Search

Wildfire risk prediction is a critical component of disaster prevention and mitigation, often closely associated with local human activities in most regions. Recent studies demonstrate that employing joint modeling techniques using diverse datasets alongside Convolutional Neural Networks-Long Short-Term Memory Networks (ConvLSTM) produces favorable predictive results. However, previous research inadequately explored the different impact of factors across different categories and spatial orientations, and neglected the impact of fuels and human activities inside the samples. This study focuses on the six eastern provinces of China, utilizing a multi-source dataset comprising satellite-monitored wildfire products from 2012 to 2022, along with various factors indicating terrestrial and human activities, simulated meteorological elements and high-resolution vegetation imagery. By introducing channel and spatial attention mechanisms and visual transformer mode, this research optimizes the ConvLSTM wildfire prediction model. Results indicate a noteworthy enhancement, elevating accuracy, Kappa coefficient, and AUC of ROC curves from 91.15%, 80.87%, and 97.01% to 92.79%, 84.48%, and 97.90%, respectively. Consequently, it reinforces the accuracy by increase of the structural features within samples and quantifying the differences in the importance of different factors, which is also validated by prediction application of the samples in the entire year of 2023. Sensitivity analysis reveals that the current model is still highly dependent on the meteorological factors. Notably, the impact of structural features significantly surpasses the influence of terrain and terrestrial ecology elements, which should be considered in further models. Thus, this study has developed a methodology integrating multiple attention mechanisms and sample structural features, which could furnish high-precision daily kilometer-level wildfire risk prediction products. This method could improve the efficiency of prevention and control by improving the accuracy and narrowing the high-risk areas.

In order to solve the problems of low transmission performance, inaccurate flip set construction and high complexity of existing polar codes successive cancellation list bit-flip (SCLF) algorithms, a SCLF algorithm for polar codes based on GRU network assistance and path metric exchange was proposed.First the decoding state was redivided, and a bit-flip strategy was proposed by combining the ideas of path metric exchange and reverse judgment.Secondly, a flip set construction method was proposed by training the GRU network to locate the first erroneous bit in the decoding.Finally, a multi-bit flipping rule was proposed by sorting the path metric for each low-order flip and fixing the low-order reliable bits before performing the high-order flip.Simulation results show that compared with the existing SCLF algorithms, the proposed algorithm improves the accuracy of identifying the first error bit by 18～24% at low signal noise ratio.Under single-bit and multi-bit flipping, the proposed algorithm has a performance gain of up to 0.3 dB and 0.2 dB, respectively, and the online decoding complexity is lower.

The precipitation characteristics in Hangzhou of Zhejiang Province, China under the background of global climate change are analyzed using the meteorological observation data obtained from the Hangzhou base station in this study. We investigate the climate characteristics of precipitation in Hangzhou from several aspects, such as centennial trend, seasonal change, periodicity and the variation of extreme precipitation. Our results show a linear decreasing trend and obvious interdecadal characteristics in the precipitation of Hangzhou on a centennial timescale. Significantly increased amplitude of precipitation fluctuation was observed since the beginning of the 21st century. For the interdecadal variation of seasonal precipitation on a centennial timescale, precipitation in autumn showed a decreasing trend of 8.1 mm/10a, whereas the trends for the other three seasons were statistically insignificant. The precipitation in Hangzhou showed a decreasing trend in spring and an increasing trend in winter over the past 30 years. Our analyses reveal distinct precipitation cycles, including a quasi-30-year cycle since the 1960s and a quasi-10-year cycle since the 1980s. However, the periodicity has weakened in the past 10 years. In addition, the occurrence of torrential rain has increased rapidly in the past 10 years. Furthermore, influenced by global climate change and regional processes, the variation of extreme precipitation in Hangzhou has changed, which shows strong correlations with the overall trend of annual precipitation. The annual maximum daily precipitation in Hangzhou was mainly in the range of 0–40 mm from 1951 to 1980 and in the range of 40–80 mm from 1981 to 2010 with the maximum daily precipitation occurrence rate of 4.7 times/10a and 6.3 times/10a, respectively. This study emphasizes the risk of urban waterlogging caused by short-term heavy rainfall and provides useful reference to the assessment of extreme meteorological and hydrological disaster risk in Hangzhou.

