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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]

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

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

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

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

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

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

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

Statistical techniques are needed to analyze data structures with complex dependencies such that clinically useful information can be extracted. Individual‐specific networks, which capture dependencies in complex biological systems, are often summarized by graph‐theoretical features. These features, which lend themselves to outcome modeling, can be subject to high variability due to arbitrary decisions in network inference and noise. Correlation‐based adjacency matrices often need to be sparsified before meaningful graph‐theoretical features can be extracted, requiring the data analysts to determine an optimal threshold. To address this issue, we propose to incorporate a flexible weighting function over the full range of possible thresholds to capture the variability of graph‐theoretical features over the threshold domain. The potential of this approach, which extends concepts from functional data analysis to a graph‐theoretical setting, is explored in a plasmode simulation study using real functional magnetic resonance imaging (fMRI) data from the Autism Brain Imaging Data Exchange (ABIDE) Preprocessed initiative. The simulations show that our modeling approach yields accurate estimates of the functional form of the weight function, improves inference efficiency, and achieves a comparable or reduced root mean square prediction error compared to competitor modeling approaches. This assertion holds true in settings where both complex functional forms underlie the outcome‐generating process and a universal threshold value is employed. We demonstrate the practical utility of our approach by using resting‐state fMRI data to predict biological age in children. Our study establishes the flexible modeling approach as a statistically principled, serious competitor to ad‐hoc methods with superior performance. [ABSTRACT FROM AUTHOR]

Background: Yam (Dioscorea alata L.) is the staple food of many populations in the intertropical zone, where it is grown. The lack of phenotyping methods for tuber quality has hindered the adoption of new genotypes from breeding programs. Recently, near‐infrared spectroscopy (NIRS) has been used as a reliable tool to characterize the chemical composition of the yam tuber. However, it failed to predict the amylose content, although this trait is strongly involved in the quality of the product. Results: This study used NIRS to predict the amylose content from 186 yam flour samples. Two calibration methods were developed and validated on an independent dataset: partial least squares (PLS) and convolutional neural networks (CNN). To evaluate final model performances, the coefficient of determination (R2), the root mean square error (RMSE), and the ratio of performance to deviation (RPD) were calculated using predictions on an independent validation dataset. The tested models showed contrasting performances (i.e., R2 of 0.72 and 0.89, RMSE of 1.33 and 0.81, RPD of 2.13 and 3.49 respectively, for the PLS and the CNN model). Conclusion: According to the quality standard for NIRS model prediction used in food science, the PLS method proved unsuccessful (RPD < 3 and R2 < 0.8) for predicting amylose content from yam flour but the CNN model proved to be reliable and efficient method. With the application of deep learning methods, this study established the proof of concept that amylose content, a key driver of yam textural quality and acceptance, can be predicted accurately using NIRS as a high throughput phenotyping method. © 2023 The Authors. Journal of The Science of Food and Agriculture published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

The importance of satellite altimetry for monitoring inland water bodies is increasing, with the increasing need for fresh water. With the addition of the Sentinel-6 Michael Freilich (MF) in 2020 to the satellite altimetry record, we have parallel high- and low-resolution inland water observations. In this study, we validate Sentinel-6MF low- and high-resolution lake water levels, as determined with a state-space model, with data from 124 gauges in 85 lakes (of size > 20 km2) from the United States, Canada, Sweden, and Australia. Furthermore, we estimate the scatter of the observations at 1330 globally distributed lakes. We address the difference in performance when using a physical (ocean) and empirical (OCOG) retracker. Using the 124 gauges as ground truth, we determine a best-case unbiased root mean squared error (u-RMSE) of 6.4 cm for Sentinel-6MF, when using the high-resolution mode and the OCOG retracker. The u-RMSE is between 6.4 and 12.3 cm when correcting for ice-covered lakes, and 15.6 to 31.3 cm for all data, depending on retracker and resolution mode. The corresponding median standard deviation of water surface elevations per transect from the 1330 lakes is 3.5 cm and 8.1 cm for high and low-resolution OCOG-based water levels, respectively. The corresponding median standard deviation of Sentinel-3A/B OCOG (enhanced measurement, 20 Hz mode, baseline-3/4) is 5.9 cm and 6.0 cm. We find a lower u-RMSE and standard deviation for high-resolution data, and the ability to observe more lakes, compared to the low-resolution mode. From this, we see that Sentinel-6MF yields valid estimates of the water surface elevation, including smaller lakes, and would provide additional value to future water level monitoring via remote sensing. [ABSTRACT FROM AUTHOR]

