Vadose zone infiltration well (VZW) is an important method to implement managed aquifer recharge (MAR) when groundwater table is relatively deep or sufficiently permeable soils at ground and/or ...sizable land areas for surface infiltration systems are not available. However, the theoretical model for this kind of artificial recharge has rarely been developed. A mathematical model for the coupled unsaturated‐saturated flow process induced by a VZW is presented in this study with the purpose of understanding the overall hydrodynamics of unsaturated flow and recharge efficiency. Semianalytical solutions for hydraulic heads and recharge rate are derived using Laplace‐Hankel transforms. The solutions are tested using a finite‐element numerical solution built with COMSOL. The performance and capacity of VZW are assessed for different unsaturated zone parameters and well structures. For the case of an unsaturated zone with a small storage capacity, a greater proportion of injected water will be stored in the saturated zone, thus is a favored situation for the implement of MAR using VZWs. Both anisotropy and infiltration well structures have nonnegligible effects on the recharge rate of the saturated zone at early times but have negligible influence at later times. The proposed semianalytical solutions can be used to estimate unsaturated parameters and can also be used to assess the effectiveness of VZWs for the purpose of MAR.
Key Points
A novel analytical model for managed aquifer recharge using vadose zone infiltration wells was developed
An unsaturated zone with a small storage capacity is a favored situation for managed aquifer recharge
Both aquifer anisotropy and well structures have negligible influence on efficiency of managed aquifer recharge
Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure ...management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.
Various quantum applications can be reduced to estimating expectation values, which are inevitably deviated by operational and environmental errors. Although errors can be tackled by quantum error ...correction, the overheads are far from being affordable for near-term technologies. To alleviate the detrimental effects of errors on the estimation of expectation values, quantum error mitigation techniques have been proposed, which require no additional qubit resources. Here we benchmark the performance of a quantum error mitigation technique based on probabilistic error cancellation in a trapped-ion system. Our results clearly show that effective gate fidelities exceed physical fidelities, i.e., we surpass the break-even point of eliminating gate errors, by programming quantum circuits. The error rates are effectively reduced from (1.10 ± 0.12) × 10
to (1.44 ± 5.28) × 10
and from (0.99 ± 0.06) × 10
to (0.96 ± 0.10) × 10
for single- and two-qubit gates, respectively. Our demonstration opens up the possibility of implementing high-fidelity computations on a near-term noisy quantum device.
The sixth generation (6G) networks are expected to provide a fully connected world with terrestrial wireless and satellite communications integration. The design concept of 6G networks is to leverage ...artificial intelligence (Ai) to promote the intelligent and agile development of network services. intelligent services inevitably involve the processing of large amounts of data, such as storage, computing, and analysis, such that the data may be vulnerable to tampering or contamination by attackers. in this article, we propose a blockchain-based data security scheme for Ai applications in 6G networks. Specifically, we first introduce the 6G architecture (i.e., a space-air-ground-underwater integrated network). Then we discuss two Ai-enabled applications, indoor positioning and autonomous vehicle, in the context of 6G. Through a case study of an indoor navigation system, we demonstrate the effectiveness of blockchain in data security. The integration of Ai and blockchain is developed to evaluate and optimize the quality of intelligent service. Finally, we discuss several open issues about data security in the upcoming 6G networks.
•A new deep RNN model was developed to predicate highly nonlinear daily runoff.•The selection of input variables has a huge impact on deep RNN forecasting results.•PCA method was applied to improve ...the accuracy of deep RNN Model.
In recent years, deep Recurrent Neural Network (RNN) has been applied to predict daily runoff, as its wonderful ability of dealing with the high nonlinear interactions among the complex hydrology factors. However, most of the existing studies focused on the model structure and the computational load, without considering the impact from the selection of multiple input variables on the model prediction. This article presents a study to evaluate this influence and provides a method of identifying the best meteorological input variables for a run off model. Rainfall data and multiple meteorological data have been considered as input to the model. Principal Component Analysis (PCA) has been applied to the data as a contrast, to reduce dimensionality and redundancy within this input data. Two different deep RNN models, a long-short term memory (LSTM) model and a gated recurrent unit (GRU) model, were comparatively applied to predict runoff with these inputs. In this study, the Muskegon river and the Pearl river were taken as examples. The results demonstrate that the selection of input variables have a great influence on the predictions made using the RNN while the RNN model with multiple meteorological input data is shown to achieve higher accuracy than rainfall data alone. PCA method can improve the accuracy of deep RNN model effectively as it can reflect core information by classifying the original data information into several comprehensive variables.