In order to better implement the water environmental management policies, water quality evaluation is the basic step, that is to reasonably divide it into specific water quality category according to multiple water quality parameters in a certain water area.Aimed at this problem, an improved Naive Bayes classification method was proposed, which endowed different attributes with different weights, weakened the assumption of Naive Bayes conditional independence, and made the classification result closer to the actual category.Firstly, referred to the data released by the national surface water quality automatic monitoring station, 500 water quality data were selected as samples, and an evaluation system with four indicators was established, including dissolved oxygen, permanganate index, ammonia nitrogen and total phosphorus.And then, the improved Naive Bayes classification method was used to learn and evaluate the samples, and its classification performance by the five fold cross validation method was verified.The results show that the accuracy, precision, recall and F1 value of the improved Naive Bayes classification method reach 96.0%, 95.9%, 93.8% and 94.8% respectively, with higher performance index of water quality data classification compared with other Naive Bayes classification method, which can provide some reference for the problem of water quality data classification encountered in actual engineering.

In order to improve the quality and planting efficiency of barley seedlings in the growth chamber, the Kalman filter algorithm was firstly used to process the data collected by the sensor, which effectively reduced the influence of environmental factors and the error of the sensor itself, improved the accuracy of the collected data, and ensured the precise control in the growth chamber and accurate test data.Then multiple nonlinear regression, radial basis function and multilayer perceptron neural network were used to analyze the average growth height, seedling weight and seed weight of barley seeds about 160 hours after germination under different conditions.The drying ratio was analyzed and compared.The results show that the multi-layer perceptron network model fits the data best.Using this model to predict the average height of barley seedlings and the ratio of seedling weight of barley seedlings in the optimal environment is basically consistent with the actual planting effect, which provides a certain reference for the planting of barley seedlings in the growth chamber.

As one of the emerging technologies of low-power wide-area Internet of things, narrow band Internet of things (NB-IoT) faces the pressure and challenge of massive connections of terminals.Aiming at practical application scenarios, a preamble allocation scheme based on Beta distribution was proposed.In this scheme, the energy consumption of each time slot device was firstly calculated.The equipment was divided into high energy consumption according to two grouping methods: average energy consumption and cumulative energy consumption.The preamble was assigned to the high-energy-consuming device group first, and the remaining preamble and the conflicting preamble of the high-energy-consuming device group were assigned to the low-energy-consuming device group.The simulation results show that compared with the traditional random allocation scheme, the proposed scheme can effectively improve the average access success rate and average throughput of the NB-IoT system, and can effectively reduce the back off time.

With the increasing demand for tooth bleaching in esthetic dentistry, its safety has been the focus of a comprehensive body of literature. In this context, the aim of the present study was to evaluate the application effects of pentalysine β-carbonylphthalocyanine zinc (ZnPc(Lys)5)-mediated photodynamic therapy in dentin bleaching and its effects on dentin collagen. We first established a new and reproducible tooth staining model using dentin blocks stained by Orange II and then bleached with ZnPc(Lys)5 (25 μM) and hydrogen peroxide (10% or 30%). Data were analyzed with one- and two-way ANOVA and a significance level of p < 0.05. ZnPc(Lys)5 effectively bleached the dentin samples to an extent comparable to hydrogen peroxide at either 10% or 30% concentrations. Further studies on the dentin morphology, chemical element distribution, and protein constituents, using an electron microscope, energy dispersive spectroscopy, X-ray photoelectron spectroscopy, and SDS-PAGE, demonstrated that treatment with the photosensitizer preserved the dentin structure and, at the same time, the major organic component, collagen type I. For comparison, hydrogen peroxide (10% or 30%) treatment significantly degraded the collagen protein. This work indicated that the photosensitizer exerts potent bleaching effects on dentin staining; importantly, does not damage dentin and its collagen content; and opens up a new strategy to further explore various photosensitizers for the bleaching of both tooth enamel and dentin.

