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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]

Rationale: The oxygen stable isotope ratio (δ18O) of the sugar‐rich fraction of fruit juice is important as a tracer of the geographical origin of raw material. This study sought to minimize the inter‐day variation of δ18O attributable to the influence of water to accurately monitor geographical origin labeling. Methods: Two drying devices (freeze dryer and vacuum oven) were compared. Then, two humidity levels (normal and low humidity) at which the samples were placed after drying were compared. The low‐humidity environment was constructed using a glove bag and pure argon gas. δ18O was measured using thermal conversion elemental analyzer/isotope ratio mass spectrometry. Improvements were made to the measurement method based on aforementioned analyses results, and the performance of the initial and improved methods was compared. Results: δ18O of juice dried in a vacuum oven was 3.30‰ lower than that of juice dried in a freeze dryer. Moreover, δ18O of juice samples exposed to normal humidity was 3.74‰ lower than that of samples exposed to low humidity. The combined inter‐day and intra‐day standard deviation was reduced from 1.20‰ in the initial method to 0.42‰ in the improved method. Conclusions: This study describes a pretreatment method for δ18O measurement in the sugar‐rich fraction of fruit juice with less inter‐day variation, and it will be useful for monitoring geographical origin labeling. [ABSTRACT FROM AUTHOR]

This study aimed to develop a digital interactive supplementary material in Biotechnology for Grade 8 learners. It aimed to help the learners in coping with the lessons and improve their performance in selected lessons in Biotechnology. Twenty-seven (27) learners from Grade 8 Special Science Class of Angono National High School in the School Year 2023-2024 were purposively selected as respondents of the study. The Digital Interactive Supplementary Material in Biotechnology was developed based on the competencies in Biotechnology during the third quarter. It sought to determine the level of performance and the significant difference in the level of performance of the learners before and after exposure to the developed supplementary material. The mean and standard deviation were utilized to determine the level of performance of the students as revealed in their pretest and posttest results. Additionally, dependent t-test was used to determine the significant difference in the level of performance of the students. The research method used was quasi-experimental design to determine the level of performance of learners in the pretest and posttest results and the difference between the pretest and posttest scores before and after the exposure to the developed material. The validity of the digital interactive material was validated by master teachers and experts Science instructors and professors. The data gathered before and after exposure to the developed material revealed from "Average" to "High" verbal interpretations in which the developed digital interactive supplementary material was utilized with respect to the different competencies which showed an improved performance of the learners after the exposure to the developed material. The result of the t-test revealed that there was a significant difference between the performance of the Grade 8 learners before and after exposure to the developed digital interactive supplementary material in Biotechnology. The researcher concluded that the level of performance of the Grade 8 learners had a significant difference before and after utilizing the developed digital interactive supplementary material in Biotechnology. Hence, the supplementary material improved the level of performance of the learners in all the competencies in which the developed material was used. [ABSTRACT FROM AUTHOR]

Bile acid (BA) is one of the main active components of bile and has multiple isomers, the structure or content of its isomers often changes due to diseases and other health problems; thus, the accurate detection of BA isomers is very important. In this study, two groups of BA isomers of glycine-conjugated BAs and taurine-conjugated BAs were simultaneously separated and quantitatively analyzed by ion mobility mass spectrometry (IM-MS). Especially, baseline mobility separation between the isomers was achieved by the formation of binary complexes via simple interaction with spiramycin (SPM), for which a separation resolution (Rp-p) of 1.96 was reached. Moreover, BA isomers were quantitatively analyzed, and the limit of detection (LOD) of absolute quantification for TCDCA/TUDCA and GUDCA/GCDCA/GHDCA was 0.514 and 0.611 ng∙mL−1, respectively; the LODs for molar ratio ranges of relative quantification for TCDCA/TUDCA, GUDCA/GHDCA, and GCDCA/GHDCA were 1:18–30:1, 1:18–21:1, and 1:19–21:1, respectively. Additionally, BA isomers analyzed in pig bile powder and bear bile powder were measured, which were in good consistency with those labeled, revealing the differences in BA composition and content between the two powders. Finally, BA detection and recovery analyses were performed on serum samples, with a recovery rate of ≥73.69%, RSD of ≤6.8%, and SR (standard deviation of recoveries, the degree of difference between measured values and average recovery) of ≤1.27. Due to the simple, rapid, and lack of need for complex sample preparation and chromatographic separation, the proposed method can be an effective method for BA detection in practical samples. [ABSTRACT FROM AUTHOR]

Electrical discharge machining (EDM) is a complex process renowned for shaping intricate geometries in hard materials, though optimizing its performance is costly due to extensive experimental testing. We introduce EDMNet, a novel machine learning (ML) benchmark framework for predicting EDM performance, specifically in smart powder mixed-electrical discharge machining (PM-EDM) with electrode vibration, enabling in situ, online monitoring of the EDM process. EDMNet integrates ML into the PM-EDM process, reducing reliance on trial-and-error experimentation and allowing real-time performance optimization. We evaluate 12 ML regressor models, including deep neural networks (DNN), support vector regression (SVR), and ensemble methods, across four key metrics: mean squared error (MSE), mean absolute error (MAE), root mean squared error (RMSE), and R-squared (R2). This multi-metric evaluation highlights the strengths and weaknesses of different models, with DNN and ensemble methods demonstrating superior performance. EDMNet serves as a standardized, reproducible benchmarking framework, facilitating the comparison of ML models in the EDM context. The integration of in situ monitoring with predictive accuracy provided by EDMNet holds significant potential for improving manufacturing processes, reducing costs, and boosting efficiency in engineering applications. Our results validate EDMNet as an effective tool for optimizing EDM, paving the way for enhanced precision in smart manufacturing. [ABSTRACT FROM AUTHOR]

