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Abstract Coronae, which are weak electrical discharges, have long been hypothesized to form on trees under thunderstorms, though never directly observed, characterized, or quantified. Using a newly developed instrument that measures ultraviolet emissions from coronae, the first direct observations and quantifications of coronae are presented for two trees under a thunderstorm in North Carolina. Coronae moved sporadically among leaves on every tree branch in a narrow field of view while the thunderstorm was directly overhead. Coronae emitted ∼10¹¹photons at 260 nm, corresponding to electrical currents of ∼1 μA, derived from unique measurements relating corona intensity to tree electrical current. Similar results across four additional storm intercepts from Florida to Pennsylvania give rise to a vision of swaths of scintillating corona glow as thunderstorms pass over forests. Such widespread coronae have implications for the removal of hydrocarbons emitted by trees, subtle tree leaf damage, and limited thunderstorm electrification. 

Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size. 

Functional electrical stimulation (FES) is a potential technique for reanimating paralyzed muscles post neurological injury/disease. Several technical challenges including difficulty in measuring and compensating for delayed muscle activation levels inhibit its satisfactory control performance. In this paper, an ultrasound (US) imaging approach is proposed to measure delayed muscle activation levels under the implementation of FES. Due to low sampling rates of US imaging, a sampled data observer (SDO) is designed to estimate the muscle activation in a continuous manner. The SDO is combined with continuous-time dynamic surface control (DSC) approach that compensates for the electromechanical delay (EMD) in the tibialis anterior (TA) activation dynamics. The stability analysis based on the Lyapunov-Krasovskii function proves that the SDO-based DSC plus delay compensation (SDO-DSC-DC) approach achieves semi-global uniformly ultimately bounded (SGUUB) tracking performance. Simulation results on an ankle dorsiflexion neuromusculoskeletal system show the root mean square error (RMSE) of desired trajectory tracking is reduced by 19.77 % by using the proposed SDO-DSC-DC compared to the DSC-DC without the SDO. The findings provide potentials for rehabilitative devices, like powered exoskeleton and FES, to assist or enhance human limb movement based on the corresponding muscle activities in real-time. 

Massive data center (DC) energy demands lead to water consumption concerns. This study quantifies on-site and off-site DC water consumption and its holistic impact on regional water availability. This study proposes a new DC sustainability metrics, Water Scarcity Usage Effectiveness (WSUE), that captures the holistic impacts of water consumption on regional water availability by considering electricity and water source locations and their associated water scarcity. We examine the water consumption of various DC cooling systems by tracking on-site water consumption along with the direct and indirect water transfers associated with electricity transmission at the contiguous U.S. balancing authority (BA) level. This study then applies the WSUE metric for different DC cooling systems and locations to compare the holistic water stress impact by large on-site water consuming systems (e.g., via cooling towers) versus systems with higher electrical consumption and lower on-site water consumption such as the conventional use of computer room air conditioner (CRAC) units. Results suggest that WSUE is strongly dependent on location, and a water-intensive cooling solution could result in a lower WSUE than a solution requiring no or less on-site water consumption. The use of the WSUE metric aids in DC siting decisions and DC cooling system design from a sustainability point of view. 

We propose a new theoretical approach for building anonymous mixing mechanisms for cryptocurrencies. Rather than requiring a fully uniform permutation during mixing, we relax the requirement, insisting only that neighboring permutations are similarly likely. This is defined formally by borrowing from the definition of differential privacy. This relaxed privacy definition allows us to greatly reduce the amount of interaction and computation in the mixing protocol. Our construction achieves O(n * polylog(n)) computation time for mixing n addresses, whereas all other mixing schemes require O(n^2) total computation across all parties. Additionally, we support a smooth tolerance of fail-stop adversaries and do not require any trusted setup. We analyze the security of our generic protocol under the UC framework, and under a stand-alone, game-based definition. We finally describe an instantiation using ring signatures and confidential transactions. 

Web tracking and advertising (WTA) nowadays are ubiquitously performed on the web, continuously compromising users' privacy. Existing defense solutions, such as widely deployed blocking tools based on filter lists and alternative machine learning based solutions proposed in prior research, have limitations in terms of accuracy and effectiveness. In this work, we propose WtaGraph, a web tracking and advertising detection framework based on Graph Neural Networks (GNNs). We first construct an attributed homogenous multi-graph (AHMG) that represents HTTP network traffic, and formulate web tracking and advertising detection as a task of GNN-based edge representation learning and classification in AHMG. We then design four components in WtaGraph so that it can (1) collect HTTP network traffic, DOM, and JavaScript data, (2) construct AHMG and extract corresponding edge and node features, (3) build a GNN model for edge representation learning and WTA detection in the transductive learning setting, and (4) use a pre-trained GNN model for WTA detection in the inductive learning setting. We evaluate WtaGraph on a dataset collected from Alexa Top 10K websites, and show that WtaGraph can effectively detect WTA requests in both transductive and inductive learning settings. Manual verification results indicate that WtaGraph can detect new WTA requests that are missed by filter lists and recognize non-WTA requests that are mistakenly labeled by filter lists. Our ablation analysis, evasion evaluation, and real-time evaluation show that WtaGraph can have a competitive performance with flexible deployment options in practice.