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To improve the quality of industrial internet network security assessment, this paper proposes an improved industrial internet network security situation assessment model. First,we analyze the factors that affect the network security of the industrial internet and determine the evaluation indicators; Second, the ER iterative algorithm and mutation method are used to fuse the evaluation indicators; Then, according to the fusion results, the industrial Internet network security situation assessment model is established; Finally, the selection covariance matrix adaptive evolution strategy (S-CMA-ES) is used to optimize the model parameters. This method establishes one-way selection of better strategies among different subclass groups, achieving better evaluation results. This method effectively solves the problem of reduced modeling accuracy caused by insufficient data. This paper conducts evaluation experiments on an industrial data set, and verifies the feasibility and effectiveness of the industrial Internet network security situation assessment model and S-CMA-ES optimization algorithm proposed in this paper.

As a vital part of the geo-environment and water cycle, ecosystem health and human development are dependent on water resources. Water supply and demand are influenced significantly by land use and cover change (LUCC) which shapes the surface ecosystems by altering their structure and function. Under future climate change scenarios, LUCC may greatly impact regional water balance, yet the impact is still not well understood. Therefore, examining the spatial relationship between LUCC and water yield services is crucial for optimizing land resources and informing sustainable development policies. In this study, we focused on the Hanjiang River Basin and used the patch-generating land use simulation (PLUS) model, coupled with the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model, to assess water yield services under three Shared Socioeconomic Pathway and Representative Concentration Pathway (SSP-RCP) scenarios. For the first time, we considered the impact of future changes in socio-economic and water use indicators on water demand using correction factors and ARIMA projections. The relationship between water supply and demand was explored using this approach, and LUCC’s effects on this balance are also discussed. Results indicate that: (1) The patterns of LUCC are similar for the three scenarios from 2030 to 2050, with varying levels of decrease for cropland and significant growth of built-up areas, with increases of 6.77% to 19.65% (SSP119), 7.66% to 22.65% (SSP245), and 15.88% to 46.69% (SSP585), respectively, in the three scenarios relative to 2020; (2) The future supply and demand trends for the three scenarios of produced water services are similar, and the overall supply and demand risks are all on a downward trend. Water demand continues to decline, and by 2050, the water demand of the 3 scenarios will decrease by 96.275×108t, 81.210×108t, and 84.13×108t relative to 2020, respectively; while supply decreases from 2030 to 2040 and rises from 2040 to 2050; (3) Both water supply and demand distributions exhibit spatial correlation, and the distribution of hotspots is similar. The water supply and demand are well-matched, with an overall supply-demand ratio greater than 1.5; (4) LUCC can either increase or decrease water yield. Built-up land provides more water supply compared to other land types, while forest land has the lowest average water supply. Limiting land use type conversions can enhance the water supply.

With the rapid advancements in information technology and industrialization, the sustainability of industrial production has garnered significant attention. Industrial control systems (ICS), which encompass various facets of industrial production, are deeply integrated with the Internet, resulting in enhanced efficiency and quality. However, this integration also introduces challenges to the continuous operation of industrial processes. This paper presents a novel security assessment model for ICS, which is based on evidence-based reasoning and a library of belief rules. The model consolidates diverse information within ICS, enhancing the accuracy of assessments while addressing challenges such as uncertainty in ICS data. The proposed model employs evidential reasoning (ER) to fuse various influencing factors and derive security assessment values. Subsequently, a belief rule base is used to construct an assessment framework, grounded in expert-defined initial parameters. To mitigate the potential unreliability of expert knowledge, the chaotic mapping adaptive whale optimization algorithm is incorporated to enhance the model’s accuracy in assessing the security posture of industrial control networks. Finally, the model’s effectiveness in security assessment was validated through experimental results. Comparative analysis with other assessment models demonstrates that the proposed model exhibits superior performance in ICS security assessment.