The emergence of on-machine measurement (OMM) technology has opened up new possibilities for error compensation in complex surface machining. Due to the harsh working environments of machine tools, the error data obtained from OMM often contains a considerable degree of random errors. To separate the systematic errors from the random errors and improve the effectiveness of the measurement results and the accuracy of surface parts machining, a method known as ICEEMDAN-VMD is proposed. This method, based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), permutation entropy, correlation coefficient, power spectral entropy, and variational mode decomposition (VMD), is entirely data-driven and does not necessitate manual intervention, making it well-suited for OMM. The accuracy of error separation using the ICEEMDAN-VMD method was assessed through simulation, utilizing root mean square error and signal-to-noise ratio as evaluation metrics. Application of this method in machining experiments resulted in a substantial reduction of 73.58% in the average error, 64.33% in the maximum error, and 67.86% in the standard deviation on surfaces. This implementation notably enhanced surface consistency and effectively avoided over-cutting. The results conclusively demonstrated the theoretical feasibility, high accuracy, and practical effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In response to the current situation where Beijing's benchmark maintenance relies solely on GPS data, we conduct in-depth research on the positioning accuracy of different dual frequency and orbit combinations of the BeiDou system. Gamit software is used to calculate the benchmark station data for seven consecutive days, and we compared the positioning performance characteristics of the BeiDou and GPS systems with different frequency and orbit combinations. The positioning accuracy of the BeiDou system is evaluated from three aspects: standardized root mean square error, baseline repetition rate, and coordinate difference. The results show that in the Beijing area, the positioning accuracy of different frequency combinations of the BeiDou-3 satellite system is higher than that of the BeiDou-2 system. The B1C/B2a frequency positioning accuracy of BeiDou-3 is higher than that of B1I/B3I frequency points, and the orbit combination of MEO+IGSO has the highest positioning accuracy among all combinations. This discovery not only provides new technological options for our city's benchmark maintenance work, but also provides more scientific and accurate surveying and mapping benchmark support for geographic spatial analysis and natural resource management. [ABSTRACT FROM AUTHOR]

Metabolomics aims to study the downstream effects of variables like diet, environment, or disease on a given biological system. However, inconsistencies in sample preparation, data acquisition/processing protocols lead to reproducibility and accuracy concerns. A systematic study was conducted to assess how sample preparation methods and data analysis platforms affect metabolite susceptibility. A targeted panel of 25 metabolites was evaluated in 69 clinical metabolomics samples prepared following three different protocols: intact, ultrafiltration, and protein precipitation. The resulting metabolic profiles were characterized by 1D 1H nuclear magnetic resonance (NMR) spectroscopy and analyzed with Chenomx v8.3 and SMolESY software packages. Greater than 90% of the metabolites were extracted more efficiently using protein precipitation than filtration, which aligns with previously reported results. Additionally, analysis of data processing software suggests that metabolite concentrations were overestimated by Chenomx batch-fitting, which only appears reliable for determining relative fold changes rather than absolute quantification. However, an assisted-fit method provided sufficient guidance to achieve accurate results while avoiding a time-consuming fully manual-fitting approach. By combining our results with previous studies, we can now provide a list of 5 common metabolites [2-hydroxybutyrate (2-HB), choline, dimethylamine (DMA), glutamate, lactate] with a high degree of variability in reported fold changes and standard deviations that need careful consideration before being annotated as potential biomarkers. Our results show that sample preparation and data processing package critically impact clinical metabolomics study success. There is a clear need for an increased degree of standardization and harmonization of methods across the metabolomics community to ensure reliable outcomes. [ABSTRACT FROM AUTHOR]

Toxic ginkgolic acids (GAs) are a challenge for Ginkgo biloba-related food. Although a detection method for GAs is available, bulky instruments limit the field testing of GAs. Herein, by assembling gold nanoclusters with copper tannic acid (CuTA), CuAuTA nanocomposites were designed as peroxidase mimics for the colorimetric determination of GAs. Compared with single CuTA, the obtained CuAuTA nanocomposites possessed enhanced peroxidase-like properties. Based on the inhibitory effect of GAs for the catalytic activity of CuAuTA nanozymes, CuAuTA could be utilized for the colorimetric sensing of GAs with a low limit of quantitation of 0.17 μg mL−1. Using a smartphone and the ImageJ software in conjunction, a nanozyme-based intelligent detection platform was developed with a detection limit of 0.86 μg mL−1. This sensing system exhibited good selectivity against other potential interferents. Experimental data demonstrated that GAs might bind to the surface of CuAuTA, blocking the catalytically active sites and resulting in decreased catalytic activity. Our CuAuTA nanozyme-based system could also be applied to detect real ginkgo nut and ginkgo powder samples with recoveries of 93.12–111.6% and relative standard deviations less than 0.3%. Our work may offer a feasible strategy for the determination of GAs and expand the application of nanozymes in food safety detection. [ABSTRACT FROM AUTHOR]

Projections of commodity prices have long been a significant source of dependence for investors and the government. This study investigates the challenging topic of forecasting the daily regional steel price index in the northeast Chinese market from January 1, 2010, to April 15, 2021. The projection of this significant commodity price indication has not received enough attention in the literature. The forecasting model that is used is Gaussian process regressions, which are trained using a mix of cross-validation and Bayesian optimizations. The models that were built precisely predicted the price indices between January 8, 2019, and April 15, 2021, with an out-of-sample relative root mean square error of 0.5432%. Investors and government officials can use the established models to study pricing and make judgments. Forecasting results can help create comparable commodity price indices when reference data on the price trends suggested by these models are used. [ABSTRACT FROM AUTHOR]

The purpose of this descriptive quantitative inquiry was to determine the level of writing difficulties of grade 3 pupils of Clementa F. Royo Elementary School. It aimed to find out the effectiveness of the intervention and provide valuable insights into the writing challenges faced by third graders through the adaptation of the instrument of Crocker et al., 2020, contingencies of self-worth theory and measurement. It aimed to find out if there was a difference of the mean from the pre-test and post-test of the CATS (Copy, Alter, Transform, and Supply). This inquiry utilized descriptivequantitative method in which numerical and non-numerical responds was analyzed. The participant of this research were the 40 of grade 3 section orange and pineapple in Clementa F. Royo Elementary School. The data collection was though Likert Scale and survey questionnaire which compose of In-Depth Interview (IDI) and Focus Group Discussion (FGD). The result found out that there was a difference between the pre-test and post-test based on its overall mean. The statistical evidence, indicated by the mean of the pre-test was 2.37 with 0.18 standard deviation while the post-test was 3.90 with 0.32 standard deviation. The pre-test determine that the pupils having difficulties in writing while the post-test, after implementation of the CATS determines that the pupils are good in writing. The success of this program could serve as a model for similar educational interventions aiming to bolster academic skills among young learners. The results shows implementing CATS can improve in writing skills in elementary pupils with writing difficulties. [ABSTRACT FROM AUTHOR]

