rSeqTU—A Machine-Learning Based R Package for Prediction of Bacterial Transcription Units

A transcription unit (TU) is composed of one or multiple adjacent genes on the same strand that are co-transcribed in mostly prokaryotes. Accurate identification of TUs is a crucial first step to delineate the transcriptional regulatory networks and elucidate the dynamic regulatory mechanisms encoded in various prokaryotic genomes. Many genomic features, for example, gene intergenic distance, and transcriptomic features including continuous and stable RNA-seq reads count signals, have been collected from a large amount of experimental data and integrated into classification techniques to computationally predict genome-wide TUs. Although some tools and web servers are able to predict TUs based on bacterial RNA-seq data and genome sequences, there is a need to have an improved machine learning prediction approach and a better comprehensive pipeline handling QC, TU prediction, and TU visualization. To enable users to efficiently perform TU identification on their local computers or high-performance clusters and provide a more accurate prediction, we develop an R package, named rSeqTU. rSeqTU uses a random forest algorithm to select essential features describing TUs and then uses support vector machine (SVM) to build TU prediction models. rSeqTU (available at https://s18692001.github.io/rSeqTU/) has six computational functionalities including read quality control, read mapping, training set generation, random forest-based feature selection, TU prediction, and TU visualization

Dynamic Responses of Continuous Girder Bridges with Uniform Cross-Section under Moving Vehicular Loads

To address the drawback of traditional method of investigating dynamic responses of the continuous girder bridge with uniform cross-section under moving vehicular loads, the orthogonal experimental design method is proposed in this paper. Firstly, some empirical formulas of natural frequencies are obtained by theoretical derivation and numerical simulation. The effects of different parameters on dynamic responses of the vehicle-bridge coupled vibration system are discussed using our own program. Finally, the orthogonal experimental design method is proposed for the dynamic responses analysis. The results show that the effects of factors on dynamic responses are dependent on both the selected position and the type of the responses. In addition, the interaction effects between different factors cannot be ignored. To efficiently reduce experimental runs, the conventional orthogonal design is divided into two phases. It has been proved that the proposed method of the orthogonal experimental design greatly reduces calculation cost, and it is efficient and rational enough to study multifactor problems. Furthermore, it provides a good way to obtain more rational empirical formulas of the DLA and other dynamic responses, which may be adopted in the codes of design and evaluation

Learning-aided UAV-cooperation reduces the age-of-information in wireless networks

Unmanned aerial vehicles (UAVs) can enhance data collection for ground sensing nodes (SNs). Given the modest battery capacity of UAVs and the limited communication range of SNs, it is crucial to conceive efficient trajectory coordination for UAVs. However, existing studies simply decouple the joint trajectory planning policy of multiple UAVs into independent local policies, preventing their cooperation and hence limits the performance. Inspired by the observation that sharing messages among agents can promote their cooperation, we investigate the communication-assisted decentralized trajectory planning policy of multi-UAV wireless networks. Our goal is to minimize the overall energy consumption of UAVs and the average age of information of all SNs. To harness the encoded messages for learning a sophisticated policy, we conceive a communicationassisted distributed training and execution framework, and propose a communication-aided decentralized trajectory control algorithm. Our simulation results show that the proposed algorithm substantially outperforms the state-of-the-art deep reinforcement learning based methods, at a modest communication overhead

Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis

Extracting weak fault features from noisy measured signals is critical for the diagnosis of wind turbine faults. In this paper, a novel group-sparse feature extraction method via an ensemble generalized minimax-concave (GMC) penalty is proposed for machinery health monitoring. Specifically, the proposed method tackles the problem of formulating large useful magnitude values as isolated features in the original GMC-based sparse feature extraction method. To accurately estimate group-sparse fault features, the proposed method formulates an effective unconstrained optimization problem wherein the group-sparse structure is incorporated into non-convex regularization. Moreover, the convex condition is proved to maintain the convexity of the whole formulated cost function. In addition, the setting criteria of the regularization parameter are investigated. A simulated signal is presented to verify the performance of the proposed method for group-sparse feature extraction. Finally, the effectiveness of the proposed group-sparse feature extraction method is further validated by experimental fault diagnosis cases

Group-Sparse Feature Extraction via Ensemble Generalized Minimax-Concave Penalty for Wind-Turbine-Fault Diagnosis

Extracting weak fault features from noisy measured signals is critical for the diagnosis of wind turbine faults. In this paper, a novel group-sparse feature extraction method via an ensemble generalized minimax-concave (GMC) penalty is proposed for machinery health monitoring. Specifically, the proposed method tackles the problem of formulating large useful magnitude values as isolated features in the original GMC-based sparse feature extraction method. To accurately estimate group-sparse fault features, the proposed method formulates an effective unconstrained optimization problem wherein the group-sparse structure is incorporated into non-convex regularization. Moreover, the convex condition is proved to maintain the convexity of the whole formulated cost function. In addition, the setting criteria of the regularization parameter are investigated. A simulated signal is presented to verify the performance of the proposed method for group-sparse feature extraction. Finally, the effectiveness of the proposed group-sparse feature extraction method is further validated by experimental fault diagnosis cases

Study on the Sulfuration Mechanism of Concrete: Microstructure and Product Analysis

This paper presents an experimental investigation of the sulfuration mechanism of concrete. The microstructure, mineral phase composition, substance content, and pH of the concrete were determined using scanning electron microscopy, X-ray diffraction, comprehensive thermal analysis, and pore-solution pH test. It was observed that light-grey spots appeared on the surface of the specimen, and a large amount of powdery precipitated substances appeared. At the initial stage of sulfuration reaction, the formation of ettringite blocked the concrete pores and densified its cracks and voids. Subsequently, ettringite reacted with H+ to form gypsum, and the continuous increase in gypsum in the pores increased the number of cracks and broadened their width. Gypsum was the final product of the sulfuration reaction, and the mass percentage of gypsum in the powdery precipitated substances at different water–cement ratios was more than 50%. When the water–cement ratios was 0.37, 0.47, and 0.57, the highest Ca(OH)2 content was found for the lowest water–cement ratio. As the water–cement ratios increased, the amount of powdery precipitated substances decreased and the CaCO3 content and pH increased

Communication-assisted multi-agent reinforcement learning improves task-offloading in UAV-aided edge-computing networks

Equipping unmanned aerial vehicles (UAVs) with computing servers allows the ground-users to offload complex tasks to the UAVs, but the trajectory optimization of UAVs is critical for fully exploiting their maneuverability. Existing studies either employ a centralized controller having prohibitive communication overhead, or fail to glean the benefits of interaction and coordination among agents. To circumvent this impediment, we propose to intelligently exchange critical information among agents for assisting their decision-making. We first formulate a problem for maximizing the number of offloaded tasks and the offloading fairness by optimizing the trajectory of UAVs. We then conceive a multi-agent deep reinforcement learning (DRL) framework by harnessing communication among agents, and design a communication-assisted decentralized trajectory control algorithm based on value-decomposition networks (VDN) for fully exploiting the benefits of messages exchange among agents. Simulation results demonstrate the superiority of the proposedalgorithm over the state-of-the-art DRL-based algorithms