While antidepressants are frequently used, less is known about contemporary discontinuation patterns, especially across different sociodemographic populations. Patients 16–84 years initiating antidepressants between 2016 and 2019 within a large US health insurer were identified. The association between patient characteristics and time until antidepressant discontinuation was evaluated using adjusted Cox proportional hazard regression. Across 1,365,576 patients, mean time to discontinuation was 168.1 days (SD: 223.6). Men were more likely to discontinue than women (HR: 0.94, 95%CI: 0.94–0.94). Younger patients (16–24 years) were more likely to discontinue than older patients. Patients who were non-White (Asian HR: 1.33, 95%CI: 1.31–1.34; Black HR: 1.27, 95%CI: 1.27–1.28; Hispanic HR: 1.34, 95%:CI 1.34–1.35), with evidence of a substance use disorder (HR: 1.31, 95%CI: 1.27–1.35), or taking tricyclic antidepressants (HR:1.26, 95%CI: 1.25–1.27) were more likely to discontinue. Information on reasons for discontinuation was not available, and wide standard deviations for the primary outcome were reported. The results may not be generalized to non-commercially insured beneficiaries. Discontinuation is common within the first 6 months of treatment but varies across populations, highlighting patients who may benefit from potential intervention. • Most patients discontinued their antidepressants around 6 months post-initiation. • Discontinuation patterns varied across different patient characteristics. • Younger, male, non-White race patients were more likely to discontinue. • Patients with certain diagnoses and drugs were more likely to discontinue. [ABSTRACT FROM AUTHOR]

Objective. Deep learning reconstruction (DLR) algorithms exhibit object-dependent resolution and noise performance. Thus, traditional geometric CT phantoms cannot fully capture the clinical imaging performance of DLR. This study uses a patient-derived 3D-printed PixelPrint lung phantom to evaluate a commercial DLR algorithm across a wide range of radiation dose levels. Method. The lung phantom used in this study is based on a patient chest CT scan containing ground glass opacities and was fabricated using PixelPrint 3D-printing technology. The phantom was placed inside two different size extension rings to mimic a small- and medium-sized patient and was scanned on a conventional CT scanner at exposures between 0.5 and 20 mGy. Each scan was reconstructed using filtered back projection (FBP), iterative reconstruction, and DLR at five levels of denoising. Image noise, contrast to noise ratio (CNR), root mean squared error, structural similarity index (SSIM), and multi-scale SSIM (MS SSIM) were calculated for each image. Results. DLR demonstrated superior performance compared to FBP and iterative reconstruction for all measured metrics in both phantom sizes, with better performance for more aggressive denoising levels. DLR was estimated to reduce dose by 25%–83% in the small phantom and by 50%–83% in the medium phantom without decreasing image quality for any of the metrics measured in this study. These dose reduction estimates are more conservative compared to the estimates obtained when only considering noise and CNR. Conclusion. DLR has the capability of producing diagnostic image quality at up to 83% lower radiation dose, which can improve the clinical utility and viability of lower dose CT scans. Furthermore, the PixelPrint phantom used in this study offers an improved testing environment with more realistic tissue structures compared to traditional CT phantoms, allowing for structure-based image quality evaluation beyond noise and contrast-based assessments. [ABSTRACT FROM AUTHOR]