As the extraction of mineral resources intensifies, ground subsidence in mining areas has escalated, posing substantial challenges to sustainable development and operational safety. This subsidence, resulting directly from mining activities, significantly compromises the safety of nearby residents by damaging residential structures and infrastructure. Thus, developing precise and dependable methods for predicting ground subsidence is crucial. This study introduces an innovative Cabs-Unet model, which enhances the U-Net architecture by integrating a Convolutional Block Attention Module (CBAM) and Depthwise Separable Convolutions (DSC). This model aims to predict the spatiotemporal dynamics of the Interferometric Synthetic Aperture Radar (InSAR) time series. Employing Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS InSAR) technology, we gathered and validated data on ground subsidence at the Pengzhuang coal mine from May 2017 to November 2021, covering 130 scenes, with its accuracy corroborated by levelling survey results. An empirical evaluation of the Cabs-Unet model in two distinct subsidence zones demonstrated superior performance over conventional methods like Convolutional Long Short-Term Memory (ConvLSTM) and Predictive Recurrent Neural Network (PredRNN), with Root Mean Square Error (RMSE) values of 1.44 and 1.70, respectively. These findings highlight the model's efficacy in accurately predicting spatiotemporal InSAR ground subsidence. Further predictive analysis using InSAR data indicated an expected increase in subsidence, projecting cumulative declines of −457 mm in Area A and −1278 mm in Area B by 17 July 2022. Our model proves effective in assessing subsidence, promptly detecting potential risks and facilitating the rapid implementation of risk mitigation strategies. [ABSTRACT FROM AUTHOR]

Rotating fan-beam scatterometer (RFSCAT) is a radar scatterometer system for sea surface wind vector measurement. Compared with other available scatterometers, RFSCAT can provide more combination of azimuth angles and incidence angles for a single WVC (wind vector cell), this observation mechanism is more conducive to the sea surface wind direction retrieval. In this paper, the NSCAT-4DS GMF (geophysical model function) with SST (sea surface temperature) correction, and the MSS (Multiple Solution Scheme) combination with a 2DVAR (2-dimensional variational analysis) are adopted to retrieve the sea surface winds from RFSCAT on the CFOSAT (Chinese-French Oceanography Satellite). The retrieved RFSCAT sea surface winds and ASCAT (advanced scatterometer) sea surface winds are compared and tested, and the feasibility of the RFSCAT measuring sea surface winds under high wind speeds is analysed. The results show that the RMSE of the RFSCAT sea surface wind speed using improved algorithm has decreased by 0.292 m s−1, the correlation coefficient has increased by 0.032, and the residual standard deviation has decreased by 0.194 m s−1. The RMSE of RFSCAT sea surface wind direction has decreased by 5.950°, the correlation coefficient has increased by 0.002, and the residual standard deviation has decreased by 5.567°. It is shown that the changes in winds RMSE using pre-improved and improved algorithms have statistical significance. In a word, the spaceborne Ku-band rotating fan-beam scatterometer can capture the winds structure of ocean cyclones. Although there may be high wind speeds saturation phenomena, the detecting sea surface winds at high wind speeds show preferable performance. [ABSTRACT FROM AUTHOR]

Lidar sensors are active remote sensing instruments that provide data on forest structure, which serve as inputs in the prediction of forest parameters. The objective of this study was to evaluate the performance of mixed-effects models to predict basal area (BA), aboveground biomass (AGB), and wood volume (VOL) from GEDI (Global Ecosystem Dynamics Investigation) predictor variables. Linear mixed and generalized linear mixed models (LMM and GLMM) were fitted with a clustering structure as a random effect between forest parameters and GEDI metrics. LMM and GLMM performed similarly regarding root mean square error (RMSE) and correlation coefficients (r) between observed and predicted data. Our results showed moderate correlations between RH50 and BA (r = 0.53), AGB (r = 0.54), and RH80 with VOL (r = 0.62). Another finding was the correlation between GEDI predictions of the AGB density and the biomass predicted in this study, with an agreement of 79% and 77% and a RMSE of 76.95 and 82.34 Mg ha−1 for LMM and GLMM, respectively. We conclude that the use of mixed models allowed for the inclusion of both fixed and random effects that capture the systematic and random variation in the data, and they provide a flexible framework for modelling forest parameters. [ABSTRACT FROM AUTHOR]

Guanabara Bay, located in the Metropolitan Region of Rio de Janeiro, one of the largest urban coastal areas in the Southern Hemisphere, is subject to intense maritime traffic due to the presence of several ports. These facilities are affected by sea level fluctuations, influenced by atmospheric and astronomical forces, which motivates synoptic and climatological analysis, including meteorological and astronomical tidal phenomena. This study aimed to assess the evolution of tidal components and the relative mean sea level (MSL) between 1990 and 2021, as well as the atmospheric influence on extreme meteorological tide events, in which the MSL exceeded ±2 and ±3 (±29.98 and ±44.97 cm) standard deviations. The results have shown that, albeit small, the main tidal components (M2, S2, O1, M4, Q1, K1, K2 and N2) indicated positive trends in the increase of amplitude and phase, despite the small quantities. For the relative MSL, an increase of 0.30 cm·year−1 was observed in agreement with estimates from global series from satellite altimeters and climate model predictions. Pressure and wind field analyses led to the identification of seasonal variability and the pattern of evolution of atmospheric systems associated with extreme events. In the case of positive surges, very intense winds from the SW, extending along the S/SE Brazilian coast, are caused by the presence of a high‐pressure centre over the continent and low‐pressure centres over the ocean, leading to water piling up. In turn, negative surges are caused by the presence of an anticyclone over the ocean, generating NE winds parallel to the coast of Rio de Janeiro, inducing Ekman transport and lowering the sea level. Therefore, the extreme sea level events in the Guanabara Bay are not triggered by local forces, but rather depend on the temporal persistence and direction of winds along the S/SE Brazilian coast. [ABSTRACT FROM AUTHOR]

Background: Evidence on associations of remnant cholesterol (RC) and its variability with cognitive function is still lacking. Objective: To explore the association of RC and its variability with cognitive function. Methods: Participants were recruited from a population-based cohort, the China Health and Retirement Longitudinal Study (CHARLS). Cognitive function was assessed by a standardized questionnaire from CHARLS, with domains of episodic memory and mental intactness. A linear mixed effects model was used to analyze the association of RC with cognitive function, along with its variability (calculated as standard deviation [SD], coefficient of variation [CV], variability independent of the mean [VIM]), with results expressed as β (95%CI). Potential subgroup differences in the association of RC and its variability with cognitive function were also explored. Results: 4234 participants were eventually included, with mean (SD) age of 57.4 (8.0) years. Each 10 mg/dL increase in RC was associated with 0.053 (95%CI: 0.096, 0.009) points, 0.021 (95%CI: 0.042, 0.000) points, 0.032 (95%CI: 0.064, 0.001) points decrease in global cognitive function, episodic memory, and mental intactness scores, respectively. Compared with the first tertile (T1) group of RC variability (calculated as SD, VIM), T3 showed a lower level in global cognition and episodic memory after multivariate adjustment. The potential modification effects of educational level on RC and its variability in relation to cognitive function were also identified. Conclusions: Among Chinese middle-aged and older adults, higher RC level were associated with worse cognitive function. Greater RC variability was also associated with worse cognitive performance, especially in memory function. [ABSTRACT FROM AUTHOR]

