This letter considers optimizing user association in a heterogeneous network via utility maximization, which is a combinatorial optimization problem due to integer constraints. Different from existing solutions based on convex optimization, we alternatively propose a cross-entropy (CE)-based algorithm inspired by a sampling approach developed in machine learning. Adopting a probabilistic model, we first reformulate the original problem as a CE minimization problem which aims to learn the probability distribution of variables in the optimal association. An efficient solution by stochastic sampling is introduced to solve the learning problem. The integer constraint is directly handled by the proposed algorithm, which is robust to network deployment and algorithm parameter choices. Simulations verify that the proposed CE approach achieves near-optimal performance quite efficiently.

Reconfigurable intelligent surfaces (RISs) can shape the wireless environment for enhancing the communication performance. In this letter, we propose a cooperative multi-RIS assisted transmission scheme for a millimeter-wave multi-antenna orthogonal frequency division multiplexing system. We first put forward a delay matching based scheme for simultaneously estimating the multipath channels and the transmission delays of distributed RISs, which requires limited training overhead and feedback. Based on this scheme, we obtain a closed-form solution for the RIS phase shift. Then, we derive an analytical expression of the downlink rate, which is proved to increase logarithmically with the square number of RIS reflecting elements. Simulations are conducted to verify these observations.

Backgroud: PIWI-interacting RNAs (piRNAs) are a kind of small non-coding RNAs which interact with PIWI proteins and play a vital role in safeguarding genome. Single nucleotide polymorphisms (SNPs) are widely distributed variations which are associated with diseases and have rich information. Up to now, various studies have proved that SNPs on piRNA were related to diseases. Objective: In order to create a comprehensive source about piRNA-related SNPs, we developed a publicly available online database piRSNP. Methods: We systematically identified SNPs on human and mouse piRNAs. piRSNP contains 42,967,522 SNPs on 10,773,081 human piRNAs and 29,262,185 SNPs on 16,957,706 mouse piRNAs. Results: 7,446 SNPs on 519 cancer-related piRNAs and their flanks are investigated. Impacts of 2,512 variations of cancer-related piRNAs on piRNA-mRNA interactions are analyzed. Conclusion: Abstract: Backgroud: PIWI-interacting RNAs (piRNAs) are a kind of small non-coding RNAs which interact with PIWI proteins and play a vital role in safeguarding genome. Single nucleotide polymorphisms (SNPs) are widely distributed variations which are associated with diseases and have rich information. Up to now, various studies have proved that SNPs on piRNA were related to diseases. Objective: In order to create a comprehensive source about piRNA-related SNPs, we developed a publicly available online database piRSNP. Methods: We systematically identified SNPs on human and mouse piRNAs. piRSNP contains 42,967,522 SNPs on 10,773,081 human piRNAs and 29,262,185 SNPs on 16,957,706 mouse piRNAs. Results: 7,446 SNPs on 519 cancer-related piRNAs and their flanks are investigated. Impacts of 2,512 variations of cancer-related piRNAs on piRNA-mRNA interactions are analyzed. Conclusion: All these useful data and piRNA expression profiles of 12 cancer types in both tumor and pericarcinomatous tissues are compiled into piRSNP. piRSNP characterizes human and mouse piRNA-related SNPs comprehensively and could be beneficial for researchers to investigate subsequent piRNA functions. Database URL is http://www.ibiomedical.net/piRSNP/.

Railway Point Machines (RPMs) condition monitoring has attracted engineers’ attention due to safe train operation and accident prevention. To realize the fast and accurate fault diagnosis of RPMs, this paper proposes a method based on entropy measurement and Broad Learning System (BLS). Firstly, the Modified Multi-scale Symbolic Dynamic Entropy (MMSDE) module extracts dynamic characteristics from the collected acoustic signals as entropy features. Then the Fuzzy BLS takes the above entropy features as input to complete model training. Fuzzy BLS introduces Takagi-Sugeno fuzzy system into BLS, which improves the model’s classification performance while considering computational speed. Experimental results indicate that the proposed method significantly reduces the running time while maintaining high accuracy.

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