This paper predicts the network security posture of an ICS, focusing on the reliability of Industrial Control Systems (ICSs). Evidence reasoning (ER) and belief rule base (BRB) techniques are employed to establish an ICS network security posture prediction model, ensuring the secure operation and prediction of the ICS. This model first integrates various information from the ICS to determine its network security posture value. Subsequently, through ER iteration, information fusion occurs and serves as an input for the BRB prediction model, which necessitates initial parameter setting by relevant experts. External factors may influence the experts’ predictions; therefore, this paper proposes the Projection Equalization Optimization (P-EO) algorithm. This optimization algorithm updates the initial parameters to enhance the prediction of the ICS network security posture through the model. Finally, industrial datasets are used as experimental data to improve the credibility of the prediction experiments and validate the model’s predictive performance in the ICS. Compared with other methods, this paper’s prediction model demonstrates a superior prediction accuracy. By further comparing with other algorithms, this paper has a certain advantage when using less historical data to make predictions.

Soft Computing Techniques (SCT) are extensively used to estimate Labyrinth Weir's (LW) flow-rate. Due to the multiplicity of these techniques, identifying the most competent SCT is indispensable. This study aims to estimate the flow-rate of a sharp-crest triangular LW as a function of its side leg angle α and total head ratio (H/P) through several SCTs such as Adaptive Neuro-Fuzzy Inference System (ANFIS), Multi-Layer Perceptron (MLP), Support Vector Regression, and Radial Basis Function Neural Network. Additionally, these SCTs' potential combinations with Firefly Optimization Algorithm (FA) and Particle Swarm Optimization (PSO) are also investigated and used for estimation. The conducted experimental studies on LW examined a wide range of H/P in some limited α values. Correspondingly, all the proposed models and techniques are incapable of estimating the flow rate for intermediate α values without interpolation. Therefore, SCT's Interpolation accuracy is of the utmost importance. Besides the standard evaluation in the testing stage, a novel approach is utilized to evaluate the SCT's accuracy in the interpolation task. The SCTs are evaluated based on several statistical criteria, the Taylor diagram, Kruskal-Wallis, and Mann-Whitney tests. It is concluded that the competence of an SCT in the testing stage cannot guarantee its accuracy in the interpolation task. Subsequently, ANFIS-PSO and MLP-FA show the highest accuracy in the testing stage and interpolation task, respectively. Eventually, according to a systematic investigation in the implemented diagnostic test results, two rankings are presented for the applied SCTs based on their performance in the testing stage and their interpolation accuracy.

Labyrinth Weir (LW) is a popular control structure that passes a significantly higher flow rate compared to the linear weirs. In order to approach the optimal design of a trapezoidal LW, a multi-objective problem is defined to concurrently minimize the LW consumed concrete volume and maximize its discharge capacity. Simultaneously, a Radial Basis function Neural Networks (RBFNN) is designed and used for estimating LW discharge coefficient (Cd) according to the existing experimental results. An improved multi-objective particle swarm optimization (MOPSO) algorithm named TOPSIS Fuzzy MOPSO (TFMOPSO) is proposed to solve the LW optimization problem. This algorithm utilizes the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) to rank the solutions, while a fuzzy inference system is developed to select the algorithm strategy for finding two leaders among the non-dominated solutions. The performance of the proposed TFMOPSO has been tested on the optimization problem of the LW of the Ute dam. The results of TFMOPSO, along with three other state-of-the-art multi-objective algorithms, are explored in terms of hypervolume, coverage, and spacing metrics. It is demonstrated that the TFMOPSO outperforms other algorithms and studies for solving the LW multi-objective optimization problem for the case of Ute dam. Also, RBFNN is found to be one of the most appropriate approaches among studied algorithms in estimating the discharge coefficient of LW, while Pareto optimal solutions from TFMOPSO exhibit a significant improvement compared to the original design of Ute dam LW.

