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The cell consistency is a significant factor affecting the performance, durability, and reliability for the commercial proton exchange membrane fuel cell stack due to the barrel effect. At present, the previous researches are mainly investigating the relationship between current and voltage consistency, lacking the deepgoing discussions on the fluctuation and dynamic variation of cell consistency. Regarding to such deficiency, this paper proposes a novel perspective to study the relationship between cell consistency and voltage since their variation features in time domain after current change. Cell consistency (coefficient of variation) and voltage satisfy an exponential relation and its feasibility is validated by the polarization curves of three different stacks. This novel consistency-voltage relationship exhibits a distinct advantage representing the dynamic consistency response in comparison with the consistency-current relationship. For instance, its root mean square error of 5.723 × 10−4 under loading operation is evidently lower than that of 1.275 × 10−3 for the later. Moreover, the Spearman's correlation analysis indicates that the cell consistency shows a stronger correlation to voltage than current according to their coefficients of −0.9983 and 0.9968, respectively. [Display omitted] • Cell voltage consistency inside stack is studied from the novel voltage perspective. • The cell consistency and stack mean voltage meet an exponential relationship. • The exponential CV-V expression has a better fitting effect to experimental data. • The CV - V expression can well represent the dynamic response in the time domain. • The cell consistency exhibits a stronger correlation with voltage than with current. [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]

Background: Currently, there is no wind park operating along the Egyptian Mediterranean Coast. Therefore, this study aims to find suitable locations for such projects. Wind data from five coastal meteorological stations were used. These are Marsa Matruh (MM), Ras El-Tin (RE), Abu Qir (AQ), Port Said (PS), and Arish (Ar), in that order from west to east. The wind regime dataset, comprising velocity and direction measurements at a 10-m elevation, was collected from January 2007 to December 2022 (16 years), with a complete record of all data points. The Weibull distribution function, along with its different parameters, was used to characterize wind energy along the Egyptian Mediterranean Coast. The coefficient of determination (R2), root mean squared error (RMSE), and relative root mean squared error (RRMSE) for the Weibull parameters, along with the relative percentage errors (RPE) for the wind power density were calculated to assess the concordance between outcomes derived from observed data and those predicted by the Weibull function. Results: Results revealed that the dominant wind direction along the Egyptian Mediterranean Coast was the NNW to N wind, except at Ar where the dominant wind was S. The wind velocity range of 4–6 m/s dominated RE, AQ, and PS. At MM and Ar, this was reduced to 2–4 m/s. The analysis of wind power density outlined significant insights into the potential for wind energy generation in the region. The overall analysis showed that AQ and PS were potentially the most suitable locations for wind energy projects. However, the high variability at the AQ site required robust system designs to manage the fluctuating wind conditions. PS might be more suitable for projects prioritizing stability and consistency over maximum energy output. Although Arish, characterized by its lower wind power density, may be less conducive for large-scale wind energy projects, it could still be viable for smaller installations or when integrated with other renewable energy sources. Conclusions: The different statistical indices reflected good model fitting, displaying the reliability of the Weibull distribution as a tool for preliminary wind resource assessment along the Egyptian Mediterranean Coast and facilitating accurate predictions of wind power availability. [ABSTRACT FROM AUTHOR]

Three different enzymes alkaline phosphatase, Urease and Dehydrogenase were measured during this study to monitor the organic matter dynamics during semi-industrial composting of mixture A with 1/3 sludge+2/3 palm waste and mixture B with ½ sludge+1/2 palm waste. The phosphatase activity was higher for Mix-A (398.7 µg PNP g−1 h−1) than Mix-B (265.3 µg PNP g−1 h−1), while Mix-B (103.3 µg TPF g−1d−1) exhibited greater dehydrogenase content than Mix-A (72.3 µg TPF g−1 d−1). That could contribute to the dynamic change of microbial activity together with high amounts of carbonaceous substrates incorporated with the lignocellulosic. The gradual increase in the dehydrogenase from the compost Mix-A implies that high lignocellulosic substrate requires gradual buildup of dehydrogenase activity to turn the waste into mature compost. A higher pick of urease with a maximum activity of 151.5 and 122.4 µg NH4-N g−1 h−1 were reported, respectively for Mix-A and B. Temperature and pH could also influence the expression of enzyme activity during composting. The machine learning well predicted the compost quality based on NH3/NO3, C/N ratio, decomposition rate and, humification index (HI). The root mean square error (RMSE) values were 1.98, 1.95, 4.61%, and 4.1 for NH+3/NO−3, C/N ratio, decomposition rate, and HI, respectively. The coefficient of determination between observed and predicted values were 0.87, 0.93, 0.89, and 0.94, for the r NH3/NO3, C/N ratio, decomposition rate, and HI. Urease activity significantly predicted the C/N ratio and HI only. The profile of enzymatic activity is tightly linked to the physico-chemical properties, proportion of lignocellulosic-composted substrates. Enzymatic activity assessment provides a simple and rapid measurement of the biological activity adding understunding of organic matter transformation during sludge-lignocellulosic composting. [ABSTRACT FROM AUTHOR]

Rapid and non-destructive diagnosis of plant nitrogen (N) status is crucial to optimize N management during the growth of summer maize. This study aimed to evaluate the effectiveness of continuous wavelet analysis (CWA) in estimating the nitrogen nutrition index (NNI), to determine the most suitable wavelet analysis method, and to identify the most sensitive wavelet features across the visible to near-infrared spectrum (325–1,025 nm) for accurate NNI estimation. Field experiments were conducted across two sites (Kaifeng and Weishi) during the 2022 and 2023 growing seasons using four summer maize cultivars (XD20, ZD958, DH661, and DH605) under varying N application rates (0, 80, 160, 240, and 320 kg N ha-1). Canopy reflectance spectra and plant samples were collected from the V6 to V12 growth stages. The wavelet features for each spectral band were calculated across different scales using the CWA method, and their relationships with NNI, plant dry matter (PDM), and plant N concentration (PNC) were analyzed using four regression models. The results showed that NNI varied from 0.61 to 1.19 across different N treatments during the V6 to V12 stages, and the Mexican Hat wavelet was identified as the most suitable mother wavelet, achieving an R ² value of 0.73 for NNI assessment. The wavelet features derived from the Mexican Hat wavelet were effective in estimating NNI, PDM, and PNC under varying N treatments, with the most sensitive wavelet features identified as 745 nm at scale 7 for NNI, 819 nm at scale 5 for PDM, and 581 nm at scale 6 for PNC using linear regression models. The direct and indirect methods for NNI estimation were compared using independent field data sets. Both methods proved valid to predict NNI in summer maize, with relative root mean square errors of 10.8% for the direct method and 13.4% for the indirect method. The wavelet feature at 745 nm, scale 7, from the direct method (NNI = 0.14 WF (745 nm, 7) + 0.3) was found to be simpler and more accurate for NNI calculation. These findings provide new insights into the application of the CWA method for precise spectral estimation of plant N status in summer maize. [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]

The Surface Water Ocean Topography (SWOT) mission significantly improves on the capabilities of current nadir altimeters by enabling two-dimensional mapping. Assimilating this advanced data into high-resolution models poses challenges. To address this, Observing System Simulation Experiments (OSSEs) were conducted to evaluate the effects of both simulated and actual SWOT data on the Regional Ice Ocean Prediction System (RIOPS). This study examines the OSSEs' design, focusing on the simulated observations and assimilation systems used. The validity of the OSSE designs is confirmed by ensuring the deviations between the assimilation system and the Nature Run (NR) align with discrepancies observed between actual oceanic data and OSSE simulations. The study measures the impact of assimilating SWOT and two nadir altimeters by calculating root mean square forecast error for sea surface height (SSH), temperature, and velocities, along with performing wave-number spectra and coherence analyses of SSH errors. The inclusion of SWOT data is found to reduce RMS SSH errors by 16% and RMS velocity errors by 6% in OSSEs. The SSH error spectrum shows that the most notable improvements are for scales associated with the largest errors in the range of 200-400 km, with a 33% reduction compared to traditional data assimilation. Additionally, spectral coherence analysis shows that the limit of constrained scales is reduced from 280 km for conventional observations to 195 km when SWOT is assimilated as well. This study also represents our first attempt at assimilating early-release SWOT data. A set of Observing System (data denial) experiments using early-release SWOT measurements shows similar (but smaller) responses to OSSE experiments in a two nadir-altimeter context. In a six-altimeter constellation setup, a positive impact of SWOT is also noted, but of significantly diminished amplitude. These findings robustly advocate for the integration of SWOT observations into RIOPS and similar ocean analysis and forecasting frameworks. [ABSTRACT FROM AUTHOR]

