Key Impact Factors Research Articles

Monitoring and Assessment for the Susceptibility of Landslide Changes After the 2017 Ms 7.0 Jiuzhaigou Earthquake Using the Remote Sensing Technology

Monitoring the change of post-seismic landslides could provide valuable information for geological disaster treatment. The 2017 Jiuzhaigou Ms 7.0 earthquake has triggered a large number of landslides in the Jiuzhaigou United Nations Educational, Scientific and Cultural Organization (UNESCO) Natural Heritage site, which provides a unique opportunity for monitoring the spatio-temporal characteristics and exploring the impact factors of post-seismic landslides change. In this study, the spatio-temporal characteristics of landslides and their post-seismic changes are analyzed using multi-source, multi-temporal, and multi-scale remote sensing data combining with the field study. The Support Vector Machine classification, visual interpretation, field investigation, and Geographic Information System technology are employed to extract landslides and analyze their spatial distribution patterns. Moreover, the Certainty Factor method is used to explore the susceptibility of landslides and to find key impact factors. Our results show that the net increase area of landslide is 1.2 km2 until September 27th, 2019, which are induced by the expansion of coseismic landslide, the post-seismic landslide, and the expansion of vegetation degradation. Moreover, the area expansion of the coseismic and post-seismic landslides is mainly related to the increase of debris flow induced by the post-seismic torrential rainfalls. The highest net increase rate of post-seismic landslide change does not distribute on the regions with the highest density of coseismic landslides. The susceptibility of post-seismic landslide change is greatly influenced by slope, altitude, aspect, peak ground acceleration fault, and strata. It is higher in the coseismic landslide area with low susceptibility. This study also suggests that the potential landslides will most likely occur in the unstable slope region affected by the additional driving force. Therefore, great attention should be paid to identify and prevent the potential landslides on unstable slopes in addition to treatments of the sliding slopes. This study provides a good example for the monitoring and assessment of post-seismic landslides in mountainous regions with a steep slope and deep valley.

Industry 4.0 and its associated technologies

Rapid implementation of Information and Communication Technology in manufacturing enables the industrial processes to evolve as smart for mass customization, making it something unique and spectacular with the ways of adapting to work facilitated with better communication approaches, and novel classifications in manufacturing are changing the terminology of the Industrial Upheaval. The key impact factor offers a cutting edge to compete with the application of the IoT. Thus facilitating functional requirements to unravel towards the encounters erupting from the technological disruption. The enhancing parameter in novel manufacturing uses digital format information, which supports the content interstellar by considering the people's needs on the dependable data and the progress in the information and communication infrastructure. The potential of people being linked with mobile devices to admittance knowledge sources, bourgeoned by emergent technology, influences the Industry 4.0 to unceasing growth in the IoT, Cloud computing, autonomous vehicles, Big Data, Augmented/Virtual Reality, 3-D printing, energy storage, nanotechnology, robotics, Artificial intelligence, materials science, biotechnology, and quantum computing. Smart Manufacturing is an inclusive strategy, which authorizes the production and generation Ecology to catapult the demand-supply equations on a physical goods/service perspective. This inclusive growth trajectory positions the future of industries to evolve towards industry 4.0. This necessitates the importance of skills and quality as the important parameter for providing cutting-edge deliverables to industries. While preparing themselves for employability in the future, potential and prospective students need to pick and equip themselves with industry 4.0 competent skills for a better opportunity.

Distribution of Metal Resistance Genes in Estuarine Sediments and Associated Key Impact Factors.

