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Burst is a common pattern in data streams which is characterized by a sudden increase in terms of arrival rate followed by a sudden decrease. Burst detection has attracted extensive attention from the research community. To detect bursts accurately in real time, we propose a novel sketch, namely BurstSketch, which consists of two stages. Stage 1 uses the technique Running Track to select potential burst items efficiently. Stage 2 monitors the potential burst items and captures the key features of burst pattern by a technique called Snapshotting. We further propose an optimization, namely Dynamic Buckets, which can improve the accuracy of BurstSketch. We provide theoretical error bounds for Stage 1, Stage 2 and the optimized version. Experimental results show that, compared with the strawman solution, Burstsketch achieves 2.00 to 11.63 times higher F1 score, and 1.56 times higher throughput. We also integrate BurstSketch into Apache Flink, and show that using BurstSketch can be faster than simply using the built-in APIs provided by Apache Flink.

Logging-while-drilling (LWD) sonic data are critical for marine gas hydrate reservoir evaluation and production prediction. However, acquiring complete acoustic logs, particularly shear wave, poses significant challenges and incurs high costs. To tackle this issue, we develop a two-branch hybrid framework for predicting LWD sonic logs of hydrate-bearing sediments from existing logging data. One branch based on a rock physics model is utilized to generate background (no-hydrate/no-gas) elastic wave velocity profiles, while the other deep learning branch (DLB) compensates for the residuals between actual observations and the outputs of the first branch. The state-of-the-art Transformer encoder block is employed in the DLB to extract potentially intricate patterns within logging sequences. Such a scientific knowledge-guided network architecture with additional physics-based feature construction provides an explainable process that improves the physical consistency of predictions. Our method is tested with two publicly available datasets from the Cascadia continental margin. The hybrid model greatly enhances the predictive accuracy of the physical process model (with a minimum mean absolute percentage error of 0.73% and 4.33% for P- and S-wave velocities, respectively) and demonstrates outstanding generalization performance compared to pure data-driven approaches. The well-trained model offers impressive extrapolation beyond observed conditions for unmeasured high hydrate saturation (>40%) intervals in the region.

To measure the contribution of collegiate club sports to the economic and social environment of higher education institutions, this case study developed a pragmatic model—integrating self-generated revenue, stimulated student spending, student attraction, and student retention—to quantify the economic value or worth of club sports to the university, and to consider their broader utility for the attainment of the university's mission. For the university studied, results show that undergraduate club sport athletes render approximately $21.7 million in institutional expenditure annually, and club sports can serve the university mission through their contribution to student recruitment, retention, and recommendation. Over 50% of participants credited their enrollment, continued attendance, and willingness to recommend the university to the availability of club sports. Student retention was more positive for participants in club sports than for the university population as a whole. Additionally, international students and students living off-campus viewed club sports as particularly positive.

As the most advanced energy storage devices, lithium ion batteries (LIBs) have captured a great deal of attention and have been developed swiftly during the past decades. However, the improved fast-charging performance is more urgent than ever for time-saving and convenience, which is generally limited by the graphite anode. Recent studies have revealed that the fast-charging performance of graphite anodes is highly dictated by the properties of the electrolyte. Therefore, the investigations on fast-charging graphite based on designs of electrolytes are summarized from two aspects: solid electrolyte interphase (SEI) structures and solvated lithium ion structures. Finally, challenges and prospects for further research toward fast-charging graphite anodes are proposed.

Layered double hydroxide (LDH) nano‐ and microstructures with controllable size and morphology have been fabricated on “bivalent metal” substrates such as zinc and copper by a one‐step, room‐temperature process, in which metal substrates act as both reactants and supports. By manipulating the concentration of NH3· H2O, the thickness and lateral size of the LDH materials can be tuned from several tens of nanometers to several hundreds of nanometers and from several hundreds of nanometers to several micrometers, respectively. This method is general and may be readily extended to any other alkali‐resisted substrate coated with Zn and Cu. As an example, Zn‐covered stainless steel foil has been shown to be effective for the growth of a ZnAl LDH film. After calcinating the as‐grown LDH at high temperature (650 °C) in argon gas, a ZnO/ZnAl2O4porous nanosheet film is obtained, which is then directly used for the first time as the anode material for Li‐ion batteries with the operating voltage window of 0.05–2.5 V (vs. Li). The result demonstrates that ZnO/ZnAl2O4has higher discharge and charge capacities and considerably better cycling stability compared to pure ZnO (Li insertion/extraction rate: 200 or 500 mA g−1). The improved electrochemical performance can be ascribed to the buffering effect of the inactive matrix ZnAl2O4by relieving the stress caused by the volume change during charge–discharge cycling. This work represents a successful example for the development of promising ZnO‐based anode materials for Li‐ion batteries.

We report a highly effective growth of vertically aligned ZnO one-dimensional (1D) nanostructures on conducting alloy substrate (Fe−Co−Ni) in mild solutions (T ≤ 70 °C) in the absence of any seeds, catalysts, and surfactants. The growth conditions such as NH3·H2O concentration, temperature, and nature of the substrate are correlated to affect the nanostructure formation. Different ZnO single-crystal nanostructures including nanoneedles, hexagonal nanorods, and nanopencils oriented normal to the substrate can be selectively formed in high quantity. The ordered ZnO nanostructures show strong UV excitonic emissions and good field emission (FE) properties. Other metal substrates such as Ti and Ni are also proven to be effective for ZnO nanoarray growth. Since metal substrates are much more economical and scalable than Si, sapphire/Al2O3, GaN, etc., we believe that our approach presents a general economical route toward mass production of controllable ZnO arrays and will facilitate flexible design of device architectures for nanoelectronics.

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