Purpose: Positron emission tomography (PET) range verification is an important method that can help improve the confidence in proton therapy for clinical applications. Two kinds of verification methods are implemented and compared based on clinical cases in this study. Method: The study is conducted on 14 breast cancer patients following proton irradiation treatment. Verification is done by calculating the depth error between the numerically predicted values with the measured PET image along the beam direction. Point-based and segment-based methods are applied and compared. The verification results are presented as depth error means and standard deviations in a region of interest (ROI). Results: The mean value of the depth error of all 14 cases is within the range of [−3, 3] mm for both point-based and segment-based methods, and only one case result calculated by the point-based method is slightly beyond −3 mm. When comparing the mean depth error from the two methods, the paired t -test result shows that the p -value is 0.541, and the standard deviation of the segment-based method is smaller than that of the point-based method. Conclusion: In breast cancer case verification application, point-based and segment-based methods show no significant difference in the mean value of results. Both methods can quantify the accuracy of proton radiotherapy to the millimeter level. [ABSTRACT FROM AUTHOR]

Energy is fundamental to all significant human endeavors and is crucial for sustaining life and realizing human potential. With the advent of smart homes, energy consumption is increasing as new technologies are introduced, leading to shifts in both lifestyle and societal norms. This scenario presents a unique energy challenge that requires extraordinary efforts to meet the anticipated energy demands. Various innovative strategies are being implemented to overcome the drawbacks and address the growing consumer demand for energy. Today, smart homes offer much more than just basic functions; they also focus on resource management, energy efficiency, and enhancing quality of life. Machine Learning (ML) plays a vital role in smart homes as it allows for the training, adjustment, and optimization of various functions. This intelligent, purposeful capacity has the potential to turn homes into dynamic and practical environments that improve daily performance, ease, and personalization. In this research, an ML-based multivariate model is proposed utilizing Long Short-Term Memory (LSTM) for smart homes, aiming to optimize energy utilization and improve management in the realm of energy consumption. This model offers precise predictions of energy consumption, ensuring minimal random errors. Prominent metrics include a low Mean Squared Error (MSE) of 0.02284, a high Mean Absolute Error (MAE) of 0.184, a Mean Absolute Percentage Error (MAPE) of 0.123, the lowest Root Mean Squared Error (RMSE) at 0.15113, a significant Mean Absolute Scaled Error (MASE) of 0.996, and a strong R-squared value (R2) of 0.694. The proposed model delivers exceptional predictive performance as compared to the previous approaches, ensuring high reliability, which aligns with the standards needed for advancing toward a smart and sustainable future. [ABSTRACT FROM AUTHOR]

AbstractBackground: Telehealth-only provision of buprenorphine for opioid use disorder was made possible during the COVID-19 pandemic, but policymakers are considering ending this practice. A primary concern is potential diversion of buprenorphine, although it is unknown how many patients enter telehealth-only treatment already using diverted buprenorphine. Methods: We performed a retrospective chart review of 500 consecutive newly enrolled patients in our large telehealth-only program in January 2023. We recorded if the patient was already taking buprenorphine at the time of the intake visit and if the buprenorphine was from a diverted source. Results: Five hundred patients enrolled from January 3-20, 2023. Patients were 61.0% male; mean age was 40.3 (standard deviation 10.3) years. Buprenorphine use at the time of enrollment was reported by 46.4% (n = 232) of patients, and 18.4% (n = 92) reported using diverted buprenorphine. Conclusions/Importance: Over one in six patients initiating care with our telehealth-only practice reported taking diverted buprenorphine. These findings indicate that the telehealth care setting possibly decreases diversion of buprenorphine when patients use this modality to obtain legitimate treatment. [ABSTRACT FROM AUTHOR]

Background: The genus Sapovirus in the family Caliciviridae comprises of a genetically diverse group of viruses that are responsible for causing acute gastroenteritis in both human and animals globally. As the number of sequences continues to grow and more recombinant sequences are identified, the classification criteria of genogroups and genotypes of sapovirus need to be further refined. In this study, we aimed to optimize the classification of sapoviruses. Results: Through evolutionary clustering and genetic distance analysis, we have updated the classification criteria for VP1 genogroup and genotypes. We adjusted the original mean values ± 3 standard deviations (SD) of genetic distances to mean values ± 2.5SD, resulting the corresponding cutoff values for the same genotype and genogroup set at <0.161 and <0.503, respectively. Additionally, we established classification criteria for RdRp types and groups, referred to as P-types and P-groups,, with mean values ± 2SD and cutoff values of <0.266 and <0.531 for the same type and group, respectively. This refinement has expanded the VP1 genogroups to thirty-four and identified twenty-four P-groups. For human sapoviruses, the new criteria have resulted in the addition of one genotype, GV.PNA1. Moreover, the new criteria defined three P-groups and 21 P-types for human sapoviruses. Spatial-temporal analysis revealed no specific distribution pattern for human sapoviruses. Conclusions: We established a dual typing system on classification based on VP1 and RdRp nucleotide sequences for sapoviruses. [ABSTRACT FROM AUTHOR]

Coal mining-induced surface subsidence can significantly impact resident safety and hinder regional sustainable development, making precise subsidence monitoring and prediction critical. Existing mining subsidence monitoring technologies often exhibit low spatiotemporal resolution, while subsidence prediction models suffer from heavy dependence on data quality and model assumptions, as well as imprecise parameters. This study addresses these limitations by proposing a novel mining subsidence monitoring and prediction method based on Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) and the Convolutional Neural Network—Bidirectional Gated Recurrent Unit—Attention (CNN-BiGRU-Attention) model. Focusing on the Banji mining area in Anhui Province, ground subsidence was monitored from July 15, 2021, to September 3, 2023, utilizing SBAS-InSAR technology with Sentinel-1A satellite data. The monitoring results were validated using leveling measurement data. A CNN-BiGRU-Attention prediction model was subsequently constructed based on the time-series monitoring data. The results indicate that the surface subsidence rate in the study area decreases progressively from northwest to southeast, with an average subsidence rate ranging from -49.844 mm/year to -14.810 mm/year. At feature points, the CNN-BiGRU-Attention model effectively captures the characteristics of subsidence time-series changes. For regional subsidence prediction, this model maintains the smallest error, with Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) values of 1.27 mm and 1.44 mm, respectively, and an absolute prediction error of less than 1 mm in most areas. This study integrates SBAS-InSAR technology with the CNN-BiGRU-Attention model to enable unmanned monitoring and prediction of mining subsidence. In comparison to traditional methods, this approach not only reduces monitoring costs but also enhances the accuracy of subsidence predictions, offering critical technical support for the sustainable development of mining areas. [ABSTRACT FROM AUTHOR]

Maximum likelihood estimation (frequentist) and Bayesian estimation are two common parameter estimation methods. However, maximum likelihood estimation faces limitations, including the effect of outliers, computational complexity, and issues with ordinal categorical data, leading to biased estimates and inaccurate coverage probabilities. To address these limitations, this study employed a latent trait model with a Bayesian marginal likelihood of rank-based estimation for parameter estimation. The simulation results demonstrated favorable performance of the proposed method. Trace plots of all parameters showed good distribution and quick convergence, with the potential scale reduction factor not exceeding 1, indicating no convergence issues. Furthermore, the posterior predictive check showed the simulated data closely resembled the observed data, indicating the method effectively captures within-region variation through a latent trait parameter. Performance metrics like mean absolute error, root mean square error, and 95% confidence interval coverage probability revealed the estimates from the proposed Bayesian method surpassed those from classical approaches. In conclusion, a latent traits model with Bayesian marginal likelihood and rank-based estimation is considered a superior parameter estimation technique compared to classical methods, particularly for dealing with ordinal categorical data. [ABSTRACT FROM AUTHOR]

