroni post hoc test. Data analysis was carried out using a computer and SPSS software version 26. Findings: The findings indicate that the significance level shows that responsibility training based on choice theory had a significant impact on the dependent variables of the study, namely academic performance (F = 25.80, P < 0.01) and academic motivation (F = 23.70, P < 0.01). The changes resulting from this intervention were significant in the three time intervals of pre-test, post-test, and follow-up. The effectiveness of this intervention was also sustained in the follow-up phase according to the Bonferroni post hoc test. Conclusion: Based on the available findings, it can be concluded that responsibility training based on choice theory can be used to increase academic motivation and improve students' academic performance. Therefore, it is suggested that educational workshops on this method be held for specialists. [ABSTRACT FROM AUTHOR]

This study investigates the impact of the Touch Math approach on enhancing kindergarten readiness and mathematical achievement. A total of 40 pupils, aged five to six, were divided into two groups: a control group using traditional methods and an experimental group employing the Touch Math approach. Initial assessments revealed varied levels of numerical readiness among the children, with 88% able to identify and work with shapes, while only 25% demonstrated proficiency in recognizing basic addition concepts. The analysis of pretest scores indicated no significant difference between the two groups, with both performing similarly. Post-intervention results demonstrated a marked improvement in the experimental group, which achieved a mean score of 30.75 and a standard deviation of 5.88, compared to the control group's mean score of 20.9 with a standard deviation of 4.33. The difference of 9.85 was statistically significant, as confirmed by a t-test result of 5.89, which exceeded the critical values for both the 5% and 1% confidence levels. Consequently, the null hypothesis was rejected, indicating that the Touch Math approach significantly enhanced the mathematical abilities of the kindergarten pupils. Overall, the study concludes that the Touch Math method is more effective than traditional teaching methods in fostering essential mathematical skills in young learners. This finding underscores the importance of innovative teaching strategies in early childhood education, aiming to better prepare children for future academic success in mathematics. [ABSTRACT FROM AUTHOR]

Highlights In this study, the impact performance of carbon fiber‐reinforced polymer (CFRP) composites under low‐speed impact conditions was investigated using a data‐driven approach. Both material properties and impact parameters were determined through experimental methods and finite element (FE) analysis. FE analysis was conducted on CFRP composite structures to generate impact force and absorbed energy datasets. Various impact conditions, such as impactor height (0.5–1.25 m), impactor shape (flat, truncated cone, bullet, and cone), and composite plate thickness (1–4 mm), were incorporated into an artificial neural network (ANN) model to predict the impact behavior of CFRP composites. Using the optimal plate thickness identified from the data‐driven model, CFRP plates were fabricated using vacuum‐assisted resin transfer molding and tested under different impactor shapes and heights using drop impactors. The FE analysis revealed that increasing the impactor height improved the impact force by 37.8% and the absorbed energy by 178%. The impactor shape also significantly influenced the results, with a flat‐to‐cone‐shaped impactor increasing the impact force and absorbed energy by 167.2% and 440%, respectively. Additionally, the plate thickness analysis showed that a 2 mm plate provided optimal impact force and absorbed energy, with values of 1.09 kN and 8.12 J, respectively. The prediction of the force and energy experienced by CFRP material under different impact conditions was validated using root mean squared error (RMSE), mean square error (MSE), mean absolute error (MAE), and R2 metrics. The model demonstrated excellent performance with the lowest RMSE (0.0118), MSE (0.0003), and MAE (0.0096), indicating that the predicted impact forces closely matched the actual forces. The highest R2 value (0.9999) suggests that the model accurately captures the variance in impact force across varied impact conditions. Similarly, R2 values close to one indicate that the model effectively explains the variability in energy absorption, making it highly reliable. The ANN model also showed that predictions for absorbed energy were more accurate than those for impact force under varying impact conditions. Furthermore, the predicted impact force and absorbed energy from the FE analysis and ANN model closely aligned with the experimental results. Damage morphology observations indicated matrix cracking at higher impact velocities, with more significant penetration occurring with cone‐shaped impactors. These findings demonstrate a strong correlation between experimental and numerical outcomes, validating the effectiveness of this combined approach for evaluating the impact resistance of CFRP composites. The impact performance of CFRP composites under different impact conditions was modeled. An Artificial Neural Network model was developed to predict impact performance. The VARTM method was adopted for the development of optimized CFRP composites. Impactor height and shape were found to be the most influential factors Impact damage areas were related based on impactor height and shape. [ABSTRACT FROM AUTHOR]

