A study on the micro-endmilling surface prediction model with non-dynamic errors

داشتن درک درستی از چگونگی تغییرات زمانی و مکانی متغیر های هواشناسی در مطالعات محیطی از اهمیت بسزایی برخوردار است. اخیرا از مدل‌های منطقه ای پیش بینی عددی هواشناسی در تحقیقات بسیاری استفاده می شود. با این وجود برای بررسی چگونگی تغییرات زمانی و مکانی نتایج حاصل از مدل ها تحقیقات زیادی صورت نگرفته است. بر این اساس و با توجه به اهمیت این موضوع یک رویکرد زمانی - مکانی در این مقاله بررسی می شود. در این رویکرد نتایج پیش‌بینی مدل میان مقیاس 4MM5 با مجموعه داده های مشاهده ای، برای میانگین ماهانه و سالانه دما و بارش در دوره سال‌های 1990-2010 در منطقه شمال شرق ایران مورد تحلیل و بررسی قرار گرفت. در این تحقیق ارزیابی کمی شباهت مکانی و زمانی بین مجموعه داده‌ها و مدل آب و هوایی، و نیز شناسایی مناطق جغرافیایی و فصولی که در مدل سازی‌ها مشکل‌ساز می‌باشند انجام شده است. این کار با استفاده از نقشه‌های تهیه شده بر مبنای الگوریتم تشابه5 جبری، که یک روش موثر برای تحلیل مجموعه داده‌های مکانی مختلف، نقشه‌ها و مدل‌ها می‌باشد، انجام شد. مشخص شد که شباهت داده‌های واقعی با نتایج مدل برای متغیر دما بیش‌تر از بارندگی است. نتایج حاصل از مدل برای بارش و دما در فصل‌های گرم سال در مقایسه با داده‌های واقعی از لحاظ پراکندگی مقادیر تشابه، تناقض داشت. هم‌چنین در فصل‌های سرد سال پیش‌بینی دقیق‌تری به وسیله مدل ارائه شده است. علاوه بر این چند منطقه که در آنجا خطای پیش‌بینی بیش‌تر بود به وسیله الگوریتم نقشه‌پردازی تشابه شناسایی شدند. این تحلیل نشان داد که در محدوده‌های کوچکی از جنوب و ناحیه مرکزی منطقه مورد مطالعه بین مدل و داده‌های واقعی تشابه کم‌تری وجود دارد.

China has highly emphasized the research and operational application of numerical weather prediction. This paper determines the objective function parameters, such as CAPE and SRH, to apply an ensemble numerical prediction model in weather forecasting. Preprocessing and evaluating rainfall data is necessary to construct the WRF-ARW numerical weather prediction model. The WRF-ARW model is applied to simulate the weather forecasts in Henan Province, and the difficulties and challenges faced in the efficient implementation of the parameterized scheme are outlined. The WRFARW model’s prediction errors for the maximum rainfall and total rainfall in Henan Province range from 1.78%-13.51% and 0.16%-3.78%, respectively, which are significantly less than 15%, and the model is more predictive than the others. The raw data test set’s credibility ranges from 0.957 to 0.997, which is close to 1, indicating that the raw data collected in this paper are highly credible. The WRF-ARW model’s qualification rates for forecasting maximum rainfall and total rainfall are 86.7% and 93.3%, respectively, and its overall accuracy is grade B and grade A, respectively. The pass rates for the peak occurrence time of maximum rainfall and total rainfall were 93.3% and 86.7%, respectively, and the overall prediction accuracy was Grade A and Grade B, respectively. The WRF-ARW model is effective in weather forecasting throughout Henan Province. In summary, the WRF-ARW model is very effective in improving the efficiency of ensemble numerical weather prediction and parameterization schemes in Henan Province.

Objective To evaluate the effect of setup errors on the 2D image projection and image registration, and then propose an improved registration method based on mutual information. Methods An anthropomorphic head phantom was used to simulate the rotational and translational setup errors. The geometric disparities were reflected by the changes of mutual information. Known setup errors were intentionally introduced to twenty cases divided into two groups demarcated by 3 mm translation error and 3° rotation error: ten cases with larger errors and ten with smaller errors. Then the anterior-posterior and lateral portal images were captured by the electronic portal imaging device (EPID), based on which the setup errors were calculated using two mutual information registration method respectively: the vender provided one, and the improved method as proposed. The calculated errors were compared with the actual setup errors to evaluate robustness of the method. Results For the ten cases with smaller setup errors, the average translational registration disparities using the conventional method were 0.3, 0.4, and 0.3 mm in x, y and z directions respectively. The rotational disagreements were 0.4° in both x and z directions. The average time consumption was 28.7 s. The corresponding discrepancies analyzed using the improved method were 0.3, 0.4, 0.3 mm, 0.5° and 0.4°, respectively. On average, 31.1 s was needed for registration. For the ten cases with larger setup errors, the mean disparities of the conventional method were 0.9, 0.7, 0.8 mm, 0.9° and 0.8°, 29.9 s taken on average. The corresponding result of the improved method was 0.5, 0.4, 0.5 mm, 0.6° and 0.5°, 33.2 s taken on average. Conclusions Regarding smaller setup errors, the two methods showed little difference and both had good performance in image registration accuracy. For larger setup errors, however, the improved mutual information registration method exhibited significantly higher accuracy than the conventional method, at cost of clinically acceptable registration time. Key words: Setup errors; Electronic portal imaging device; Portal image; Image registration

