Absolute Prediction Error Research Articles

External evaluation of intravenous vancomycin population pharmacokinetic models in adults receiving high-flux intermittent haemodialysis.

Patients undergoing haemodialysis (HD) are at greater risk of methicillin-resistant Staphylococcus aureus infections requiring intravenous vancomycin. Close vancomycin therapeutic drug monitoring is warranted in HD patients as renal clearance is the primary elimination pathway. Clinically, population pharmacokinetics (popPK) model-informed dosing is commonly used. This study aimed to perform an external evaluation of published vancomycin popPK models developed for adults undergoing high-flux intermittent HD, and to create a dosing nomogram derived from the model that performed best. A literature review was conducted through PubMed and EMBASE to identify relevant popPK models. an external dataset was collected retrospectively from patients of 2 healthcare centres in Quebec, Canada. Selected models were implemented in NONMEM (v7.5; ICON Development Solutions). Predictive performance was assessed through prediction and simulation-based diagnostics. In total, 2386 vancomycin concentrations were collected from 274 patients and 476 antibiotic courses. Four vancomycin popPK models were selected for evaluation. None of the models demonstrated overall satisfactory or clinically acceptable predictive performance. Nonetheless, Bae et al.'s model performed best with a median prediction error of 16.25% and median absolute prediction error of 34.66%. Different predictive performance was also observed for vancomycin concentrations from samples collected during and between HD sessions. All evaluated models presented poor overall predictive performance. Further studies are required, through existing popPK model parameter re-estimation or new model development, to adequately describe vancomycin pharmacokinetics for our high-flux intermittent HD patient cohort.

Automatic Landmark Detection for Preoperative Planning of High Tibial Osteotomy Using Traditional Feature Extraction and Deep Learning Methods.

Automatic High Tibial Osteotomy (HTO) landmark detection methods promise to improve the effectiveness and standardisation of HTO preoperative planning. Unfortunately, due to the limited number of HTO datasets, existing methods are less robust when dealing with patients with varied deformities than traditional manual planning, severely limiting their clinical viability and application in practical surgical settings. Here, we present a new HTO landmark detection framework using an integration of optimised heatmap-offset aggregation method and traditional feature extraction. Subjective and objective approaches were employed to reflect the final clinical acceptance of our model. Average Mean Absolute Error of prediction results compared to the surgeon's gold standard was 0.35° for the hip-knee-ankle angle. The objective score rated by surgeons reached 4.4 on a scale of 5. The study demonstrated that the automatic detection method has great potential serving as an alternative to manual radiological analysis in practical surgical pre-operative planning.

The Effect of Lifting Straps on the Prediction of the Maximal Neuromuscular Capabilities and 1 Repetition Maximum During the Prone Bench Pull Exercise.

Miras-Moreno, S and García-Ramos, A. The effect of lifting straps on the prediction of the maximal neuromuscular capabilities and 1 repetition maximum during the prone bench pull exercise. J Strength Cond Res 38(11): e638-e644, 2024-This study examined the effects of using lifting straps in the prone bench pull exercise on (a) the magnitude of the load-velocity (L-V) relationship variables (load-axis intercept, velocity-axis intercept, and area under the L-V relationship line) and (b) the accuracy of the L-V relationship in predicting the 1 repetition maximum (1RM). In a counterbalanced sequence, 20 resistance-trained male subjects performed 2 identical experimental sessions, 1 with and 1 without lifting straps. During each session, subjects sequentially lifted 4 loads (14 kg, 85% 1RM, and 2 intermediate loads), followed by 1RM attempts. The L-V relationship was modelled using both multiple-point (4 loads) and 2-point (14 kg-85% 1RM) methods, whereas 3 types of minimal velocity thresholds (MVT) were used for 1RM prediction: (a) general MVT (averaged across the subjects velocity of the 1RM trial), (b) individual MVT (individual velocity of the 1RM trial), and (c) average optimal MVT (averaged across the subjects velocity that eliminates the differences between the actual and predicted 1RM). Irrespective of lifting straps use, which had no impact on the L-V relationship or the accuracy of 1RM prediction, we observed: (a) greater L-V relationship variables when obtained by the 2-point method compared with the multiple-point method (p < 0.001; effect size range = 0.353-0.639) and (b) the lowest absolute errors (<4.0 kg) in 1RM prediction for the 2-point method combined with the average optimal MVT (p < 0.001). These results reinforce the 2-point method applied in field conditions and the average optimal MVT for the routine testing of the L-V relationship.