Wireless sensor networks (WSNs) are vulnerable to selective forwarding attacks that can maliciously drop a subset of forwarding packets to degrade network performance and jeopardize the information ...integrity. Meanwhile, due to the unstable wireless channel in WSNs, the packet loss rate during the communication of sensor nodes may be high and vary from time to time. It poses a great challenge to distinguish the malicious drop and normal packet loss. In this paper, we propose a channel-aware reputation system with adaptive detection threshold (CRS-A) to detect selective forwarding attacks in WSNs. The CRS-A evaluates the data forwarding behaviors of sensor nodes, according to the deviation of the monitored packet loss and the estimated normal loss. To optimize the detection accuracy of CRS-A, we theoretically derive the optimal threshold for forwarding evaluation, which is adaptive to the time-varied channel condition and the estimated attack probabilities of compromised nodes. Furthermore, an attack-tolerant data forwarding scheme is developed to collaborate with CRS-A for stimulating the forwarding cooperation of compromised nodes and improving the data delivery ratio of the network. Extensive simulation results demonstrate that CRS-A can accurately detect selective forwarding attacks and identify the compromised sensor nodes, while the attack-tolerant data forwarding scheme can significantly improve the data delivery ratio of the network.
Over the last century, land use and land cover (LULC) in the United States Corn Belt region shifted from mixed perennial and annual cropping systems to primarily annual crops. Historical LULC change ...impacted the annual water balance in many Midwestern basins by decreasing annual evapotranspiration (ET) and increasing streamflow and base flow. Recent expansion of the biofuel industry may lead to future LULC changes from increasing corn acreage and potential conversion of the industry to cellulosic bioenergy crops of warm or cool season grasses. In this paper, the Soil and Water Assessment Tool (SWAT) model was used to evaluate potential impacts from future LULC change on the annual and seasonal water balance of the Raccoon River watershed in west‐central Iowa. Three primary scenarios for LULC change and three scenario variants were evaluated, including an expansion of corn acreage in the watershed and two scenarios involving expansion of land using warm season and cool season grasses for ethanol biofuel. Modeling results were consistent with historical observations. Increased corn production will decrease annual ET and increase water yield and losses of nitrate, phosphorus, and sediment, whereas increasing perennialization will increase ET and decrease water yield and loss of nonpoint source pollutants. However, widespread tile drainage that exists today may limit the extent to which a mixed perennial‐annual land cover would ever resemble pre‐1940s hydrologic conditions. Study results indicate that future LULC change will affect the water balance of the watershed, with consequences largely dependent on the future LULC trajectory.
The vehicular social networks (VSNs) supports diverse kinds of services such as traffic management, road safety, and sharing data (videos, audios, roads photos, air quality, and so on). However, its ...complex, large-scale and dynamic network structure poses new security challenges. Among these challenges, secure data transmission has turned to be a spotlight. Ciphertext-policy attribute-based encryption (CP-ABE) may be adopted to realize one-to-many data sharing in VSNs. In traditional CP-ABE schemes, access policy is stored and granted by the could, which lacks credibility due to centralization. In this article, we propose a secure and verifiable one-to-many data sharing scheme to solve the above problem. We use blockchain to record the access policy, realizing user self-certification and cloud non-repudiation. Considering the computing capabilities of the vehicular user, we propose an effective scheme for certificating. Meanwhile, considering the sensitive information included in the access policy, we propose a policy hiding scheme. Our scheme also supports data revocation when a vehicular user no longer wants to share the data in VSNs. Finally, security analysis and simulation show our scheme is both secure and efficient.
Under the pressure of climate change, the demands for alternative green hydrogen (H 2 ) production methods have been on the rise to conform to the global trend of transition to a H 2 society. This ...article proposes a multirenewable-to-hydrogen production method to enhance the green H 2 production efficiency for renewable-dominated hydrogen fueling stations (HFSs). In this method, the aqueous electrolysis of native biomass can be powered by wind and solar generations based on electrochemical effects, and both electrolysis current and temperature are taken into account for facilitating on-site H 2 production and reducing the electricity consumption. Moreover, a capsule network based H 2 demand forecasting model is formulated to estimate the gas load for HFS by extracting the underlying spatial features and temporal dependencies of traffic flows in the transportation network. Furthermore, a hierarchical coordinated control strategy is developed to suppress high fluctuations in electrolysis current caused by volatility of wind and solar outputs based on model predictive control framework. Comparative studies validate the superior performance of the proposed methodology over the power-to-gas scheme on electrolysis efficiency and economic benefits.
The emerging Internet-of-Things (IoT) are vulnerable to Sybil attacks where attackers can manipulate fake identities or abuse pseudoidentities to compromise the effectiveness of the IoT and even ...disseminate spam. In this paper, we survey Sybil attacks and defense schemes in IoT. Specifically, we first define three types Sybil attacks: SA-1, SA-2, and SA-3 according to the Sybil attacker's capabilities. We then present some Sybil defense schemes, including social graph-based Sybil detection (SGSD), behavior classification-based Sybil detection (BCSD), and mobile Sybil detection with the comprehensive comparisons. Finally, we discuss the challenging research issues and future directions for Sybil defense in IoT.
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