The nonhydrostatic multiscale model on the B grid (NMMB) was employed to forecast an episode of intense local Kosava wind in northeast Serbia. Kosava, a vigorously turbulent local wind, originates from the east or southeast near the Danube's "Iron Gate," moves westward over Belgrade, and then extends northward into the regions of Romania and Hungary. Typically attributed to a jet-effect wind within the narrow gorge of the "Iron Gate," it can reach maximum speeds exceeding 30 m/s. The NMMB model, with a horizontal resolution of 1.2 km, was utilized for the 2019 Kosava episode forecast. The high resolution, that surpasses the typical standards in numerical prediction models used by national meteorological services and other centers, can be crucial for accurately predicting strong wind gusts and capturing the specific dynamics and characteristics of the wind associated with the narrow gorge. The NMMB model results are compared with measured wind data and the results from models with lower resolutions.

As the requirement of LCD products which are large screen and have high brightness increases, the role of light guide panel (LGP) of which micro-features diffuse the light uniformly on surface is getting important. In general, there are many errors in machining like machine tool error, process error, setup error and etc. The amount of setup error in general machining is not so big in comparison with the others, so it is mostly neglected. But, especially in v-groove micromachining, setup error has a significant effect on micro-features. Low quality product and high cost are resulted from setup error. In v-groove micromachining, to confirm the effect of setup error, it is identified and then setup error synthesis model is derived from analysis of tool and workpiece setup. In addition, to predict the micro-features affected by setup error and enhance the production efficiency, the setup condition satisfying the tolerance of micro-features is geometrically analyzed and presented.

A simulation experiment is presented which tests a procedure that assimilates temperature soundings from geostationary satellites using a method developed by Gal-Chen (1983). The simulation is used to decide whether the increased temporal resolution of geostationary satellite data can be used to improve numerical simulation of a baroclinically unstable wave, and to assess the impact of gaps in the satellite sounding fields on the model simulations. Background information on the simulation experiment is presented, including discussion of the variational approach utilized, the numerical model, updating procedures, and control run. The simulation results indicate that, for the case of a baroclinically unstable wave, considerable improvements in the short-range forecast may be realized if geostationary satellite data are inserted with a frequency near 1 hr during a 6 hr analysis cycle compared with a single insertion.

Numerical runout models are important tools for predicting the potential downstream impacts of tailings dam breaches that generate tailings flows, which is a crucial step in emergency preparedness and planning, and risk management. Most existing runout models were originally developed for the analysis of water floods or flow-like landslides (e.g. debris flows). In this study, we back-analyze two well-documented historical tailings dam breaches (1985 Stava, Italy and 1994 Merriespruit, South Africa) using four numerical models (DAN3D, MADflow, FLO-2D and FLOW-3D). The main objective of this multi-case, multi-model benchmarking exercise is to identify collective opportunities to adapt these types of models and associated modelling methods to tailings dam breach runout applications. Comparing best-fit simulation results, we find that all four models are capable of reproducing the bulk behaviour of the real events; however, (i) multiple sets of rheological parameters may produce very similar output results, (ii) the best-fit input parameter combinations are non-transferable between models and inconsistent with independently measured rheological properties of stored tailings, and (iii) choosing an appropriate set requires sufficient understanding of material rheological properties and expert judgment. Using a systematic sensitivity analysis with the First-Order Second-Moment (FOSM) approach, we also find that each model is sensitive to different input parameters, although the total released volume is among the main high-influence parameters in every scenario. We conclude that more case study back-analyses are needed to enhance our understanding of these sensitivities and develop better guidance on the use of these types of numerical models for tailings flow runout prediction.