Based on phase space reconstruction theory, the root mean square error is used as a quantitative criterion for identifying the appropriate embedding dimension and time step and selecting the optimal configuration for these factors. The phase space is then reconstructed, and the convergent cross-mapping algorithm is applied to analyse the causality between time series. The causality among the variables in the Lorenz equation is first discussed, and the response of this causality to the integration step of numerical solutions to the Lorenz equation is analyzed. We conclude that changes in the integration step do not alter the causality but will affect its strength. Variables X and Y drive each other, whereas variable Z drives variables X and Y in a unidirectional manner. Second, meteorological data from 1948–2022 are used to analyse the effect of the Southern Hemisphere annular mode on the East Asian summer monsoon index and surface air temperature driving capacity. From a dynamic perspective, it is concluded that the Southern Hemisphere annular mode is the driving factor affecting the East Asian summer monsoon index and surface air temperature. Based on ideal test results and the observation data, the collaborative selection of the embedding dimension and time step is more reliable in terms of determining causality. This provides the ability to determine causality between climate indices and theoretically guarantees the selection of climate predictors. [ABSTRACT FROM AUTHOR]

The rapid development of high-speed railways has brought significant attention to the coupled vibration issues of the train–bridge system. Vertical track irregularities, as one of the primary excitation sources of train–bridge system vibrations, is essentially a stochastic process. In this study, a beam model considering spatial vibrations is established to analyze the dynamic response under the passage of a train. Statistical methods, Laplace transform and Duhamel’s integral technique, are employed to derive the mean square value (MSV) and autocorrelation function (ACF) of the train’s vertical response, as well as the average and standard deviation (SD) of the beam’s bending–torsional coupled vibration response. The accuracy of the proposed analytical approach was verified using the Newmark-β algorithm and Monte Carlo simulation. In numerical examples, the displacement response of the beam is mainly explored for the influence of train speed and beam span. The results indicate that the maximum SD of displacement at mid-span occurs when the train is about to leave, which diminishes with higher train speeds and shorter beam spans. For a speed ranging from 250km/h to 350km/h, an increase in speed leads to a noticeable rise in the peak value of vertical average displacement. Due to high-frequency excitation causing the attenuation of low-frequency structural response, the MSV of the train’s displacement decreases with increasing speed. [ABSTRACT FROM AUTHOR]

ABSTRACT Objectives Material and Methods Results Conclusions Trial Registration To compare the accuracy of CAD‐CAM and conventional guides in the static computer‐assisted implant surgery (sCAIS) placement of distal free‐end implants.A prospective, controlled, and blinded quasi‐experimental study was done involving 27 patients (76 implants) distributed into two groups according to the surgical guide manufacturing approach used: conventional (control group [CG]) or CAD‐CAM (test group [TG]). The implants were planned in the software and the surgical guides were manufactured. Fully guided implant placement was carried out and the deviations were measured along with secondary variables as potential confounding factors. Descriptive analyses were performed on mean, standard deviation (SD), and interquartile range (IQR). In the comparative/inferential analysis hypothesis, contrasts were made of the quantitative and qualitative variables and multiple linear models were generated to adjust for the different confounding variables recorded.Coronal horizontal deviation (CHD) was significantly greater in CG (1.52 mm) versus TG (1.04 mm) (p = 0.004). Apical horizontal deviation (AHD) in turn was 1.67 versus 1.46 mm, respectively; angular deviation was 2.87 versus 3.64; and vertical deviation was −0.1 versus −0.05 mm, with no significant differences between the groups (p > 0.05). A greater sleeve height, positioning in premolars versus molars, and the use of shorter implants, were associated with greater accuracy in relation to CHD and/or AHD (p < 0.05). The implant success rate at 1 year was 92.1%, 90.7% in TG, and 100% in CG, being statistically significant (p = 0.026) at the implant level, but not significant at the patient level.The CAD‐CAM surgical guides proved to be more accurate than the conventional guides in the sCAIS placement of distal free‐end implants, with statistically significant differences being observed in terms of CHD. All implant failures occurred in TG (6 implants/3 patients).ClinicalTrials.gov identifier: NCT06404385 [ABSTRACT FROM AUTHOR]

The thermodynamic radii of 15 metal ions Ag+, Al3+, Am3+, Ba2+, Be2+, Fe3+, Ga3+, Hg2+, In3+, K+, Li+, Na+, NpO2+, Sr2+, and UO22+ were calculated by linear free energy relationship modelling using 8269 experimental stability constants (logK) of ML complexes of 42 metal ions (M) and 1990 diverse organic ligands (L) in water. The predictive performance of relationships on the base of the thermodynamic radii was estimated on 5272 combinations of ligands and metal ion pairs of external test sets. Squared determination coefficient ($R_{det}^2$Rdet2) and root mean squared error (RMSE) of logK predictions are 0.801 and 2.4 for combined data of the test sets, respectively. The corresponding performances for the 3003 combinations of training data are $R_{det}^2$Rdet2 0.921 and RMSE 1.7. [ABSTRACT FROM AUTHOR]

Background: The structural abnormality of the heart and its blood vessels at the time of birth is known as congenital heart disease. Every year in Pakistan, sixty thousand children are born with CHD, and 44 in 1000 die before they are a month old. Various studies used different techniques to estimate the risk factors of congenital heart disease, but these techniques suffer from a deficiency of capacity to present human understanding and a deficiency of adequate data. The current study provided an innovative approach by defining the latent variables to handle this issue and building a reasonable model. Method: Data used in this study has been collected from mothers and hospital records of the children. The dataset contains information on 3900 children who visited the OPD of the Chaudry Pervaiz Elahi Institute of Cardiology (CPEIC) Multan, Pakistan from October 2021 to September 2022. The latent variables were defined from the data and structural equation modeling was used to model them. Result: The results show that there are 53.6% of males have acyanotic CHD and 54.5% have cyanotic CHD. There are 46.4% of females have acyanotic CHD and 45.5% have cyanotic CHD. The children who have no diabetes in the family are 64.0% and children who have diabetes in the family are 36.0% in acyanotic CHD, the children who have no diabetes in the family are 59.7% and children have diabetes in the family are 40.3% in cyanotic CHD. The value of standardized root mean residual is 0.087 is less than 0.089 which shows that the model is a good fit. The value of root mean square error of approximation is 0.113 is less than 0.20 which also shows the good fit of the model. Conclusion: It was concluded that the model is a good fit. Also, the latent variables, socioeconomic factors, and environmental factors of mothers during pregnancy have a significant effect in causing cyanotic while poor general health factor increases the risk of Acyanotic congenital heart disease. [ABSTRACT FROM AUTHOR]

The electricity sector deregulation has led to the formation of short‐term power markets where the consumers can purchase electricity by bidding at the electricity market. The electricity market price is volatile and changes are due to change in electricity demand and the bid price at different span of time during the day. The availability of the electricity price forecast is essential for the electricity market participants to make informed decisions. In this paper, the modified LSTM approach, wavelet‐LSTM, and Hilbert‐LSTM are proposed to predict the electricity price for bidding in the short‐term electricity market. The objective is to improve the precision and adaptability of electricity price predictions by utilizing the temporal dependence identification capability of LSTM and the multiresolution analysis capability of the transforms. The proposed models combine these two effective methods in order to capture both the long‐term trends and short‐term variations present in electricity price time series data. In this approach, the 8‐year dataset is used for training the models, and based on this the day‐ahead price is calculated and compared with the testing data. The proposed techniques show better performance in terms of rank correlation, mean square error, and root mean square error compared to the existing algorithms of LSTM and CNN‐LSTM. The prediction results achieved with wavelet‐LSTM and Hilbert‐LSTM (1‐month dataset of 8 years) are rank correlation 0.9746 and 0.9749, MSE 0.2962 and 0.1363, and RMSE 0.5443 and 0.3692, respectively. The results achieved with the proposed methods are better than the existing forecasting models, and the RMSE for Hilbert‐LSTM and wavelet‐LSTM techniques is improved by 61% and 43%, respectively, compared to the LSTM method. Also, results are calculated for the complete 8 years all 12 months with Hilbert‐LSTM, and the results achieved are rank correlation 0.9645, MSE 0.3876, and RMSE 0.6225. The results achieved with the proposed models are improved in terms of performance parameters compared to the conventional approaches. The proposed models can be used in the day‐ahead electricity price forecasting to bid for electricity accurately in the day‐ahead electricity market. [ABSTRACT FROM AUTHOR]