In the current most accepted global geopotential model, EGM2008, there are often data gaps in the source data used to compute the model over inland water. As a result, EGM2008 may be less reliable over lakes. Satellite altimetry has the potential to estimate gravity anomalies and update EGM2008 over lakes. Here, we evaluate the first attempt to extract gravity anomalies from ICESat-2 laser altimetry over several medium (100–1000 km 2 ) and large (>1000 km 2 ) lakes and compare them with conventional radar altimetry from CryoSat-2 and SARAL to investigate the performance of ICESat-2 for gravity determination over lakes. Aerial gravimetry from the GRAV-D project over the United States are utilized as the best estimate of the gravity field over the lakes. Gravity determination from altimetry is done using Fast Fourier Techniques (FFT) within a remove-restore geoid-to-gravity approach. The resulting altimetry derived gravity anomalies are then compared to the EGM2008 geoid over each lake with respect to GRAV-D. 18 lakes with area ranging from 108 km 2 to 82,220 km 2 across the United States were considered. Overall, gravity determination from ICESat-2 provides more reliable estimates than the other two radar altimetry missions. For all considered lakes, the performance of ICESat-2, measured in terms of standard deviation with GRAV-D, is comparable or better than the EGM2008 field over the same lake. Lake Pend Orielle is the best performing case, in which the standard deviation of the ICESat-2 derived gravity field is 2.14 mGal and the standard deviation of the EGM2008 gravity field is 2.66 mGal with respect to the GRAV-D measurements. Over Lake Tahoe, which is surrounded by mountainous terrain, ICESat-2 performs comparably to EGM2008 and captures clear gravity signal related to the lake's bathymetry, whereas CryoSat-2 produces very unstable results. The method presented here for deriving gravity anomalies from altimetry applied to ICESat-2 laser altimetry data produces results that validate in supplement to the GRAV-D project over medium to large lakes in the United States. [ABSTRACT FROM AUTHOR]

The Ionosphere, a crucial region of Earth's atmosphere extending from approximately 50 to 1000 km above the Earth's surface, plays a pivotal role in global communication, navigation, and satellite-based technologies. Among its various parameters, Total Electron Content (TEC) stands out as a key metric, representing the integrated electron density along a specific path through the ionosphere. This research focuses on analyzing TEC variations during Indonesian earthquakes between 2004 and 2024 using a Recurrent Neural Network (RNN) model. The study investigates the ionospheric response to seismic activity, particularly in Indonesia, a region prone to earthquakes due to its location within the Pacific Ring of Fire and complex tectonic setting involving multiple lithospheric plates. The RNN model, designed to predict TEC variations during earthquakes, utilizes data on solar and geomagnetic activity, including the Kp index, solar wind, and geomagnetic storm activity, obtained from the OMNIWEB data centre, along with TEC data from the IONOLAB data servers for the BAKO station in Cibinong, Indonesia. The earthquakes considered for observation include significant events such as the 2004 Indian Ocean earthquake and tsunami, the 2012 Wharton Basin earthquake, and subsequent seismic events up to 2024. Statistical metrics such as Mean Bias Deviation (MBD), Relative Error (REL_E), Absolute Error (ABS_E), and Root Mean Square Error (RMSE) are used to evaluate the predictive capability of the RNN model and compare it with the International Reference Ionosphere (IRI) 2020 model, a widely accepted empirical model for describing ionospheric parameters. The comparison reveals the performance of the RNN model in capturing TEC variations during seismic events, providing valuable insights for earthquake monitoring and early warning systems. [ABSTRACT FROM AUTHOR]

Water content as an important physiological status variable in plants, is closely linked to transpiration, photosynthesis, water stress, and biomass productivity. Obtaining plant water information that provides sufficient spatial and temporal resolution for such applications remains a challenge. In this study, the data distribution space of fractional vegetation cover (FVC) and shortwave infrared transformed reflectance (STR) with linear and nonlinear edges was used to monitor plant water content. Four indicators of plant water status, equivalent water thickness (EWT), leaf water content (LWC), relative water content (RWC), and leaf water potential (LWP), were measured in a 10-ha corn field at the Karkaj Agricultural Research Station of Tabriz University, Tabriz, Iran. Furthermore, the Sentinel-2 Level 2 prototype processor (SL2P) was utilized to estimate EWT and compare the results with FVC-STR space. The FVC-STR space with nonlinear edges (FSNLE) provided better estimation accuracy of plant water status than the FVC-STR space with linear edges (FSLE). The root mean square errors of the EWT, LWC, RWC and LWP estimates for FSLE were 0.00306 g/cm2, 4.03 %, 6.56 %, and 0.38 bar, respectively, while those for FSNLE were 0.00303 g/cm2, 3.75 %, 5.57 %, and 0.37 bar, respectively. In addition, the R2 value for FSNLE was higher than that for FSLE (0.43–0.70 vs. 0.39–0.64). The RMSE and R2 for SL2P were 0.0041 g/cm2 and 0.401, respectively. Among the four measured indicators, the highest and lowest estimation accuracies in both FSLE and FSNLE were obtained with EWT and RWC, respectively. It can be concluded that FVC-STR space model based on Sentinel-2 imagery data provide acceptable accuracy for estimating plant water content. The FVC-STR space with nonlinear edges provided a better estimation accuracy for plant water indicators than the FVC-STR space with linear edges. [ABSTRACT FROM AUTHOR]

Onboard generation of reentry trajectory optimization for a reusable launch vehicle (RLV) is challenging as it must meet strict requirements for real-time performance. Machine learning has brought this field fresh insights due to its great extraction of data performance and predictive accuracy. In this work, the extreme learning machine (ELM), which is often favored in real-time applications, is employed to make onboard trajectory predictions by exploiting the underlying functional relationship obtained from offline data of state-control variable pairs. To boost the generalization performance of the ELM, a regularization term is added, and the marine predator whale optimization algorithm (MPWOA) is utilized to optimize the input weights as well as hidden layer biases of the networks. Afterward, four-hidden layer feedforward neural networks (FLFNNs), a single-hidden layer hybrid MPWOA-BPNN (backpropagation neural networks), an ELM model, and three ELM-based particle swarm optimization, whale optimization algorithm and marine predators algorithm models, were developed for comparison. The results revealed that the MPWOA-ELM model outperformed the other models in the testing phase performance indices of the mean absolute error, mean absolute percentage error, root mean squared error, and determination coefficient. Meanwhile, Monte Carlo simulations showed that the model performed remarkably well in real-time, with a trajectory being generated in about 3.405 s. Specifying the current flight state of the RLV, the proposed model can directly generate guidance instructions and then achieve an onboard generation of reentry trajectory with high computational efficiency and reliability. [ABSTRACT FROM AUTHOR]