BackgroundHeart failure (HF) is a leading cause of global disease burden and mortality. Accurate prognosis assessment is critical for reducing the risk of adverse events. In recent years, numerous predictive models have been developed for different HF subtypes. However, the quality of existing models varies considerably, and there remains a lack of consensus on models suitable for widespread clinical application. This study systematically reviews the current landscape of HF prediction models, analyzes their strengths and limitations, and provides guidance for future research.MethodsThis review systematically retrieved studies on prognostic prediction models for HF from databases including PubMed and Embase, with a search period spanning from the inception of each database to 19 September 2025. The risk of bias of the included studies was assessed using the prediction model risk of bias assessment tool, and the performance of the prediction models was evaluated through metrics such as the C-index and calibration.ResultsA total of 46 prediction models from 38 studies were included. According to target population classification, 14 models were developed for predicting outcomes in HF patients with reduced ejection fraction, nine models were applicable to HF patients with preserved ejection fraction, one model targeted HF patients with mildly reduced ejection fraction, and the remaining 22 were designed for all HF patients regardless of subtype. The risk of bias assessment showed that 10 models had a high risk of bias, 21 models demonstrated an unclear risk of bias, and 15 models exhibited a low risk of bias. The study systematically summarized each model's study cohort, modeling methodology, predictors, outcomes, prediction performance, presentation format, as well as strengths and limitations.ConclusionRefining the methodological processes of model construction—including optimizing study cohort selection, updating predictor screening (such as incorporating novel biomarkers, imaging indicators, and multi-omics data), improving modeling strategies, and enhancing model presentation—will contribute to the development of more accurate and clinically applicable prediction models. Such advancements hold significant potential for improving clinical outcomes in patients across all types of HF. This review provides a substantive reference for future research in this field.

Four-axis machine tools with two rotary axes are widely used in the machining of complex parts. However, due to an irregular kinematic relationship and non-linear kinematic function with geometric error, it is difficult to analyze the influence the geometry error of each axis has and to compensate for such a geometry error. In this study, an influence analysis method of geometric error based on the homogeneous coordinate transformation matrix and a compensation method was developed, using the Newton iterative method. Geometric errors are characterized by a homogeneous coordinate transformation matrix in the proposed method, and an error matrix is integrated into the kinematic model of the four-axis machine tool as a means of studying the influence the geometric error of each axis has on the tool path. Based on the kinematic model of the four-axis machine tool considering the geometric error, a comprehensive geometric error compensation calculation model based on the Newton iteration was then constructed for calculating the tool path as a means of compensating for the geometric error. Ultimately, the four-axis machine tool with a curve tool path for an off-axis optical lens was chosen for verification of the proposed method. The results showed that the proposed method can significantly improve the machining accuracy.

Disc cutter has a large diameter and more teeth, and obvious tool runout (δi) is produced during disc milling process, so the irregular distribution of cutting force is generated due to uneven distribution of cutting output per tooth (hi*) caused by tool runout. Therefore, the tool runout (δi) must be considered when milling force model of disc milling is established. In the paper, first, milling force model of disc milling is built with a three-teeth alternating disc cutter considering the tool runout (δi). Second, tool runout (δi) is measured by the dial gauge when the revolving speed of disc cutter is 1 r/min, then cutting output per tooth considering tool runout (hi*) is obtained by the formula between cutting output of each tooth (hi) without considering tool runout (δi) and tool runout (δi). Third, the parameters of frictional angle (βn), normal shear angle (ϕn), and shear yield strength (τs) in model coefficients are calibrated by orthogonal cutting experimental. Then, the predictive model of milling force is obtained. Last, the reliability verification experiment of model is conducted. The experiment results show that the model is with high efficiency and precision and the measured force and predictive force is in good agreement in amplitude and trend, which can be used in the study of milling force of disc milling.

Introduction: Numerous prediction models have been developed to identify high-risk individuals for lung cancer screening, aimed at improving early detection and survival rates. However, there has been no comprehensive review and meta-analysis of model performance across different sociocultural contexts. Therefore, this review aims to systematically examine the performance of lung cancer risk prediction models in Western and Asian populations. Methods: PubMed and EMBASE were searched from inception to January 2023. Studies published in English and proposed a validated model on human populations with well-defined predictive performances were included. Two reviewers independently screened titles and abstracts and the Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to appraise study quality. A random-effects meta-analysis was performed and a 95% confidence interval (CI) for model performance was provided. Between-study heterogeneity was adjusted for using the Hartung-Knapp-Sidik-Honkman test. Results: Fifty-four studies were included, with 42 from Western and 12 from Asian countries. Most Western studies were designed for ever-smokers (19/42; 45.2%) and the general population (17/42; 40.5%), and only two Asian studies developed models exclusively for never-smokers. Across both Western and Asian prediction models, the three most consistently included risk factors are age, sex, and family cancer history. In 45.2% (19/42) of Western and 50.0% (6/12) of Asian studies, models incorporated both traditional risk factors and biomarkers; 14.8% (8/54) directly compared their biomarker-based models with those incorporating only traditional risk factors, demonstrating better discrimination. Machine-learning algorithms were applied in eight Western models and two Asian models. External validation of PLCOM2012 (AUC=0.748; 95% CI: 0.719-0.777) outperformed prediction models such as Bach (AUC=0.710; 95% CI: 0.674-0.745) and Spitz models (AUC=0.698; 95% CI: 0.640-0.755). Conclusion: Despite showing promising results, the majority of Asian risk models in our study lack external validation. Our review also reveals a significant gap in prediction models for never-smokers. Future research should externally validate existing Asian models or incorporate relevant Asian risk factors in widely-used Western models (PLCOM2012) to account for their unique risk profiles and lung cancer progression patterns. Citation Format: Yah Ru Juang, Lina Ang, Wei Jie Seow. The predictive performance of risk prediction models in determining lung cancer incidence in Western and Asian countries: A systematic review and meta-analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 6210.

