A new penaeoid shrimp collected from the Middle Triassic Member II of the Guanling Formation in the vicinity of the city of Luxi, Yunnan, southwest China, is a new species, Aeger luxii n. sp. The new species possesses prominent spinose third maxillipeds, which is one of the typical characteristics of Aeger . The new species differs from the type species, Aeger tipularius from the Jurassic Solnhofen Plattenkalk, in having a long, smooth rostrum with no subrostral spines. The new taxon increases the diversity of Chinese decapods, and further expands our knowledge of the phylogeny and evolution of the Mesozoic decapods. The find is the first complete specimen of Aeger in the Middle Triassic, and reveals a close biogeographic connection of the marine ecosystem between Eastern and Western Tethys.
With the large-scale application of LiFePO4 (LFP) in energy storage, recycling spent lithium LFP batteries has drawn more interest. However, the low economic value of Fe products makes recycling spent LFP less economically viable, which presents a problem for both efficiency and economic challenge. This work proposes a highly economical acid-free mechanochemical approach for the efficient and selective extraction of lithium (Li) from spent LFP. The selective release of 98.9% of Li from the LFP crystal structure is achieved at a reaction time of 5 h, a rotational speed of 500 rpm, and sodium citrate (Na3Cit) to LFP mass ratio of 10. Meanwhile, Fe is reserved in the form of FePO4 in the olivine structure. The use of Na3Cit as a co-milling agent ensures a pollution-free recovery process and efficient extraction of Li+. The chelation of Li+ with organic ligands (Cit3- ) is the key to the efficient selective recovery of Li + from the olivine LFP structure through a mechanochemical process. The economic analysis indicates that the method is feasible and guarantees industrial viability. The acid-free mechanochemical (MC) process reported in this work provides a novel route to selectively recover Li from spent LFP efficiently and highly economically.
Wireless sensing technology based on channel state information (CSI) has broad application prospects in human–computer interaction, smart homes, and other fields due to its advantages, which include no special equipment deployment, no privacy leakage, and no light intensity and line of sight influence. The existing studies have achieved satisfactory recognition accuracy for human localization or activity information. However, many applications need to recognize not only the activity of humans but also the location of humans. Therefore, multidimensional information recognition for human targets has become an urgent problem to be solved. For this problem, a multidimensional information recognition algorithm for human targets based on CSI decomposition (called the CD-MDIR algorithm) is proposed. Specifically, we first decompose the CSI time series into dynamic location CSI (DLC) components affected by human location and dynamic activity CSI (DAC) components affected by human activity, according to the independent characteristics of the influence of human location and activity on CSI. Then, the linear discriminant analysis (LDA) algorithm is used to transform the DLC component to enhance the location information, and the features of the DAC component are extracted to enhance the activity information. Finally, we designed a long short-term memory (LSTM) multidimensional information recognition network that successively recognizes the location and activity information of humans. Experimental results show that the proposed CD-MDIR algorithm achieves both higher localization and activity recognition accuracy.
We demonstrate generation and dynamical control of self-accelerating beams simply using tilted UV-resin pendant-droplets. The curved trajectories of such large-angle self-bending beams are directly observed through scattering of yeast-cell suspensions, in excellent agreement with simulations.
To address the issues of inadequate fault diagnosis accuracy and suboptimal generalisation performance of rolling bearings in the presence of significant noise and varying operational conditions, a fault diagnosis approach utilising a dual-stream interactive convolutional neural network (DSICNN) is presented. To fully leverage the fault characteristics in vibration signals, both time-domain and frequency-domain signals are concurrently employed as inputs to the neural network. Then, dual attention mechanisms are presented, among which the Dynamic Weighted Channel Attention Mechanism (DWCAM) dynamically calibrates the channel weights of different inputs based on the importance of different channels, and the Aggregated Spatial Self Attention Mechanism (ASSAM) assigns greater weights to important regions while enhancing feature expression ability. Meanwhile, an arctangent linear unit function (AT-LU) is constructed to improve the problem of information loss contained in the signal when the linear rectifier unit has negative input. Finally, the signals are input into DSICNN, and the features are extracted more fully by interacting with two information streams. The model is trained and evaluated against other fault diagnosis models. The experimental results indicate that the suggested method exhibits superior classification performance, generalisation capability and robustness in the presence of significant noise and varying operating conditions.
Development of power packs suspended under the diesel train towards high power and high integration has been driven by the development of technology. The wind environment of the railway lines is complex and variable, strong crosswind would exacerbate the complexity of the airflow around the train, impacting the cooling-fan performance of power pack, smoke dispersion, posing safety concerns. Using incompressible RANS equations and SST k-ω model, flow field around a diesel train, and effect of train speed, crosswind, and skirt board on cooling-fan flow and smoke dispersion are simulated and analyzed. Results reveal that airflow in power pack fans is affected by train speed and location, with the upstream fan exhibiting slightly higher airflow, especially in tail car power packs, with a difference of up to 7% (at 160 km/h). Skirt boards reduce fan airflow by about 6%. Crosswinds positively correlate with fan airflow variation, with windward and leeward fans experiencing increased and decreased airflow, respectively. Skirt board effectively reduces crosswind effects on power pack fans. Smoke intake into air-conditioning units (ACUs) correlates positively with train speed, particularly in downstream air-intakes of ACUs near smoke vents. Crosswinds significantly alter smoke distribution between leeward and windward sides of ACUs, especially at low crosswind speed. Smoke rarely enters ACUs when crosswind speed exceeds train speed. These findings offer insights for combustion-powered train operation and smoke vent design on train roofs.
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