Comparison of HLA ligand elution data and binding predictions reveals varying prediction performance for the multiple motifs recognized by HLA‐DQ2.5

Event-based social networks (EBSNs) connect online and offline lives. They allow online users with similar interests to get together in real life. Attendance prediction for activities in EBSNs has attracted a lot of attention and several factors have been studied. However, the prediction accuracy is not very good for some special activities, such as outdoor activities. Moreover, a very important factor, the weather, has not been well exploited. In this work, we strive to understand how the weather factor impacts activity attendance, and we explore it to improve attendance prediction from the organizer’s view. First, we classify activities into two categories: the outdoor and the indoor activities. We study the different ways that weather factors may impact these two kinds of activities. We also introduce a new factor of event duration. By integrating the above factors with user interest and user-event distance, we build a model of attendance prediction with the weather named GBT-W , based on the Gradient Boosting Tree. Furthermore, we develop a platform to help event organizers estimate the possible number of activity attendance with different settings (e.g., different weather, location) to effectively plan their events. We conduct extensive experiments, and the results show that our method has a better prediction performance on both the outdoor and the indoor activities, which validates the reasonability of considering weather and duration.

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Prediction of Healing Performance of Autogenous Healing Concrete Using Machine Learning

Materials ◽

10.3390/ma14154068 ◽

2021 ◽

Vol 14 (15) ◽

pp. 4068

Author(s):

Xu Huang ◽

Mirna Wasouf ◽

Jessada Sresakoolchai ◽

Sakdirat Kaewunruen

Keyword(s):

Machine Learning ◽

Search Algorithm ◽

Weather Conditions ◽

Prediction Performance ◽

Machine Learning Algorithms ◽

Coefficient Of Determination ◽

Gradient Boosting ◽

Support Vector ◽

Self Healing ◽

Artificial Neural Network Ann

Cracks typically develop in concrete due to shrinkage, loading actions, and weather conditions; and may occur anytime in its life span. Autogenous healing concrete is a type of self-healing concrete that can automatically heal cracks based on physical or chemical reactions in concrete matrix. It is imperative to investigate the healing performance that autogenous healing concrete possesses, to assess the extent of the cracking and to predict the extent of healing. In the research of self-healing concrete, testing the healing performance of concrete in a laboratory is costly, and a mass of instances may be needed to explore reliable concrete design. This study is thus the world’s first to establish six types of machine learning algorithms, which are capable of predicting the healing performance (HP) of self-healing concrete. These algorithms involve an artificial neural network (ANN), a k-nearest neighbours (kNN), a gradient boosting regression (GBR), a decision tree regression (DTR), a support vector regression (SVR) and a random forest (RF). Parameters of these algorithms are tuned utilising grid search algorithm (GSA) and genetic algorithm (GA). The prediction performance indicated by coefficient of determination (R2) and root mean square error (RMSE) measures of these algorithms are evaluated on the basis of 1417 data sets from the open literature. The results show that GSA-GBR performs higher prediction performance (R2GSA-GBR = 0.958) and stronger robustness (RMSEGSA-GBR = 0.202) than the other five types of algorithms employed to predict the healing performance of autogenous healing concrete. Therefore, reliable prediction accuracy of the healing performance and efficient assistance on the design of autogenous healing concrete can be achieved.

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Urinary exosomal CD26 is associated with recovery from acute kidney injury in intensive care units: a prospective cohort study

Clinical Chemistry and Laboratory Medicine (CCLM) ◽

10.1515/cclm-2021-0040 ◽

2021 ◽

Vol 0 (0) ◽

Author(s):

Juan Du ◽

Yihui Li ◽

Qiang Sun ◽

Zhihao Wang ◽

Feng Wang ◽

...

Keyword(s):

Acute Kidney Injury ◽

Cohort Study ◽

Prospective Cohort Study ◽

Prospective Cohort ◽

Kidney Injury ◽

Prediction Performance ◽

Control Cohort ◽

Clinical Factors ◽

Qilu Hospital ◽

Major Adverse Kidney Events

Abstract Objectives Currently there is no validated method to predict renal reversal and recovery after acute kidney injury (AKI). As exosomes have the potential for AKI prognosis and CD26 is involved in the mechanisms in AKI, this study aims to investigate whether urinary exosomal CD26 is associated with renal-related outcomes and explore its prospect as a novel prognosis biomarker. Methods This was a single-center, prospective cohort study. A total of 133 AKI patients and 68 non-AKI patients admitted to ICU in Qilu Hospital Shandong University from January 2017 to January 2018. Urine samples were collected at enrollment and the relative expression of CD26 (CD26 percentage) in urinary exosomes was examined, that was then categorized into a low-CD26 level and a high-CD26 level. Results CD26 percentage was significantly lower in the AKI cohort than in the control cohort. Within the AKI cohort, a high-CD26 level was associated with lower incidence of major adverse kidney events within 90 days, but higher incidence of reversal within 28 days. In AKI survivors, a high-CD26 level had a 4.67-, 3.50- and 4.66-fold higher odds than a low-CD26 level for early reversal, recovery and reversal, respectively, after adjustment for clinical factors. Prediction performance was moderate for AKI survivors but improved for non-septic AKI survivors. Conclusions Urinary exosomal CD26 is associated with renal reversal and recovery from AKI and is thus a promising prognosis biomarker.

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Recurrent neural networks with long term temporal dependencies in machine tool wear diagnosis and prognosis

SN Applied Sciences ◽

10.1007/s42452-021-04427-5 ◽

2021 ◽

Vol 3 (4) ◽

Author(s):

Jianlei Zhang ◽

Yukun Zeng ◽

Binil Starly

Keyword(s):

Neural Network ◽

Tool Wear ◽

Machine Tool ◽

Recurrent Neural Network ◽

Machine Tools ◽

Prediction Performance ◽

Sequential Data ◽

Diagnosis And Prognosis ◽

Proposed Model

AbstractData-driven approaches for machine tool wear diagnosis and prognosis are gaining attention in the past few years. The goal of our study is to advance the adaptability, flexibility, prediction performance, and prediction horizon for online monitoring and prediction. This paper proposes the use of a recent deep learning method, based on Gated Recurrent Neural Network architecture, including Long Short Term Memory (LSTM), which try to captures long-term dependencies than regular Recurrent Neural Network method for modeling sequential data, and also the mechanism to realize the online diagnosis and prognosis and remaining useful life (RUL) prediction with indirect measurement collected during the manufacturing process. Existing models are usually tool-specific and can hardly be generalized to other scenarios such as for different tools or operating environments. Different from current methods, the proposed model requires no prior knowledge about the system and thus can be generalized to different scenarios and machine tools. With inherent memory units, the proposed model can also capture long-term dependencies while learning from sequential data such as those collected by condition monitoring sensors, which means it can be accommodated to machine tools with varying life and increase the prediction performance. To prove the validity of the proposed approach, we conducted multiple experiments on a milling machine cutting tool and applied the model for online diagnosis and RUL prediction. Without loss of generality, we incorporate a system transition function and system observation function into the neural net and trained it with signal data from a minimally intrusive vibration sensor. The experiment results showed that our LSTM-based model achieved the best overall accuracy among other methods, with a minimal Mean Square Error (MSE) for tool wear prediction and RUL prediction respectively.

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