The virtual crop stands as a vital content in crop model research field, and has become an indispensable tool for exploring crop phenotypes. The focal objective of this undertaking is to realize three-dimensional (3D) dynamic visualization simulations of rice individual and rice populations, as well as to predict rice phenotype using virtual rice. Leveraging our laboratory's existing research findings, we have realized 3D dynamic visualizations of rice individual and populations across various growth degree days (GDD) by integrating the synchronization relationship between the above-ground parts and the root system in rice plant. The resulting visualization effects are realistic with better predictive capability for rice morphological changes. We conducted a field experiment in Anhui Province in 2019, and obtained leaf area index data for two distinct rice cultivars at the tiller stage, jointing stage, and flowering stage. A method of segmenting leaf based on the virtual rice model is employed to predict the leaf area index. A comparative analysis between the measured and simulated leaf area index yielded relative errors spanning from 7.58% to 12.69%. Additionally, the root mean square error, the mean absolute error, and the coefficient of determination were calculated as 0.56, 0.55, and 0.86, respectively. All the evaluation criteria indicate a commendable level of accuracy. These advancements provide both technical and modeling support for the development of virtual crops and the prediction of crop phenotypes. [ABSTRACT FROM AUTHOR]

Measles is an important infectious disease system both for its burden on public health and as an opportunity for studying nonlinear spatio-temporal disease dynamics. Traditional mechanistic models often struggle to fully capture the complex nonlinear spatio-temporal dynamics inherent in measles outbreaks. In this paper, we first develop a high-dimensional feed-forward neural network model with spatial features (SFNN) to forecast endemic measles outbreaks and systematically compare its predictive power with that of a classical mechanistic model (TSIR). We illustrate the utility of our model using England and Wales measles data from 1944-1965. These data present multiple modeling challenges due to the interplay between metapopulations, seasonal trends, and nonlinear dynamics related to demographic changes. Our results show that while the TSIR model yields similarly performant short-term (1 to 2 biweeks ahead) forecasts for highly populous cities, our neural network model (SFNN) consistently achieves lower root mean squared error (RMSE) across other forecasting windows. Furthermore, we show that our spatial-feature neural network model, without imposing mechanistic assumptions a priori, can uncover gravity-model-like spatial hierarchy of measles spread in which major cities play an important role in driving regional outbreaks. We then turn our attention to integrative approaches that combine mechanistic and machine learning models. Specifically, we investigate how the TSIR can be utilized to improve a state-of-the-art approach known as Physics-Informed-Neural-Networks (PINN) which explicitly combines compartmental models and neural networks. Our results show that the TSIR can facilitate the reconstruction of latent susceptible dynamics, thereby enhancing both forecasts in terms of mean absolute error (MAE) and parameter inference of measles dynamics within the PINN. In summary, our results show that appropriately designed neural network-based models can outperform traditional mechanistic models for short to long-term forecasts, while simultaneously providing mechanistic interpretability. Our work also provides valuable insights into more effectively integrating machine learning models with mechanistic models to enhance public health responses to measles and similar infectious disease systems. Author summary: Mechanistic models have been foundational in developing an understanding of the transmission dynamics of infectious diseases including measles. In contrast to their mechanistic counterparts, machine learning techniques including neural networks have primarily focused on improving forecasting accuracy without explicitly inferring transmission dynamics. Effectively integrating these two modeling approaches remains a central challenge. In this paper, we first develop a high-dimensional neural network model to forecast spatiotemporal endemic measles outbreaks and systematically compare its predictive power with that of a classical mechanistic model (TSIR). We illustrate the utility of our model using a detailed dataset describing measles outbreaks in England and Wales from 1944–1965, one of the best-documented and most-studied nonlinear infectious disease systems. Our results show that overall, our neural network model outperforms the TSIR in all forecasting windows. Furthermore, we show that our neural network model can uncover the mechanism of hierarchical spread of measles where major cities drive regional outbreaks. We then develop an integrative approach that explicitly and effectively combines mechanistic and machine learning models, improving simultaneously both forecasting and inference. In summary, our work offers valuable insights into the effective utilization of machine learning models, and integration with mechanistic models, for enhancing outbreak responses to measles and similar infectious disease systems. [ABSTRACT FROM AUTHOR]

ABSTRACT Background Methods Results Conclusions Childhood height is commonly used to measure children's health and nutritional status. It is used to define stunting, where a child is considered stunted if their height is shorter than two standard deviations below the Child Growth Standards median. Studies have shown that genetic and environmental factors contribute to childhood height, so understanding these contributions is important for stunting research. This systematic review and meta‐analysis examined the contribution of these factors across study designs and ancestries.A systematic search found 28 twin and 26 family studies spanning from birth to 17 years. We identified 162 293 twin, 380 195 parent‐offspring, and 19 965 sibling pairs born between 1929 and 2004. These datasets were meta‐analyzed using a random effects model. The review is registered in PROSPERO (ID CRD42023432550).The estimated heritability (h2) of height from twin design increased from birth to 17 years. For children under 5 years, the estimated heritability was 0.57 (95%CI, 0.52–0.61) for European, 0.48 (0.39–0.57) for Asian, and 0.46 (0.40–0.51) for multi ancestries. Heritability estimated from parent‐offspring design was 0.46 (95%CI, 0.28–0.62) at birth and 0.76 (95%CI, 0.70–0.80) at 17 years. Meanwhile, the estimated heritability from sibling design remained constant across ages at 0.70 (95%CI, 0.52–0.83).The heritability estimates of childhood height were slightly higher in European ancestries, but in general, they did not significantly differ across ancestry groups. While the shared environmental effects on height peak in early childhood, the estimated heritability increase in late teens. Unique environmental factors play a small, but significant role throughout childhood. [ABSTRACT FROM AUTHOR]

Point cloud video delivery will be an important part of future immersive multimedia. In it, objects represented as sets of points are embedded within a video which is streamed and displayed to remote users. This opens possibilities towards remote presence scenarios such as tele-conferencing, remote education and virtual training. Due to its infeasibly high bandwidth requirements, encoding is unavoidable. The introduced artifacts and network degradations can have an important but unpredictable impact on the end-user's Quality of Experience (QoE). Thus, real-time quality monitoring and prediction mechanisms are key to allow for fast countermeasures in case of QoE decrease. Since current state-of-the-art research is focusing on either continuous QoE monitoring of traditional video streaming services or objective delivery optimizations of point cloud content without any QoE validation, we believe this work brings a valuable contribution to current literature. Therefore, we present a no-reference (NR) QoE model, consisting of KMeans clustering and sigmoidal mapping, that works on video-level, group-of-pictures (GOP)-level and frame-level granularity. Results show the value of the sigmoidal mapping across all granularity levels. The clustering algorithm shows its value at the video-level and in the role of an outlier detector on the more fine-grained levels. Satisfying results are yet obtained with correlation values often going above 0.700 on GOP- and frame-level while maintaining root mean squared error (RMSE) below 10 on a 0–100 scale. In addition, a Command Line Interface (CLI) Video Metric Tool is presented that allows for easy and modular calculation of NR metrics on a given video. [ABSTRACT FROM AUTHOR]