Currently, little is known about the distribution of metal resistance genes (MRGs) in estuarine sediments. In this study, we used the high-throughput quantitative real-time polymerase chain reaction (HT-qPCR) to determine the distribution of MRGs in the sediments of an estuary system and the associated key impact factors. The relative abundance of the detected MRGs showed a decreasing trend from the river inlet toward the sea and a decrease from the middle area of the estuary to the near-shore areas on both sides; these decreases were higher in the summer than in the winter. In the estuary system during the summer, the abundance of Zn- and Cu-MRGs from the river inlet to the sea decreased by 99.5% and 93.6%, whereas those of Hg- and Cd-Zn-Co-MRGs increased by 51.5% and 16.7%, respectively. Moreover, the abundance of Zn- and Cu-MRGs in the winter decreased by 88.6% and 97.7%, respectively, whereas that of Cd-Bi-Zn-Pb-MRGs increased by 729.6%. Furthermore, the abundances of MRGs and mobile genetic elements (MGEs) were significantly positively correlated with the levels of antibiotic residues and heavy metals as well as with the particle size and total organic carbon content of the sediment; however, they were significantly negatively correlated with seawater salinity and the oxidation and reduction potential (Eh) and pH of the sediment. The abundance of MGEs was significantly positively correlated with the abundance of MRGs in the sediment. Our findings suggest that antibiotic residues facilitated the proliferation and propagation of MRGs by promoting MGEs in estuarine sediments.

The challenges of cybersecurity insurance development: The case of Latvia

Purpose. This paper aims to provide an overview of the current challenges of cybersecurity insurance, focusing on the identification of development constraints and opportunities and the key impact factors of this recently emerging insurance market in Latvia. Methodology. The authors used theoretical and empirical research methods, e.g., a literature review, surveys of experts from principal insurance companies and professionals from prominent mobile network operators, and interviews with experts in cybersecurity and pioneers of cybersecurity insurance in Latvia. Findings. The research results illustrate the immense difficulties in providing cyber risk insurance in Latvia. The lack of historical knowledge and evolving nature of cyber risks create significant challenges in quantifying and expressing cyber threats in monetary value. Cyber risk modelling is in its infancy, as events and threat vectors are still evolving. Due to the evolving nature of cyber risks, simulating events and fitting into traditional risk management forms are the primary challenges for insurance companies. Despite the increasing relevance of cyber risks to businesses, research on guidelines and methods of mitigating cyber risks with cyber insurance is still limited. Social implications. Cyber insurance is a direct result of public awareness of cyber threats and preparedness to mitigate business risks. Thus, cyber insurance is an integral part of educating and building a more resilient society. Originality/Value. The authors propose various approaches to cybersecurity insurance development, such as technology awareness building, standardization of mandatory reporting requirements and public–private partnerships in which the government covers part of the risk to overcome insurability limitations. Keywords: cyber insurance, cybersecurity, cyber insurance market, cyber risks

An integrated Bayesian least-squares-support-vector-machine factorial-analysis (B-LSVM-FA) method for inferring inflow from the Amu Darya to the Aral Sea under ensemble prediction

An integrated Bayesian least-squares-support-vector-machine factorial-analysis (B-LSVM-FA) method is developed through integrating techniques of Bayesian inference, least squares support vector machine (LSVM), and factorial analysis (FA) into a general framework. B-LSVM-FA has advantages in: (i) capturing the complicated nonlinear relationship between input factors and streamflow, (ii) optimizing the key parameters of LSVM through a maximum posterior density estimation, and (iii) quantifying the contributions of individual and interactive effects of multiple factors to streamflow variation. B-LSVM-FA is then applied to inferring the changes in inflow from the Amu Darya to the Aral Sea (named as IATA). Results obtained cannot only identify the key impact factors reducing IATA during the period of 1960–2015, but also predict future trends in IATA for 2020–2050. Comparing to the conventional ANN, SVM and LSVM, the proposed method performs better in describing the IATA changes with anthropogenic, hydrometeorological, and ecological factors in terms of NSE, RMSE, and PBS. Results disclose that the major factors affecting IATA at annual/seasonal scale are upstream streamflow, agricultural water use in Uzbekistan, reservoir water storage, and evapotranspiration. The significant differences in the contributions of main factors to IATA at seasonal scale are observed because each season has unique characteristics of human activities, meteorological conditions, and vegetation coverages. In order to seek the feasible strategies of recovering the IATA level in the future, 162 scenarios based on ensemble prediction are analyzed. Results indicate that the IATA would restore to its 1970s condition if the drip irrigation rate reaches 50% at the end of 2050, and the reservoir water storage level reduces to the average value of 1960–1970. The findings can provide valuable suggestions for decision makers to increase IATA, and thus ameliorating the ecological crisis within the Aral Sea Basin.

