An incentive mechanism for data sharing based on blockchain with smart contracts

© 2020 Data sharing techniques have progressively drawn increasing attention as a means of significantly reducing repetitive work. However, in the process of data sharing, the challenges regarding formation of mutual-trust relationships and increasing the level of user participation are yet to be solved. The existing solution is to use a third party as a trust organization for data sharing, but there is no dynamic incentive mechanism for data sharing with a large number of users. Blockchain 2.0 with smart contract has the natural advantage of being able to enable trust and automated transactions between a large number of users. This paper proposes a data sharing incentive model based on evolutionary game theory using blockchain with smart contract. The smart contract mechanism can dynamically control the excitation parameters and continuously encourages users to participate in data sharing

Quantified mass loss of the Laohugou ice core and its precipitation signal during 1961–2005 at high elevation in the northeastern Tibetan Plateau

Ice records provide a qualitative rather than a quantitative indication of the trend of climate change. Using the bulk aerodynamic method and degree day model, this study quantified ice mass loss attributable to sublimation/evaporation (S/E) and meltwater on the basis of integrated observations (1960–2006) of glacier-related and atmospheric variables in the northeastern Tibetan Plateau. During 1961–2005, the average annual mass loss in the ice core was 95.33 ± 20.56 mm w.e. (minimum: 78.97 mm w.e. in 1967, maximum: 146.67 mm w.e. in 2001), while the average ratio of the revised annual ice accumulation was 21.2 ± 7.7% (minimum: 11.0% in 1992, maximum 44.8% in 2000). A quantitative formula expressing the relationship between S/E and air temperature at the monthly scale was established, which could be extended to estimation of S/E changes of other glaciers in other regions. The elevation effect on alpine precipitation determined using revised ice accumulation and instrumental data was found remarkable. This work established a method for quantitative assessment of the temporal variation in ice core mass loss, and advanced the reconstruction of long-term precipitation at high elevations. Importantly, the formula established for reconstruction of S/E from temperature time series data could be used in other regions

Seasonal variation of atmospheric elemental carbon aerosols at Zhongshan Station, East Antarctica

Elemental carbon (or black carbon) (EC or BC) aerosols emitted by biomass burning and fossil fuel combustion could cause notable climate forcing. Southern Hemisphere biomass burning emissions have contributed substantially to EC deposition in Antarctica. Here, we present the seasonal variation of EC determined from aerosol samples acquired at Zhongshan Station (ZSS), East Antarctica. The concentration of EC in the atmosphere varied between 0.02 and 257.81 ng·m−3 with a mean value of 44.87±48.92 ng·m−3. The concentration of EC aerosols reached its peak in winter (59.04 ng·m−3) and was lowest (27.26 ng·m−3) in summer. Back trajectory analysis showed that biomass burning in southern South America was the major source of the EC found at ZSS, although some of it was derived from southern Australia, especially during winter. The 2019–2020 Australian bush fires had some influence on EC deposition at ZSS, especially during 2019, but the contribution diminished in 2020, leaving southern South America as the dominant source of EC

Cryosphere as a temporal sink and source of microplastics in the Arctic region

Microplastics (MPs) pollution has become a serious environmental issue of growing global concern due to the increasing plastic production and usage. Under climate warming, the cryosphere, defined as the part of Earth's layer characterized by the low temperatures and the presence of frozen water, has been experiencing significant changes. The Arctic cryosphere (e.g., sea ice, snow cover, Greenland ice sheet, permafrost) can store and release pollutants into environments, making Arctic an important temporal sink and source of MPs. Here, we summarized the distributions of MPs in Arctic snow, sea ice, seawater, rivers, and sediments, to illustrate their potential sources, transport pathways, storage and release, and possible effects in this sentinel region. Items concentrations of MPs in snow and ice varied about 1–6 orders of magnitude in different regions, which were mostly attributed to the different sampling and measurement methods, and potential sources of MPs. MPs concentrations from Arctic seawater, river/lake water, and sediments also fluctuated largely, ranging from several items of per unit to >40,000 items m−3, 100 items m−3, and 10,000 items kg−1 dw, respectively. Arctic land snow cover can be a temporal storage of MPs, with MPs deposition flux of about (4.9–14.26) × 108 items km−2 yr−1. MPs transported by rivers to Arctic ocean was estimated to be approximately 8–48 ton/yr, with discharge flux of MPs at about (1.65–9.35) × 108 items/s. Average storage of MPs in sea ice was estimated to be about 6.1×1018 items, with annual release of about 5.1×1018 items. Atmospheric transport of MPs from long-distance terrestrial sources contributed significantly to MPs deposition in Arctic land snow cover, sea ice and oceanic surface waters. Arctic Great Rivers can flow MPs into the Arctic Ocean. Sea ice can temporally store, transport and then release MPs in the surrounded environment. Ocean currents from the Atlantic brought high concentrations of MPs into the Arctic. However, there existed large uncertainties of estimation on the storage and release of MPs in Arctic cryosphere owing to the hypothesis of average MPs concentrations. Meanwhile, representatives of MPs data across the large Arctic region should be mutually verified with in situ observations and modeling. Therefore, we suggested that systematic monitoring MPs in the Arctic cryosphere, potential threats on Arctic ecosystems, and the carbon cycle under increasing Arctic warming, are urgently needed to be studied in future