The goal of this research was a quantitative-spatial high-resolution analysis of population distribution based on residential building data extracted from topographic objects database. Attribute information on residential buildings (location, volume, function) provides opportunities to estimate the number of residents. The recalculation of the population from the urban units of Cracow into new spatial units was based on the area-weighted aggregation method. The location of residential buildings constituted a limiting variable, and the total square meterage (calculated as the area of the buildings and the number of their floors) constituted the binding variable. The introduction of additional binding variables related to the type of building and its location, as well as various methods of determining the square meterage per building type, resulted in the creation of a total of nineteen maps of population. As a result, the best methods for the correct geographic scale and segmentation of residential building type—single family or multifamily—were identified. For the input data, based solely on the amount of population in urban units, the calculated value of the mean absolute percentage error (MAPE) in the 1 × 1 km grid was 310.8 percent, and for the root mean square error (RMSE) was 1,476 people. In the dasymetric method, directly associating the population with the volume of residential buildings, the errors fell to 21.9 percent and 632 people, respectively. The best result was obtained for the variant based on minimizing the RMSE, associating the number of residents to single-family buildings (2.88 people/building) and associating the number of residents to the square footage in multifamily buildings (37.1 m2/person; MAPE = 19.2 percent, RMSE = 556 people). [ABSTRACT FROM AUTHOR]

OBJECTIVE: We investigated direct and indirect relationships between historic redlining and prevalence of diabetes in a U.S. national sample. RESEARCH DESIGN AND METHODS: Using a previously validated conceptual model, we hypothesized pathways between structural racism and prevalence of diabetes via discrimination, incarceration, poverty, substance use, housing, education, unemployment, and food access. We combined census tract–level data, including diabetes prevalence from the Centers for Disease Control and Prevention PLACES 2019 database, redlining using historic Home Owners' Loan Corporation (HOLC) maps from the Mapping Inequality project, and census data from the Opportunity Insights database. HOLC grade (a score between 1 [best] and 4 [redlined]) for each census tract was based on overlap with historically HOLC-graded areas. The final analytic sample consisted of 11,375 U.S. census tracts. Structural equation modeling was used to investigate direct and indirect relationships adjusting for the 2010 population. RESULTS: Redlining was directly associated with higher crude prevalence of diabetes within a census tract (r = 0.01; P = 0.008) after adjusting for the 2010 population (χ2(54) = 69,900.95; P < 0.001; root mean square error of approximation = 0; comparative fit index = 1). Redlining was indirectly associated with diabetes prevalence via incarceration (r = 0.06; P < 0.001), poverty (r = −0.10; P < 0.001), discrimination (r = 0.14; P < 0.001); substance use (measured by binge drinking: r = −0.65, P < 0.001; and smoking: r = 0.35, P < 0.001), housing (r = 0.06; P < 0.001), education (r = −0.17; P < 0.001), unemployment (r = −0.17; P < 0.001), and food access (r = 0.14; P < 0.001) after adjusting for the 2010 population. CONCLUSIONS: Redlining has significant direct and indirect relationships with diabetes prevalence. Incarceration, poverty, discrimination, substance use, housing, education, unemployment, and food access may be possible targets for interventions aiming to mitigate the impact of structural racism on diabetes. [ABSTRACT FROM AUTHOR]