Selection of product recycling channels based on extended TODIM method

The evaluation and selection of product recovery channels is a key decision issue in closed-loop supply chain management. Although existing research does not take into account the presence of inaccurate information and the influence of the decision maker’s psychological state during the selection of recycling channels, we study the issue of recycling channel selection under the consideration of inaccurate information and the influence of the decision maker’s psychological state. (1) First, we establish a new evaluation index system for recycling channels for policy makers. (2) We propose a TODIM (an acronym in Portuguese of Interactive and Multi-criteria Decision Making) method based on fuzzy evaluation and Shapley index for decision makers to imitate the psychological behavior characteristics of FMEA (Failure mode and effect analysis) team members. It can also be used to estimate the risk priority of several failure modes. The interval type 2 fuzzy is used to describe the uncertainty in the risk assessment process. (3) Finally, we obtain the practical significance of the method proposed in this paper through case studies, sensitivity analysis and comparative analysis of traditional methods. We also find the key impact factors in the choice of recycling channels.

Random Network Transmission and Countermeasures in Containing Global Spread of COVID-19-Alike Pandemic: A Hybrid Modelling Approach

Since the outbreak of the novel coronavirus disease (COVID-19) at the beginning of December 2019, there have been more than 28.69 million cumulative confirmed cases worldwide as of 12th September 2020, affecting over 200 countries and regions with more than 920,463 deaths. The COVID-19 pandemic has been sweeping worldwide with unexpected rapidity. In this paper, a hybrid modelling strategy based on tessellation structure- (TS-) configured SEIR model is adopted to estimate the scale of the pandemic spread. Building on the data pertaining to the global pandemic transmission over the last six months around the world, key impact factors in the transmission and control procedure have been analysed, including isolation rate, number of the infected cases before taking prevention measures, degree of contact scope, and medical level, so as to capture the fundamental factor influencing the pandemic. The quantitative evaluation allowed us to illustrate the magnitude of risks of pandemic and to recommend appropriate national health policy of prevention measures for effectively controlling both intra- and interregional pandemic spread. Our modelling results clearly indicate that the early-stage preventive measures are the most effective action to be taken to contain the pandemic spread of the highly contagious nature of the COVID-19.

Development of an integrated PCA-SCA-ANOVA framework for assessing multi-factor effects on water flow: A case study of the Aral Sea

An integrated PCA-SCA-ANOVA framework (abbreviated as PSAF) is advanced to analyze the impacts of multiple factors on water-flow variation. PSAF incorporates techniques of principle component analysis (PCA), stepwise-cluster analysis (SCA), and analysis of variance (ANOVA) within a general framework. PSAF cannot only quantify the sensitivity of water flow to individual and interactive factors, but also simulate water flow effectively under different scenarios. A case study of the Aral Sea is conducted to demonstrate the effectiveness and practicability of PSAF. The Nash-Sutcliffe coefficients for calibration and validation are 0.96 and 0.84, respectively. Results reveal that the key impact factors that cause the variability of water flow from the Amu Darya into the Aral Sea are runoff in the upper reach (contributing 53%), agricultural water use (occupying 23%) and water storage in reservoirs (occupying 3%). Results also disclose that runoff in the upper reach and agricultural water use have obvious interaction; the high level of runoff in the upper reach can enhance the negative effects of agricultural water use on water flow. In the future, to recover the water flow from the Amu Darya into the Aral Sea to the level of that in the 1990s (about 8 km3), the decreasing rates of annual agricultural water use and water storage in reservoirs should be controlled at 1.0% and 0.5%, respectively. The findings are helpful for supporting water resources management and restoring the ecological environment of the Aral Sea.