Fraction of green vegetation, fg, and green leaf area index, Lg, are needed as a regular space-time gridded input to evapotranspiration schemes in the two National Weather Service (NWS) numerical prediction models regional Eta and global medium range forecast. This study explores the potential of deriving these two variables from the NOAA Advanced Very High Resolution Radiometer (AVHRR) normalized difference vegetation index (NDVI) data. Obviously, one NDVI measurement does not allow simultaneous derivation of both vegetation variables. Simple models of a satellite pixel are used to illustrate the ambiguity resulting from a combination of the unknown horizontal (fg) and vertical (Lg) densities. We argue that for NOAA AVHRR data sets based on observations with a spatial resolution of a few kilometres the most appropriate way to resolve this ambiguity is to assume that the vegetated part of a pixel is covered by dense vegetation (i.e., its leaf area index is high), and to calculate fg=(NDVI-NDVI0)/(NDVI8-NDVI0), where NDVIo (bare soil) and NDVI (dense vegetation) are specified as global constants independent of vegetation/soil type. Global (0.15o)2 spatial resolution monthly maps of fg were produced from a 5-year NDVI climatology and incorporated in the NWS models. As a result, the model surface fluxes were improved.

Rainstorm often causes inland flooding and mudslides that threaten lives and properties. In this study, rainstorm is used as a forecasting object, and an interpretation prediction model for rainstorm based on the European Center for medium-range weather forecasting (ECMWF) numerical prediction model is constructed through the generalized regression neural network method. Model inputs are forecasted through principal component analysis, and dual-factor feature extraction is performed on the primary predictors to obtain new irrelevant variables and optimize network structures. The experimental forecast results of the 24 h aging test using an independent sample of large-scale rainstorm in Guangxi, China from 2012 to 2016, the actual forecast results of selected rainstorm cases with great influence on Guangxi, and different influencing systems show that the new prediction scheme is sophisticated. Thus, the scheme has a certain universal applicability. The results of the comparative analysis between the new program and ECMWF show that the forecasting ability of the new method is more accurate than that of the direct numerical forecasting model. The threat score of the new forecast model for 5 years has a 58.4% increase relative to that of the ECMWF. The forecasting skills are positive and good and can thus improve the rainstorm forecasting ability of ECMWF and provide a better guidance for forecasters.

A new experimental and numerical simulation method for measuring ash slag fluidity using VOF based on OpenFOAM

: LONG-TERM GOAL. Our long-term goal is to improve the accuracy of numerical prediction models of weather and climate. We shall concentrate on sources of prediction error involving interactions between physical phenomena having different timescales. OBJECTIVES. We examine how inadequate representation of rapidly-varying (i.e., stochastic) atmospheric effects in General Circulation Models (GCMs) can systematically affect prediction on a variety of scales, and how errors arising from this inadequacy may be ameliorated. Concurrently, we develop useful, efficient and accurate methods of accounting for these systematic effects of stochastic forcing, and also provide methods for estimating the spread of ensemble predictions taking into account multiplicative stochastic effects. APPROACH. We approach the problem both analytically and numerically. Simple barotropic models present cases that are nontrivial, yet simple enough to solve analytically, and we use these models to identify and solve some of the numerical problems that will arise in realistic, more complex models. We intend to apply our results to studying the impact of stochastic forcing in more complete models, culminating with long runs of stochastically forced, full-fledged GCMs and numerical weather prediction (NWP) models. Although routinely ignored in current models, the theory of stochastic white noise is well known and analytic solutions are available, at least in principle. We have evaluated the effect of white stochastic forcing on the mean response of a global barotropic model to steady forcing. In addition, we have considered the more realistic problem of stochastic colored noise forcing, deriving analytic estimations of the systematic change in the mean response, and have compared these analytic approximations with results from stochastically forced numerical models.