In this study, the linear and non‐linear multivariate relationships between 25 teleconnection indices (tele‐indices) as independent variables and annual P24max as the dependent variable were analyzed using multivariate linear regression (MLR) and decision tree regression models (M5), in selected synoptic weather stations of Iran over a statistical period of 30 years (1992–2021). No strong and statistically significant correlation between each tele‐index and P24max was observed. Therefore, it is not appropriate to attribute climate changes in the region to a single factor such as El Niño, but rather consider the combined influence of multiple factors. The M5 model demonstrated higher performance, indicating a non‐linear relationship between tele‐indices and P24max. The stepwise execution of the M5 model tree showed that the algorithm follows a greedy approach, and it is not necessary to use all variables to predict P24max. The normalized root mean square error (NRMSE) of P24max estimation was found to be 15%, 13%, 15%, 8%, 20%, 14%, and 12% with the coefficients of determination of 0.78, 0.79, 0.72, 0.85, 0.81, 0.82, and 0.84 in Hashemabad‐Gorgan, Rasht, Kermanshah, Ahvaz, Bandar Abbas, Isfahan, and Birjand, respectively. Finally, it is possible to forecast P24max using tele‐indices measured in the previous year. [ABSTRACT FROM AUTHOR]

This study presents a new method for simultaneously quantifying a complex anti-migraine formulation containing five components (ergotamine, propyphenazone, caffeine, camylofin, and mecloxamine) using UV spectrophotometry and chemometric models. The formulation presents analytical challenges due to the wide variation in component concentrations (ERG: PRO: CAF: CAM: MEC ratio of 0.075:20:8:5:4) and highly overlapping UV spectra. To create a comprehensive validation dataset, the Kennard-Stone Clustering Algorithm was used to address the limitations of arbitrary data partitioning in chemometric methods. Three different chemometric models were evaluated: Classical Least Squares (CLS), Partial Least Squares (PLS), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). Among these, MCR-ALS demonstrated excellent performance, achieving recovery values of 98–102% for all components, accompanied by minimal root mean square errors of calibration (0.072–0.378) and prediction (0.077–0.404). Moreover, the model exhibited high accuracy, with relative errors ranging from 1.936 to 3.121%, bias-corrected mean square errors between 0.074 and 0.389, and a good sensitivity (0.2097–1.2898 μg mL−1) for all components. The Elliptical Joint Confidence Region analysis further confirmed the predictive performance of the models, with MCR-ALS consistently showing the smallest ellipses closest to the ideal point (slope = 1, intercept = 0) for most analytes, indicating superior accuracy and precision. The approach's sustainability was rigorously assessed using six advanced metrics, validating its environmental friendliness, economic viability, and practical application. This approach effectively resolves complex pharmaceutical formulations, contributing to sustainable development objectives in quality control processes. [ABSTRACT FROM AUTHOR]

This study conducts an evaluation of the performance of seven ultra-high-degree Global Geopotential Models (GGMs) across Egypt, utilizing GNSS/leveling data as a basis for assessment. The models under investigation include SGG-UGM-2, XGM2019e_2159, SGG-UGM-1, GECO, EIGEN-6C4, EGM2008, and GGMplus. The evaluation procedure comprises three principal steps: an absolute assessment, the implementation of Residual Terrain Modelling (RTM), and the fitting of GGMs to GNSS/leveling data. Among the models assessed, GECO exhibited the highest performance in the absolute assessment, achieving a standard deviation (STD) of 0.310 m, while SGG-UGM-1 recorded the largest STD at 0.353 m. Given Egypt’s predominantly flat topography, the application of RTM yielded only modest benefits. Nonetheless, all models demonstrated significant enhancements in accuracy through the fitting process, with EIGEN-6C4 emerging as the most successful model, attaining an STD of 0.116 m in external assessment. The accuracy improvements following the fitting procedure ranged from 49 % to 63 % across all models assessed. [ABSTRACT FROM AUTHOR]

Aim Methods Results Conclusion To evaluate the effectiveness of a Bayesian adaptive dosing strategy in achieving target busulfan exposure in adult patients undergoing haematopoietic cell transplantation (HCT).This study included 71 adult patients scheduled to receive high‐dose busulfan. Busulfan was administered to achieve a cumulative area under the curve (AUC) of 66.0 mg/L/h (16 000 μM/min), 82.60 mg/L/h (20 000 μM/min) or 87.6 mg/L/h (21 200 μM/min) depending on the regimen. Individual pharmacokinetic (PK) parameters of busulfan were estimated from three blood samples using a one‐compartment model and Bayesian estimation after the first standard dose. Individual PK parameters were used to adjust subsequent doses to achieve the target exposure.All patients had their dose adjusted after the first dose administration. The final deviation from the target AUC was significantly improved compared to the initial deviation after standard mg/kg dosing (mean absolute deviation 19.5% vs 11.7%, P < .01). In addition, the proportion of patients with marked deviation from target exposure (ie, >25%) decreased significantly from 31% after standard dosing to 10% after PK‐guided dosing (P < .01). Canonical busulfan‐related toxicity, specifically veno‐occlusive disease, was observed in 5% of patients who achieved successful PK‐guided dosing. In contrast, one‐third of patients with off‐target exposure with poor dosing experienced toxicity.The Bayesian adaptive dosing strategy significantly improves the accuracy of achieving the target busulfan AUC in patients undergoing HCT. This approach not only reduces marked deviations from target exposure, but also reduces the incidence of busulfan‐related toxicity, thereby maintaining a favourable toxicity/efficacy ratio. [ABSTRACT FROM AUTHOR]

We developed a method to evaluate the failure rate (FR) for the cylindrical part of a composite high-pressure hydrogen vessel with arbitrary dimensions, number of layers, and stacking sequences by giving the mean and standard deviation of the fiber strength development rate and the stiffness reduction rate under various damage types. The FR is the rate at which the residual strength of a vessel is below the maximum service pressure (125%NWP). Assuming a case in which the vessel is designed with the damage-tolerance design, the FR was calculated for the case of all-layer matrix cracking (assuming beginning-of-life) and fiber failure in two layers in addition to the matrix cracking (assuming the most severe damage that is tolerable during service). The observations demonstrate the importance of improving the probability of detection for non-destructive testing in ensuring the safety of damage-tolerance-designed vessels after fiber failure. The mean strength of the fibers mainly affected the mean residual strength of the composite high-pressure hydrogen vessels when the dimensions and stacking sequence remained unchanged. [ABSTRACT FROM AUTHOR]

Limited research has assessed the accuracy of imputation methods in aerobiological datasets. We conducted a simulation study to evaluate, for the first time, the effectiveness of Gappy Singular Value Decomposition (GSVD), a data-driven approach, comparing it with the moving mean interpolation, a statistical approach. Utilizing complete pollen data from two monitoring stations in northeastern Italy for 2022, we randomly generated missing data considering the combination of various proportions (5%, 10%, 25%) and gap lengths (3, 5, 7, 10 days). We imputed 4800 time series using the GSVD algorithm, specifically implemented for this study, and the moving mean algorithm of the "AeRobiology" R package. We assessed imputation accuracy by calculating the Root Mean Square Error and employed multiple linear regression models to identify factors independently affecting the error (e.g. pollen variability, simulation settings). The results showed that the GSVD was as good as the well-established moving mean method and demonstrated its strong generalization capabilities across different data types. However, the imputation error was primarily influenced by pollen characteristics and location, regardless of the imputation method used. High variability in pollen concentrations and the distribution of missing data negatively affected imputation accuracy. In conclusion, we introduced and tested a novel imputation method, demonstrating comparable performance to the statistical approach in aerobiological data reconstruction. These findings contribute to advancing aerobiological data analysis, highlighting the need for improving imputation methods. [ABSTRACT FROM AUTHOR]