A spatio-temporal forecasting method of fracture distribution using dynamically exposed rock images in tunnel: Methodology and application

Forecasting the geometric characteristics of rock fractures in unexcavated tunnel sections is crucial for construction arrangements. This paper presents a method for predicting fracture distribution near the tunnel face from a spatiotemporal perspective. We Innovatively analogize the tunnel excavation mileage as the time series data. Using fractal geometry and geostatistics, we extract geometric features of peripheral rock fractures and establish a spatiotemporal dataset for dynamic prediction. The SCINet model, a spatial-aware recursive neural network, is employed for deterministic-stochastic dynamic prediction. Numerical simulations are conducted to validate the predictive accuracy of the model for various distributions, including trace length, dip angle, and density. Notably, the Mean Absolute Percentage Error (MAPE) for trace length prediction is remarkably low at 5.49 %, and the Kullback-Leibler (KL) divergence is as low as 4.81 %. The method is further applied to an underground oil storage cavern, China, revealing structural surface information. By incorporating continuously exposed fracture data, prediction accuracy is progressively improved, demonstrating the method's potential as a predictive tool for enhancing tunnel construction safety and efficiency.

A Deep Learning Approach for Enhanced Real-Time Prediction of Winter Road Surface Temperatures in High-Altitude Mountain Areas

Low temperatures and icing in winter are significant factors that severely affect highway safety and traffic mobility. To enhance the precision and reliability of real-time winter road surface temperature (RST) prediction, a short-term prediction model is developed that harnesses both feature selection and deep learning. Leveraging meteorological data from a mountain highway in Yunnan, China, the key environmental variables affecting road surface temperature were first extracted using a random forest (RF) model for feature selection. These features were then combined with RST data to construct multiple groups of input variable combinations for the prediction model. A short-term prediction model with a 10-minute update frequency was built using a long short-term memory neural network (LSTM), namely RF-LSTM. The best input variable combination and preset parameters for the prediction model were determined through comparative testing with prevalent machine learning models, and the transferability of the prediction model was verified. The results showed that the best input variable combination for the RF-LSTM prediction model was road surface temperature and air temperature. The model recognised that the short-term RST was affected by long and short-term memory characteristics within a two-hour timeframe. When compared to the RF model, backpropagation (BP) neural network model and the standard LSTM model, the proposed model reduces prediction errors by 59.15%, 31.10% and 20.26%, respectively, while the prediction accuracy is 99.13% within an error margin of ±0.5℃. On the verification dataset, the proposed model maintains its time transferability with an average prediction absolute error of 0.0478. In all, the proposed model not only achieves a higher level of precision in real-time RST predictions but also ensures a more consistent and reliable performance under the challenging conditions of high altitude and mountainous terrain, offering enhanced support for traffic safety and road maintenance decision-making.

Towards a Better Uncertainty Quantification in Automated Valuation Models

AbstractThis study introduces a novel framework for quantifying prediction uncertainty in automated valuation models (AVMs), crucial tools in modern real estate finance. While non-linear AVMs excel in predictive performance, their limited methods for assessing prediction uncertainty reduces reliability and practical utility. We address this gap by proposing an approach for quantifying the uncertainty associated with predicted house prices and by introducing a model-specific AVM uncertainty estimate (AVMU) for AVM comparisons. Using a dataset of 51,747 historical apartment transactions in Oslo, Norway, we train three AVMs (XGBoost, random forest, support vector machine) to predict sales prices. Thereafter, we develop three base uncertainty estimators (direct loss estimation, bootstrap ensemble, quantile regression) and three meta estimators (average regressor, voting regressor, stacked generalization) for uncertainty quantification. Conformal calibration aligns the outputted uncertainty estimates from the six estimators with standard deviations of corresponding prediction errors. Having strong positive correlations with observed absolute prediction errors, the calibrated uncertainty estimators are shown to effectively capture prediction uncertainty. While the direct loss estimation excels among base estimators, the voting regressor and stacked generalization meta estimators consistently outperform it. Furthermore, by using the AVMU estimate from the stacked generalization meta estimator we can successfully identify the best-performing AVM for three separate apartment portfolios without knowing true sales prices. This alignment of the mean estimated prediction uncertainty with observed deviations underlines the utility of pre-factual AVMU estimates for model comparisons. In conclusion, our framework helps bridge prediction accuracy and uncertainty for AVMs, enhancing their reliability and supporting informed decision making for stakeholders.