Plume rise from most sources is an important factor in determining ground-level concentration of air pollutants. Various attempts have been done to compute plume rise from stationary sources. Volkov (1979) proposed a formula for calculation of plume dimensions (height and length) from a stack, while most of the well-known plume rise equations can only compute plume rise or plume height. Both plume height and plume length should be determined in some cases, for example in study of stack and cooling tower plume mergence, plume behavior (lofting, fanning, coning, looping, trapping and fumigation) etc. Because there are little studies in the world regarding the accuracy of the Volkov equation, the aim of the present work is to investigate the validity of the Volkov formula based on 5 statistical tests including the relative error, mean square error, root mean square error, coefficient of determination and Nash-Sutcliffe coefficient. The results revealed that (1) the Volkov equation better predicts plume rise at the distance of 60 m than 30 m from a typical stack, (2) considering the value of 0.5 instead of 0.4 or 0.65 for n in the Volkov formula will lead to more accurate results and (3) plume dispersion pattern was categorized as lofting. Overall, the Volkov equation can be an acceptable method to study of plume rise; nevertheless more studies must be conducted in the future with regard to the accuracy of the Volkov formula [ABSTRACT FROM AUTHOR]

Learners' behavior is basically an offshoot of the environment and effect different factors which include parenting. This study determined the Parenting Style of the parents and its effect on the behavior of the leaners. Data were statistically treated with the suitable statistical tools such as mean and standard deviation, frequency and percentage Distribution, and Cramer's V. The respondents are learners and teachers from Dansalan Annex Elementary School located at Dansalan, Sapad, Lanao del Norte. The researcher used Parenting Style and Dimensions Questionnaire (PSDQ) and Children Behavior Checklist (CBC). Results showed that the respondents had experienced an authoritative way of parenting and revealed to have positive effect on the behavior of the learners. It revealed that there is a significant relationship between the authoritative, permissive, and uninvolved parenting to the learner's behavior. In contrast, authoritarian, has no significant relationship on the behavior of the learners. It is therefore recommended that the participation and support from the parents is needed in establishing a positive outcome in terms in the behavioral development of the learners and linked to have an influence on their academic performance. [ABSTRACT FROM AUTHOR]

Sulfur (S) in the mantle is conventionally assumed to be exclusively stored in accessory sulfide phases, but recent work shows that the major silicate minerals that comprise >99% of the mantle could be capable of hosting trace amounts of S. Assessing the incorporation of trace S in nominally S-free mantle minerals and determining equilibrium S partitioning between these minerals and basaltic melt requires analyzing small experimental phases with low S contents. Here, we develop a protocol for EPMA analysis of the trace levels of S in silicate phases. We use a suite of natural and experimental basaltic glass primary and secondary standards with S contents ranging from 44 ppm to 1.5 wt%. The effects of beam current and counting time are assessed by applying currents ranging from 50 to 200 nA and total counting times between 200 and 300 s at 15 kV accelerating voltage. We find that the combination of 200 nA beam current with a 200 s counting time (80 s peak, 60 s each for upper and lower background, respectively) achieves precise yet cost-effective measurements of S down to a calculated detection limit of ~5 ppm and a blank-derived, effective detection limit of ~17 ppm. Close monitoring of the S peak intensity and position throughout the duration of each spot also shows that high currents and extended dwell times do not compromise the accuracy of measurements, and even low S contents of 44 ppm can be reproduced to within one standard deviation. Using our developed recipe, we analyzed a small suite of experimental clinopyroxenes (Cpx) and garnets (Gt) from assemblages of silicate partial melt + Cpx ± Gt ± sulfide, generated at 1.5 to 3.0 GPa and 1200 to 1300 °C. We find S contents of up to 71 ± 35 ppm in Cpx and 63 ± 28 ppm in Gt and calculate mineral-melt partition coefficients ( (D s min/melt) ) of up to 0.095 ± 0.064 and 0.110 ± 0.064 for D s Cpx/melt and D s Gt/melt , respectively. The sulfur capacity and mineral-partitioning for Cpx are in good agreement with SXRF measurements in a prior study by Callegaro et al. (2020), serving as an independent validation of our EPMA analytical protocol. [ABSTRACT FROM AUTHOR]