We proposed the numerical studies and machine learning prediction for the multilayered Borophene-GST-silica-Ag-based refractive index sensor for the 1.3–1.5 µm wavelength range. The top layers of the proposed sensor incorporate Ag gratings. The proposed structure utilizes a mode based on surface plasmonic resonance, which has been created using a finite element model through computational analysis. Resonance is observed across a range of refractive index values from 1 to 2.5, which primarily aligns with the refractive indices of key biomolecules such as haemoglobin, saliva, urine, and cancerous cells. The proposed structure has also been investigated for the different physical parameters such as material height, grating space, the incident wave's wide incident angle, and the GST material's phase. We demonstrate the modulation of reflectance values in response to varying phases of the GST material (amorphous and crystalline), which can be controlled through external temperature changes. The distribution of the electric and magnetic fields of the structure is also provided in order to examine the field distribution across the grating and other layers of material. This sensor offers a wide angle of stability (0 to 80°) for the respective resonating points. An artificial neural network is employed to analyze the simulated data and make predictions regarding the behaviour of the structure. We have found the R2 = 0.97 for the proposed artificial neural network (ANN) model. The ANN model of this structure also presented the values of mean square error (MSE), mean absolute error (MAE), and root mean square error (RMSE) for different epoch values. Results shown in this research can help to sense the wide range of biomolecule samples whose refractive index ranges from 1 to 2.5. The wide wavelength range makes this device applicable for developing wideband infrared biosensors. [ABSTRACT FROM AUTHOR]

Montessori is a highly individualized pedagogy that prioritizes following each child's individual interests over standardized test scores. This quasi-experimental study compares groups of Montessori students (treatment groups, N = 535–710) with control groups of non-Montessori students, matched through the simple matching method, which controls for the effect of background variables on schooling outcomes. The tested hypothesis is that the treatment groups achieve mean scores that are not significantly lower and have a significantly higher standard deviation on standardized tests of mathematics and language. The study encompassed students in Grade 2, 5, 8, and 10 across two separate cohorts. Results indicate that Montessori students attain either comparable or significantly higher scores than non-Montessori students, depending on grade and cohort, with greater score variability observed, particularly at Grade 8 and in mathematics. This finding implies the necessity for future investigations into the effect of Montessori teachers' instructional practices. [ABSTRACT FROM AUTHOR]

Reservoir operations involve determining the quantity of water to be released or stored from the reservoir at any given time, based on the reservoir's current condition. This research presents optimizing techniques for reservoir operating policies on Ravishankar Sagar reservoir during monsoon season. Metaheuristics algorithms – GA, NSGA II, NSGA III, and Eps MOEA were used to optimize the objective function for maximization of storage along with a different set of constraints in Ravishankarsagar reservoir. Sensitivity analysis on all algorithms was performed to calibrate different evaluation parameters. The results were analyzed to find the effectiveness of soft computing algorithms in real-world problems. The standard deviation of 359.52, 351.93,349.68 and 361.09 and Median of 512.5, 525.36,568.96 and 433.79 was observed in GA, NSGA II, Eps MOEA, and NSGA IIII so all the algorithms performed well and were in close approximation, with the least standard deviation and the most optimum storage in Eps MOEA. [ABSTRACT FROM AUTHOR]

Clozapine is characterized by a large within‐ and between‐patient variability in its pharmacokinetics, attributed to non‐genetic and genetic factors. A cross‐sectional analysis of clozapine trough concentration (Clz C0) issued from Tunisian schizophrenic patients was collected and analysed using a nonparametric modelling approach. We assessed the impact of demographic covariates (age, weight and sex), patient's habits (smoking status, alcohol and caffeine intake) and the genetic factors (CYP1A2*1C, CYP1A2*1F and CYP2C19*2 polymorphisms) on each pharmacokinetic parameter. An external validation of this pharmacokinetic model using an independent data set was performed. Fit goodness between observed‐ and individual‐predicted data was evaluated using the mean prediction error (% MPE), the mean absolute prediction error (% MAPE) as a measure of bias, and the root mean squared error (% RMSE) as a measure of precision. Sixty‐three CLz C0 values issued from 51 schizophrenic patients were assessed in this study and divided into building and validation groups. CYP1A2*1F polymorphism and smoking status were the only covariates significantly associated with clozapine clearance. Precision parameters were as follows: 1.02%, 0.95% and 22.4%, respectively, for % MPE, % MAPE and % RMSE. We developed and validated an accurate pharmacokinetic model able to predict Clz C0 in Tunisian schizophrenic patients using the two parameters CYP1A2*1F polymorphism and smoking. [ABSTRACT FROM AUTHOR]

