Investigation of wind characteristics of typhoon boundary layer through field experiments and CFD simulations

High-resolution observations of typhoon boundary layer above 100 m are rare as traditional wind towers are generally below 100 m, which limits the study of typhoon boundary layer and engineering applications such as wind-resistant design of tall buildings and wind turbines in typhoon-prone regions. In this study, boundary layer winds of super typhoon Lekima (2019) are observed, simulated and analyzed. Together with traditional wind tower, Doppler wind lidar is utilized for observations of typhoon boundary layer in order to obtain measured data above 100 m. Besides, Computational Fluid Dynamics (CFD) simulation based on Large Eddy Simulation (LES) method is conducted to further investigate the impact of complex terrain on the near-surface wind characteristics. The results show that the power law fits the mean wind speed profile well below 100 m. However, before and after the typhoon lands, a local reverse or low-level jet occurs in the mean wind speed profile at the height of 100–300 m, which cannot be depicted by the power law. Meanwhile, the turbulence intensity increases with height and experiences larger fluctuations. In addition, there is a significant negative correlation between the ground elevation and power exponents of the fitted mean wind speed profiles. This study provides useful information to better understand wind characteristics of the typhoon boundary layer

DCPT: Darkness Clue-Prompted Tracking in Nighttime UAVs

Existing nighttime unmanned aerial vehicle (UAV) trackers follow an "Enhance-then-Track" architecture - first using a light enhancer to brighten the nighttime video, then employing a daytime tracker to locate the object. This separate enhancement and tracking fails to build an end-to-end trainable vision system. To address this, we propose a novel architecture called Darkness Clue-Prompted Tracking (DCPT) that achieves robust UAV tracking at night by efficiently learning to generate darkness clue prompts. Without a separate enhancer, DCPT directly encodes anti-dark capabilities into prompts using a darkness clue prompter (DCP). Specifically, DCP iteratively learns emphasizing and undermining projections for darkness clues. It then injects these learned visual prompts into a daytime tracker with fixed parameters across transformer layers. Moreover, a gated feature aggregation mechanism enables adaptive fusion between prompts and between prompts and the base model. Extensive experiments show state-of-the-art performance for DCPT on multiple dark scenario benchmarks. The unified end-to-end learning of enhancement and tracking in DCPT enables a more trainable system. The darkness clue prompting efficiently injects anti-dark knowledge without extra modules. Code and models will be released.Comment: Under revie

Decoupling, quantifying, and restoring aging-induced Zn-anode losses in rechargeable aqueous zinc batteries

The search for batteries beyond Li-ion that offer better performance, reliability, safety, and/or affordability has led researchers to explore a diverse array of candidates. The advantages of Zn-ion batteries reside in zinc’s relatively low reactivity, raising the prospect of a rechargeable battery with a simple aqueous electrolyte and a cheaper, safer option to the organic electrolytes that must be paired with reactive lithium. However, water still reacts with the zinc in corrosion reactions. These consume zinc, lowering the battery’s capacity, and generate gas that accumulates in the sealed cell. We diagnose the contribution of corrosion to performance decay in zinc batteries and reveal the critical role of gas accumulation in deactivating large sections of electrode, which cripples cell performance. Fortunately, electrodes can be reactivated by removal of the gas, demonstrating the importance of designing future cells that either prevent gas formation or facilitate its safe release

Achieving ultra‐high rate planar and dendrite‐free zinc electroplating for aqueous zinc battery anodes

Despite being one of the most promising candidates for grid-level energy storage, practical aqueous zinc batteries are limited by dendrite formation, which leads to significantly compromised safety and cycling performance. In this study, by using single-crystal Zn-metal anodes, reversible electrodeposition of planar Zn with a high capacity of 8 mAh cm−2 can be achieved at an unprecedentedly high current density of 200 mA cm−2. This dendrite-free electrode is well maintained even after prolonged cycling (>1200 cycles at 50 mA cm−2). Such excellent electrochemical performance is due to single-crystal Zn suppressing the major sources of defect generation during electroplating and heavily favoring planar deposition morphologies. As so few defect sites form, including those that would normally be found along grain boundaries or to accommodate lattice mismatch, there is little opportunity for dendritic structures to nucleate, even under extreme plating rates. This scarcity of defects is in part due to perfect atomic-stitching between merging Zn islands, ensuring no defective shallow-angle grain boundaries are formed and thus removing a significant source of non-planar Zn nucleation. It is demonstrated that an ideal high-rate Zn anode should offer perfect lattice matching as this facilitates planar epitaxial Zn growth and minimizes the formation of any defective regions

Home leaving behavior of American youth

This paper addresses three major issues: whether predictors of home leaving differ by age at home leaving, whether predictors differ by first time and last time home leaving, and what variables best predict repeated home leaving. The 1988 National Survey of Families and Households provides the data set from which respondents age 30 and below are selected. Discriminant analysis and event history are the major methods by which these issues are addressed. Four major categories of variables are included in the analysis: family structure, family formation, personal characteristics, and control variables. Respondents are divided into three groups according to their ages at first time and last time home leaving: early home leavers (left at age 17 and below), on-time home leavers (left between 18 and 24), and late home leavers (left at 25 and above). It is found that predictors of home leaving differ according to the group of home leavers under study. Growing up in a non-nuclear household (adoptive, stepfamily, or other) generally affects early home leaving. Single-parent families are associated with on-time home leaving. When early marriage is controlled, marital status emerges as a better predictor of late than on-time home leaving. The predictors of home leaving vary little by whether we look at first time or last time home leaving. The effect of family structure is significant but very similar in both cases. The observable changes are largely associated with parenthood, divorce/separation status, and personal income. Repeated home leaving is best predicted by respondents\u27 age at first home leaving, race, and divorce/separation status. Children from non-biological or non-intact families are less likely to repeat home leaving. In spite of the fact that the previous literature suggests a high home return rate for those who once lived in barracks or dormitories, military service and student status fail to predict repeated home leaving