Comparison of Active COVID-19 Cases per Population Using Time-Series Models

This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the thirty-six different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered and death cases as of 4 November 2020 of COVID-19 and populace of each state. A 14-multi step ahead forecast system for active coronavirus cases was built, analyzed and compared for six (6) different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.

Comparison of Active COVID-19 Cases Per Population Using Time-Series Models

This research explored the precision of diverse time-series models for COVID-19 epidemic detection in all the thirty-six different states and the Federal Capital Territory (FCT) in Nigeria with the maximum count of daily cumulative of confirmed, recovered and death cases as of 4 November 2020 of COVID-19 and populace of each state. A 14-multi step ahead forecast system for active coronavirus cases was built, analyzed and compared for six (6) different deep learning-stimulated and statistical time-series models using two openly accessible datasets. The results obtained showed that based on RMSE metric, ARIMA model obtained the best values for four of the states (0.002537, 0.001969.12E-058, 5.36E-05 values for Lagos, FCT, Edo and Delta states respectively). While no method is all-encompassing for predicting daily active coronavirus cases for different states in Nigeria, ARIMA model obtains the highest-ranking prediction performance and attained a good position results in other states.

Location-Centered House Price Prediction: A Multi-Task Learning Approach

Accurate house prediction is of great significance to various real estate stakeholders such as house owners, buyers, and investors. We propose a location-centered prediction framework that differs from existing work in terms of data profiling and prediction model. Regarding data profiling, we make an important observation as follows – besides the in-house features such as floor area, the location plays a critical role in house price prediction. Unfortunately, existing work either overlooked it or had a coarse grained measurement of locations. Thereby, we define and capture a fine-grained location profile powered by a diverse range of location data sources, including transportation profile, education profile, suburb profile based on census data, and facility profile. Regarding the choice of prediction model, we observe that a variety of approaches either consider the entire data for modeling, or split the entire house data and model each partition independently. However, such modeling ignores the relatedness among partitions, and for all prediction scenarios, there may not be sufficient training samples per partition for the latter approach. We address this problem by conducting a careful study of exploiting the Multi-Task Learning (MTL) model. Specifically, we map the strategies for splitting the entire house data to the ways the tasks are defined in MTL, and select specific MTL-based methods with different regularization terms to capture and exploit the relatedness among tasks. Based on real-world house transaction data collected in Melbourne, Australia, we design extensive experimental evaluations, and the results indicate a significant superiority of MTL-based methods over state-of-the-art approaches. Meanwhile, we conduct an in-depth analysis on the impact of task definitions and method selections in MTL on the prediction performance, and demonstrate that the impact of task definitions on prediction performance far exceeds that of method selections.

A Roadway Safety Sustainable Approach: Modeling for Real-Time Traffic Crash with Limited Data and Its Reliability Verification

With the development of freeway system informatization, it is easier to obtain the traffic flow data of freeway, which are widely used to study the relationship between traffic flow state and traffic safety. However, as the development degree of the freeway system is different in different regions, the sample size of traffic data collected in some regions is insufficient, and the precision of data is relatively low. In order to study the influence of limited data on the real-time freeway traffic crash risk modeling, three data sets including high precision data, small sample data, and low precision data were considered. Firstly, Bayesian Logistic regression was used to identify and predict the risk of three data sets. Secondly, based on the Bayesian updating method, the migration test towards high and low precision data sets was established. Finally, the applicability of machine learning and statistical methods to low precision data set was compared. The results show that the prediction performance of Bayesian Logistic regression improves with the increasing of sample size. Bayesian Logistic regression can identify various significant risk factors when data sets are of different precision. Comparatively, the prediction performance of the support vector machine is better than that of Bayesian Logistic. In addition, Bayesian updating method can improve the prediction performance of the transplanted model.

A Novel Integrated Learning Model for Rainfall Prediction CEEMD- FCMSE -Stacking

Scientific prediction of precipitation changes has important guiding value and significance for revealing regional spatial and temporal patterns of precipitation changes, flood climate prediction, etc. Based on the fact that CEEMD can effectively overcome the interference of modal aliasing and white noise, fine composite multi-scale entropy can reorganize the same FCMSE value to reduce the modal component and improve the computational efficiency, and Stacking ensemble learning can effectively and conveniently improve the fitting effect of machine learning, a rainfall prediction method based on CEEMD-fine composite multi-scale entropy and Stacking ensemble learning is constructed, and it is applied to the prediction of monthly precipitation in the Xixia. The results show that, under the same conditions, the CEEMD-RCMSE-Stacking model reduces the root mean square error by 83.48% and 62.08%, and the mean absolute error by 83.25% and 61.84%, respectively, compared with the single Stacking model and CEEMD-LSTM, while the goodness-of-fit coefficients improve by 15.94% and 2.34%, respectively, which means that the CEEMD-RCMSE-Stacking model has higher prediction performance. The CEEMD-RCMSE-Stacking model has higher prediction performance.

