RFLMDA: A Novel Reinforcement Learning-Based Computational Model for Human MicroRNA-Disease Association Prediction

Numerous studies have confirmed that microRNAs play a crucial role in the research of complex human diseases. Identifying the relationship between miRNAs and diseases is important for improving the treatment of complex diseases. However, traditional biological experiments are not without restrictions. It is an urgent necessity for computational simulation to predict unknown miRNA-disease associations. In this work, we combine Q-learning algorithm of reinforcement learning to propose a RFLMDA model, three submodels CMF, NRLMF, and LapRLS are fused via Q-learning algorithm to obtain the optimal weight S. The performance of RFLMDA was evaluated through five-fold cross-validation and local validation. As a result, the optimal weight is obtained as S (0.1735, 0.2913, 0.5352), and the AUC is 0.9416. By comparing the experiments with other methods, it is proved that RFLMDA model has better performance. For better validate the predictive performance of RFLMDA, we use eight diseases for local verification and carry out case study on three common human diseases. Consequently, all the top 50 miRNAs related to Colorectal Neoplasms and Breast Neoplasms have been confirmed. Among the top 50 miRNAs related to Colon Neoplasms, Gastric Neoplasms, Pancreatic Neoplasms, Kidney Neoplasms, Esophageal Neoplasms, and Lymphoma, we confirm 47, 41, 49, 46, 46 and 48 miRNAs respectively.

Practical health indicator construction methodology for bearing ensemble remaining useful life prediction with ISOMAP-DE and ELM-WPHM

To improve the accuracy of our previous bearing ensemble Remaining Useful Life (RUL) prediction model using the Genetic Algorithm (GA), Support Vector Regression (SVR), and the Weibull Proportional Hazard Model (WPHM) (see reference [1]), we proposed a more practical Health Indicator (HI) construction methodology for bearing ensemble RUL prediction. A weighted coefficient determination method for four prognostic metrics-monotonicity, robustness, trendability, and consistency-was proposed to select sensitive health features accurately using the Analytic Hierarchy Process (AHP). The selected sensitive health features were fused through isometric feature mapping (ISOMAP), and Differential Evolution (DE) was employed to replace GA for computing the optimal weight coefficients of each input fused feature. One-dimensional HI was constructed by multiplying each input fused feature with the corresponding optimal weight coefficient, and RUL prediction was implemented through an extreme learning machine (ELM) and WPHM. The accuracy and effectiveness of the proposed method were validated by a bearing experiment. The results show that HI construction with ISOMAP-DE has achieved the best performance, and the proposed ELM-WPHM model is compared with BP-WPHM, SVM-WPHM, LSTM-WPHM, and DLSTM-WPHM in terms of RMSE criteria. The minimum error and training time appear in ELM-WPHM, indicating the superiority of the proposed bearing ensemble RUL prediction model.

Effect of Al2O3 nanoparticles on the mechanical behaviour of aluminium-based metal matrix composite synthesized via powder metallurgy

The effects of variation in aluminium oxide nanoparticles in aluminium-based metal matrix composite on the compressive and sliding wear deformation have been investigated. The compressive and sliding wear resistance of the composite increase significantly with the addition of nanoparticles in the matrix. The 5% aluminium oxide nanoparticles in the composite were found to be the optimal weight fraction of added nanoparticles that produced higher static yield strength, hardness, scratch resistance and lower material loss in wear in the composite. The addition of nanoparticles, beyond 5% weight fraction, in the matrix showed adverse effects in the performance of the composite due to its higher brittleness. The effects on wear properties of the composite with added nanoparticles beyond optimal weight fraction were more detrimental than those with lower weight fraction of nanoparticles.

Iterative learning control for consensus of second-order multi-agent systems with interval uncertain topologies

This paper is devoted to the robust consensus tracking problem of second-order nonlinear multi-agent systems (MASs) with the interval uncertain topologies. For the second-order MASs including one leader agent and multiple follower agents, a control protocol is proposed by combining the iterative learning control scheme with the sliding mode control method. By analyzing the convergence of sliding mode variables, the consensus conditions including the unknown eigenvalues and the undetermined weight coefficient are obtained. In order to deal with the difficulties of weight coefficient design caused by the unknown eigenvalues of graphs, a min-max optimization problem is formulated based on the fastest convergence of the λ-norm of sliding mode variables, then the optimal weight coefficient is obtained by solving the min-max optimization problem. Moreover, for the undirected and directed interval uncertain graphs, two algorithms about the optimal weight coefficients are proposed, respectively. Finally, three numerical simulation examples are presented to demonstrate the effectiveness of the proposed methods.

A Decomposition-Based Multiobjective Optimization Evolutionary Algorithm with Adaptive Weight Generation Strategy

Multiobjective evolutionary algorithm based on decomposition (MOEA/D) is the seminal framework of multiobjective evolutionary algorithms (MOEAs). To alleviate the nonuniformly distributed solutions generated by a fixed set of evenly distributed weight vectors in the presence of nonconvex and disconnected problems, an adaptive vector generation mechanism is proposed. A coevolution strategy and a vector generator are synergistically cooperated to remedy the weight vectors. Optimal weight vectors are generated to replace the useless weight vectors to assure that optimal solutions are distributed evenly. Experiment results indicate that this mechanism is efficient in improving the diversity of MOEA/D.

Decision Support System for Tour Package Recommendation in Bali Using BWM-MARCOS Method

Bali is one of the provinces with profitable tourism opportunities. It has led to many businesses related to tourism, which is a travel agent. Travel agents in Bali usually offer variety of tour packages with different prices and specifications. The problem experienced by tourists in determining tour packages is that the price of tour packages is quite high and does not match the tourist budget. In addition, the schedule of visits from tour packages is also inflexible. This problem can be overcome by making a decision support system for forming tour packages. This study uses the BWM method to determine each criterion’s optimal weight and the MARCOS to rank alternative tourism objects that will form a tour package. Testing results using confusion matrix get an accuracy value of 74.19%, precision of 81.25%, recall / sensitivity of 72.22% and specificity of 76.92%.

KESETIMBANGAN ADSORPSI ISOTERMIS LOGAM Cr (VI) PADA FLY ASH

The Purpose of this study was to determine the optimum adsorption time of fly ash from the PT. Pupuk Kaltim in adsorbing Cr metal, optimal stirring speed, and optimal weight of fly ash under isothermic conditions in batches using the Langmuir equation has been carried out at atmospheric conditions, temperature 27oC. Fly ash varies in weight and stirring speed. The filtrate was added with 2 mL of 0.1% NH4Cl after equilibrium, then analyzed using AAS AA-7000. The optimal adsorption time of fly ash in adsorbing Cr metal is 60 minutes. The optimal adsorption weight of fly ash is in 50 mL of Cr solution is 2 grams with an average efficiency value of 91.22%. The optimum stirring speed of fly ash to adsorb Cr metal is 200 rpm. Keywords: isothermic adsorption, fly ash, Cr metal