Environmental performance evaluation of enterprises using internal resource loss and external environmental damage costs

Environmental performance evaluation is an essential part of environmental management—however, current methods of environmental performance overlook efficiencies in individual operational processes. Environmental pollutant waste not only causes internal resource loss but also leads to external environmental damage through emissions and generation of other byproducts, and these dual factors are critical in environmental performance evaluation. This paper develops an “Internal Loss-External Damage” (ILED) environmental performance evaluation method. This two-dimensional method is applied to a coal-fired plant's environmental performance evaluation to demonstrate that the ILED method can identify key environmental pollution impact factors and quantify internal resource loss costs and environmental damage costs. Results show that combustion is a priority process for cost reduction efforts. This assessment will be helpful for enterprises seeking to improve environmental management effectiveness and reduce environmental pollution risk. Also, this performance evaluation provides a new perspective on environmental management and enriches the theoretical framework for environmental management.

Impacts of inland pollution input on coastal water quality of the Bohai Sea

Inland pollutants input is a key impact factor for the coastal water quality of the Bohai Sea. The coastal and inland water pollutant inputs were analyzed by using monitoring data of recent years from the State Oceanic Administration. The results showed that more than 56% of the Bohai Sea area was unclean seawater in 2012, although the water quality improved gradually after that time. In 2017, about one-third of the Bohai Sea area still had unclean seawater. Inorganic nitrogen, reactive phosphate, and petroleum hydrocarbons are the main pollutants in the seawater. A total of approximately 840,000 t of pollutants was transported to the sea each year by major rivers during 2010–2017. Significant correlations (p < 0.05) were found between the third-grade level seawater area and the pollutants of CODcr, petroleum, NO3−–N, NH4+–N, NO2−–N, Cu, and Pb and between the inferior fourth-grade level seawater area and the pollutants of petroleum, NO2−–N, Pb, and NO3−–N. The standard discharge rate of terrestrial-source sewage outlets was no more than 50%. The low standard discharge rate of the major terrestrial-source sewage pollutants of CODcr, NH4+–N, TP, BOD5, and SS caused more than 80% of the monitored sea areas adjacent to the selected key sewage outlets to not meet the water quality requirements of the marine functional area. The results suggest that implementing a coastal water management plan is necessary to reduce the heavy ecological burdens on the coastal zone of the Bohai Sea.

Connected Vehicle Based Distributed Meta-Learning for Online Adaptive Engine/Powertrain Fuel Consumption Modeling

A microscopic fuel consumption model with respect to comprehensive fuel consumption key impact factors that can reflect dynamic engine operations and various real-world driving conditions is essential for fuel efficient model-based vehicle and powertrain control (MB-VPC). Existing microscopic fuel consumption models are mostly steady state models with limited vehicle parameters as dynamic variables. Data-driven solutions can make the model comprehensive by introducing broad impact factors. However, without the required accessible online computational capability and fast model adaptation mechanisms, those data-driven solutions cannot quickly adapt to unseen driving conditions and engine condition changes based on real-world vehicle data to support MB-VPC. In this paper, a connected vehicle-based data mining (CV-DM) framework is proposed to achieve online adaptive dynamic fuel consumption modeling through knowledge sharing over CVs and the CV remote data center. Based on the CV-DM framework, CV-supported Distributed Meta-regression (CV-DMR) algorithms are developed to realize a fast few-shot adaptation with limited training data. Extensive proof-of-concept experiments are conducted with steady-state and transient vehicle engine data. Compared to the baseline physical model and the existing non-adaptive grey-box model, prediction accuracy is improved by 47%-85% and 38%-80% respectively with only limited training data needed for the subject vehicle. Accordingly, a fuel savings of up to 9.4% can be achieved owing to the improvement of prediction accuracy.