The article titled "Estimation of missing ordinal data: A comment on Wissler et al. (2022)" discusses the accuracy of multiple imputation methods for handling missing ordinal or continuous data. The authors comment on the accuracy metric used in the original study and propose an alternative metric that provides a more realistic assessment of imputation accuracy. They also evaluate the performance of one imputation method, predictive mean matching (PMM), on a dataset of fibrocartilaginous entheseal changes. The results show that PMM performs better on this dataset compared to the dataset used in the original study. The authors conclude by highlighting the importance of considering the number of stages in recording a trait and the intercorrelation among variables when imputing missing values. [Extracted from the article]

Conditional Spectrum (CS), characterized by a conditional mean and conditional standard deviation, serves as a target spectrum linking seismic hazard information to ground motion selection for seismic demand analysis. Current Ground Motion Selection and Modification (GMSM) methods aim to align with this target spectrum but face challenges due to the limited availability of recorded ground motions and potential modifications, leading to difficulties in ensuring selected ground motions accurately reflect site characteristics. Addressing this issue, the study presents an algorithm to select hazard-consistent CS-based records utilizing a stochastic ground motion model with a dual objective: (1) alleviate the need for scaling during the selection process, and (2) select records consistent with the target hazard and the contributing causal scenarios. To achieve both objectives, the study generates a database of hazard-targeted ground motions utilizing the kriging surrogate with scenarios sampled from the disaggregation matrix. Then, a postprocessing step explicitly considers causative parameters in the selection process. The algorithm's potential to select site-specific ground motions is demonstrated using sites in the Western United States, providing insights into the computational cost and accuracy. Additionally, statistical comparisons are conducted to explore circumstances where similar hazard consistency can be achieved without the postprocessing step, reducing the overall computational cost. Finally, the selected ground motions are favorably compared to those selected through traditional conditional spectrum-based approaches based on their spectral shape and ground motion intensity measures. [ABSTRACT FROM AUTHOR]

Reduced social support has been associated with presence of depression and reduced quality of life among older adults. The relationships may be better understood by exploring the interactions of individual domains among the constructs. This cross-sectional survey involved a consecutive sample of 206 (116 females and 90 males) older adults living in a Southern Nigeria community. The Multidimensional Scale of Perceived Social Support, Geriatric Depression Scale, and World Health Organization Quality of Life-OLD Questionnaire, were used to measure social support, depression, and quality of life respectively. Data was analyzed using frequency counts, percentages, mean, standard deviation, multiple regression and Spearman rank-order correlation coefficient, at 0.05 alpha level. Prevalence rate of depression among participants was 45.5%. Social support was perceived to be low by 37.4% of participants with the lowest mean social support score coming from friends domain. Participants' quality of life was generally fairly good (> 60%) with the lowest scores coming from the intimacy domain. Significant correlations between social support domains and each of quality of life (p < 0.05) and depression were respectively positive and negative; but weakest for the friend and strongest for significant others domains. All quality of life domains were significantly correlated with social support except the death and dying domain. All the domains of social support (family and significant other) were significant predictors of depression except the friend domain. Significant others around individual older adults particularly those with depression ought to be educated on the importance of their roles. Stakeholders including healthcare providers may create and support programmes for improved social networking for the older adults in order to enhance their general wellbeing and quality of life. [ABSTRACT FROM AUTHOR]

