Comprehensive Assessment of Seawater Quality Based on an Improved Attribute Recognition Model

The attribute recognition model (ARM) has been widely used to make comprehensive assessment in many engineering fields, such as environment, ecology, and economy. However, large numbers of experiments indicate that the value of weight vector has no relativity to its initial value but depends on the data of Quality Standard and actual samples. In the present study, the ARM is enhanced with the technique of data driving, which means some more groups of data from the Quality Standard are selected with the uniform random method to make the calculation of weight values more rational and more scientific. This improved attribute recognition model (IARM) is applied to a real case of assessment on seawater quality. The given example shows that the IARM has the merits of being simple in principle, easy to operate, and capable of producing objective results, and is therefore of use in evaluation problems in marine environment science.

Comprehensive benefits assessment of the ecological management model in Hobq Desert of China

The Hobq Desert is the seventh largest desert in China. Since 1988, the effects of ecological management in Hobq Desert have been obvious and a typical desertification control model developed gradually, which is well known as the ＂Hobq model＂. It is important to evaluate the comprehensive benefits of the ＂Hobq model＂, but this has not been addressed in previous studies. Thus, we established an index system to comprehensively evaluate the benefits of the ＂Hobq model＂, using an analytic hierarchy process method from 1988 to 2013. The results show the following： ecological benefits of the ＂Hobq model＂ had a positive trend, but with fluctuations during 2008 and 2009; economic benefits increased by 74% and the maximum value occurred in 2013; and social benefits increased steadily, but with fluctuations in 2010 and 2011. The social benefits were higher than ecological and economic benefits in the same period. Trends in overall benefits of the ＂Hobq model＂ were similar to changes in ecological benefits, which increased each year.

Development and Application of Oil-Spill Risk Assessment Model for Offshore Pipeline

To the potential oil-spill risk caused by offshore pipeline more attention has been paid after the Dalian oil spill incident from oil-pipeline explosion. Since then an issue about how to prevent and control the sudden oil-spill from the offshore pipeline has been raised. In this paper, we proposed an optimized model to analyze the main causes(probability) of spill and the consequence with the fuzzy comprehensive assessment model. Considering the complicated assessment process for oil-spill, the assessment factor system involving the spill probability and consequence was established based on the operative manual and statistic leakage/damage data of offshore pipeline in order to estimate the integrated spill risk score automatically. The evaluated factors of spill probability could be grouped into five aspects: corrosion, fatigue, national damage, third party, and operational fault; the consequence evaluated factors of spill included hazard of oil and impact-controlling capability. With some modifications based on experts' opinions, each of the evaluated factors in our work was developed with a relative weight and evaluation criterion. A test example for an offshore pipeline in the Bohai waters was described to show how the model can be used for an actual case in more detail. By using the oil-spill risk assessment model, it is easy to determine the risk level associated with the ongoing activity and management level and hence to take the risk mitigation action immediately.

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

Regional comprehensive assessment on environment-health of China

The aim of the study was to assess the environment-health development in different regions of China. 175 indicators, such as average life expectancy at birth, emission intensity of waste gas, GDP etc. were chosen to describe various aspects of the environment, health and development of China. Of all the indicators, life expectancy can sufficiently reflect health situation of population. Consequently, life expectancy was identified as key indicator, and 42 out of 175 indicators were selected for establishing the environment-health indicator framework with three grades of integrative indices to assess the development of environment-health of China. Based on the hierarchical relation between various grades of indices, the comprehensive environment-health index was calculated and contributed to classify the environment-health situation of 30 provinces, municipalities and autonomous regions in China which were divided into five grades by four predefined limits. Comprehensive assessment indicates that the environment-health situation of the eastern and coastal areas is superior to that of inland which is the western regions with underdeveloped economy and rigorous natural condition. Especially, the Qinghai-Tibet and Yunnan-Guizhou plateaus in southwestern China are most vulnerable in the environment and population health. These fit in with the pattern of national socio-economic development, which fully shows that socio-economic context plays a dominant role in the improvement of environment-health in China.