The multi-spectral imagery based Satellite Derived Bathymetry (SDB) provides an efficient and cost-effective approach for acquiring bathymetry data of nearshore shallow water regions. Compared with conventional pixelwise inversion models, Deep Learning (DL) models have the theoretical capability to encompass a broader receptive field, automatically extracting comprehensive spatial features. However, enhancing spatial features by increasing the input size escalates computational complexity and model scale, challenging the hardware. To address this issue, we propose the Multiple Scale Resolution Fusion Model (MuSRFM), a novel DL-based SDB model, to integrate information of varying scales by utilizing temporally fused Sentinel-2 L2A multi-spectral imagery. The MuSRFM uses a Multi-scale Center-aligned Hierarchical Resampler (MCHR) to composite large-scale multi-spectral imagery into hierarchical scale resolution representations since the receptive field gradually narrows its focus as the spatial resolution decreases. Through this strategy, the MuSRFM gains access to rich spatial information while maintaining efficiency by progressively aggregating features of different scales through the Cropped Aligned Fusion Module (CAFM). We select St. Croix (Virgin Islands) as the training/testing dataset source, and the Root Mean Square Error (RMSE) obtained by the MuSRFM on the testing dataset is 0.8131 m (with a bathymetric range of 0–25 m), surpassing the machine learning based models and traditional semi-empirical models used as the baselines by over 35 % and 60 %, respectively. Additionally, multiple island areas worldwide, including Vieques, Oahu, Kauai, Saipan and Tinian, which exhibit distinct characteristics, are utilized to construct a real-world dataset for assessing the generalizability and transferability of the proposed MuSRFM. While the MuSRFM experiences a degradation in accuracy when applied to the diverse real-world dataset, it outperforms other baseline models considerably. Across various study areas in the real-world dataset, its RMSE lead over the second-ranked model ranges from 6.8 % to 38.1 %, indicating its accuracy and generalizability; in the Kauai area, where the performance is not ideal, a significant improvement in accuracy is achieved through fine-tuning on limited in-situ data. The code of MuSRFM is available at https://github.com/qxm1995716/musrfm. [ABSTRACT FROM AUTHOR]

Snow depth (SD) is a crucial property of snow, its spatial and temporal variation is important for global change, snowmelt runoff simulation, disaster prediction, and freshwater storage estimation. Polarimetric Synthetic Aperture Radar (PolSAR) can precisely describe the backscattering of the target and emerge as an effective tool for SD retrieval. The backscattering component of dry snow is mainly composed of volume scattering from the snowpack and surface scattering from the snow-ground interface. However, the existing method for retrieving SD using PolSAR data has the problems of over-reliance on in-situ data and ignoring surface scattering from the snow-ground interface. We proposed a novel SD retrieval method for PolSAR data by fully considering the primary backscattering components of snow and through multi-parameter estimation to solve the snow backscattering model. Firstly, a snow backscattering model was formed by combining the small permittivity volume scattering model and the Michigan semi-empirical surface scattering model to simulate the different scattering components of snow, and the corresponding backscattering coefficients were extracted using the Yamaguchi decomposition. Then, the snow permittivity was calculated through generalized volume parameters and the extinction coefficient was further estimated through modeling. Finally, the snow backscattering model was solved by these parameters to retrieve SD. The proposed method was validated by Ku-band UAV SAR data acquired in Altay, Xinjiang, and the accuracy was evaluated by in-situ data. The correlation coefficient, root mean square error, and mean absolute error are 0.80, 4.49 cm, and 3.95 cm, respectively. Meanwhile, the uncertainties generated by different SD, model parameters estimation, solution method, and underlying surface are analyzed to enhance the generality of the proposed method. [ABSTRACT FROM AUTHOR]