Wheat is a very vital cereal crop in the development of a nation's economy and agricultural sector. A crucial element that profoundly affects the growth and productivity of wheat is the soil temperature. As an insulating layer, straw mulch controls the soil temperature. Field measurement of the soil temperature is time-consuming and costly; therefore, the present study investigates the applicability of random forest (RF), support vector machine (SVM) and generalized regression neural network (GRNN) models for estimating soil temperature in wheat fields covered with straw mulch using meteorological, soil cover and agronomical parameters. A field study was conducted on the wheat cultivar with two soil cover treatments for measuring the soil temperature at three depths (5, 50 and 80 cm). Soil plant analysis development value; soil cover and depth; daily meteorological data such as average air temperature, average relative humidity, sunshine hours and vapor pressure were used as inputs for machine learning models in training and testing. Statistical performance indicators showed applicability of three models with a root mean square error (RMSE) ranging from 0.245–0.595°C, 0.867–0.886°C and 0.557–0.812°C, Nash-Sutcliffe efficiency (NSE) ranging from 0.917–0.987, 0.823–0.836 and 0.804–0.935 and Lin's concordance correlation coefficient (CCC) ranging from 0.949–0.991, 0.887–0.897 and 0.881–0.957 for the RF, SVM and GRNN, respectively. Results indicated better performance of the RF model for predicting the soil temperature at different depths in the case of both crop and soil cover conditions. Also, the uncertainty analysis indicated the better performance of RF with R-factor and P-factor value of 0.40 and 0.84, respectively. [ABSTRACT FROM AUTHOR]

[Display omitted] • An Implicit knowledge-guided machine learning framework is proposed. • The effect is validated by modelling plasma gasification of medical waste. • RMSEs decrease by 36.44% and 2.58% for ANN and SVM. • Knowledge-based errors decrease by 83.22% and 100% for ANN and SVM. • All implicit knowledge-based monotonicity relationships are captured. Ensuring the interpretability of machine learning models in chemical engineering remains challenging due to inherent limitations and data quality issues, hindering their reliable application. In this study, a qualitatively implicit knowledge-guided machine learning framework is proposed to improve plasma gasification modelling. Starting with a pre-trained machine learning model, parameters are further optimized by integrating the heuristic algorithm to minimize the data fitting errors and resolving implicit monotonic inconsistencies. The latter is comprehensively quantified through Monte Carlo simulations. This framework is adaptive to different machine learning techniques, exemplified by artificial neural network (ANN) and support vector machine (SVM) in this study. Validated by a case study on plasma gasification, the results reveal that the improved models achieve better generalizability and scientific interpretability in predicting syngas quality. Specifically, for ANN, the root mean square error (RMSE) and knowledge-based error (KE) reduce by 36.44% and 83.22%, respectively, while SVM displays a decrease of 2.58% in RMSE and a remarkable 100% in KE. Importantly, the improved models successfully capture all desired implicit monotonicity relationships between syngas quality and feedstock characteristics/operating parameters, addressing a limitation that traditional machine learning struggles with. [ABSTRACT FROM AUTHOR]

[Display omitted] • Six machine learning models were used to predict MR and CT. • GPR model demonstrated the best predictive performance. • SHAP and PDP were introduced for model interpretation. • An easy-to-use GUI was developed for the prediction of MR and MC. Preparation of activated carbons is an important way to utilize municipal sludge (MS) resources, while drying is a pretreatment method for making activated carbons from MS. In this study, machine learning techniques were used to develop moisture ratio (MR) and composting temperature (CT) prediction models for the thermally assisted biodrying process of MS. First, six machine learning (ML) models were used to construct the MR and CT prediction models, respectively. Then the hyperparameters of the ML models were optimized using the Bayesian optimization algorithm, and the prediction performances of these models after optimization were compared. Finally, the effect of each input feature on the model was also evaluated using SHapley Additive exPlanations (SHAP) analysis and Partial Dependence Plots (PDPs) analysis. The results showed that Gaussian process regression (GPR) was the best model for predicting MR and CT, with R2 of 0.9967 and 0.9958, respectively, and root mean square errors (RMSE) of 0.0059 and 0.354 ℃. In addition, graphical user interface software was developed to facilitate the use of the GPR model for predicting MR and CT by researchers and engineers. This study contributes to the rapid prediction, improvement, and optimization of MR and CT during thermally assisted biodrying of MS, and also provides valuable guidance for the dynamic regulation of the drying process. [ABSTRACT FROM AUTHOR]

In indoor environments, air quality significantly impacts human health and well-being, with carbon monoxide (CO) posing a particular hazard due to its colorless and odorless nature and potential to cause severe health issues. Integrating the Internet of Things and remote sensing technologies has revolutionized data monitoring, collection, and evaluation, especially within the context of 'smart' homes. This study leverages these technologies to enhance indoor air quality monitoring. By collecting data on key indoor atmospheric quality indicators—carbon dioxide (CO2), methane (CH4), alcohol, liquefied petroleum gas (LPG), particulate matter (PM1 and PM2.5), humidity, and temperature—the study aims to predict indoor carbon monoxide levels. A custom dataset was compiled from August to October, consisting of 61,710 observations recorded at one-minute intervals. The methodology employs a stacking ensemble approach, integrating multiple machine learning models to boost prediction accuracy and reliability. In the stacking ensemble, six distinct models are employed: Random Forest, Multi-Layer Perceptron, Lasso, Elastic Net, XGBoost, and Support Vector Regression. Each model is individually trained and fine-tuned using the Grid Search method to optimize parameter combinations. These optimized models are then combined in the stacking ensemble, which achieves a Mean Squared Error (MSE) of 0.0140, a Root Mean Squared Error (RMSE) of 0.1185, and a Mean Absolute Error (MAE) of 0.0291. The results demonstrate that the proposed system significantly enhances the precision of CO prediction, underscoring its critical role in air quality surveillance within smart environments. [ABSTRACT FROM AUTHOR]

We propose a new reaction device for the microdiffusion analysis of CO2 in fluid samples from volcanic areas to improve the conventional analysis procedure, which requires a fixative and good handling skills. To accomplish this, a small resin reaction cell for the sample solution and acid (initiator for CO2 liberation) was devised and placed in a commercially available air-tight glass container with a Petri dish to form a microdiffusion analysis unit. The analysis was based on the principles of the conventional manual titration method that uses Ba(OH)2 as the CO2 absorbent. Using the new device, 99.5% recovery, with a relative standard deviation of 4.5% (n = 8), was achieved in a test analysis for 0.03 mmol of CO2 liberated from a Na2CO3 solution as a simulated sample. The analytical performance of the new device was almost comparable to that of conventional units under the experimental conditions of this study. Applying this new device for analyzing fluid samples from volcanic areas will contribute to enhancing the performance by reducing the possibility of CO2 contamination and by simplifying the process by eliminating the need for preparatory cleaning for fixative application. [ABSTRACT FROM AUTHOR]