Spatial and Temporal Differences of Urban Land Comprehensive Carrying Capacity in Guangxi

The comprehensive carrying capacity of urban land can reflect the re- source level, economic scale, social development and environmental pressure of ur- ban land carrying. The assessment indicator system of urban land comprehensive carrying capacity was constructed from the 4 aspects of resource, economy, society, environment, and principal component analysis and cluster analysis were used to evaluate the urban land comprehensive carrying capacity of Guangxi and the 14 cities in 2005-2014, and analyzed its spatial and temporal characteristics as well as the driving forces, with the aim to provide references for improving the urban land comprehensive carrying capacity. The results showed that, the overall urban land comprehensive carrying capacity in Guangxi increased in 2005-2014, and there were significant differences in the land comprehensive carrying capacities among the cities in Guangxi in 2005-2014, in which Liuzhou, Guilin, Nanning belonged to the regions with the highest carrying capacity, while Beihai, Yulin, Wutong belonged to the regions with high carrying capacity, and the carrying capacities of the other cities changed with the changes of time. The economic development degree was an important factor influencing urban land comprehensive carrying capacity, but could not directly represent the urban land comprehensive carrying capacity level.

Safety culture assessment of petroleum enterprises based on SMART

This paper introduces an improved fuzzy comprehensive evaluation model specially designed for the safety culture assessment of petroleum enterprises. The model is established based on the authors' years of experience in this field. The index system of the safety assessment mentioned herein is based on SMART safety culture principles, covering safety material, safety behavior, safety system, safety belief, persistence of safety, and openness of safety. The proposed system also includes the safety material culture, in which on-the-spot analysis of the key factors of safety culture is conducted together with duty consultants. All the fuzzy membership indexes and their degrees were weighed and calculated with the final evaluation results, presented in accordance with the analytic hierarchy process. All the safety culture statuses of petroleum enterprises Z, S, and C were then incorporated into detailed analysis. The results of the application confirm that the levels of safety culture follow the order of Z petroleum enterprise <S petroleum enterprise <C petroleum enterprise. The safety culture grades of the S and C petroleum enterprises are "qualified," and it indicates that production can proceed. By contrast, the assessment result for Z petroleum enterprise is "not qualified", and it means that its production has to be prohibited. These results show that the fuzzy comprehensive evaluation model is suitable for evaluating the degree of safety of the studied petroleum enterprises. The reward and safety material culture, as well as safety conditions and safety reliability, should be taken as the key factors in safety culture construction for petroleum enterprises. Thus, the proposed model heightens production levels, and plays an important role in the construction of business safety culture.

Taking Comfort in Points:The Appeal of the Norwegian Model in Sweden

Purpose: The "Norwegian model" has become widely used for assessment and resource allocation purposes. This paper investigates why this model has becomes so widespread and influential. Approach: A theoretical background is outlined in which the reduction of "uncertainty" is highlighted as a key feature of performance measurement systems. These theories are then drawn upon when revisiting previous studies of the Norwegian model, its use, and reactions to it, in Sweden.Findings: The empirical examples, which concern more formal use on the level of universities as well as responses from individual researchers, shows how particular parts—especially the "publication indicator"—are employed in Swedish academia. The discussion posits that the attractiveness of the Norwegian model largely can be explained by its ability to reduce complexity and uncertainty, even in fields where traditional bibliometric measurement is less applicable. Research limitations: The findings presented should be regarded as examples that can be used for discussion, but one should be careful to interpret these as representative for broader sentiments and trends.Implications: The sheer popularity of the Norwegian model, leading to its application in contexts for which it was not designed, can be seen as a major challenge for the future.Originality: This paper offers a novel perspective on the Norwegian model by focusing on its general "appeal", rather than on its design, use or(mis)-use.

