Development and characterization of microsatellite loci for Fenneropenaeus penicillatus Alcock

Eight novel microsatellite loci from the genome of Fenneropenaeus penicillatus Alcock were developed using the protocol of fast isolation by amplified fragment length polymorphism of sequences containing repeats (FIASCO). Thirty (30) wild individuals were used to analyze the polymorphism of these eight microsatellite markers. The results show that the number of alleles per locus and the polymorphism information content ranged from 2 to 7 and from 0.2076 to 0.7484, respectively. The observed and expected heterozygosity were 0.1724 to 0.9130 and 0.1639 to 0.7314, respectively. These microsatellite primers will be used for further population genetic studies, constructing genetic linkage maps or locating quantitative trait locus (QTL) of F. penicillatus Alcock.Keywords: Genetic markers, Fenneropenaeus penicillatus Alcock, microsatellite

Multi-Level Data-Driven Battery Management: From Internal Sensing to Big Data Utilization

Battery management system (BMS) is essential for the safety and longevity of lithium-ion battery (LIB) utilization. With the rapid development of new sensing techniques, artificial intelligence and the availability of huge amounts of battery operational data, data-driven battery management has attracted ever-widening attention as a promising solution. This review article overviews the recent progress and future trend of data-driven battery management from a multi-level perspective. The widely-explored data-driven methods relying on routine measurements of current, voltage, and surface temperature are reviewed first. Within a deeper understanding and at the microscopic level, emerging management strategies with multi-dimensional battery data assisted by new sensing techniques have been reviewed. Enabled by the fast growth of big data technologies and platforms, the efficient use of battery big data for enhanced battery management is further overviewed. This belongs to the upper and the macroscopic level of the data-driven BMS framework. With this endeavor, we aim to motivate new insights into the future development of next-generation data-driven battery management

6,6-Dimethyl-2H,5H,6H,7H-1,3-dithiolo[4,5-f][1,5,3]dithia­silepin-2-one

In the structure of the title compound, C7H10OS4Si, the carbonyl O atom lies in the plane of the five-membered dithiole ring with a deviation of only 0.022 (2) Å. The seven-membered ring adopts a chair conformation. The crystal packing is stabilized by S⋯O [3.096 (4) Å] and S⋯S [3.620 (4) Å] contacts, together with C—H⋯S inter­actions