PSO-FDM (Particle Swarm Optimization-Finite Difference Method)-Based Simulation Model of Temperature and Velocity of Full-Scale Continuous Annealing Furnace

Improving the accuracy of the temperature field prediction model for continuous annealing line strips and enhancing the model’s adaptability to full-size strips are key technical challenges in continuous annealing lines. This paper developed a continuous annealing temperature prediction model based on a variable step-size strategy for the heating section, even-heat section, slow-cooling section, and fast-cooling section of the continuous annealing line. To improve the prediction accuracy for different strip sizes, the PSO optimization algorithm was employed to determine the optimal heat transfer coefficient for each strip size. Additionally, due to the limited production of certain strip gauges, providing insufficient data for optimization, this study introduces a combined file approach to address gauge vacancies. The experimental results indicate that the optimized model with variable step size can control the absolute prediction error to less than 4 °C, improving prediction accuracy by 61.9% and prediction speed by 26.8% compared to the traditional equal-step prediction model. This study verified that the merger method is effective for addressing side gauge vacancies, while the proposed method is suitable for resolving middle gauge vacancies. The main technical contribution of this study is the establishment of a high-precision prediction model for continuous annealing temperature of variable step length strips, ensuring high temperature control accuracy for full-gauge strips when passing through the continuous annealing production line.

Deformation Evolution and Perceptual Prediction for Additive Manufacturing of Lightweight Composite Driven by Hybrid Digital Twins

This paper proposes a deformation evolution and perceptual prediction methodology for additive manufacturing of lightweight composite driven by hybrid digital twins (HDT). In order to improve manufacturing quality of irregular lightweight composite through boosting conceptual design in aeronautic and aerospace engineering, the HDT meaning hybridization of physical and digital domains, including deformation and energy efficiency can be built, where the essential parameters can be perceptually predicted in advance, by virtue of the fusion of physical sensors and digital information. The long short term memory (LSTM) can be employed to void vanishing gradient problem and improve predicting precision via Recurrent Neural Networks, thereby laying a foundation for the HDT. The diverse manufacturing requirements of different regions are integrated into the parameters designing phase by attaching region weights confirmed via empiricism and in-service simulation. The effects of slicing strategy and external support structures on manufacturing quality are considered from the perspective of improving dimensional accuracy. The manufacturing efficiency and comprehensive costs are accounted as consideration factors, which are perceptually predicted via LSTM. The designed manufacturing parameters through HDT were virtually examined by evaluating the deformation and equivalent stress distributions of fabricated lightweight component with composite material through AM process simulation. The physical experiments were conducted to verify the HDT-based pre-designing and optimization method of manufacturing parameters via fused deposition modeling (FDM). The energy consumption of actual manufacturing process was measured via digital power meter and applied to evaluate accuracy of perceptual prediction outcomes. The dimensional accuracy and distortion distribution of the manufactured lightweight prototype made with composite material were measured through the coordinate measuring machine (CMM) and 3D optical scanner. The proposed method demonstrates effectiveness in improving manufacturing quality and accurately predicting energy consumption, which have been verified with a three-way solenoid valve element, in which the maximum deformation was reduced by 39.78% and the mean absolute percentage error for perceptual prediction was 3.76%.

Comparison of intraocular lens power formulas for negative-diopter intraocular lens implantation for high myopia.