Assessment of Climate Change over South America under RCP 4.5 and 8.5 Downscaling Scenarios

Four sets of downscaling simulations based on the Eta Regional Climate Model forced by two global climate models, the HadGEM2-ES and the MIROC5, and two RCP scenarios—8.5 and 4.5, have been carried out. The objective of this work is to assess the climate change over South America based on the Eta simulations. The future changes are shown in timeslices of 30 years: 2011-2040;2041-2070 and 2071-2100. The climate change response of the Eta simulations nested in HadGEM2-ES is larger than the Eta nested in MIROC5. Major warming area is located in the central part of Brazil. In austral summer, the reduction of precipitation in the central part and the increase in the southeastern part of the continent are common changes in these simulations, while the EtaHadGEM2-ES intensifies the decrease of precipitation in central Brazil, the Eta-MIROC5 expands the area of increase of precipitation in southern Brazil toward the end of the century. In austral winter, precipitation decrease is found in the northern part of South America and in most of Central America, whereas the reduction in southeastern South America is limited to near coastal region. The time series of temperatures show that warming trends are larger in the Eta-HadGEM2-ES than in the Eta-MIROC5 simulations. Heavier precipitation rates are projected in the Central-South of Brazil toward the end of the century. Increase in the length of consecutive dry days (CDD) in Northeast of Brazil and the decrease of consecutive wet days (CWD) in the Amazon region are common features in these simulations.

Artificial Intelligence Providing a More Optimized Assessment Tool for Comprehensive Geriatric Assessment

With the continuous development of science and technology,artificial intelligence(AI)is coming into our lives and changing our lives.Since China entered the aging society in 2000,the degree of population aging has deepened.Comprehensive geriatric assessment(CGA)is now the accepted gold standard for the care of older people in hospitals.However,some problems limit the clinical application,such as complexity and time consuming.Therefore,by analyzing previous studies,we summarize some existing AI tools in order to find a more optimized assessment tool to complete the entire CGA process.

A risk assessment method considering risk attributes and work safety informational needs and its application

The technological revolution has spawned a new generation of industrial systems,but it has also put forward higher requirements for safety management accuracy,timeliness,and systematicness.Risk assessment needs to evolve to address the existing and future challenges by considering the new demands and advancements in safety management.The study aims to propose a systematic and comprehensive risk assessment method to meet the needs of process system safety management.The methodology first incorporates possibility,severity,and dynamicity(PSD)to structure the“51X”evaluation indicator system,including the inherent,management,and disturbance risk factors.Subsequently,the four-tier(risk point-unit-enterprise-region)risk assessment(RA)mathematical model has been established to consider supervision needs.And in conclusion,the application of the PSD-RA method in ammonia refrigeration workshop cases and safety risk monitoring systems is presented to illustrate the feasibility and effectiveness of the proposed PSD-RA method in safety management.The findings show that the PSD-RA method can be well integrated with the needs of safety work informatization,which is also helpful for implementing the enterprise's safety work responsibility and the government's safety supervision responsibility.

Dynamic comprehensive quality assessment of postharvest grape in different transportation chains using SAHP–CatBoost machine learning

Objectives:Food quality assessment is critical for indicating the shelf-life and ensuring food safety or value.Due to high environmental sensitivity,the post-harvest quality of fresh fruit will undergo complex changes in the supply chain,with various dynamic quality-related features.It is diffcult to effciently and accurately extract comprehensive quality feature of post-harvest fruits from high-dimensional monitoring data with heterogeneous characteristics(numerical and categorical).Therefore,we proposed a dynamic comprehensive quality assessment method based on self-adaptive analytic hierarchy process(SAHP)integrated with the CatBoost model.Materials and Methods:By adaptive weight optimization,the SAHP was utilized to analyze the multi-source quality information and obtain the quantized fusion value,as an output sample of CatBoost machine learning.Then,using heterogeneous monitoring data as input,the CatBoost model was directly trained through unbiased boosting with categorical features for dynamic assessment of overall quality status.Results:Three quality index monitoring data sets for‘Jufeng’grape in different transportation chains(normal temperature,cold insulation,and cold chain)were individually constructed as the research samples.Furthermore,compared to other machine learning methods,the SAHP-CatBoost had more accurate results in comprehensive quality feature extraction.In actual transportation chains,the mean absolute error,mean absolute percentage error,and root mean squared error of dynamic comprehensive assessment were limited to 0.0044,1.012%,and 0.0078,respectively.Conclusions:The proposed method is effcient in handling heterogeneous monitoring data and extracting comprehensive quality information of post-harvest grape as a robust shelf-life indicator.It can reasonably guide post-harvest quality management to reduce food loss and improve economic benefts.