The aim of this study was to determine the validity of three smartphone applications measuring barbell movement velocity in resistance training and comparing them to a commercially available linear transducer. Twenty competitive powerlifters (14 male and 6 female) completed a progressive loading protocol in the squat, bench press and deadlift (sumo or conventional) until reaching 90% of the highest load they had achieved in a recent competition. Mean velocity was concurrently recorded with three smartphone applications: Qwik VBT (QW), Metric VBT (MT), MyLift (ML), and one linear transducer: RepOne (RO). 3D motion capturing (Vicon) was used to calculate specific gold standard trajectory references for the different systems. A total of 589 repetitions were recorded with a mean velocity of (mean ± standard deviation [min-max]) 0.44 ± 0.17 [0.11–1.04] m·s-1, of which MT and ML failed to identify 52 and 175 repetitions, respectively. When compared to Vicon, RO and QW consistently delivered valid measurements (standardized mean bias [SMB] = 0 to 0.21, root mean squared error [RMSE] = 0.01 to 0.04m·s-1). MT and ML failed to deliver a level of validity comparable to RO (SMB = -0.28 to 0.14, RMSE = 0.04–0.14m·s-1), except for MT in the bench press (SMB = 0.07, RMSE = 0.04m·s-1). In conclusion, smartphone applications can be as valid as a linear transducer when assessing mean concentric barbell velocity. Out of the smartphone applications included in this investigation, QW delivered the best results. [ABSTRACT FROM AUTHOR]

Steroid 5α-reductase (5αR) converts testosterone into dihydrotestosterone (DHT), a potent androgen driving prostate cell proliferation via the androgen receptor (AR). Both 5αR and AR play crucial roles in androgen-mediated disorders, making them key therapeutic targets in drug development. Current treatments target these enzymes individually and often cause significant side effects, highlighting the need for safer alternatives. Through in silico screening, 13 pyrrolizidine alkaloids of Heliotropium indicum (HI) were assessed for their inhibitory potential against 5αR and AR. Using machine learning, six alkaloids showed promising pIC50 values. The accuracy of the models was assessed using key statistical parameters, including the score, correlation coefficient for training sets (R2), correlation coefficient for test sets (Q2), standard deviation (SD), and root mean square error (RMSE). For 5αR, the results were 0.763 (R2), 0.781 (Q2), 0.748 (score), 0.362 (SD), and 0.832 (RMSE), while for AR, the values were 0.817 (R2), 0.783 (Q2), 0.713 (score), 0.427 (SD), and 0.782 (RMSE), indicating reliability. Europine-N-oxide (-10.27 kcal/mol) and Heliotridine-N-oxide (-9.72 kcal/mol) displayed stronger 5αR binding than Finasteride, while Heliotrine (-10.09 kcal/mol) and Europine-N-oxide (-8.76 kcal/mol) outperformed Enzalutamide in AR binding. Key hydrogen bonds and MD simulations confirmed stable interactions. Pharmacokinetic screening revealed favorable drug-like profiles, including good solubility and absorption with minimal CYP enzyme inhibition. These findings suggest that HI alkaloids are promising multi-target inhibitors for BPH treatment, warranting further in vivo validation and optimization. [ABSTRACT FROM AUTHOR]

Background Purpose Methods Results Conclusions PET/CT and planning CT are commonly used medical images in radiotherapy for esophageal and nasopharyngeal cancer. However, repeated scans will expose patients to additional radiation doses and also introduce registration errors. This multimodal treatment approach is expected to be further improved.A new Transformer model is proposed to obtain pseudo‐PET/CT fusion images for esophageal and nasopharyngeal cancer radiotherapy.The data of 129 cases of esophageal cancer and 141 cases of nasopharyngeal cancer were retrospectively selected for training, validation, and testing. PET and CT images are used as input. Based on the Transformer model with a “focus‐disperse” attention mechanism and multi‐consistency loss constraints, the feature information in two images is effectively captured. This ultimately results in the synthesis of pseudo‐PET/CT fusion images with enhanced tumor region imaging. During the testing phase, the accuracy of pseudo‐PET/CT fusion images was verified in anatomy and dosimetry, and two prospective cases were selected for further dose verification.In terms of anatomical verification, the PET/CT fusion image obtained using the wavelet fusion algorithm was used as the ground truth image after correction by clinicians. The evaluation metrics, including peak signal‐to‐noise ratio, structural similarity index, mean absolute error, and normalized root mean square error, between the pseudo‐fused images obtained based on the proposed model and ground truth, are represented by means (standard deviation). They are 37.82 (1.57), 95.23 (2.60), 29.70 (2.49), and 9.48 (0.32), respectively. These numerical values outperform those of the state‐of‐the‐art deep learning comparative models. In terms of dosimetry validation, based on a 3%/2 mm gamma analysis, the average passing rates of global and tumor regions between the pseudo‐fused images (with a PET/CT weight ratio of 2:8) and the planning CT images are 97.2% and 95.5%, respectively. These numerical outcomes are superior to those of pseudo‐PET/CT fusion images with other weight ratios.This pseudo‐PET/CT fusion images obtained based on the proposed model hold promise as a new modality in the radiotherapy for esophageal and nasopharyngeal cancer. [ABSTRACT FROM AUTHOR]

Objective Background Methods Results Conclusion To evaluate the safety and tolerability of ubrogepant for the acute treatment of migraine in participants taking atogepant for the preventive treatment of episodic migraine (EM).Atogepant is an oral calcitonin gene‐related peptide (CGRP) receptor antagonist approved for the preventive treatment of migraine in adults and ubrogepant is an oral CGRP receptor antagonist approved for the acute treatment of migraine in adults, with or without aura. The safety and tolerability of the concomitant use of ubrogepant and atogepant have not been previously evaluated in a clinical setting.The TANDEM study, a phase 4, two‐period, multicenter, open‐label study conducted in the United States, enrolled adults with migraine, with or without aura, and <15 headache days/month. In Treatment Period 1, participants took atogepant 60 mg once daily (QD) for 12 weeks and their own non‐gepant acute headache medication for breakthrough migraine attacks. In Treatment Period 2, participants continued taking atogepant 60 mg QD and ubrogepant 100 mg was taken as needed (PRN) for the treatment of breakthrough migraine attacks (up to eight per 4‐week interval) for 12 weeks. In Treatment Period 2, an optional second ubrogepant dose or the participant's own acute medication could be used to rescue headaches that did not resolve within 2–24 h post initial ubrogepant dose. The primary objective evaluated the safety and tolerability of the concomitant use of ubrogepant and atogepant.Of 263 participants enrolled, 262 were treated in Treatment Period 1 (Safety Population 1) and 218 continued and were treated in Treatment Period 2 (Safety Population 2). The mean (standard deviation) number of ubrogepant use days in Treatment Period 2 was 6.6 (5.03) over the 12 weeks. In Treatment Periods 1 and 2, 49.6% and 43.1% of participants experienced a treatment‐emergent adverse event (TEAE), respectively. The most common TEAEs (≥5%) in Treatment Period 1 and Treatment Period 2 were COVID‐19 (8.4%, 3.2%), fatigue (6.5%, 1.4%), nausea (6.1%, 0.9%), decreased appetite (5.7%, 0.9%), and constipation (5.3%, 0.9%). In Treatment Period 2, no increase in the incidence and types of TEAEs in relation to the number of ubrogepant use days or doses taken were identified. During the whole treatment period, 9.9% of participants discontinued atogepant or ubrogepant treatment due to TEAEs. There was one serious TEAE in Treatment Period 1 (ureterolithiasis) and one in Treatment Period 2 (cervical myelopathy), and both were considered not related to study treatment by the study investigators.The use of atogepant 60 mg QD for the preventive treatment of EM and ubrogepant 100 mg PRN for the acute treatment of migraine over the 12‐week open‐label concomitant use treatment period was safe and well tolerated. The overall safety results were consistent with the known safety profiles of atogepant and ubrogepant when used alone and no new safety signals were identified. [ABSTRACT FROM AUTHOR]