As the most abundant greenhouse gas in the atmosphere, CO2 has a significant impact on climate change. Therefore, the determination of the temporal and spatial distribution of CO2 is of great significance in climate research. However, existing CO2 monitoring methods have great limitations, and it is difficult to obtain large-scale monitoring data with high spatial resolution, thus limiting the effective monitoring of carbon sources and sinks. To obtain complete Chinese daily-scale CO2 information, we used OCO-2 XCO2 data, Carbon Tracker XCO2 data, and multivariate geographic data to build a model training data set, which was then combined with various machine learning models including Random Forest, Extreme Random Forest, XGBoost, LightGBM, and CatBoost. The results indicated that the Random Forest model presented the best performance, with a cross-validation R2 of 0.878 and RMSE of 1.123 ppm. According to the final estimation results, in terms of spatial distribution, the highest multi-year average RF XCO2 value was in East China (406.94 ± 0.65 ppm), while the lowest was in Northwest China (405.56 ± 1.43 ppm). In terms of time, from 2016 to 2018, the annual XCO2 in China continued to increase, but the growth rate showed a downward trend. In terms of seasonal effects, the multi-year average XCO2 was highest in spring (407.76 ± 1.72 ppm) and lowest in summer (403.15 ± 3.36ppm). Compared with the Carbon-Tracker data, the XCO2 data set constructed in this study showed more detailed spatial changes, thus, can be effectively used to identify potentially important carbon sources and sinks.

Ambient exposure to fine particulate matter (PM2.5) in eastern China, a densely populated region with very high-level PM2.5 pollution, has attracted great concern from the public, government, and scientific community. By taking advantage of advanced statistical methods and a high-resolution PM2.5 dataset, this study explicitly investigated the spatiotemporal changes in PM2.5 in eastern China from 2001 to 2018 at multiple spatial and temporal scales and examined its links with natural and socioeconomic factors to explore their effects on PM2.5 changes. This study found that the PM2.5 concentration in most of eastern China declined recently, while most of the discernable decreasing trends occurred in the southern and western areas of the study domain, and the statistically significant increasing trends were primarily in the North China Plain. The influencing factors analysis found that, among the selected four natural and five anthropogenic factors, temperature, and population density exerted more potent effects than the other influencing factors, and all the influencing factors were found to impose complex effects on the PM2.5 concentration over space and time. Our study draws a complete picture of the changes in PM2.5 and its possible influences, which could guide future actions to mitigate PM2.5 pollution in eastern China.

Abstract As of July 27, 2020, COVID‐19 has caused 640,000 deaths worldwide and has had a major impact on people's productivity and lives. Analyzing the spatial distribution characteristics of COVID‐19 cases and their relationships with meteorological and environmental factors might help enrich our knowledge of virus transmission and formulate reasonable epidemic prevention strategies. Taking the cumulative confirmed cases in Hubei province from January 23, 2020, to April 8, 2020, as an example, this study analyzed the spatial evolution characteristics of confirmed COVID‐19 cases in Hubei province using exploratory spatial data analysis and explored the spatial relationship between the main environmental and meteorological factors and confirmed COVID‐19 cases using a geographically weighted regression (GWR) model. Results show that there was no obvious spatial clustering of confirmed COVID‐19 cases in Hubei province, while the decline and end of the newly confirmed cases revealed relatively obvious negative spatial correlations. Due to the lockdown in Hubei province, the main air quality indexes (e.g., AQI and PM2.5) decreased significantly and environmental quality was better than historical contemporaneous levels. Meanwhile, the results of the GWR model suggest that the impacts of environmental and meteorological factors on the development of COVID‐19 were not significant. These findings indicate that measures such as social distancing and isolation played the primary role in controlling the development of the COVID‐19 epidemic.