Objective Animals Studied Procedure Results Conclusions To further investigate quantitative and qualitative tear‐film dynamics through tear‐film osmolarity, meibometry, meibography, and non‐invasive tear‐film break‐up time and to describe a survey of parameters in normal eyes of healthy non‐brachycephalic dogs.Thirty‐two veterinary hospital staff and student owned dogs between 1 and 7 years with no known comorbidities or ocular disease.Complete ophthalmic examination, tear‐film osmolarity (I‐PEN® VET), meibometry (Meibometer MB560), meibography, and non‐invasive tear‐film break‐up time (handheld Ocular Surface Analyzer) were performed. Significance threshold was 0.05. Q‐Q plots and histograms were used to assess normality of measurements. Data were summarized descriptively with mean, standard deviation, median, and interquartile range (IQR). Pearson and Spearman's correlations were used to test for correlations between diagnostics with each other and tear‐film break‐up times.Thirty‐two dogs (64 eyes) met the inclusion criteria. Mean tear‐film osmolarity level was 293.6 ± 14.1 mOsm/L (IQR 282.5–302.3). Median meibomian lipid level was 30.4 Meibometry Units (MU) (IQR 11.5–59.1). Median percent loss of meibomian glands in the upper eyelids was 25.3% (IQR 13.5–26.8). Median percent loss of meibomian glands in the lower eyelids was 26.0% (IQR 13.5–52.3). Automatic assessment of meibomian gland loss showed that grade 0 was present in 4/128 (3.1%) eyelids, grade 1 in 78/128 (60.9%) eyelids, grade 2 in 30/128 (23.4%) eyelids, and grade 3 in 16/128 (12.5%) eyelids.Tear‐film osmolarity, meibometry, meibography, and non‐invasive tear break‐up time can easily be incorporated into routine ophthalmic examinations to provide clinical information about meibomian gland morphology and function. [ABSTRACT FROM AUTHOR]

Recent studies revealed the importance of tracking continuous blood pressure (BP) changes in monitoring and controlling hypertension and diagnosing cardiovascular diseases. However, current evaluation protocols utilize distance measures as primary metrics, which cannot properly evaluate the ability of the estimation model to track BP changes. This paper proposes a comprehensive evaluation framework which evaluates the distance and trend similarity metrics, and the composite metric of both between the reference and estimated BPs. The results of applying both widely used conventional metrics and the new proposed metrics for BP estimations are demonstrated in an example of comparing the reference with a set of different BP estimations. Then, the metrics are applied to BP estimations obtained using state-of-the-art (SOTA) algorithms. It is shown that even though SOTA algorithms have a low mean and standard deviation of absolute difference, they are not capable of tracking short-term blood pressure changes. Additionally, the proposed metrics are normalized metrics and range from -1 to 1, making them intuitively interpretable, similar to well-known correlation coefficients. Therefore, we suggest that the proposed evaluation framework should be used regularly in evaluating continuous BP monitoring systems. [ABSTRACT FROM AUTHOR]

Background and Aims Methods Results Interpretation Hyaluronidase‐facilitated subcutaneous immunoglobulin (fSCIG) consists of subcutaneous human immunoglobulin G (IgG) 10% with recombinant human hyaluronidase (rHuPH20) and can be administered at the same dose and interval as intravenous IgG (IVIG). fSCIG recently received US approval as maintenance therapy for adults with chronic inflammatory demyelinating polyradiculoneuropathy (CIDP) and European approval for adults and children with CIDP after stabilization with IVIG.ADVANCE‐CIDP 3 (NCT02955355) was an open‐label long‐term extension of the Phase 3 double‐blind randomized placebo‐controlled ADVANCE‐CIDP 1 study (NCT02549170) that examined fSCIG safety and efficacy as maintenance CIDP therapy. Primary outcomes were safety, tolerability, and immunogenicity. Efficacy was an exploratory outcome.The study provided 220 patient‐years of follow‐up data from 85 patients. Median (range) exposure was 33 (0–77) months. Patients received fSCIG every 4 weeks (88.2%) or every 3 weeks (11.8%). Median (range) 4‐weekly IgG dose equivalent was 64.0 (28.0–200.0) g. Mean (standard deviation) infusion duration was 135.5 (62.8) minutes. Most adverse events (AEs) were mild or moderate and self‐limiting. Of the 1406 AEs, only 48 were severe and 30 were serious. fSCIG‐related AEs (n = 798) included infusion site reactions such as pain, redness, and pruritus. Three infusions (0.1%) were reduced in rate, interrupted, or stopped due to intolerability. Relapse occurred in 10 of 77 patients (13.0%); annual relapse rate was 4.5%. An anti‐rHuPH20 antibody titer ≥1:160 was detected in 14 of 84 patients (16.7%); patients who tested positive (≥1:160) had similar relapse rates versus those who tested negative (16.7% vs. 12.3%, respectively).ADVANCE‐CIDP 3 demonstrated favorable fSCIG long‐term safety and tolerability consistent with its established safety profile, and a low relapse rate, supporting use as maintenance CIDP treatment. [ABSTRACT FROM AUTHOR]

Commercial automation systems for small- and medium-sized laboratories, including research environments, are often complex to use. For liquid handling systems (LHS), development is required not only for the robot's movements but also for adapting the bioanalytical method to the automated system. This study investigates whether a more human-like automation strategy—using a robotic system (RS)—is more suitable for research laboratories than a professional automation approach utilizing a commercial automated LHS. We conducted a series of measurements for protein determination using a Bradford assay manually, with a fully automated LHS, and with our human-like RS. Although the hand-like RS approach requires more than twice the time of the LHS, it achieved the best standard deviation in this setup (RS = 0.5, manual = 0.71, LHS = 0.86). Due to the low limit of detection (LOD) and limit of quantification (LOQ), most protein samples could be quantified with the RS (samples below LOQ = 9.7%, LOD = 0.23; LOQ = 0.25) compared to manual (samples below LOQ = 28.8%, LOD = 0.24; LOQ = 0.26) and the LHS (samples below LOQ = 36.1%, LOD = 0.27; LOQ = 0.31). In another time-dependent enzymatic assay test, the RS achieved results comparable to the manual method and the LHS, although the required time could be a constraint for short incubation times. Our results demonstrate that a more hand-like automation system closely models the manual process, leading easier to accurate bioanalytical results. We conclude that such a system could be more suitable for typical research environments than a complex LHS. [ABSTRACT FROM AUTHOR]