In this paper, we obtained the mean mass density parameter (MMDP) ρm(Z,N) based on the nuclear mass AME2020 and nuclear charge radius CR2013 databases. On this basis, we study the dependence relationship between neutron number N and the two neighboring isotopes MMDP difference δρ(Z,N), and then obtained the empirical formula for δρ(Z,N). By combining this empirical formula with the AME2020 and CR2013 databases, the root-mean-square deviation (RMSD) for 606 nuclear charge radii with Z,N≥ 21 between the calculated and experimental values is 0.0046fm. Considering the odd–even staggering and isospin term correction, the study found that the RMSD is reduced to 0.0040fm. This work also uses Levenberg–Marquart (L-M) neural network approach to study the nuclear charge radius. The RMSD of nuclear charge radius is only 0.0030 fm, where the accuracy is increased by about 25%. Furthermore, the predicted values of nuclear charge radius obtained using this modified empirical formula and Neural Network model are competitive with the results of other models. These results indicate that nuclear charge radius relation and Neural Network model based on δρ(Z,N) proposed in this work have a certain accuracy, simplicity and reliability. [ABSTRACT FROM AUTHOR]

ABSTRACT Supplementary run rules have been employed since the 1950s to enhance the performance of control charts, such as the X¯$\bar{X}$ control chart, which aims to control the process mean, or S2$S^2$ control chart, which aims to control the process variance. One simple, effective, and popular suggestion is Klein's 2‐of‐2, which signals an out‐of‐control process only when two sequential sample averages are observed above the upper control limit or below the lower control limit. We propose to implement Klein's 2‐of‐2 for a more complex control chart, the X¯−S2$\bar{X}-S^2$ joint control chart, which aims to simultaneously monitor the mean and the variance of a process. Compared with the traditional control chart, using this rule for the X¯−S2$\bar{X}-S^2$ control chart reduces the average run length (ARL) and standard deviation of the run length (SDRL) for detecting small process shifts X¯−S2$\bar{X}-S^2$ while maintaining high applicability owing to the ease of use in employing the 2‐of‐2 rule in practical environments. Klein's run rule was implemented in the X¯−S2$\bar{X}-S^2$ control chart through a 15‐state Markov chain, and its performance was compared with the basic X¯−S2$\bar{X}-S^2$ control chart as well as the exponentially weighted moving average (EWMA) chart, known to have excellent capabilities in detecting small process average shifts at the cost of a more complex control scheme. The newly proposed X¯−S2$\bar{X}-S^2$ Klein control chart demonstrated superior performance compared to the traditional X¯−S2$\bar{X}-S^2$ control chart and competitive performance even against the more complex joint EWMA control chart. [ABSTRACT FROM AUTHOR]

Using heart rate (HR) measurements to detect mental stress in naturalistic settings is hampered by the physiological impact of hemodynamic and metabolic demands. Correcting HR for these demands can help isolate fluctuations in HR associated with psychosocial stress responses, a concept termed additional heart rate (aHR). This study examined whether adding predictors for posture, activity type, and lagged movement intensity for the prolonged impact of physical activity (PA) improved aHR estimation across various manipulations of mental stress, posture, and PA in a controlled laboratory environment (n = 197). Accelerometer signals were used to obtain the movement intensity and to classify posture and activity type. Posture, activity type, and lagged movement intensity each led to a significant improvement in HR estimation, as measured by adjusted R2 and root mean squared error. However, HR was overestimated during quiet sitting. The aHR, computed as the difference between observed and predicted HR, generally underestimated observed task‐baseline reactivity but was sensitive to individual differences in reactivity to mental stressors. Between‐subject correlations of aHR with task‐baseline reactivity ranged from 0.62 to 0.93 across conditions. On a within‐subject level, the ability of aHR to differentiate between exposure to physical stress and mental stress was limited (recall = 0.32, precision = 0.31), but better than that of observed HR (recall = 0.02, precision = 0.02). Future research should explore the potential of this novel aHR estimation method in differentiating physical and mental demands on HR in daily life, and its predictive value for health outcomes. [ABSTRACT FROM AUTHOR]

Underground hydrogen storage (UHS) critically relies on cushion gas to maintain pressure balance during injection and withdrawal cycles, prevent excessive water inflow, and expand storage capacity. Interfacial tension (IFT) between brine and hydrogen/cushion gas mixtures is a key factor affecting fluid dynamics in porous media. This study develops four machine learning models— Decision Trees (DT), Random Forests (RF), Support Vector Machines (SVM), and Multi-Layer Perceptrons (MLP)—to predict IFT under geo-storage conditions. These models incorporate variables such as pressure, temperature, molality, overall gas density, and gas composition to evaluate the impact of different cushion gases. A group-based data splitting method enhances the realism of our tests by preventing information leakage between training and testing datasets. Shapley Additive Explanations (SHAP) reveal that while the MLP model prioritizes gas composition, the RF model focuses more on operational parameters like pressure and temperature, showing distinct predictive dynamics. The MLP model excels, achieving coefficients of determination (R2) of 0.96, root mean square error (RMSE) of 2.10 mN/m, and average absolute relative deviation (AARD) of 3.25%. This robustness positions the MLP model as a reliable tool for predicting IFT values between brine and hydrogen/cushion gas (es) mixtures beyond the confines of the studied dataset. The findings of this study present a promising approach to optimizing hydrogen geo-storage through accurate predictions of IFTs, offering significant implications for the advancement of energy storage technologies. • ML techniques were used to estimate IFT in H 2 /cushion gas (es)-brine systems. • A novel data splitting method focused on gas composition over sample quantity. • Inputs were pressure, temperature, molality, overall gas density, and gas composition. • The MLP model outperformed other models. • Shapley Additive Explanations (SHAP) approach was used to interpret the results. [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]

At present, there are many reconstructed datasets at the global scale. To test the applicability of these datasets in the China seas, the study comprehensively analyzes the reliability and accuracy of reconstructed sea level datasets in capturing nuanced temporal patterns of sea level changes in the China Seas. This study applied analysis methods or indicators such as time series, Taylor plots, correlation coefficients, growth rates, and standard deviations. Ocean Data Assimilations (ODAs) outperform Tide Gauge Reconstructions (TGRs) in terms of correlation with measured data in the nearshore, while TGRs exhibit superior capability in capturing oceanic sea level variability. Although the ODAs and TGRs both suffer from the underestimation of sea level variability in China as well as in neighboring seas, the TGRs perform better than the former. ODAs show inconsistency in reflecting the rate of sea level rise, but they, particularly the China Ocean Reanalysis (CORA), demonstrate a better correlation with satellite altimetry datasets. Meanwhile, both of them can reflect the Pacific Decadal Oscillation (PDO) well. TGRs, relying on oceanic tide gauge stations, suffer from poor correlation with tide gauge stations due to limited coverage. Reconstruction discrepancies are attributed to methodological differences and data assimilation techniques. Future studies should explore alternative variables like sea surface temperature and so on to enhance sea-level reconstruction, especially in regions with sparse tide gauge coverage. [ABSTRACT FROM AUTHOR]

Accurate estimation of farmland evapotranspiration (ET) is crucial for agricultural production. The accuracy of the widely used Penman–Monteith (PM) equation for estimating crop ET depends on the quality of input data and their ability to accurately model the canopy resistance (r c). In this study, we evaluated the PM equation in estimating winter wheat ET using nine r c models, with both original and recalibrated parameters, including the Farias (FA), Monteith (MT), Garcίa-Santos (GA), Idso (IS), Jarvis (JA), Katerji-Perrier (KP), Stannard (ST), Todorovic (TD), and Coupled surface resistance (CO) models. We used long-term measurements (2018 to 2023) from the Bowen ratio energy balance method at both daily and seasonal scales. Parameterization was performed using data from the 2020–2021 growing season, while the remaining 4 years were used for verification. The results showed that the FA, KP, and ST models performed better in estimating daily ET with original parameters, achieving a root mean square error (RMSE) of 1.07–1.16 mm d−1 and a mean bias error (MBE) of −0.59–0.02 mm d−1. After parameterization, the performance of acceptable r c models based on RMSE (ranging from 1.07 to 1.22 mm d−1, averaged 1.16 mm d−1) ranked as follows on the daily scale: FA > CO > KP > ST > IS > GA > JA > MT. The r c models were more accurate in simulating ET on a seasonal scale than on the daily scale. Before calibration, the acceptable FA, KP, and MT models overestimated seasonal ET with the MBE ranging from 2.83 to 75.32 mm and RMSE from 29.79 to 82.38 mm. After correction, the suitable r c models based on RMSE values decreased by FA > CO > KP > IS > ST > GA > JA on the seasonal scale, which ranged from 29.79 to 76.35 mm. The performance of the revised r c models improved on both daily and seasonal scales, with RMSE reductions of 29.03% and 68.18%, respectively. Considering both the accuracy and calculation complexity, the FA and KP models were recommended to be used in the PM equation to estimate daily and seasonal ET in semiarid regions. The CO, GA, ST, IS, and JA models can also be used as alternatives, depending on the availability of meteorological parameters. [ABSTRACT FROM AUTHOR]