Because of the high nonlinearity and complexity, it is still a challenge to forecast traffic flow accurately. Most of the existing methods, which lack the ability to model the dynamic spatio-temporal correlations of traffic flows, rely on a priori knowledge of the graph structure to capture static spatial correlations. However, spatial correlation can be dynamic and the physical structure does not reflect the true relationship between roads. To better capture complex spatio-temporal dependencies and predict traffic conditions on road networks, a new forecasting model based on Transformer and Diffusion Graph Attention Network (T-DGAN) is proposed. This model adopts the Transformer encoder-decoder architecture, where the encoder and decoder contain multiple Spatio-Temporal Convolutional Network Blocks (ST-CB) and Diffusion Graph Attention Blocks (DGA-B), which are utilized to capture the spatio-temporal dependency and the dynamic spatial correlation of traffic flow separately. In addition, an information auxiliary module is added to the decoder to summarize the traffic flow information between the encoder and the decoder. Experiments on different datasets verify that the T-DGAN model has achieved higher forecasting performance than the baseline methods. In PEMS03, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of T-DGAN to 60 min are improved by 4.28% and 3.66%, compared with the state-of-the-art model, respectively. [ABSTRACT FROM AUTHOR]

In this study, a method is proposed to deal with the variable light conditions in a greenhouse to non-destructively predict the soluble solid content (SSC) of tomatoes on the plant. It was investigated how well the SSC – measured as °Brix – of tomatoes could be predicted based on spectral data in the range of 470–900 nm, where data acquired in situ (in the greenhouse) was compared to post-harvest data captured under controlled laboratory conditions. To deal with the variation in illumination in the greenhouse, a dynamic-calibration method is proposed, using a grey reference in the image. Ground-truth SSC data of the tomatoes was acquired using a refractometer. Data was collected of three different types of truss tomatoes with a wide range of SSC. Different PLS regression models were then trained on the spectral data in relation to the refractometer values. Trained and tested on all types, the in situ measurements showed a predicted coefficient of determination on the test set, Q2, of 0.95 with a Root Mean Squared Error of Prediction (RMSEP) of 0.29 °Brix using the dynamic calibration, and a Q2 of 0.93 with RMSEP of 0.35 °Brix without using the dynamic-calibration method. The post-harvest measurements resulted in a Q2 of 0.95 with RMSEP of 0.31 °Brix. The results show that spectral imaging using dynamic calibration is applicable for in situ non-destructive prediction of SSC. This method enables high-throughput and non-destructive quality estimation of fruits on the plant in commercial greenhouse conditions. • Vis-NIR spectral imaging compared between lab and greenhouse conditions. • Prediction of SSC based on PLS models trained on spectral imaging data. • Calibration of spectral data enables accurate SSC prediction under changing illumination. • Performance analysis of soluble solid content in tomato using spectral imaging. [ABSTRACT FROM AUTHOR]

Summary: Aim: The aim of this study is to examine which interventions lead to clinically significant weight loss among people with physical disabilities. Methods: We systematically searched three electronic databases (PubMed, Scopus, and CENTRAL) including studies until May 2022 to find randomized controlled trials on behavioral interventions and weight‐related outcomes in people with physical disabilities. Pharmacological or surgical interventions were excluded. Study quality was evaluated using the Cochrane Risk of Bias Tool. Interventions were grouped as dietary, physical activity, education/coaching, or multi‐component. Mean weight changes, standard deviations, confidence intervals, and effect sizes were extracted or calculated for assessment of the intervention effect. Results: Sixty studies involving 6,511 participants were included in the qualitative synthesis. Most studies (n = 32) included multi‐component interventions, incorporating dietary and physical activity components. Limited evidence suggests that extensive dietary interventions or long‐term multi‐component interventions might lead to a clinically relevant weight reduction of at least 5% for older individuals (age > 50) with mild‐to‐moderate mobility impairments. Discussion: Due to the high heterogeneity of studies and low study quality, it can be assumed that the range of applicability of the findings is questionable. Further research should examine younger age groups (i.e., children, adolescents, and adults under 40 years) and compare different settings such as schools, clinics, nursing homes, and assisted living facilities. [ABSTRACT FROM AUTHOR]