In order to improve the performance of digital modulation recognition under high dynamic signal-to-noise ratio, a joint modulation recognition method based on high-order cumulant and discriminative restricted Boltzmann machine was proposed, which extracted the high-order cumulant of digital signals as signal features, comprehensively utilized the generation ability and classification ability of the discriminative restricted Boltzmann machine, analyzed the recognition rate of digital signals in environments containing Gaussian noise, time-varying phase offset or Rayleigh fading.Experimental results show that compared with traditional classification methods, the recognition performance of the proposed method is obviously improved.In addition, the use of the model’s generation ability to reconstruct the input features can effectively improve the signal recognition rate under low signal-to-noise ratio.

In vehicular edge computing (VEC), some tasks can be processed either locally or on the mobile edge computing (MEC) server at a base station (BS) or a nearby vehicle. In fact, tasks are offloaded or not, based on the status of vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communication. In this paper, device-to-device (D2D)-based V2V communication and multiple-input multiple-output and nonorthogonal multiple access (MIMO-NOMA)-based V2I communication are considered. In actual communication scenarios, the channel conditions for MIMO-NOMA-based V2I communication are uncertain, and the task arrival is random, leading to a highly complex environment for VEC systems. To solve this problem, we propose a power allocation scheme based on decentralized deep reinforcement learning (DRL). Since the action space is continuous, we employ the deep deterministic policy gradient (DDPG) algorithm to obtain the optimal policy. Extensive experiments demonstrate that our proposed approach with DRL and DDPG outperforms existing greedy strategies in terms of power consumption and reward.

The oceanic absorption of atmospheric CO _2 acidifies seawater, which accelerates CaCO _3 dissolution of calcifying organisms, a process termed dissolution effect. Promoted CaCO _3 dissolution increases seawater ALK (alkalinity), enhancing ocean carbon sink and mitigating ocean acidification. We incorporate different parameterizations of the link between CaCO _3 dissolution and ocean acidification into an Earth System Model, to quantify the feedback of the dissolution effect on the global carbon cycle. Under SRES A2 CO _2 emission scenario and its extension with emissions of 5,000 PgC in ∼400 years, in the absence of the dissolution effect, accumulated ocean CO _2 uptake between year 1800 and 3500 is 2,041 PgC. The consideration of the dissolution effect increases ocean carbon sink by 195–858 PgC (10%–42%), and mitigates the decrease in surface pH by 0.04–0.17 (a decrease of 10%–48% in [H ^+ ] (hydrogen ion concentration)), depending on the prescribed parameterization scheme. In the epipelagic zone, relative to the Arc-Atlantic Ocean, the Pacific-Indian Ocean experiences greater acidification, leading to greater dissolution effects and the resultant stronger feedbacks on ocean carbon sink and acidification in the Pacific-Indian Ocean. Noteworthy, the feedback of dissolution effect on ocean carbon sink can be comparable with or stronger than the feedback from CO _2 -induced radiative warming. Our study highlights the potentially critical role played by CaCO _3 dissolution effect in the ocean carbon sink, global carbon cycle and climate system.

Utilizing the inherent spontaneous polarization of single-domain ferroelectric materials is crucial for achieving spatial charge separation via the induction of a built-in electric field. Herein, a single-domain Z-scheme heterojunction of BiFeO3/CsPbBr3 was rationally designed by in situ growth of CsPbBr3 nanocrystals on the surface of single-domain BiFeO3 nanosheets. Detailed experimental characterization and theoretical simulations reveal that forming the single-domain BiFeO3/CsPbBr3 heterojunction induces a polarization electric field within the BiFeO3 and a built-in-electric field at the heterojunction interface, both of which act as dual driving forces (DDFs) for charge separation. Under light irradiation, the DDFs enable significant spatial charge separation within the heterojunction following a Z-scheme charge transfer mode. Consequently, the single-domain BiFeO3/CsPbBr3 heterojunction yields a sound photocatalytic CO production rate of 53.1 mmol g-1 h-1 in a solid-vapor reaction system under visible light irradiation, representing a 6.4-fold increase in activity compared to pristine CsPbBr3. This work demonstrates the significant impact of developing single-domain heterojunction photocatalysts to enhance visible light-driven photocatalytic systems. [ABSTRACT FROM AUTHOR]