Background: Cross-validation techniques in genetic evaluations encounter limitations due to the unobservable nature of breeding values and the challenge of validating estimated breeding values (EBVs) against pre-corrected phenotypes, challenges which the Linear Regression (LR) method addresses as an alternative. Furthermore, beef cattle genetic evaluation programs confront challenges with connectedness among herds and pedigree errors. The objective of this work was to evaluate the LR method's performance under pedigree errors and weak connectedness typical in beef cattle genetic evaluations, through simulation. Methods: We simulated a beef cattle population resembling the Argentinean Brangus, including a quantitative trait selected over six pseudo-generations with a heritability of 0.4. This study considered various scenarios, including: 25% and 40% pedigree errors (PE-25 and PE-40), weak and strong connectedness among herds (WCO and SCO, respectively), and a benchmark scenario (BEN) with complete pedigree and optimal herd connections. Results: Over six pseudo-generations of selection, genetic gain was simulated to be under- and over-estimated in PE-40 and WCO, respectively, contrary to the BEN scenario which was unbiased. In genetic evaluations with PE-25 and PE-40, true biases of − 0.13 and − 0.18 genetic standard deviations were simulated, respectively. In the BEN scenario, the LR method accurately estimated bias, however, in PE-25 and PE-40 scenarios, it overestimated biases by 0.17 and 0.25 genetic standard deviations, respectively. In herds facing WCO, significant true bias due to confounding environmental and genetic effects was simulated, and the corresponding LR statistic failed to accurately estimate the magnitude and direction of this bias. On average, true dispersion values were close to one for BEN, PE-40, SCO and WCO, showing no significant inflation or deflation, and the values were accurately estimated by LR. However, PE-25 exhibited inflation of EBVs and was slightly underestimated by LR. Accuracies and reliabilities showed good agreement between true and LR estimated values for the scenarios evaluated. Conclusions: The LR method demonstrated limitations in identifying biases induced by incomplete pedigrees, including scenarios with as much as 40% pedigree errors, or lack of connectedness, but it was effective in assessing dispersion, and population accuracies and reliabilities even in the challenging scenarios addressed. [ABSTRACT FROM AUTHOR]

Typhoon waves possess significant destructive potential, and their numerical simulation relies on accurate sea surface wind fields. An evaluation of different combinations of the radial air pressure distribution coefficient B and the radius of maximum wind speed (R max) in the Holland wind field (HWF) model was conducted to determine the optimal configuration. The HWF and the ERA5 wind field (EWF) were used as input wind fields to drive the typhoon wave model for China's coastal waters. Validation results indicated that neither wind field accurately reflected real conditions; therefore, a hybrid wind field (HBWF) was created by combining HWF and EWF using weighting coefficients that vary with the radius of wind speed to enhance accuracy. Simulation results showed that the HBWF improved the accuracy of significant wave heights (SWHs), with a mean relative error of 25.29%, compared to 32.48% for HWF and 27.94% for EWF. Additionally, HBWF also demonstrated the best performance in terms of root mean square error (RMSE) and consistency index. Overall, the HBWF enhances the simulation accuracy of typhoon waves in China's coastal waters. [ABSTRACT FROM AUTHOR]

This study introduces an innovative water depth estimation method for complex coastal environments, focusing on Yantian Port. By combining Random Forest algorithms with a Coordinate Attention mechanism, we address limitations of traditional bathymetric techniques in turbid waters. Our approach incorporates geographical coordinates, enhancing spatial accuracy and predictive capabilities of conventional models. The Random Forest Lon./Lat. model demonstrated exceptional performance, particularly in shallow water depth estimation, achieving superior accuracy metrics among all evaluated models. It boasted the lowest Root Mean Square Error (RMSE) and highest coefficient of determination (R²), outperforming standard techniques like Stumpf and Log-Linear approaches. These findings highlight the potential of advanced machine learning in revolutionizing bathymetric mapping for intricate coastal zones, opening new possibilities for port management, coastal engineering, and environmental monitoring of coastal ecosystems. We recommend extending this research to diverse coastal regions to validate its broader applicability. Additionally, exploring the integration of additional geospatial features could further refine the model's accuracy and computational efficiency. This study marks a significant advancement in bathymetric technology, offering improved solutions for accurate water depth estimation in challenging aquatic environments. As we continue to push boundaries in this field, the potential for enhanced coastal management and environmental stewardship grows, paving the way for more sustainable and informed decision-making in coastal zones worldwide. [ABSTRACT FROM AUTHOR]

Nadolol is a hydrophilic β‐adrenoceptor blocker with a relatively long half‐life and negligible metabolism. It is a substrate of P‐glycoprotein and organic anion transporting polypeptide 1A2, and may serve as an in vivo probe drug for the assessment of drug–drug and food–drug interactions mediated by these transporters. In the present study, we aimed to develop limited sampling strategy (LSS) models for predicting the area under the plasma concentration–time curve (AUC0‐∞) of nadolol. Plasma concentration data (Ct) in healthy volunteers reported in four previous studies were randomly divided into a training dataset for model development (n = 15) and a test dataset for model validation (n = 16). By multiple linear regression analysis, we confirmed that four out of the eight models using two time points and all models using three time points met the acceptable criteria. In particular, the three time point models using (C3, C6, and C24) and (C4, C8, and C24) showed better predictive performances with r2 values of 0.983 and 0.980, respectively. In drug interaction studies of nadolol with itraconazole, rifampicin, grapefruit juice, and green tea extract, both LSS models accurately predicted the AUC0‐∞ with percent mean absolute error ≤11% and percent root mean square error ≤12%. In addition, using digitized pharmacokinetic data of nadolol, both LSS models were further validated by predicting the AUC0‐∞ in different doses. The results suggest that the LSS models using three time points allow a reliable prediction of AUC0‐∞ of nadolol in healthy individuals. [ABSTRACT FROM AUTHOR]

Background: The fixed effects model is a useful alternative to the mixed effects model for analyzing stepped-wedge cluster randomized trials (SW-CRTs). It controls for all time-invariant cluster-level confounders and has proper control of type I error when the number of clusters is small. While all clusters in a SW-CRT are typically designed to crossover from the control to receive the intervention, some trials can end with unexposed clusters (clusters that never receive the intervention), such as when a trial is terminated early due to safety concerns. It was previously unclear whether unexposed clusters would contribute to the estimation of the intervention effect in a fixed effects analysis. However, recent work has demonstrated that including an unexposed cluster can improve the precision of the intervention effect estimator in a fixed effects analysis of SW-CRTs with continuous outcomes. Still, SW-CRTs are commonly designed with binary outcomes and it is unknown if those previous results extend to SW-CRTs with non-continuous outcomes. Methods: In this article, we mathematically prove that the inclusion of unexposed clusters improves the precision of the fixed effects intervention effect estimator for SW-CRTs with binary and count outcomes. We then explore the benefits of including an unexposed cluster in simulated datasets with binary or count outcomes and a real palliative care data example with binary outcomes. Results: The simulations show that including unexposed clusters leads to tangible improvements in the precision, power, and root mean square error of the intervention effect estimator. The inclusion of the unexposed cluster in the SW-CRT of a novel palliative care intervention with binary outcomes yielded smaller standard errors and narrower 95% Wald Confidence Intervals. Conclusions: In this article, we demonstrate that the inclusion of unexposed clusters in the fixed effects analysis can lead to the improvements in precision, power, and RMSE of the fixed effects intervention effect estimator for SW-CRTs with binary or count outcomes. [ABSTRACT FROM AUTHOR]