To quickly and accurately obtain the moisture status of grape plants at the field scale, the treatments included three irrigation levels i.e. W3 (100%M, M as the irrigation quota), W2 (75% M) and W1 (50%M) and four fertilizer application rates i.e. F0 (0 kg/hm2), F1 (324 kg/hm2), F2 (486 kg/hm2) and F3 (648 kg/hm2). Grape leaf water content (LWC) was monitored nondestructively by an unmanned aerial vehicle (UAV) carrying multispectral (MS), visible light (RGB) and thermal infrared (TIR) cameras to extract band reflectance (BR), canopy coverage (CC) and canopy temperature (T) information, respectively. Using BR (included six bands: B, R, G, RE, NIR800, and NIR900), CC and T and their combinations as input variables brought into the partial least squares (PLS), random forest (RF), support vector machine (SVM) and extreme learning machine (ELM) algorithms. The prediction models for grape LWC were established by using four machine learning algorithms, and the optimal combination of variables was finally determined. The results represented that (1) the model built with BR + CC + T as predictor variables under different water treatments was better than other combinations of variables, with the coefficient of determination (R2) above 0.69 and the relative root mean square error (RRMSE) less than 2.5%; (2) modeling the LWC of grapes at different fertility periods based on the combination of BR + CC + T, the R2 ranged from 0.51 to 0.78 at the shoot-growing, anthesis, and fruit-inflating stages; (3) the top three important variables were T, NIR800, and NIR900 in the shoot-growing, anthesis, and fruit-inflating stages, while the top three important variables were RE, B, and T in the fruit-inflating stage. In summary, UAV multimodal data fusion has good application in predicting the LWC of grapes using RF algorithm modeling during the different growth stages. This study can supply a technical support for precise management of vineyard water regime using a UAV platform. [ABSTRACT FROM AUTHOR]

Early monitoring of within-field yield variability and forecasting yield potential is critical for farmers and other key stakeholders such as policymakers. Remote sensing techniques are progressively being used in yield prediction studies due to easy access and affordability. Despite the increasing use of remote sensing techniques for yield prediction in agriculture, there is still a need for medium-resolution satellite imagery when predicting canola yield using a combination of crop and soil information. In this study, we investigated the utility of remotely sensed flowering information from PlanetScope (at 4 m) satellite imagery combined with derived soil and topography parameters to predict canola yield. Our yield prediction model was trained and validated using data from 21 fields managed under variable rate seed and fertilizer application, including cleaned harvester yield maps, soil, and topography maps. To quantify the flowering intensity of canola, 9 vegetation indices (VIs) were calculated using spectral bands from PlanetScope imagery acquired for the reproductive stages of canola. We created five random forest regression models using different subsets of covariates, including VIs, soil, and topography features, to predict canola yield within the season. Using a random forest regression algorithm, we recorded accuracies ranging from poor to best performing using coefficient of determination and root mean squared error (R2: 0.47 to 0.66, RMSE: 325 to 399 kg ha−1). The optimal subset of covariates identified electrical conductivity (EC), Normalized Difference Yellowness Index, and Canola Index as the key variables explaining within-spatial variability in canola yield. Our final model exhibited a validation R2 of 0.46 (RMSE = 730 kg ha−1), demonstrating the potential of medium-resolution satellite imagery during the flowering stage to detect and quantify sub-field spatial and temporal floral phenology changes when predicting canola yield. [ABSTRACT FROM AUTHOR]

Cephalometric analysis has an important and essential role to treat the patients with craniofacial and dentofacial deformities. Cephalometric analysis is a relationship of human geometry which can be quantified and derived from the linear and angular measurements. To treat any patient, such analysis is required to be performed on the Head X‐ray image of the patient. The objective of the proposed work is to detect cephalometric landmarks automatically on CT (computational tomography) images. Twenty cephalometric landmarks were automatically localized on 100 CT scans using hybrid coarse‐to‐fine computational method. The mean error for landmark detection was computed as 2.88 mm and standard deviation of 1.85 mm. The highest detection rate for cephalometric landmarks was received as 100% for Nasion landmark under 4‐mm error and the highest detection rate was received as 99% for Nasion landmark under 3‐mm error. The less number of datasets were used for the training and higher number of datasets were used for the testing. Compared to the literature methods, our method used higher number of datasets to demonstrate the accuracy of the proposed method. [ABSTRACT FROM AUTHOR]