Electro-hydraulic actuators offer advantages over traditional hydraulic actuators, particularly for high-precision force or position control, but their nonlinear dynamics complicate modelling and control. This study proposes a predictor-integrated adaptive controller to address these challenges. By combining a proportional-integral-derivative controller with a sliding surface, the system tracks desired trajectories, while a parameter optimization rule minimizes errors. Additionally, a smart grey prediction model with dynamic step size adjusts parameters and generates control signals to reduce noise and disturbances. Simulations show improved accuracy and control performance, with a root mean square error reduction of over 50% compared to traditional control algorithms. This adaptive approach ensures precise control in varying conditions, making it suitable for aerospace, automotive, and robotics applications. [ABSTRACT FROM AUTHOR]

In the paper there are presented models for moisture assessment applying the two reflectometric sensors in the cellular concrete samples. The readouts express the dependence between the cellular concrete moisture, measured in gravimetric way and the apparent permittivity values achieved by the Time Domain Reflectometry method and two surface sensors. According to observed relationships, the two types of calibration models were derived -- the first model is a traditional one-variable model covering only time of signal propagation and the second one two-variable model which together with signal propagation time takes into account signal attenuation. The aim of this paper is to verify the efficiency of multiple regression to improve the accuracy of moisture estimation using the TDR technique. The applied models that consider amplitude attenuation are used for this type of analysis for the first time. With the conducted research and analyses, it was shown that the measurement quality of the method could be improved by obtaining more favorable values of the determination coefficient, Residual Standard Error, Root Mean Squared Error. Also the correlation analysis shows a better fit of two-variable models than one-variable to the obtained data. [ABSTRACT FROM AUTHOR]

Image enhancement is the process of improving the quality of a digital image. Improved image quality is one of the goals of the ongoing effort to improve the information content and interpretability of satellite images. Two sets of remote sensing data were collected for this region: one from the SPOT-4 (2018) satellite and the other from the Enhanced Thematic Mapper Plus (ETM+) Landsat 7 (2020) satellite. This study used two images taken at various spatial resolutions of the same location. Two images are shown here: one with a 30 m spatial resolution and the other with a 5 m resolution with multispectral processing. The results indicate that integrating spatial and spectral resolution using geomatics techniques significantly benefits various applications. Before merging the images, it was root mean square error (RMSE) 11.55 Easting, 5.77 Northing and became 1.52 Easting, 1.45 Northing after merging the images. After implementing the approach, the resulting fusing image exhibits enhanced spatial resolution, and the resulting multispectral image has excellent spatial as well as spectral resolution. Finally, the improved combined image with great spatial and spectral resolution is prepared for analysis and classification. [ABSTRACT FROM AUTHOR]

In order to realize stable propagation of the ethylene-air rotating detonation wave with less velocity deficit in the annular combustion chamber, a series of experiments have been carried out while changing the combustor width (i.e. 10 mm, 15 mm, 25 mm, and a hollow combustor), mass flow rate of air, and equivalence ratio. The air and ethylene are supplied into the combustor through a slot-orifice impinging injection scheme, where air is injected through a slot with a height of 0.5 mm and ethylene is supplied by 160 orifices with a diameter of 0.35 mm. Experimental results indicate that four propagation modes have been obtained, i.e. the single wave mode, the sawtooth wave mode, the periodic oscillation mode, and the deflagration mode. The sawtooth wave mode and the periodic oscillation mode are considered unstable modes. In the single wave mode, the average propagation velocity of rotating detonations in the annular combustion chamber with a combustor width of 15 mm is between 1121 and 1215 m/s. The minimum velocity deficit is 21.9%, and the relative standard deviation of the propagation velocity is less than 5%, which is much lower than the experimental results of other studies utilizing ethylene and air in the annular combustors. In addition, the reasons may lead to the failure of producing rotating detonations have been analyzed. The effects of the chemical reactivity on the generation of rotating detonation waves have been demonstrated by installing a convergent nozzle at the exit of the combustor. [ABSTRACT FROM AUTHOR]

Air pollution is one of the main public health and safety issues facing humanity. PM2.5 concentration prediction (PCP) helps the public to prevent and make government decisions in advance. PCP is a typical knowledge mining problem based on spatiotemporal sequential data, which still faces great challenges up to now. Aiming at the complex conundrum of meteorological, geographical, and temporal factors interference and concentration sudden changes, a dynamic spatiotemporal graph neural network (DST_GNN) method for PCP is proposed by using the advantages of graph neural network (GNN) and mechanism model. Its main methods are: The graph structure is used to construct the spatial relationship of PM2.5 among different monitoring stations, the mechanism model HYSPLIT is used to construct the dynamic edge relationship among graph nodes, and the gate recurrent unit of attention mechanism is used to learn the timing of PM2.5 concentration, thus forming a GNN architecture that integrates machine learning and domain knowledge. In addition, a loss function based on trend and shape is proposed when the model objective function is designed. The proposed model innovatively uses HYSPLIT to assist in building a dynamic spatiotemporal graph network and uses trend loss function for model training, which provides a new way for the dynamic construction of GNN, and provides a reference for PCP by combining domain knowledge and deep learning. Experimental results show that the proposed method has the best prediction accuracy among GNN based methods, which reduced the mean absolute error by about 14% and root mean square error by about 13% compared with the advanced GNN methods. The mean absolute error within 48 h forecast is less than 50, which predictive performance is far superior to the traditional mechanism model, and it also has the characteristics of flexible deployment and easy implementation. [ABSTRACT FROM AUTHOR]

Summary: In previous studies on facial video depression recognition, although convolutional neural network (CNN) has become a mainstream method, its performance still has room for improvement due to the insufficient extraction of global and local information and the neglect of the correlation of temporal and spatial information. This paper proposes a novel dual‐task enhanced global–local temporal–spatial network (DTE‐GLTS) to enhance the extraction capability of global and local features and deepen the analysis of temporal–spatial information correlation. We design a dual‐task learning mode that utilizes the data‐efficient image transformer (Deit) as the main body to learn the global features of video sequences and guides Deit to learn local features with the pre‐trained temporal–spatial fusion network (TSF). In addition, we propose the TSF mechanism to more effectively fuse temporal–spatial information in video sequences, strengthen the correlation between frames and pixels, and embed it in Resnet to form the TSF network. To the best of our knowledge, this is the first application of Deit and dual‐task learning mode in the field of facial video depression recognition. The experimental results on AVEC 2013 and AVEC 2014 show that our method achieves competitive performance, with mean absolute error/root mean square error (MAE/RMSE) scores of 6.06/7.73 and 5.91/7.68, respectively, while significantly reducing the number of parameters. [ABSTRACT FROM AUTHOR]