NO2 (nitrogen dioxide) is a common pollutant in the atmosphere that can have serious adverse effects on the health of residents. However, the existing satellite and ground observation methods are not enough to effectively monitor the spatiotemporal heterogeneity of near-surface NO2 concentrations, which limits the development of pollutant remediation work and medical health research. Based on TROPOMI-NO2 tropospheric column concentration data, supplemented by meteorological data, atmospheric condition reanalysis data and other geographic parameters, combined with classic machine learning models and deep learning networks, we constructed an ensemble model that achieved a daily average near-surface NO2 of 0.03° exposure. In this article, a meteorological hysteretic effects term and a spatiotemporal term were designed, which considerably improved the performance of the model. Overall, our ensemble model performed better, with a 10-fold CV R2 of 0.89, an RMSE of 5.62 µg/m3, and an MAE of 4.04 µg/m3. The model also had good temporal and spatial generalization capability, with a temporal prediction R2 and a spatial prediction R2 of 0.71 and 0.81, respectively, which can be applied to a wider range of time and space. Finally, we used an ensemble model to estimate the spatiotemporal distribution of NO2 in a coastal region of southeastern China from May 2018 to December 2020. Compared with satellite observations, the model output results showed richer details of the spatiotemporal heterogeneity of NO2 concentrations. Due to the advantages of using multi-source data, this model framework has the potential to output products with a higher spatial resolution and can provide a reference for downscaling work on other pollutants.

Hybrid Ensemble Empirical Mode Decomposition (EEMD) and Least Square Support Vector Machine (LSSVM) is proposed to improve short-term wind speed forecasting precision. The EEMD is firstly utilized to decompose the original wind speed time series into a set of subseries. Then the LSSVM models are established to forecast these subseries. Partial autocorrelation function is adopted to analyze the inner relationships between the historical wind speed series in order to determine input variables of LSSVM models for prediction of every subseries. Finally, the superposition principle is employed to sum the predicted values of every subseries as the final wind speed prediction. The performance of hybrid model is evaluated based on six metrics. Compared with LSSVM, Back Propagation Neural Networks (BP), Auto-Regressive Integrated Moving Average (ARIMA), combination of Empirical Mode Decomposition (EMD) with LSSVM, and hybrid EEMD with ARIMA models, the wind speed forecasting results show that the proposed hybrid model outperforms these models in terms of six metrics. Furthermore, the scatter diagrams of predicted versus actual wind speed and histograms of prediction errors are presented to verify the superiority of the hybrid model in short-term wind speed prediction.

In recent decades, various kinds of remote sensing-based drought indexes have been proposed and widely used in the field of drought monitoring. However, the drought-related software and platform development lag behind the theoretical research. The current drought monitoring systems focus mainly on information management and publishing, and cannot implement professional drought monitoring or parameter inversion modelling, especially the models based on multi-dimensional feature space. In view of the above problems, this paper aims at fixing this gap with a component-based system named RSDMS to facilitate the application of drought monitoring by remote sensing. The system is designed and developed based on Component Object Model (COM) to ensure the flexibility and extendibility of modules. RSDMS realizes general image-related functions such as data management, image display, spatial reference management, image processing and analysis, and further provides drought monitoring and evaluation functions based on internal and external models. Finally, China's Ningxia region is selected as the study area to validate the performance of RSDMS. The experimental results show that RSDMS provide an efficient and scalable support to agricultural drought monitoring.

Water shortage is one of the main factors limiting urban construction and development. Scientific forecasting of water consumption is an important approach for the rational allocation of water resources. Taking the hospitals in Wuhan City as an example and basing the analysis on the characteristics of actual water consumption, we proposed a modified grey GM (0, 1∣sin) model of oscillation sequence. Using the grey theory, the variable weight-strengthening buffer operator (VWSBO) was introduced into this model to weaken the interference of the disturbance term on the data sequence. The actual quarterly total water consumption data for hospitals in Wuhan City during the period from 2010 to 2012 were used to verify the effectiveness and practicality of this modified grey GM (0, 1∣sin) model in predicting water consumption. In terms of the model’s fitting performance, the mean absolute percentage error (MAPE) of the modified model was 3.77%, indicating a higher prediction accuracy than the traditional grey GM (0, 1∣sin) model of oscillation sequences. Therefore, the modified grey GM (0, 1∣sin) model we established in this study can provide a scientific reference for administrative departments to forecast water consumption.