To compare the accuracy of intraocular lens (IOL) power calculation formulas for myopic eyes requiring negative diopter powered IOLs. Retrospective case series. K… hospital and Y… Hospital, …, …. Sixty-one eyes that underwent phacoemulsification with implantation of a negative power IOL were investigated. The trueness, precision and accuracy of IOL power calculation were assessed for the Barrett Universal II (BUII), EVO 2.0, Haigis, Hoffer QST, Holladay 1 and SRK/T formulas using the Eyetemis online tool. The analysis was performed using 1) the ULIB IOL constants and 2) after constant optimization. With ULIB constants, the Haigis, Holladay 1 and SRK/T resulted in a hyperopic mean prediction error (PE) >1.00 diopter (D), which was significantly different from zero (adjusted p <0.05). The mean PE of the remaining formulas was closer to zero. The absolute PE was significantly higher with the Holladay 1 and SRK/T (adjusted p <0.05) with respect to the remaining formulas. After constant optimization, the outcomes of traditional formulas improved and no statistically significant differences were found among any of the formulas in terms of trueness, precision and accuracy. The percentage of eyes with an absolute PE within 0.50 D was low (<50%) even after constant optimization. With ULIB constants, the BUII, EVO 2.0 and Hoffer QST were more accurate than traditional formulas in eyes with negative-diopter IOLs. The results of IOL power calculation in these eyes remain poor even after constant optimization.

As good as it gets? A new approach to estimating possible prediction performance.

How much information does a dataset contain about an outcome of interest? To answer this question, estimates are generated for a given dataset, representing the minimum possible absolute prediction error for an outcome variable that any model could achieve. The estimate is produced using a constrained omniscient model that mandates only that identical observations receive identical predictions, and that observations which are very similar to each other receive predictions that are alike. It is demonstrated that the resulting prediction accuracy bounds function effectively on both simulated data and real-world datasets. This method generates bounds on predictive performance typically within 10% of the performance of the true model, and performs well across a range of simulated and real datasets. Three applications of the methodology are discussed: measuring data quality, model evaluation, and quantifying the amount of irreducible error in a prediction problem.

Competing and Noncompeting Risk Models for Predicting Kidney Allograft Failure.

Prognostic models are becoming increasingly relevant in clinical trials as potential surrogate endpoints, and for patient management as clinical decision support tools. However, the impact of competing risks on model performance remains poorly investigated. We aimed to carefully assess the performance of competing risk and noncompeting risk models in the context of kidney transplantation, where allograft failure and death with a functioning graft are two competing outcomes. We included 11,046 kidney transplant recipients enrolled in 10 countries. We developed prediction models for long-term kidney graft failure prediction, without accounting (i.e., censoring) and accounting for the competing risk of death with a functioning graft, using Cox, Fine-Gray, and cause-specific Cox regression models. To this aim, we followed a detailed and transparent analytical framework for competing and noncompeting risk modelling, and carefully assessed the models' development, stability, discrimination, calibration, overall fit, clinical utility, and generalizability in external validation cohorts and subpopulations. More than 15 metrics were used to provide an exhaustive assessment of model performance. Among 11,046 recipients in the derivation and validation cohorts, 1,497 (14%) lost their graft and 1,003 (9%) died with a functioning graft after a median follow-up post-risk evaluation of 4.7 years (IQR 2.7-7.0). The cumulative incidence of graft loss was similarly estimated by Kaplan-Meier and Aalen-Johansen methods (17% versus 16% in the derivation cohort). Cox and competing risk models showed similar and stable risk estimates for predicting long-term graft failure (average mean absolute prediction error of 0.0140, 0.0138 and 0.0135 for Cox, Fine-Gray, and cause-specific Cox models, respectively). Discrimination and overall fit were comparable in the validation cohorts, with concordance index ranging from 0.76 to 0.87. Across various subpopulations and clinical scenarios, the models performed well and similarly, although in some high-risk groups (such as donors over 65 years old), the findings suggest a trend towards moderately improved calibration when using a competing risk approach. Competing and noncompeting risk models performed similarly in predicting long-term kidney graft failure.

Refractive outcomes following simultaneous silicone oil removal and ciliary sulcus intraocular lens implantation.