Climate change : strategies for mitigation and adaptation

The sustainability of life on Earth is under increasing threat due to humaninduced climate change. This perilous change in the Earth's climate is caused by increases in carbon dioxide and other greenhouse gases in the atmosphere, primarily due to emissions associated with burning fossil fuels. Over the next two to three decades, the effects of climate change, such as heatwaves, wildfires, droughts, storms, and floods, are expected to worsen, posing greater risks to human health and global stability. These trends call for the implementation of mitigation and adaptation strategies. Pollution and environmental degradation exacerbate existing problems and make people and nature more susceptible to the effects of climate change. In this review, we examine the current state of global climate change from different perspectives. We summarize evidence of climate change in Earth’s spheres, discuss emission pathways and drivers of climate change, and analyze the impact of climate change on environmental and human health. We also explore strategies for climate change mitigation and adaptation and highlight key challenges for reversing and adapting to global climate change

A budget allocation strategy minimizing the sample set quantile for initial experimental design

The increased complexity of manufacturing systems makes the acquisition of the system performance estimate a black-box procedure (e.g., simulation tools). The efficiency of most black-box optimization algorithms is affected significantly by initial designs (populations). In most population initializers, points are spread out to explore the entire domain, e.g., space-filling designs. Some population initializers also consider exploitation procedures to speed up the optimization process. However, they are either application-dependent or require an additional budget. This article proposes a generic method to generate, without an additional budget, several good solutions in the initial design. The aim of the method is to optimize the quantile of the objective function values in the generated sample set. The proposed method is based on a clustering of the solution space; feasible solutions are clustered into groups and the budget is allocated to each group dynamically based on the observed information. The asymptotic performance of the proposed method is analyzed theoretically. The numerical results show that, if proper clustering rules are applied, an unbalanced design is generated in which promising solutions have higher sampling probabilities than non-promising solutions. The numerical results also show that the method is robust to wrong clustering rules

A new partition-based random search method for deterministic optimization problems

The Nested Partition (NP) method is efficient in large-scale optimization problems. The most promising region is identified and partitioned iteratively. To guarantee the global convergence, a backtracking mechanism is introduced. Nevertheless, if inappropriate partitioning rules are used, lots of backtracking occur reducing largely the algorithm efficiency. A new partition-based random search method is developed in this paper. In the proposed method, all generated regions are stored for further partitioning and each region has a partition speed related to its posterior probability of being the most promising region. Promising regions have higher partition speeds while non-promising regions are partitioned slowly. The numerical results show that the proposed method finds the global optimum faster than the pure NP method if numerous high-quality local optima exist. It can also find all the identical global optima, if exist, in the studied case

Multi-fidelity surrogate-based optimization for decomposed buffer allocation problems

The buffer allocation problem (BAP) for flow lines has been extensively addressed in the literature. In the framework of iterative approaches, algorithms alternate an evaluative method and a generative method. Since an accurate estimation of system performance typically requires high computational effort, an efficient generative method reducing the number of iterations is desirable, for searching for the optimal buffer configuration in a reasonable time. In this work, an iterative optimization algorithm is proposed in which a highly accurate simulation is used as the evaluative method and a surrogate-based optimization is used as the generative method. The surrogate model of the system performance is built to select promising solutions so that an expensive simulation budget is avoided. The performance of the surrogate model is improved with the help of fast but rough estimators obtained with approximated analytical methods. The algorithm is embedded in a problem decomposition framework: several problem portions are solved hierarchically to reduce the solution space and to ease the search of the optimum solution. Further, the paper investigates a jumping strategy for practical application of the approach so that the algorithm response time is reduced. Numerical results are based on balanced and unbalanced flow lines composed of single-machine stations

Online Detection of Surface Defects Based on Improved YOLOV3

Aiming at the problems of low efficiency and poor accuracy in the product surface defect detection. In this paper, an online surface defects detection method based on YOLOV3 is proposed. Firstly, using lightweight network MobileNetV2 to replace the original backbone as the feature extractor to improve network speed. Then, we propose an extended feature pyramid network (EFPN) to extend the detection layer for multi-size object detection and design a novel feature fusing module (FFM) embedded in the extend layer to super-resolve features and capture more regional details. In addition, we add an IoU loss function to solve the mismatch between classification and bounding box regression. The proposed method is used to train and test on the hot rolled steel open dataset NEU-DET, which contains six typical defects of a steel surface, namely rolled-in scale, patches, crazing, pitted surface, inclusion and scratches. The experimental results show that our method achieves a satisfactory balance between performance and consumption and reaches 86.96% mAP with a speed of 80.96 FPS, which is more accurate and faster than many other algorithms and can realize real-time and high-precision inspection of product surface defects