Abstract Many different encoding algorithms for systematic polar codes (SPC) have been introduced since SPC was proposed in 2011. However, the number of the computing units of exclusive OR (XOR) has not been optimized yet. According to an iterative property of the generator matrix and particular lower triangular structure of the matrix, we propose an optimized encoding algorithm (OEA) of SPC that can reduce the number of XOR computing units compared with existing non-recursive algorithms. We also prove that this property of the generator matrix could extend to different code lengths and rates of the polar codes. Through the matrix segmentation and transformation, we obtain a submatrix with all zero elements to save computation resources. The proportion of zero elements in the matrix can reach up to 58.5% from the OEA for SPC when the code length and code rate are 2048 and 0.5, respectively. Furthermore, the proposed OEA is beneficial to hardware implementation compared with the existing recursive algorithms in which signals are transmitted bidirectionally.

As naturally derived material, cellulose aerogels have excellent thermal insulation properties due to their unique high porosity and three-dimensional mesoporous structure. However, its hydrophilic properties limit its application in the field of building insulation. Here, we propose a method to prepare high hydrophobicity by adopting the sol-gel method and chemical vapor reaction strategy using cellulose acetate type II as raw material and 2,4-toluene diisocyanate as the cross-linking agent. Thermal properties of cellulose acetate aerogels (CAAs) were measured, where pyridine was the catalyst, acetone was the solvent, and perfluorodecyltriethoxysilane (PFDS), hexamethyldisilazane (HMDS), and methyltriethoxysilane (MTES) were used as hydrophobic agents (by process hydrophobic test). Compared with MTES-modified cellulose acetate aerogels (M-CAAs) and HMDS (H-CAAs)-modified cellulose acetate aerogels, PFDS-modified (P-CAAs) cellulose acetate aerogels are the most hydrophobic. By implementing hydrophobic modification of PFDS both inside and outside the structure of cellulose acetate aerogels, the water contact angle can reach up to 136°, strongly demonstrating the potential of PFDS as a hydrophobic agent. The results show that the thermal conductivity and compressive strength of cellulose acetate aerogel with the best hydrophobic properties are 0.035 W m−1 K−1 at normal pressure and 0.39 MPa at 3% strain, respectively. This work shows that the highly hydrophobic cellulose acetate aerogel has potential as a waterproof material in the field of building thermal-insulation materials.

Understanding the response of tropospheric ozone (O3) to nitrogen dioxide (NO2) change is important for local O3 control. The relationship between O3 and NO2 at county scale in China has been extensively studied using models, but there is a lack of results from direct measurements. In this study, we used measurements of O3, NO2 and meteorological conditions from a dense network in the Yangtze River Delta (YRD), and satellite observed formaldehyde (HCHO) and NO2 column densities for the analysis of O3 variabilities and its relationship to NO2. As a result, severe O3 pollution occurred mainly in Shanghai city, southern Jiangsu and northern Zhejiang provinces in YRD during April–September. In addition, meteorological conditions could explain 54% the diurnal O3 variation over YRD. During April–September 2015–2021, O3 showed a significant positive relationship (r = 0.61 ± 0.10) with NO2 after removing the impact from meteorological conditions. However, the relationship could be reversed with NO2 concentration change. Our result suggested that the controllable O3 related to NO2 change is up to 100 μg·m−3 in megacities over Shanghai and northern Zhejiang province. The O3 is much more sensitive to the NO2 reduction in megacities than surrounding areas. Our results evaluate the different impacts of NO2 changes on O3 formation, which provides explanation for the simultaneously alleviated O3 pollution and reduced NO2 in 2020 in Shanghai and northern Zhejiang, as well as the increased O3 in most counties before 2019 with reduced NO2 during October–March. The driving mechanism as revealed from this study for O3 and NO2 will be valuable for the O3 abatement through NO2 reduction at sub-county scale over YRD in China.