This study aimed to assess the refractive outcomes following the combined intervention of silicon oil (SO) removal and ciliary sulcus intraocular lens (IOL) implantation in aphakic eyes after phacovitrectomy. A retrospective examination of medical records from patients who underwent a combined procedure of SO removal and ciliary sulcus IOL implantation from 2019 to 2022 was performed. The primary outcomes of interest included uncorrected distance visual acuity (UDVA), predictive error (PE) and mean absolute predictive error (MAE). Subgroup analyses were performed to compare outcomes between patients with and without intraoperative posterior capsulotomy. The cohort comprised 40 eyes from 40 patients, with a mean duration of SO tamponade of 5.17 ± 1.48 months. A significant improvement in UDVA was observed from 1.58 ± 0.31 logMAR to 0.99 ± 0.31 logMAR one month postoperatively (P < 0.01). The PE was -0.73 ± 0.86 diopters (D), and the MAE was 0.90 ± 0.67 D, with 37.50% and 57.50% of cases achieving PE within ±0.5 D and ±1.0 D, respectively. No significant differences in UDVA, PE, or MAE were found between the capsulotomy and non-capsulotomy subgroups (all P > 0.05). However, a lower proportion of eyes in the capsulotomy group achieved PE within ±1.0 D compared to the non-capsulotomy group one month postoperatively (38.90% vs. 72.70%, P = 0.03). The combined procedure of SO removal and sulcus IOL implantation resulted in a mild myopic shift in postoperative refraction. Intraoperative posterior capsulotomy seems to increase the lability of postoperative refractive outcomes.

Nutritional composition analysis in food images: an innovative Swin Transformer approach.

Accurate recognition of nutritional components in food is crucial for dietary management and health monitoring. Current methods often rely on traditional chemical analysis techniques, which are time-consuming, require destructive sampling, and are not suitable for large-scale or real-time applications. Therefore, there is a pressing need for efficient, non-destructive, and accurate methods to identify and quantify nutrients in food. In this study, we propose a novel deep learning model that integrates EfficientNet, Swin Transformer, and Feature Pyramid Network (FPN) to enhance the accuracy and efficiency of food nutrient recognition. Our model combines the strengths of EfficientNet for feature extraction, Swin Transformer for capturing long-range dependencies, and FPN for multi-scale feature fusion. Experimental results demonstrate that our model significantly outperforms existing methods. On the Nutrition5k dataset, it achieves a Top-1 accuracy of 79.50% and a Mean Absolute Percentage Error (MAPE) for calorie prediction of 14.72%. On the ChinaMartFood109 dataset, the model achieves a Top-1 accuracy of 80.25% and a calorie MAPE of 15.21%. These results highlight the model's robustness and adaptability across diverse food images, providing a reliable and efficient tool for rapid, non-destructive nutrient detection. This advancement supports better dietary management and enhances the understanding of food nutrition, potentially leading to more effective health monitoring applications.

Research on short-term optimization and scheduling of multi-energy complementary systems based on forecast scenario dynamic correction

The inherent unpredictability and instability of renewable energy sources, such as wind and solar power, hinder the precise execution of power generation plans in complementary systems, posing significant challenges to their integration into power grids. Therefore, this study proposes a dynamic correction method for wind and solar output forecast scenarios in the short-term scheduling of wind-solar-hydro complementary systems. The method utilizes statistical analysis of forecast errors in wind and solar power outputs to characterize uncertainty patterns across different forecast levels and constructs a typical forecast scenario set based on single-day forecasts. This approach probabilistically models each scenario according to the temporal migration patterns of wind and solar power outputs and develops a neural network-based dynamic correction fusion model to refine the forecasts. Application of this method in a case study of the Yalong River Basin demonstrated that, after applying dynamic correction to the forecast scenarios, the mean absolute error in total wind and solar output predictions during the wet and dry seasons was reduced by 50.73 % and 47.95 %, respectively. Additionally, the dynamic correction reduced the maximum residual load on typical wet and dry days by 82.70 % and 62.37 %, respectively, and decreased the total intraday residual electricity by 91.17 % and 73.24 %, compared to single-day forecasts. The study concludes that the proposed dynamic correction method enhances power system stability and improves power generation efficiency and reliability.