PROCESSING PARAMETER OPTIMIZATION OF FDM BASED ON ROBUST DESIGN

The influence of processing parameters on the precision of parts fabricated by fused deposition modeling （FDM） technology is studied based on a series of performed experiments. Processing parameters of FDM in terms of wire-width compensation, extrusion velocity, filing velocity, and layer thickness are chosen as the control fac- tors. Robust design analysis and multi-index fuzzy comprehensive assessment method are used to obtain the opti- mal parameters. Results show that the influencing degrees of these four factors on the precision of as-processed parts are different. The optimizations of individual parameters and their combined effects are of the same impor- tance for a high precision manufacturing.

Integrated Model for Resilience Evaluation of Power-Gas Systems Under Windstorms

Integrated power-gas systems(IPGS)have developed critical infrastructure in integrated energy systems.Moreover,various extreme weather events with low probability and high risk have seriously affected the stable operation of IPGSs.Due to close interconnectedness through coupling elements between the power system(PS)and natural gas system(NGS)when a disturbance happens in one system,a series of complicated sequences of dependent events may follow in another system.Especially under extreme conditions,this coupling can lead to a dramatic degradation of system performance,resulting in catastrophic failures.Therefore,there is an urgent need to model and evaluate resilience of IPGSs under extreme weather.Following this development trend,an integrated model for resilience evaluation of IPGS is proposed under extreme weather events focusing on windstorms.First,a framework of IPGS is proposed to describe states of the system at different stages under disaster conditions.Furthermore,an evaluation model considering cascading effects is used to quantify the impact of windstorms on NGS and PS.Meanwhile,a Monte Carlo simulation(MCS)technique is utilized to characterize chaotic fault of components.Moreover,time-dependent nodal and system resilience indices for IPGS are proposed to display impacts of windstorms.Numerical results on the IPGS test system demonstrate the proposed methods.

Comprehensive Evaluation of Electric Power Prediction Models Based on D-S Evidence Theory Combined with Multiple Accuracy Indicators

A comprehensive evaluation method of electric power prediction models using multiple accuracy indicators is proposed.To obtain the preferred models,this paper selects a number of accuracy indicators that can reflect the accuracy of single-point prediction and the correlation of predicted data,and carries out a comprehensive evaluation.First,according to Dempster-Shafer(D-S)evidence theory,a new accuracy indicator based on the relative error(RE)is proposed to solve the problem that RE is inconsistent with other indicators in the quantity of evaluation values and cannot be adopted at the same time.Next,a new dimensionless method is proposed,which combines the efficiency coefficient method with the extreme value method to unify the accuracy indicator into a dimensionless positive indicator,to avoid the conflict between pieces of evidence caused by the minimum value of zero.On this basis,the evidence fusion is used to obtain the comprehensive evaluation value of each model.Then,the principle and the process of consistency checking of the proposed method using the entropy method and the linear combination formula are described.Finally,the effectiveness and the superiority of the proposed method are validated by an illustrative instance.

Risk reduction in Sechahun iron ore deposit by geological boundary modification using multiple indicator Kriging

Uncertainty on the geological contacts and the block volumes of the models along boundaries is often a major part of the global uncertainty of reserve estimation.This work introduces a geostatistical technique that has been developed and tested in an iron ore deposit at Bafq mining district,in central Iran,and that,based on a probability criterion,helps to objectively model the geometry of this iron ore deposit.The main problem in reserve estimation of this ore body is its geometrical modeling and uncertainty in geological boundaries.This work deals with the geostatistical method of multiple indicator kriging,which is used to determine the real boundaries of ore body in different categories.This approach has potential to improve project performance and decrease operational risk.For this purpose,the ore body is separated into two categories including rich iron zone(w(Fe)>45%)and poor iron zone(20%<w(Fe)<45%).It significantly benefits to decrease the risk of reserve evaluation in the deposit.This case study also highlights the value of multiple indicator kriging as a tool for estimates the position of grade boundaries within the deposit.Comparison of the resultant probability maps with the real ore/waste contacts on the extracted levels shows that the first indicator model could separate the whole ore body(poor plus rich)from the waste zone by probability of more than 0.35,which concludes the total reserve of 53 million tons.The second indicator model applied to separate the rich and poor domains and the results show that the blocks with the estimated probability of equal to or more than 0.4 lay within the rich ore zone consisting of 15.8 million tons reserve.

