Characterization and purification of fatty acid methyl esters from the liver oil of the deep sea shark (Centrophorus squamosus) by gas chromatography-mass spectrometry and countercurrent chromatography

Summary In the course of our studies on the location of marine products, we have directed our attention to the saponifiable fraction of the liver oil from the deep sea shark (Centrophorus squamosus). Different analytical and preparative techniques, such as high-performance liquid chromatography (HPLC), countercurrent chromatography (CCC), and gas chromatography-mass spectrometry [GC-MS, electron impact (EI) and positive-ion chemical ionization (PCI)] were used. These methods allowed us to characterize, after various derivatizations, most of the fatty acids formed after the saponification of the oil, and to purify two of them, namely: docosahexaenoic acid (DHA or 226) and docosamonoenoic acid (DMA or 221).     

Magnetic penetration depth in V3Si and LiTi2O4 measured by μSR

Muon spin relaxation (SR) studies have been performed in the normal spinel LiTi₂O₄ and the A-15 superconductor V₃Si to measure the magnetic penetration depth . The relaxation rate(T) 1/² in field-cooled measurements shows a sharp increase belowT _c followed by saturation at low temperatures in both systems. This feature implies an isotropic energy gap without anomalous zeros, and most likelys-wave pairing. The low temperature penetration depth (T 0) is determined to be 2100Å for LiTi₂O₄ and 1300Å for V₃Si respectively. Assuming a clean limit relation ^–2 n _s /m ^*, we derive the Fermi temperatureT _F n _s/ ^2/3 m ^* from the relaxation rate and the Sommerfeld constant asT _F ^3/4–1/4. Unlike conventional superconductors, both LiTi₂O₄ and V₃Si have a large ratio ofT _c /T _F 0.01, only slightly smaller than those ratios in more exotic superconductors.We thank C. Ballard and K. Hoyle for technical assistance. Work at Columbia University is supported by NSF Grant No. DMR-89-13784 and Packard Foundation (YJU). Ames Laboratory is operated for the U. S. Department of Energy by Iowa State University under Contract No. W-7405-Eng-82. Work at Ames was supported by the Director for Energy Research, Office of Basic Energy Sciences.     

Air-Stable Li2S Cathode for Quasi-Solid-State Anode-Free Batteries with High Volumetric Energy

Anode-free batteries can maximize the energy density but their development is hindered by a lack of Li-rich cathodes for compensating the irreversible Li loss. Li₂S cathode is particularly appealing to this desire due to 2.6–4.7 folds more Li content and 4.2–6.8 times higher capacity than conventional intercalation cathodes. But its practical application is hindered by poor stability against moisture attacking in the air. Herein, a facile expendable polymer sheathing strategy toward air-stable Li₂S cathodes with high capacities for developing high-performance quasi-solid-state anode-free batteries without risk of cell leakage is reported. Tight protection by dense polymer barrier dramatically prolongs the lifetime of Li₂S cathode by 2,000 times at least in the air. Such air-stable Li₂S cathode allows for high compatibility of anode-free battery production with commercial schemes. More attractively, the polymer protective layer can in situ transform to multifunctional gel polymer electrolyte for releasing ionic pathways and enhancing cell performance by inhibiting LiPS loss and smoothing Li plating. With air-stable Li₂S cathode, the quasi-solid-state anode-free cells are assembled in ambient environment to deliver superb volumetric energy density of 1093 Wh L⁻¹. This study may shed new light to push the commercialization of high-energy and reliable anode-free batteries forward.     

基于AI的全场景智能基站节电系统设计与实现

摘 要：在集约化网络的建设背景下,如何实现自动化、智能化、保体验的基站能耗管理,成为当前运营商的运营管理痛点。创新地提出一种全智能检测、全场景建模、全流程自助的3G/4G/5G智能基站节电方案,通过动态时间规整算法区分覆盖场景,利用SARIMA模型预测时间框构建自适应模型,实时监控指标保证用户感知,自动下发节电策略,短信告警及时拉起。在用户无感知的情况下,实现小区粒度级最大限度节约基站能耗。该方法已在某省网络试点推行,试点区域单站平均节电效率可达9.24%每日,具备实际生产指导意义。     

基于多元高斯混合模型的离线指纹数据库

针对室内环境下单次采样测量值的波动变化及信号间的相互干扰,该文提出一种基于分区多元高斯混合模型(MVGMM)的室内定位系统.根据信号接入点(AP)铺设位置与空间结构,系统采用一对多支持向量机算法对目标区域做分区操作,以精确信号变化的区域范围.利用狭小分区内信号间的耦合关系,建立基于信号间相互干扰的多元高斯混合模型,以改善信号波动所造成的定位精度下降.当室内环境发生变化时,基于分区多元高斯混合模型的自适应更新算法可对各分区指纹数据的可信度做出判断,并以自适应算法更新信号波动较大分区的模型参数,提高模型与现有环境间的耦合程度.实验结果表明,该文算法可利用相对少量样本数据,构建稳定可维护的室内信号分布模型,相较于其他算法,其定位精度也有一定程度提高.