There exist numerous multi‐path signals with impulsive noise (IN) in a multi‐path propagation environment. The direction‐of‐arrival (DOA) is highly challenging to be assessed because of the strong IN. For various military along with civilian applications, DOA estimation has turned into a hopeful technology. But, estimating DOA for multipath signals with IN environments is extremely complicated. A desirable output is not attained by several existent techniques, namely subspace‐centric approaches, maximum likelihood techniques, along with sparse representation‐centric methods, for DOA assessment. Therefore, an effective satin bowerbird optimization‐based deep learning neural network centered DOA estimation upon single snapshots is proposed in this paper for attaining robust and precise DOA estimation in multipath and IN environments. An arbitrary array configuration is employed by utilizing the proposed work as an input signal for establishing the received signal's model. Pre‐processing, time‐frequency domain conversion, feature extraction, feature reduction and DOA estimation are the proposed method's 5 disparate stages. To evince the proposed model's efficacy, its outcomes are analogized with other prevailing techniques regarding some performance metrics. The proposed model attains the root mean square error of 0.6484366‐AZ and 0.6484366‐EA, root mean squared percentage error (RMPSE) of 4.636107‐AZ and 5.628599‐EA and probability of resolution of 0.967999‐AZ and 0.962841‐EA, which are extremely less when compared to the prevailing methods; thus, depicting the superiority of the proposed model for an effective DOA estimation. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ETo) is a critical water resource management parameter, including irrigation scheduling and crop water requirements. Because large uncertainties in estimating ETo can result in equally large uncertainties in determining water budgets and crop water requirements, and vice versa, accurate determination of ETo can be challenging when direct measurement and estimation with the Penman–Monteith (FAO-56-PM) semi-empirical equation of the food and agriculture organization (FAO) is not possible. Indeed, this study explores the use of the support vector regression machine learning algorithm (SVR) to predict daily ETo with limited measured inputs. It is the first time that Julian Day (J) is included as an input to improve prediction accuracy. Ten years of meteorological data collected at the Dar-El-Beidha weather station in Algeria are used, with maximum, minimum, and mean air temperatures (TM, tm, and T), mean relative humidity (RH), mean wind speed (u2), and sunshine duration (n) as inputs, as well as J and extraterrestrial solar radiation (Ra) as auxiliary variables, and the ETo-FAO-56-PM values as target outputs. Several SVR models are developed using different combinations of inputs, and their performance is assessed relative to ETo-FAO-56-PM values. Empirical equations are also used for comparison, and several evaluation metrics are employed, including root mean square error (RMSE), mean absolute percentage error (MAPE), determination coefficient (R2), RMSE-standard deviation ratio (RSR), Nash–Sutcliffe efficiency coefficient (NSE), and Willmott's refined index (WI). The results show that the SVR models utilizing limited meteorological inputs in addition to J and/or Ra predicted ETo accurately and outperformed their corresponding estimates using empirical equations, radial basis function neural networks (RBFNN), and adaptive neuro-fuzzy inference systems (ANFIS) models obtained in previous studies. The RMSE ranged from 0.28 to 0.72 mm/day, R2 from 0.86 to 0.98, MAPE from 7 to 19%, RSR from 0.15 to 0.38, NSE from 0.86 to 0.98, and WI from 0.65 to 0.87. These findings could provide useful solutions for ETo estimation issues in areas with sparse data and agro-climatic conditions similar to those of Dar-El-Beidha. [ABSTRACT FROM AUTHOR]