To solve wind power volatility and hybrid energy storage optimization, this study proposes a two-layer optimal scheduling model integrating frequency decomposition. Firstly, Crown Porcupine Optimized Variational Mode Decomposition processes wind power signals into grid-connected direct output and hybrid energy storage references. These references are further decomposed into low and high frequency components managed by lithium batteries and supercapacitors. A two-layer optimization framework is established: the upper layer employs particle swarm optimization for cost-effective capacity and power configuration, while the lower layer provides parameter feedback through real-time scheduling. Case studies demonstrate the method's effectiveness in smoothing wind power fluctuations while maintaining economic benefits.

Implicit Discourse Relation Recognition (IDRR), which infers discourse logical relations without explicit connectives, is one of the most challenging tasks in natural language processing (NLP). Recently, pre-trained language models (PLMs) have yielded impressive results across numerous NLP tasks, but their performance still remains unsatisfactory in IDRR. We argue that prior studies have not fully harnessed the potential of PLMs, thereby resulting in a mixture of logical semantics, which determine the logical relations between discourse arguments, and general semantics, which encapsulate the non-logical contextual aspects (detailed in Sec.1). Such a mixture would inevitably compromise the logic reasoning ability of PLMs. Therefore, we propose a novel method that trains the PLMs through two semantics enhancers to implicitly differentiate logical and general semantics, ultimately achieving logical semantics enhancement. Due to the characteristic of PLM in word representation learning, these two semantics enhancers will inherently confront with each other, facilitating an augmentation of logical semantics by disentangling them from general semantics. The experimental results on PDTB 2.0 dataset show that the confrontation approach exceeds our baseline by 3.81% F1 score, and the effectiveness of the semantics confrontation method is validated by comprehensive ablation experiments.

Time-sensitive network (TSN) has emerged as one of the enhancement Ethernet technologies of real-time applications for future industrial networks. However, the data of TSN messages may be unexpectedly changed within the transmission duration due to electromagnetic interference. A cyclic redundancy check (CRC) can detect such errors and notify the sender node to retransmit the erroneous message through automatic repeat request (ARQ). The time when the error occurs is uncertain. Consequently, the time when performing the retransmission requests is also random, which results in different transmission sequences and violates the deterministic transmission of TSN. In addition, the returned CRC detection messages also might be at fault during transmission. In order to provide the fault tolerance capability of critical flows in TSN without violating the nature of deterministic transmission, this article proposes a pro-active fault-tolerant TSN scheduling algorithm (PFT-TSN), which not only transmits a certain number of instance copies for critical flows but provides network transmission services for noncritical flows. The challenge is that schedulability and safety of critical flows are tradeoffs in terms of the number of transmissions per instance of critical flows and the schedulability. Moreover, the transmission service of noncritical flows can also affect the schedulability of critical flows. We conduct comprehensive experiments with synthetic networks to show that the proposed method can meet both safety and schedulability requirements for critical flows, and noncritical flows are also allowed to be transmitted by the service degradation.

Circularized Chromosome Conformation Capture followed by deep sequencing (4C-Seq) is a powerful technique to identify genome-wide partners interacting with a pre-specified genomic locus. Here, we present a computational and statistical approach to analyze 4C-Seq data generated from both enzyme digestion and sonication fragmentation-based methods. We implemented a command line software tool and a web interface called w4CSeq, which takes in the raw 4C sequencing data (FASTQ files) as input, performs automated statistical analysis and presents results in a user-friendly manner. Besides providing users with the list of candidate interacting sites/regions, w4CSeq generates figures showing genome-wide distribution of interacting regions, and sketches the enrichment of key features such as TSSs, TTSs, CpG sites and DNA replication timing around 4C sites. AVAILABILITY AND IMPLEMENTATION: Users can establish their own web server by downloading source codes at https://github.com/WGLab/w4CSeq Additionally, a demo web server is available at http://w4cseq.wglab.org CONTACT: [email protected] or [email protected] information: Supplementary data are available at Bioinformatics online.