The use of chelators for cancer treatment has been an alternative option. Dithiocarbamates have recently attracted considerable attention owning to their diverse biological activities; thus, the preparation of new dithiocarbamate derivatives with improved antitumor activity and selectivity as well as probing the underlying molecular mechanism are required. In this study, di-2-pyridylhydrazone dithiocarbamate S-propionic acid (DpdtpA) and its copper complex were prepared and characterized, and its antiproliferative activity was evaluated. The proliferation inhibition assay showed that DpdtpA exhibited excellent antiproliferative effect in hepatocellular carcinoma (IC50 = 1.3 ± 0.3 μM for HepG2, and 2.5 ± 0.6 μM for Bel-7402). However, in the presence of copper ion, the antiproliferative activity of DpdtpA was dramatically attenuated (20–30 fold) owing to the formation of copper chelate. A preliminarily mechanistic study revealed that reactive oxygen species (ROS) generation mediated the antiproliferative activity of DpdtpA, and accordingly induced apoptosis, DNA cleavage, and autophagy. Surprisingly, the cytotoxicity of DpdtpA copper complex (DpdtpA–Cu) was also involved in ROS generation; however, a paradoxical relation between cellular ROS level and cytotoxicity was observed. Further investigation indicated that DpdtpA could induce cell cycle arrest at the S phase; however, DpdtpA–Cu lacked this effect, which explained the difference in their antiproliferative activity.

Understanding the seasonal variation characteristics of nitrogen and phosphorus nutrients in the reservoir can effectively prevent water eutrophication, which is very important to ensure that the water quality meets the standard. In order to explore the impact of the development of Shanxi In order to explore the impact of the development of Shanxi water control project on the concentration of nitrogen and phosphorus nutrients in the reservoir water, in this study Zhaoshandu reservoir and Shanxi In this study Zha-oshandu reservoir and Shanxi reservoir was selected as the research objects, the water quality indicators of the reservoir water was investigated in different months from March 2020 to February 2021. The different occurrence forms of N and P and the seasonal variation characteristics of TN/TP was analyzed, and further the principal component analysis (PCA) was The results showed that: (1) the concentration of TN in Zhaoshandu reservoir ranged from 0.67 to 1.24 mg/L, with an average of 0.86 mg/L, the concentration of NH3- N ranged from 0.00 to 0.15 mg/L, with an average of 0.055 mg/L, the The concentration of TN in Shanxi reservoir ranged from 0.50 to 0.67 mg/l, with an average of 0.57 mg/L. The concentration of NH3- N ranged from 0.00 to 0.15 mg/L, with an average of 0.055 mg/L, the concentration of NO- N ranged from 0.45 to 0.84 mg/L, The concentration of NH3- N ranged from 0.030 to 0.095 mg/L, with an average of 0.058 mg/L, and the concentration of NO3- -N ranged from 0.17 to 0.37 mg/L, with an average of 0.29 mg/L. The content of nitrogen of Zha-oshandu reservoir located in the downstream was higher than that of Shanxi reservoir, and the nitrogen The content of nitrogen of Zhaoshandu reservoir located in the downstream was higher than that of Shanxi reservoir, and the nitrogen in Zhaoshandu reservoir was high in spring and low in winter, and Shanxi reservoir was high in winter differently. (2) In Zhaoshandu reservoir, the concentration of TP ranged from 0.02 to 0.05 mg/L, and that in Shanxi reservoir was 0-0.0235 mg/L. Similarly, the content of TP in both reservoirs was (3) During the study period, most of the time of Zhaoshandu reservoir was the suitable period for algae growth, and a few times the water (4) The concentration of nitrogen and phosphorus in Zhaoshandu reservoir and Shanxi reservoir was related to the potential biological toxicity, the concentration of (4) The concentration of nitrogen and phosphorus in Zhaoshandu reservoir and Shanxi reservoir was related to the potential biological toxicity, the concentration of exogenous organic pollutants and the amount of suspended solids of plankton. In addition, the weather and temperature had a certain impact on the concentration of nitrogen and phosphorus in Shanxi reservoir. This study could provide protection suggestions for water pollution prevention and control of Shanxi reservoir according to its water quality characteristics. [ABSTRACT FROM AUTHOR]