Integrated assessment and obstacle factor diagnosis of China's scientific coal production capacity based on the PSR sustainability framework

The Scientific Coal Production Capacity (SCPC) is an important decision-making basis for the sustainable development of China's coal industry. It is important to objectively measure its evolution characteristics and identify its key impact factors. Based on the Pressure-State-Response (PSR) model and the connotation of SCPC, this paper constructs a comprehensive SCPC evaluation system from the dimensions of resources, technology, safety, environment and benefits. Furthermore, this paper proposes an integrated analysis method to evaluate and identify the evolutionary characteristics and key influencing factors of SCPC from 2000 to 2016. The results show that, a) China's SCPC assessment system reflects the current state of pressure drive, operational state and regulatory response of China's coal overcapacity and its interaction mechanism. b) The integrated assessment method of SCPC can effectively remedy the deviation caused by the method of constant power evaluation and improve the accuracy of the evaluation method and the reliability of the evaluation results. c) China's SCPC tends to improve overall during the period of 2000-2016, but its development is slow. The reason is that the operation state of SCPC and its response mechanism are improved, and the impact of the pressure-driven factors of SCPC still exists and has not be improved. d) The obstacle factors of the pressure-subsystem of SCPC mainly involve coal transportation and carbon mitigation constraints. The obstacle factors of the state-subsystem are mainly coal quality, employee health and asset operation ability, and the obstacle factors of the response-subsystem are market regulation and demand-side response mechanisms. The key to improving China's SCPC in the future is to strengthen the construction of coal transportation channels, achieve clean utilization of coal resources, pay attention to employee health, improve asset operation efficiency and promote coal market reform.

Mercury distribution in the surface soil of China is potentially driven by precipitation, vegetation cover and organic matter

BackgroundUnderstanding the mechanism of Hg accumulation in soil, which is a net Hg sink, at a national scale is important for protecting the environment and improving food safety. The mercury (Hg) distribution in surface soil in China is quite uneven, with relatively high concentrations in southeastern China and low concentrations in northwestern China. The reason for this distribution is inconclusive, especially at the continental scale. In this study, the relative contributions of the key impact factors, including dry and wet deposition, soil organic matter (SOM) and solar radiation to soil Hg, were evaluated.ResultsWet and dry deposition associated with precipitation and vegetation cover and emissions influenced by SOM are key factors controlling Hg distribution in surface soil. In southeastern China, high levels of wet deposition associated with the South Asia monsoon and dry deposition, enhanced by vegetation canopies, together with low levels of emissions caused by highly vegetated surfaces and solar radiation, are responsible for the high Hg levels in soil (> 0.08 mg/kg). In northeastern China, moderate levels of wet Hg deposition, high levels of dry deposition via throughfall and litterfall, low emissions due to weak solar radiation and high levels of SOM are responsible for high Hg accumulation in soil. In northwestern China, low levels of wet deposition, together with high emissions levels, low vegetation cover (bare soil) and SOM and strong solar radiation, contributed to the low Hg level in the surface soil (< 0.03 mg/kg).ConclusionsWe suggest that wet deposition derived from the Asian monsoon, dry deposition linked to vegetated surfaces and Hg emissions associated with vegetation cover, SOM and solar radiation play key roles in the soil Hg level in China. In other terrestrial environments worldwide, especially in regions with significantly high levels of wet deposition and high amounts of vegetation cover and soil SOM, high Hg concentrations may exist in the surface soil.