Prediction of Shoreline Change for the Calculation of the Integrated Littoral Sediment Budget

The severe coastal erosions are being accelerated along the east coast of South Korea owing to the intermittent erosions and depositions caused by the imbalance between the effective sediment volume supplied from coasts and rivers and the sediment transport rate. Consequently, many studies are being conducted to develop coastal-erosion reduction measures. To accurately determine the cause of coastal erosion, the causes of the erosion and deposition should be accurately diagnosed, and a comprehensive evaluation system for the sediment transport mechanism in the watershed and sea while considering regional characteristics is required. In particular, realizing the evaluation of the effective sediment volume that flows from the river to the sea through observations is a highly challenging task, and various research and developments are required to realize it, as it is still in the basic research stage. The purpose of this study was to systematically analyze the comprehensive sediment budget for coastal areas. First, an analytical system was developed. Then, a shoreline model was constructed by considering the size of the mixed particles. The parameters required for developing the model were determined using the observation data to improve the shoreline model. A sediment runoff model was applied to evaluate the effective sediment volume supplied from the river to the sea, and the applicability of this model was evaluated by comparing it with the sediment supply volume according to the soil and water assessment tool model. The representative wave and the input parameters of the model were set using the observation data of several years. It was found that the prediction performance of the shoreline change model improved when the effective sediment volume was considered, and the particles of the sediment on the shore were assumed to comprise multiple sizes. In particular, the prediction performance improved when the balance of the sediment budget was adjusted by applying a groin having a structurally similar performance to take into consideration the geographic features of the Deokbongsan (island) in front of the river mouth bar. The model demonstrated a good performance in reproducing long-term shoreline changes when the characteristics of the sea waves and the effective sediment volume were considered.

Using Transfer Learning to Build Physics-Informed Machine Learning Models for Improved Wind Farm Monitoring

This paper introduces a novel, transfer-learning-based approach to include physics into data-driven normal behavior monitoring models which are used for detecting turbine anomalies. For this purpose, a normal behavior model is pretrained on a large simulation database and is recalibrated on the available SCADA data via transfer learning. For two methods, a feed-forward artificial neural network (ANN) and an autoencoder, it is investigated under which conditions it can be helpful to include simulations into SCADA-based monitoring systems. The results show that when only one month of SCADA data is available, both the prediction accuracy as well as the prediction robustness of an ANN are significantly improved by adding physics constraints from a pretrained model. As the autoencoder reconstructs the power from itself, it is already able to accurately model the normal behavior power. Therefore, including simulations into the model does not improve its prediction performance and robustness significantly. The validation of the physics-informed ANN on one month of raw SCADA data shows that it is able to successfully detect a recorded blade angle anomaly with an improved precision due to fewer false positives compared to its purely SCADA data-based counterpart.

Influence of calcineurin inhibitors and genetic polymorphism of transporters on enterohepatic circulation and exposure of mycophenolic acid in Chinese adult renal allograft recipients

Aim: Study the influence of calcineurin inhibitors (CNI) and genetic polymorphisms of transporters on enterohepatic circulation (EHC) of mycophenolic acid (MPA) in Chinese adult renal allograft recipients and estimate the effect of various covariates on prediction performance of MPA AUC0-12h. Method: MPA concentrations of 125 Chinese patients were collected 0-12 hours after administration. Genotypes of transporters were determined in 64 patients. The influence of type of CNI and genetic polymorphisms on MPA exposure was studied. Shapley additive explanations method was used to study the impact of sampling times and covariates related to EHC on AUC0-12h. Extreme gradient boosting (XGboost) machine learning-based model was established to predict AUC0-12h. Results: Dn-AUC6-12h was significantly lower in patients co-administered with CsA (P<0.05). When co-administered with TAC, for SLCO1B1 T521C or ABCC C-24T, patients with wild-type genotype had significantly higher dn-AUC6-12h (P <0.05). Patients with SLCO1B3 334T/699G alleles had significantly lower dn-AUC6-12h than homozygotes (P=0.004). No significant difference was found in CsA subgroup. For estimating AUC0-12h, C0h, C2h, C8h, type of CNI, transporters genotypes and the difference between C0h and C2h were retained in the final model, which had good prediction performance (r2=0.9739). Conclusion: Patients co-administered with CsA had lower MPA EHC than those who received TAC. MPA EHC is affected by ABCC2 C-24T, SLCO1B3 T334G/G699A, and SLCO1B1 T521C genotypes in patients treated with TAC. Type of CNI and genetic polymorphisms of transporters can improve prediction performance of MPA AUC0-12h estimating model, developed using XGboost machine learning method.

GLAM: An adaptive graph learning method for automated molecular interactions and properties predictions

Improving drug discovery efficiency is a core and long-standing challenge in drug discovery. For this purpose, many graph learning methods have been developed to search potential drug candidates with fast speed and low cost. In fact, the pursuit of high prediction performance on a limited number of datasets has crystallized them, making them lose advantage in repurposing to new data generated in drug discovery. Here we propose a flexible method that can adapt to any dataset and make accurate predictions. The proposed method employs an adaptive pipeline to learn from a dataset and output a predictor. Without any manual intervention, the method achieves far better prediction performance on all tested datasets than traditional methods, which are based on hand-designed neural architectures and other fixed items. In addition, we found that the proposed method is more robust than traditional methods and can provide meaningful interpretability. Given the above, the proposed method can serve as a reliable method to predict molecular interactions and properties with high adaptability, performance, robustness and interpretability. This work would take a solid step forward to the purpose of aiding researchers to design better drugs with high efficiency.