Data‐Driven Approach Using a Hybrid Model for Predicting Oxygen Consumption in Argon Oxygen Decarburization Converter

Accurately controlling oxygen supply in argon oxygen decarburization (AOD) process is invariably desired for efficient decarburization and reducing alloying elements consumption. Herein, a data‐driven approach using a hybrid model integrating oxygen balance mechanism model and a two‐layer Stacking ensemble learning model is successfully established for predicting oxygen consumption in AOD converter. In this hybrid model, the oxygen balance mechanism model is used to calculate the oxygen consumption based on industrial data. Then the model calculation error is compensated using an optimized two‐layer Stacking model that is identified as (random forest (RF) + XGBoost + ridge regression)‐RF model by evaluating different hybrid model frameworks and Bayesian optimization. The results show that, in comparison to conventional prediction model based on oxygen balance mechanism, the present hybrid model greatly improves the control accuracy of oxygen consumption in AOD industrial production. The hit rate and mean absolute error of the present hybrid model for predicting oxygen consumption are 84.8% and 330 Nm3, respectively, within absolute oxygen consumption prediction error ±600 Nm3 (relative error of 3.8%). This data‐driven approach using the present hybrid model provides one pathway to efficient oxygen consumption control in AOD process.

Machine learning-aided quantitative detection of two mixed antibiotics in a narrow concentration range based on oxygen incorporation-induced 3D SERS substrates

Amoxicillin (AMO) and ciprofloxacin (CIP) as frequently-used antibiotics are often simultaneously taken for anti-infection to protect pig and poultry production, while their effective therapeutic window is narrow. How to quantitatively detect the two antibiotics mixed in a narrow concentration range is essential to ensure their treatment efficacy and food safety. In this study, quantitative detection of AMO and CIP mixed in animal flesh in one order of magnitude concentration range was performed using a triple SERS signal amplification sensor combined with machine learning. The SERS sensor based on oxygen incorporation (Oi)-induced 3D NiO nanoflowers (NFs) coated with Ag nanoparticles included 3D hot spot effect from strong internal electromagnetic couple in NiO NFs, the Oi effect of NiO and synergistic charge transfer effect of between NiO and Ag. Based on triple enhancement effects, sensitive SERS detection of AMO and CIP was made with the limits of detection of 1 nM and 100 nM. Through the use of machine learning techniques like principal component analysis and partial least square regression, qualitative and quantitative detection of AMO and CIP in their mixture from 1.33 μM to 8.69 μM was successfully achieved, where the average proportion of absolute prediction error (p ≤ 0.15) for concentration was 98.06 %.

Gradient boosting: A computationally efficient alternative to Markov chain Monte Carlo sampling for fitting large Bayesian spatio-temporal binomial regression models

Disease forecasting and surveillance often involve fitting models to a tremendous volume of historical testing data collected over space and time. Bayesian spatio-temporal regression models fit with Markov chain Monte Carlo (MCMC) methods are commonly used for such data. When the spatio-temporal support of the model is large, implementing an MCMC algorithm becomes a significant computational burden. This research proposes a computationally efficient gradient boosting algorithm for fitting a Bayesian spatio-temporal mixed effects binomial regression model. We demonstrate our method on a disease forecasting model and compare it to a computationally optimized MCMC approach. Both methods are used to produce monthly forecasts for Lyme disease, anaplasmosis, ehrlichiosis, and heartworm disease in domestic dogs for the contiguous United States. The data have a spatial support of 3108 counties and a temporal support of 108–138 months with 71–135 million test results. The proposed estimation approach is several orders of magnitude faster than the optimized MCMC algorithm, with a similar mean absolute prediction error.