Grain Consumption Forecasting: One Modified MLR Model Combined with Time Series Forecasting Theory

In the classical multivariate prediction model, most research studies focused on the selection of relevant behaviour factors and the stability of historical data for improving the predicting accuracy of the main behaviour factor, and the historical data of the main behaviour factor have never been considered as one relevant behaviour factor, which in fact can be the first key impact factor; besides, the historical data can directly predict the main behaviour in the time series forecasting model, such as the ARIMA model. In this paper, one modified MLR model combined with time series forecasting theory is presented and applied in grain consumption forecasting. In the proposed model, to improve the current grain consumption forecasting, how to select impact factors is also discussed by combining the grey relational degree and Pearson correlation coefficient with given weights, and the optimal preprocessing parameter by the moving average filtering is computed for eliminating the abnormal points and stabilizing the data. Finally, the selected main impact factors are inputted to the proposed modified MLR model for forecasting grain consumption. Simulation results have shown that the five-year mean absolute percentage error of ration and feed grain is 2.34% and 3.27%, respectively, and the prediction accuracy has improved up to 2 times compared with the BP model and LSTM model. Moreover, the robustness of the model is verified by prediction analysis at different time intervals of historical data.

CONSTITUTIONAL SUPPORT OF THE ENTREPRENEURSHIP LEGALITY IN UKRAINE

The article is devoted to the analysis of constitutional support of entrepreneurship legality in Ukraine and its functional purpose in the current conditions. The modern state of the constitutional instruments of government support of entrepreneurship in Ukraine is analyzed in its entirety; available accounting modes related to the official registration of natural and legal entities as economic agents as well as the prescription of regulatory requirements for conducting business in Ukraine are characterized. The authors formulate own view of the concept “entrepreneurship legality in Ukraine” taking into account modern development trends in business relations and objective necessity of their regulation through the constitutional means. Scholarly approaches to the definition of the concept “legal support of entrepreneurship” are studied. The paper proves that entrepreneurship faces the challenges, and sometimes it is impossible to solve the problems of local, regional, nationwide and global level, which evolve under the influence of non-market factors, independently. In order to resolve the above problems, it is essential to involve the state and provide conformity of the adopted policy in the business sector. The key impact factors and roadmap for further development of entrepreneurial activity are defined. The paper highlights the blocks of regulatory legal acts. Certain components of the constitutional support, which will have a favorable effect on entrepreneurial activity, are identified. The article generalizes the ways for improvement of the constitutional support of a business activity that further form four specific measures on effective and overall legal support of the entrepreneurial activity.

The Knowledge Spillover Risks Management Framework for Collaborative Innovation

Knowledge spillover is a manifestation of knowledge externality, its negative impact on collaborative innovation can not be ignored. Designing independent variables based on defining the key impact factors of knowledge spillover risks, propose hypothesis of the relationship between independent variables and dependent variables, and then verify hypothesis, analysis the minimum value and stability of knowledge spillover risks. Finally, based on the analysis results, constructing management framework of knowledge spillover risks for collaborative innovation.

Empirical research on household carbon emissions characteristics and key impact factors in mining areas

The coal-rich mining areas are a key area to deal with climate change in China, and it is of great significance to study household carbon emissions in these areas. Based on the micro family activity survey data of 3 towns and 9 townships in the Gujiao mining area in Shanxi Province, this study took the Consumer Lifestyle Approach (CLA) as the research paradigm, used descriptive statistics and multiple regression analysis to process the data, and found that household carbon emissions characteristics were closely associated with specific resource endowment, household income level, education level and social development. Specifically, the residential behaviors and travel behaviors are the direct impact on household carbon emission characteristics. The residential behaviors have a significant positive correlation with household carbon emissions, in which residential electricity consumption accounted for nearly 41%. In the travel behaviors, private cars are the largest contributors to the carbon emissions from transportation, approaching 79%, followed by bus travel (13%) and motorcycle travel (8%). Then, aiming at the indirect effects of household carbon emissions, this study also set up three scenarios, and found that education level and policy regulation can effectively reduce carbon emissions, and family scale increases carbon emissions. According to the empirical results of this study, local governments should focus closely on two aspects: strengthening household energy conservation and emission reduction awareness and improving public infrastructure construction in the future.

