Hybrid metal matrix composites (MMCs) exhibit superior overall mechanical and functional response when compared to their conventional counterparts and as a result have greater potential to be widely ...used for structural engineering and functional device applications. This review focuses on the recent developments in the fabrication techniques and properties characterization of Al, Mg, Ti, Cu, Fe/steel and Ni matrix composites containing hybrid reinforcements. The hybrid reinforcements are classified according to different types, shapes and sizes. Novel processing techniques proposed for achieving homogeneous distribution of hybrid reinforcements and forming special structures are critically reviewed. The mechanical properties of various matrix systems are summarized and analyzed, while the strengthening mechanisms triggered by hybrid reinforcements are discussed. Meanwhile, a prediction model for yield strength of hybrid MMCs is also proposed. The effects of hybrid reinforcements and fabrication conditions on functional properties, including tribological, thermal, and electrical properties, of the hybrid composites are also described systematically. Finally, future work for promoting further development of this field is also addressed.
•The hybrid reinforcements are classified and discussed in detail.•Novel processing techniques are critically reviewed.•The mechanical properties of various matrix systems are summarized and analyzed.•The strengthening mechanisms are discussed systematically.•Functional properties of the hybrid composites are also described systematically.
To systematically evaluate the performance of COVID-19 prognostic models and scores for mortality risk in older populations across three health-care settings: hospitals, primary care, and nursing ...homes.
This retrospective external validation study included 14,092 older individuals of ≥70 years of age with a clinical or polymerase chain reaction-confirmed COVID-19 diagnosis from March 2020 to December 2020. The six validation cohorts include three hospital-based (CliniCo, COVID-OLD, COVID-PREDICT), two primary care-based (Julius General Practitioners Network/Academisch network huisartsgeneeskunde/Network of Academic general Practitioners, PHARMO), and one nursing home cohort (YSIS) in the Netherlands. Based on a living systematic review of COVID-19 prediction models using Prediction model Risk Of Bias ASsessment Tool for quality and risk of bias assessment and considering predictor availability in validation cohorts, we selected six prognostic models predicting mortality risk in adults with COVID-19 infection (GAL-COVID-19 mortality, 4C Mortality Score, National Early Warning Score 2-extended model, Xie model, Wang clinical model, and CURB65 score). All six prognostic models were validated in the hospital cohorts and the GAL-COVID-19 mortality model was validated in all three healthcare settings. The primary outcome was in-hospital mortality for hospitals and 28-day mortality for primary care and nursing home settings. Model performance was evaluated in each validation cohort separately in terms of discrimination, calibration, and decision curves. An intercept update was performed in models indicating miscalibration followed by predictive performance re-evaluation.
In-hospital mortality for hospitals and 28-day mortality for primary care and nursing home setting.
All six prognostic models performed poorly and showed miscalibration in the older population cohorts. In the hospital settings, model performance ranged from calibration-in-the-large −1.45 to 7.46, calibration slopes 0.24–0.81, and C-statistic 0.55–0.71 with 4C Mortality Score performing as the most discriminative and well-calibrated model. Performance across health-care settings was similar for the GAL-COVID-19 model, with a calibration-in-the-large in the range of −2.35 to −0.15 indicating overestimation, calibration slopes of 0.24–0.81 indicating signs of overfitting, and C-statistic of 0.55–0.71.
Our results show that most prognostic models for predicting mortality risk performed poorly in the older population with COVID-19, in each health-care setting: hospital, primary care, and nursing home settings. Insights into factors influencing predictive model performance in the older population are needed for pandemic preparedness and reliable prognostication of health-related outcomes in this demographic.
ABSTRACT
We develop a state‐of‐the‐art fraud prediction model using a machine learning approach. We demonstrate the value of combining domain knowledge and machine learning methods in model building. ...We select our model input based on existing accounting theories, but we differ from prior accounting research by using raw accounting numbers rather than financial ratios. We employ one of the most powerful machine learning methods, ensemble learning, rather than the commonly used method of logistic regression. To assess the performance of fraud prediction models, we introduce a new performance evaluation metric commonly used in ranking problems that is more appropriate for the fraud prediction task. Starting with an identical set of theory‐motivated raw accounting numbers, we show that our new fraud prediction model outperforms two benchmark models by a large margin: the Dechow et al. logistic regression model based on financial ratios, and the Cecchini et al. support‐vector‐machine model with a financial kernel that maps raw accounting numbers into a broader set of ratios.
•A resource-saving environmental control method is proposed for crops.•U-chord curvature algorithm was adopted to determine knee points of a curve.•Optimal regulation space is determined based on ...knee points.•The mathematical and physiological significance of knee points was discussed.
Temperature, light and CO2 are three environmental factors that directly affect the photosynthetic rate of plants. Exploring the relationship between these three factors and the photosynthetic rate, and optimizing the environmental conditions, is the key to realize efficient production of greenhouse crops. A nested experiment was carried out to measure the photosynthetic rate of cucumber seedlings under different environmental conditions. On the basis of these data, a photosynthetic rate prediction model was established by using machine learning method. A method that avoiding the excessive consumption of light and CO2 resources and promoting the photosynthetic rate based on U-chord curvature is proposed for more efficient regulation of photosynthetic rate. Under different temperature conditions, the prediction model can be used to construct photosynthetic rate surfaces under the interaction of light and CO2. After the surface is discretized, the maximum U-chord curvature points of discrete light response and CO2 response curves are calculated. These points are fitted by polynomials as the regulation boundary to obtain the target space that can achieve efficient regulation. The prediction model, with temperature, light and CO2 as inputs, had high accuracy, and the regulation method was effective. The determination coefficient and root mean square error of the prediction model were 0.99 and 0.85 µmol·m−2·s−1, respectively. Compared with the traditional maximum photosynthetic rate regulation method, this new method reduced the photosynthetic rate by under 16%, but saved 41% of light and 49% of CO2 inputs, which illustrated this method improved production efficiency while basically maintained maximum photosynthetic rate.