This study addresses the challenges of overfitting and maintaining biological realism in deep learning algorithms (DLAs), for predicting individual tree taper using stem diameters outside bark (DOB) and total tree volume (TTV). To this end, DLAs were trained using two different approaches: a "hyperparameter-optimized DLA", which customizes specific hyperparameters such as learning rate and momentum rate, and a "regularization-optimized DLA", which incorporates optimization techniques like early stopping with root mean square error, L1 and L2 regularization, and dropout. Although obtaining the deterioration in predictive capabilities statistics from the taring dataset to the validation dataset by standard DLA with adaptive learning processes without customizing the hyperparameters and regularization parameters, the hyperparameter-optimized DLA with a momentum of 0.8, and a 7 # hidden layer for the TTV and regularization-optimized DLA with a dropout ratio of 0.000001, a 3 # hidden layer for the DOB demonstrated comparable predictive capabilities statistics across both training and validation datasets with generating biologically plausible predictions. Our results support that these hyperparameter-optimized and regularization-optimized DLAs, by improving the "black-box" nature of artificial intelligence, offer significant potential for enhanced interpretability and performance by improving the problem of overfitting and the violations biological realism in forest biometrics applications. [ABSTRACT FROM AUTHOR]

The Assessment of Quality of Life - 6 Dimensions (AQoL-6D), a generic preference-based measure, is an appealing alternative to EQ-5D-5L for assessing health status in patients with chronic heart failure (HF), given its expanded scope. However, without a Malaysian value set, the AQoL-6D cannot generate health state utility values (HSUVs) to support local economic evaluations. This study intended to develop algorithms for predicting EQ-5D-5L HSUVs from AQoL-6D in an HF population. Cross-sectional data from a multicenter cohort of 419 HF outpatients were used. Both direct and indirect mapping approaches were attempted using 5 sets of explanatory variables and 8 models (ordinary least squares, Tobit, censored least absolute deviations, generalized linear model, 2-part model [TPM], beta regression-based model, adjusted limited dependent variable mixture model, and multinomial ordinal regression [MLOGIT]). The models' predictive performance was assessed through 10-fold cross-validated mean absolute error [MAE] and root mean squared error [RMSE]). Potential prediction bias was also examined graphically. The best-performing models, with the lowest RMSE and no bias, were then identified. Among the models evaluated, TPM, which included age, sex, and 5 AQoL-6D dimension scores as predictors, appears to be the best-performing model for directly predicting EQ-5D-5L HSUVs from AQoL-6D. TPM yielded the lowest MAE (0.0802) and RMSE (0.1116), and demonstrated predictive accuracy for HSUVs >0.2 without significant bias. A MLOGIT model developed for response mapping had suboptimal predictive accuracy. This study developed potentially useful mapping algorithms for generating Malaysian EQ-5D-5L HSUVs from AQoL-6D responses among patients with HF when direct EQ-5D-5L data are unavailable. • The Assessment of Quality of Life - 6 Dimensions (AQoL-6D) is a comprehensive and valid measure of health-related quality of life in healthcare and research setting, equipped with a value set to calculate health-state utility values to support economic evaluations. However, these values do not reflect the health state preferences of Malaysian people, limiting their utility in local economic evaluations. • This study utilizes paired AQoL-6D and EQ-5D-5L data from a cross-sectional survey involving individuals living with heart failure and explores a broad range of direct and indirect mapping approaches alongside various sets of explanatory variables. • The findings of the study suggests that the 2-part model, which incorporates age, sex, and all AQoL-6D dimension scores (except for the Senses dimensions) as predictors, has better predictive accuracy compared with the other models evaluated. The model is potentially useful for directly transforming AQoL-6D responses from local heart failure patients onto EQ-5D-5L health state utility values based on Malaysian preferences. [ABSTRACT FROM AUTHOR]

Jean Blondel made many lasting contributions toward comparative politics, not least in his classification of party systems in Western democracies. Yet during the 5 decades since Blondel's original contribution, party competition has been transformed by multiple developments, including changes in the grassroots electorate, as intermediary organizations connecting citizens and the state, and at the apex in legislatures and government. Does Blondel's typology of party systems remain relevant today—or does it require substantial revision? And, does party system fragmentation predict ideological polarization? Part I sets out the theoretical framework. Part II compares trends from 1960 to 2020 in party system fragmentation in a wide range of democracies, measured by the effective number of parties in the electorate and in parliament. Not surprisingly, the effective number of electoral parties (ENEP) has generally grown in each country across Western democracies. This does not imply, however, that party systems are necessarily more polarized ideologically. Part III examines polarization in party systems across Western democracies, measured by standard deviations around the mean of several ideological values and issue positions in each country. The findings suggest that party system fractionalization and polarization should be treated as two distinct and unrelated dimensions of party competition. The conclusion reflects on the broader implications of the findings for understanding party polarization and threats of backsliding in democratic states. [ABSTRACT FROM AUTHOR]