Dependence of farmers' livelihoods on environmental resource in key ecological function area: A case study of Gannan Plateau, China

The key ecological function area plays an important role in providing ecosystem service. As the main players of economic activities in this area, the excessive dependence of local farmers on environmental resource will seriously influence ecological environment quality, and thus impair the ecosystem service. At present, it is urgent to explore the dependence of farmers' livelihoods on environmental resource and its influencing factors in key ecological function areas, which would provide reference for the formulation of environmental friendly regional development policies. We selected the Gannan Plateau as study area. Based on 581 survey data, we analyzed the dependence of farmers' livelihood on environmental resource, and used the ordinal multi-class Logistic regression model to identify its key impact factors. The results showed that local farmers were highly dependent on environmental resource in Gannan Plateau, which was mainly reflected in three aspects of food self-sufficiency, daily energy consumption and income source, with the dependence of 57.3%, 56.9% and 37.4%, respectively. There were significant differences in the dependence of different types of farmers' livelihoods on environmental resource, in that farmers with higher levels of education and higher non-agricultural levels had lower dependence and the farmers with high dependency ratio were more dependent on environmental resources. The factors including family dependency ratio, labor education level, proportion of migrant workers in labor force, family member leadership, ecological policy, and altitude had significant impact on farmers' environmental resource dependence. Among them, family dependency ratio and altitude had positive impact on it, while the proportion of migrant workers in labor force, labor education level, family member leadership, ecological policy had negative impact.

Characterization of Multiphase Flow in CHOPS Processes Using a Systematic Framework

Summary Cold heavy oil production with sand (CHOPS) technique has successfully improved oil recovery from heavy oil reservoirs due to high permeability channels resulted from sand failure and foamy oil flow enhancing heavy oil movability. However, impacts of the sand failure and the foamy oil flow on multiphase fluid flow features are still not properly understood. In this paper, an effective and systematic framework has been proposed to characterize the multiphase fluid flow and determine the three-phase relative permeability of CHOPS processes. A recently developed sand failure criterion and a relative permeability model have been integrated with a reservoir simulator to simulate the CHOPS process. The unknown parameters involved in the systematic framework are intelligently determined by an iterative ensemble smoother (IES) algorithm, which also enables sensitivity analysis on CHOPS production profiles. Subsequently, the proposed framework is tested through a laboratory CHOPS experiment. We obtained not only fairly history-matched production data (i.e., cumulative oil, gas, and sand production), but also satisfactorily converged three-phase relative permeability curves by iteratively assimilating production profiles of the CHOPS experiment. It has also been found that using two sets of three-phase relative permeability is capable of representing the segment-type nature of the multiphase flow and reproducing the transition stage on the production profiles. Furthermore, the comparison between two sets of three-phase relative permeability indicates that the sand failure phenomenon yields a reduced residual oil saturation and an increased oil/gas relative permeability. In addition, the sensitivity analysis demonstrates that the segment-type nature of production profiles results from the variation of key impact factors dominating the multiphase fluid flow in CHOPS processes. Overall, the proposed systematic framework can not only reproduce the physical phenomena of sand failure and foamy oil, but also provide a convenient tool to characterize the multiphase flow in CHOPS processes.

Study on Ultra-short-time Power Forecast of Photovoltaic System based on Ground-based Cloud Image Recognition and Key Impact Factors

In recent years, under the dual pressure of resource shortage and environmental pollution, the photovoltaic (PV) power generation industry has flourished. The irradiance forecasting technology of PV power plants is of great significance for output prediction, grid dispatching and safe operation. Cloud cover is always the key factor making the irradiance fluctuate. In this article, colorful ground-based cloud images are collected by the all-sky imager every minute as the research object. Based on the traditional threshold method, a hybrid entropy threshold method is proposed to identify cloud clusters. Using the correlation analysis, among many impact factors with high correlation, five are extracted as input parameters of a BP network optimized by genetic algorithm (GA-BP). Through verification and comparison analysis, it is concluded that the recognition accuracy of the hybrid entropy threshold method is higher, and the average relative error can be controlled at about 5%. Based on this, the irradiance prediction of GA-BP also achieved better results than other models. It can meet the application requirements of PV power plants.