Abstract
As machine learning research in the field of cardiovascular imaging continues to grow, obtaining reliable model performance estimates is critical to develop reliable baselines and compare ...different algorithms. While the machine learning community has generally accepted methods such as k-fold stratified cross-validation (CV) to be more rigorous than single split validation, the standard research practice in medical fields is the use of single split validation techniques. This is especially concerning given the relatively small sample sizes of datasets used for cardiovascular imaging. We aim to examine how train-test split variation impacts the stability of machine learning (ML) model performance estimates in several validation techniques on two real-world cardiovascular imaging datasets: stratified split-sample validation (70/30 and 50/50 train-test splits), tenfold stratified CV, 10 × repeated tenfold stratified CV, bootstrapping (500 × repeated), and leave one out (LOO) validation. We demonstrate that split validation methods lead to the highest range in AUC and statistically significant differences in ROC curves, unlike the other aforementioned approaches. When building predictive models on relatively small data sets as is often the case in medical imaging, split-sample validation techniques can produce instability in performance estimates with variations in range over 0.15 in the AUC values, and thus any of the alternate validation methods are recommended.
This article focuses on the design of model predictive control (MPC) systems for nonlinear processes that utilize an ensemble of recurrent neural network (RNN) models to predict nonlinear dynamics. ...Specifically, RNN models are initially developed based on a data set generated from extensive open‐loop simulations within a desired process operation region to capture process dynamics with a sufficiently small modeling error between the RNN model and the actual nonlinear process model. Subsequently, Lyapunov‐based MPC (LMPC) that utilizes RNN models as the prediction model is developed to achieve closed‐loop state boundedness and convergence to the origin. Additionally, machine learning ensemble regression modeling tools are employed in the formulation of LMPC to improve prediction accuracy of RNN models and overall closed‐loop performance while parallel computing is utilized to reduce computation time. Computational implementation of the method and application to a chemical reactor example is discussed in the second article of this series.
Causal treatment effects are estimated at the population level in randomized controlled trials, while clinical decision is often to be made at the individual level in practice. We aim to show how ...clinical prediction models used under a counterfactual framework may help to infer individualized treatment effects.
As an illustrative example, we reanalyze the International Stroke Trial. This large, multicenter trial enrolled 19,435 adult patients with suspected acute ischemic stroke from 36 countries, and reported a modest average benefit of aspirin (vs. no aspirin) on a composite outcome of death or dependency at 6 months. We derive and validate multivariable logistic regression models that predict the patient counterfactual risks of outcome with and without aspirin, conditionally on 23 predictors.
The counterfactual prediction models display good performance in terms of calibration and discrimination (validation c-statistics: 0.798 and 0.794). Comparing the counterfactual predicted risks on an absolute difference scale, we show that aspirin—despite an average benefit—may increase the risk of death or dependency at 6 months (compared with the control) in a quarter of stroke patients.
Counterfactual prediction models could help researchers and clinicians (i) infer individualized treatment effects and (ii) better target patients who may benefit from treatments.
Several studies have reported the superior performance of the Negative Binomial-Lindley (NB-L) compared to the commonly used Negative Binomial distribution. Consequently, different parameterisations ...of the NB-L distribution have been introduced to further improve crash data modelling. However, little is known on how these models perform for different data domains. This study is documenting a comparative analysis among previously developed and two newly proposed parameterisations of the NB-L distribution, the negative binomial weighted Lindley (NB-WLindley) and the negative binomial quasi-Lindley (NB-QL). The results show that the NB-WLindley distribution performed better for the majority of data domains. Also, its generalised linear model (NB-WLindley GLM) showed superior statistical performance relative to the NB GLM and NB-L GLM. The results of this study contribute to the advancement of current predictive models used in transportation safety and provide insights for safety analysts and researchers when these models should be used.
Convalescent plasma is a leading treatment for coronavirus disease 2019 (COVID-19), but there is a paucity of data identifying its therapeutic efficacy. Among 126 potential convalescent plasma ...donors, the humoral immune response was evaluated using a severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus neutralization assay with Vero-E6-TMPRSS2 cells; a commercial IgG and IgA ELISA to detect the spike (S) protein S1 domain (EUROIMMUN); IgA, IgG, and IgM indirect ELISAs to detect the full-length S protein or S receptor-binding domain (S-RBD); and an IgG avidity assay. We used multiple linear regression and predictive models to assess the correlations between antibody responses and demographic and clinical characteristics. IgG titers were greater than either IgM or IgA titers for S1, full-length S, and S-RBD in the overall population. Of the 126 plasma samples, 101 (80%) had detectable neutralizing antibody (nAb) titers. Using nAb titers as the reference, the IgG ELISAs confirmed 95%-98% of the nAb-positive samples, but 20%-32% of the nAb-negative samples were still IgG ELISA positive. Male sex, older age, and hospitalization for COVID-19 were associated with increased antibody responses across the serological assays. There was substantial heterogeneity in the antibody response among potential convalescent plasma donors, but sex, age, and hospitalization emerged as factors that can be used to identify individuals with a high likelihood of having strong antiviral antibody responses.
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