spVelo: RNA velocity inference for multi-batch spatial transcriptomics data by Wenxin Long, Tianyu Liu, Lingzhou Xue, Hongyu Zhao

“…However, current methods fail to utilize the spatial information inherent in spatial transcriptomics and lack scalability in multi-batch datasets. Here, we introduce spVelo, a scalable framework for RNA velocity inference of multi-batch spatial transcriptomics data. spVelo supports several downstream applications, including uncertainty quantification, complex trajectory pattern discovery, driver marker identification, gene regulatory network inference, and temporal cell-cell communication inference. spVelo has the potential to provide deeper insights into complex tissue organization and underscore biological mechanisms based on spatially resolved patterns.…”
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IHDETBO: A Novel Optimization Method of Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing by Li-Nan Zhu, Peng-Hang Li, Xiao-Long Zhou

“…In this paper, a new manufacturing service composition scheme named as Multi-Batch Subtasks Parallel-Hybrid Execution Cloud Service Composition for Cloud Manufacturing (MBSPHE-CSCCM) is proposed, and such composition is one of the most difficult combination optimization problems with NP-hard complexity. …”
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scFocus: Detecting branching probabilities in single-cell data with SAC by Chunlin Chen, Zeyu Fu, Jiajia Yang, Huaqing Chen, Jiabao Huang, Shitian Qin, Chuhuai Wang, Xiaoqian Hu

“…Furthermore, scFocus is applied to ten single-cell datasets, including small-scale datasets, common-scale datasets, and multi-batch datasets. This demonstrates its applicability on different types of datasets and showcases its potential in discovering biological changes due to experimental treatments through multi-batch dataset processing. …”
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Partially characterized topology guides reliable anchor-free scRNA-integration by Chuan He, Paraskevas Filippidis, Steven H. Kleinstein, Leying Guan

“…To address this, we propose scCRAFT, an autoencoder model that separates cell-type-related signals from batch effects for reliable multi-batch integration. scCRAFT integrates three key loss components: a reconstruction loss for observation reconstruction, a multi-domain adaptation loss to eliminate batch effects, and an innovative dual-resolution triplet loss to preserve intra-batch, introduced as an effective mechanism to counteract the over-correction effect of domain adaptation loss amid heterogeneous cell distributions across batches. …”
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Modeling and Optimization of Cable Production Scheduling by Incorporating an Ant Colony Algorithm by Changbiao Zhu, Chongxin Wang, Zhonghua Ni, Xiaojun Liu, Abbas Raza

“…With the development of small batch and multi-batch service production mode, manual scheduling by hand has been difficult to adapt to the production of a large number of complex orders. …”
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AML-Based Multi-Dimensional Co-Evolution Approach Supported by Blockchain: Architecture Design and Case Study on Intelligent Production Lines for Industry 4.0 by Kai Ding, Detlef Gerhard, Liuqun Fan

“…Its innovations are, firstly, developing a comprehensive two-layer management model, combining blockchain with the Interplanetary File System (IPFS) to build an integrated solution for private and public hybrid containers based on a collaborative model; secondly, designing a version co-evolution management method by combining smart contract workflows and AML multi-dimensional modeling processes; meanwhile, introducing a specially designed conflict resolution mechanism based on the graph model to maintain consistency in version multi-batch management and; finally, using the test cases established in the lab’s I5Blocks for verification.…”
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scRDEN: single-cell dynamic gene rank differential expression network and robust trajectory inference by Han Zhang, Wei Zhang, Xiaoying Zheng, Yuanyuan Li

“…This outstanding performance provides novel and valuable insights into large-scale, multi-batch trajectory inference and the study of transcriptional mechanism regulation during the processes of differentiation and development.…”
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micRoclean: an R package for decontaminating low-biomass 16S-rRNA microbiome data by Rachel Griffard-Smith, Emily Schueddig, Diane E. Mahoney, Prabhakar Chalise, Devin C. Koestler, Dong Pei

“…Further, we demonstrate through a multi-batch simulated microbiome sample that micRoclean matches or outperforms tools with similar objectives. …”
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