The study remediates the literacy gaps of the Grade 6 learners using the Contextualized Learning Kit (CLK) focusing the Word Recognition and Reading Comprehension to the selected public elementary schools in Cluster 1 of Carcar City Division during School Year 2023-2024. The study utilized the parallel research design employing the purposive sampling technique with forty (40) participants who were identified as Frustration and Instructional Level based on the Phil-IRI Group Screening Test. This research used the standardized Graded passage with 7-item test to assess the proficiency levels of the participants through pre-and post-assessment. The findings exhibited the following results, the Experimental Group have manifested the "improved" remark in word recognition with a weighted mean of 90.427, standard deviation of 5.792, and success rate of 55 percent and in reading comprehension with a weighted mean of 64.286, standard deviation of 13.513, and success rate of 50 percent, in contrary, the scores of Controlled Group have "remained" remark in word recognition with a weighted mean of 92.985, standard deviation of 1.673, and success rate of 25 percent and reading comprehension with a weighted mean of 78.571, standard deviation of 15.019, and success rate of 50 percent. The result recommended a thorough implementation of CLK in school since this is a great tool in developing the interest of the selected pupils while mastering the recognition and comprehension skills for the Phil-IRI assessment. [ABSTRACT FROM AUTHOR]

Background: Missing values in datasets present significant challenges for data analysis, particularly in the medical field where data accuracy is crucial for patient diagnosis and treatment. Although MissForest (MF) has demonstrated efficacy in imputation research and recursive feature elimination (RFE) has proven effective in feature selection, the potential for enhancing MF through RFE integration remains unexplored. Methods: This study introduces a novel imputation method, "recursive feature elimination-MissForest" (RFE-MF), designed to enhance imputation quality by reducing the impact of irrelevant features. A comparative analysis is conducted between RFE-MF and four classical imputation methods: mean/mode, k-nearest neighbors (kNN), multiple imputation by chained equations (MICE), and MF. The comparison is carried out across ten medical datasets containing both numerical and mixed data types. Different missing data rates, ranging from 10 to 50%, are evaluated under the missing completely at random (MCAR) mechanism. The performance of each method is assessed using two evaluation metrics: normalized root mean squared error (NRMSE) and predictive fidelity criterion (PFC). Additionally, paired samples t-tests are employed to analyze the statistical significance of differences among the outcomes. Results: The findings indicate that RFE-MF demonstrates superior performance across the majority of datasets when compared to four classical imputation methods (mean/mode, kNN, MICE, and MF). Notably, RFE-MF consistently outperforms the original MF, irrespective of variable type (numerical or categorical). Mean/mode imputation exhibits consistent performance across various scenarios. Conversely, the efficacy of kNN imputation fluctuates in relation to varying missing data rates. Conclusion: This study demonstrates that RFE-MF holds promise as an effective imputation method for medical datasets, providing a novel approach to addressing missing data challenges in medical applications. [ABSTRACT FROM AUTHOR]

Background: Neighbourhood exposure to takeaways can contribute negatively to diet and diet-related health outcomes. Urban planners within local authorities (LAs) in England can modify takeaway exposure through denying planning permission to new outlets in management zones around schools. LAs sometimes refer to these as takeaway "exclusion zones". Understanding the long-term impacts of this intervention on the takeaway retail environment and health, an important policy question, requires methods to forecast future takeaway growth and subsequent population-level exposure to takeaways. In this paper we describe a novel two-stage method to achieve this. Methods: We used historic data on locations of takeaways and a time-series auto-regressive integrated moving average (ARIMA) model, to forecast numbers of outlets within management zones to 2031, based on historical trends, in six LAs with different urban/rural characteristics across England. Forecast performance was evaluated based on root mean squared error (RMSE) and mean absolute scaled error (MASE) scores in time-series cross-validation. Using travel-to-work data from the 2011 UK census, we then translated these forecasts of the number of takeaways within management zones into population-level exposures across home, work and commuting domains. Results: Our ARIMA models outperformed exponential smoothing equivalents according to RMSE and MASE. The model was able to forecast growth in the count of takeaways up to 2031 across all six LAs, with variable growth rates by RUC (min–max: 39.4-79.3%). Manchester (classified as a non-London urban with major conurbation LA) exhibited the highest forecast growth rate (79.3%, 95% CI 61.6, 96.9) and estimated population-level takeaway exposure within management zones, increasing by 65.5 outlets per capita to 148.2 (95% CI 133.6, 162.7) outlets. Overall, urban (vs. rural) LAs were forecast stronger growth and higher population exposures. Conclusions: Our two-stage forecasting approach provides a novel way to estimate long-term future takeaway growth and population-level takeaway exposure. While Manchester exhibited the strongest growth, all six LAs were forecast marked growth that might be considered a risk to public health. Our methods can be used to model future growth in other types of retail outlets and in other areas. [ABSTRACT FROM AUTHOR]

Introduction: Discharge planning involves coordinating between different care settings. Failed coordination can lead to delayed care at the facilities receiving discharged patients. Nurses who implement discharge planning must consider the circumstances of the staff receiving the discharged patients. Objective: This study aims to develop a nurses' perspective-taking scale for measuring the cognitive process of imagining a staff's situation when receiving discharged patients. Methods: An online survey was conducted from September to November 2021 with nurses in Japanese acute care hospitals, using a 20-item questionnaire based on interviews and a literature review. Item reduction was conducted based on inter-item correlations, item-total correlations, and exploratory factor analysis. Confirmatory factor analysis was performed. For the exploratory and confirmatory factor analysis, the samples were randomly split in half and tested. Cronbach's alpha, intraclass correlation coefficients, and correlation coefficients were calculated using both self-developed and pre-existing measures. Results: The questionnaire was distributed to 1,289 individuals, and 416 valid responses were obtained. Item reduction and exploratory factor analysis resulted in a 10-item scale with two subscales, "imagine-other" (six items), which is imagining the other person's situation, and "imagine-self" (four items), which is imagining oneself in the other person's situation. The goodness-of-fit indices were as follows: comparative fit index, 0.95; root mean square error of approximation, 0.08; and standardized root mean squared residual, 0.06. Correlation coefficients with existing scales were 0.38, 0.57, and -0.33. Cronbach's alpha was 0.89, and the intraclass correlation coefficient was 0.54. Conclusion: The newly developed scale proved to be reliable, valid, and suitable for use. This scale can measure the degree of perspective-taking by nurses, which can improve collaboration between facilities and the effectiveness of discharge planning. [ABSTRACT FROM AUTHOR]