Bayesian impedance deconvolution using timescale distribution for lithium-ion battery state estimation

The distribution of relaxation times (DRT) and distribution of diffusion times (DDT) are widely recognized as effective model-free methods for deconvolving the internal properties of complex electrochemical systems using electrochemical impedance spectroscopy (EIS) data. This study proposes an integrated framework that employs a Bayesian approach to accurately estimate both DRT and DDT and machine learning techniques to enhance capacity estimation, incorporating Gaussian process regression and transformer networks. These methods utilize the inferred DRT and DDT as inputs to predict the discharge capacity. In addition, we perform a peak analysis on the estimated DRT and DDT to extract additional physically meaningful features, which have also proven to be effective inputs for capacity prediction. The applicability of the proposed framework to the EIS experimental data of lithium-ion cells is demonstrated and compared with existing capacity estimation methods. The proposed framework demonstrates a mean absolute error in capacity prediction below 3.03% when using DRT and DDT directly and 2.71% when using extracted features, outperforming existing methods by approximately one to three percentage points. Our Bayesian approach, combined with peak analysis and the integration of machine learning techniques, enables a more robust diagnosis of the internal state of lithium-ion batteries through EIS measurements.

Joint estimation of SOH and RUL for lithium batteries based on variable frequency and model integration

The safe and stable operation of lithium-ion batteries in electric vehicles is crucial. Aiming at the problems of a large workload of online estimation and prediction and inability to weigh the whole life cycle of the battery in the joint estimation of SOH (State of Health) and RUL (Remaining Useful Life) for traditional lithium-ion batteries, this paper proposes an optimization scheme for the joint analysis of SOH-RUL. First, the article extracts suitable health features. It utilizes an integrated Extreme Learning Machine (ELM) to estimate the SOH of the battery and inputs the estimates into a Regression Vector Machine (RVM) model for RUL prediction. To better cope with the phenomenon of "sudden capacity change" in batteries, this paper divides the entire life cycle into the early and late stages. A lower estimation frequency and model integration are employed in the early stage.In contrast, the estimation frequency and model integration are increased later to balance speed and safety in total life cycle estimation. Finally, validation was performed on the NASA and CALCE datasets, comparing the effect of constant estimated frequency and model integration with that of varying estimated frequency and model integration at different aging stages. The RMSE of SOH estimation error for both datasets is within 2% during the early and late stages, and the SOH estimation error for the entire lifecycle is within 2%. The absolute RUL prediction error for the NASA dataset is below five times; for the CALCE dataset, it is below 20 times in most cases.

Comparison of the Accuracy Between the Z CALC2 Calculator and Barrett Toric Calculator in Toric IOL Calculation.

To compare the accuracy of the Z CALC2 calculator and Barrett toric calculator in toric intraocular lens (IOL) calculation. Eighty-five eyes of 85 patients who underwent uneventful cataract surgery with toric IOL implantation were included. The accuracy was compared between the Z CALC2 calculator and Barrett toric calculator under two calculation modes: using simulated keratometry (SimK) from the IOLMaster 700 (Carl Zeiss Meditec AG) for posterior corneal astigmatism (PCA) prediction and employing total corneal astigmatism (total corneal refractive power [TCRP] or measured PCA) obtained from the Pentacam (Oculus Optikgeräte GmbH). The centroid of prediction errors, percentage of eyes with prediction errors within ±0.50 diopter (D), mean prediction error, and mean absolute prediction error were calculated. Subgroup analysis was conducted based on the orientation and magnitude of anterior corneal astigmatism and axial length. When using SimK, the two calculators with predicted PCA showed comparable accuracy. When employing total corneal astigmatism, the Barrett toric calculator with measured PCA showed a lower centroid error (0.15 vs 0.38 D), a higher percentage of eyes with prediction errors within ±0.50 D (47.1% vs 32.9%, P = .018), and a lower mean prediction error (0.57 vs 0.71 D, P = .033) compared to the Z CALC2 calculator with TCRP in the 4-mm zone. In subgroup analysis, when employing total corneal astigmatism, the Barrett toric calculator with measured PCA exhibited superior accuracy, especially in the with-the-rule and anterior corneal astigmatism of 2.00 D or less subgroups. When using SimK, the Z CALC2 calculator and Barrett toric calculator yield comparable accuracy. The Barrett toric calculator with measured PCA may be more recommended when employing total corneal astigmatism. [J Refract Surg. 2024;40(10):e681-e691.].