Modeling Spatio-temporal Drought Events Based on Multi-temporal,Multi-source Remote Sensing Data Calibrated by Soil Humidity

Inspired by recent significant agricultural yield losses in the eastern China and a missing operational monitoring system,we developed a comprehensive drought monitoring model to better understand the impact of individual key factors contributing to this issue.The resulting model,the‘Humidity calibrated Drought Condition Index’(HcDCI)was applied for the years 2001 to 2019 in form of a case study to Weihai County,Shandong Province in East China.Design and development are based on a linear combination of the Vegetation Condition Index(VCI),the Temperature Condition Index(TCI),and the Rainfall Condition Index(RCI)using multi-source satellite data to create a basic Drought Condition Index(DCI).VCI and TCI were derived from MODIS(Moderate Resolution Imaging Spectroradiometer)data,while precipitation is taken from CHIRPS(Climate Hazards Group InfraRed Precipitation with Station data)data.For reasons of accuracy,the decisive coefficients were determined by the relative humidity of soils at depth of 10-20 cm of particular areas collected by an agrometeorological ground station.The correlation between DCI and soil humidity was optimized with the factors of 0.53,0.33,and 0.14 for VCI,TCI,and RCI,respectively.The model revealed,light agricultural droughts from 2003 to 2013 and in 2018,while more severe droughts occurred in 2001 and 2002,2014-2017,and 2019.The droughts were most severe in January,March,and December,and our findings coincide with historical records.The average temperature during 2012-2019 is 1℃ higher than that during the period 2001-2011 and the average precipitation during 2014-2019 is 192.77 mm less than that during 2008-2013.The spatio-temporal accuracy of the HcDCI model was positively validated by correlation with agricultural crop yield quantities.The model thus,demonstrates its capability to reveal drought periods in detail,its transferability to other regions and its usefulness to take future measures.

Research Based on the Development Trend of World Language

Multicoloured languages play an irreplaceable role in the whole world as a useful communication tool. With the development of technology and science, varieties of languages have an ideal prospective tendency to evolution during the long and wonderful history. Will they be thriving or decaying? To begin with, aimed to gain general tendency about the quantity of languages' speakers, we employ the Grey prediction to capture associative curve which can be seen in Figure 1. From the trend of this vivid figure, we not only can come to the conclusion that the number of English and Chinese users tend to increase but also find that Spanish development will reach the period of stagnation. Secondly, for further improvement, we take birth rate, death rate, economic factors and the immigration into consideration and establish the language communication model. This model is deduced from the population prediction model and virus transmission model. After data normalization, the eventual curve indicates that current top-ten languages seem to be replaced by other languages. This transformation phenomenon also occurs among such top-ten languages. For instance, Hindustani will replace Spanish in the future when seen from Table 1. What's more, after predicting the migration pattern, we can draw the conclusion that some range of languages' dissemination has obvious change. As show in vivid Figure 14, we know English will popularize widely among neighboring countries such as Canada, Mexico, Cuba and Russia. Moreover, with regard to how to manage international offices' quantity and locations in the world, we construct the efficiency model with combination of the Bayes' probability theory and Fussy comprehensive assessment. As a result, we obtain 9 optimal plans to establish the international offices. Intelligible result is showed in Table 4 and Table 5. In short, our model is reasonable and feasible, which can accommodate to different situation.

基于组合赋权三维云评估技术的深部煤矿顶板事故风险分析

为了更好地对深部煤矿顶板事故展开风险评价,提出了组合赋权三维云评估技术。基于事故统计及事故致因理论,对深部煤矿顶板事故风险指标体系展开研究,采用博弈论对改进层次分析法(Analytic Hierarchy Process,AHP)和熵权法进行博弈分析,以实现风险指标的组合赋权,通过构建三维云模型,对深部煤矿顶板事故危险性进行可视化分析。结果表明:包括行为、技术、设备、环境、管理5大风险因素在内的一级指标并其下20个二级风险指标是深部煤矿顶板事故风险评价指标体系的主体内容,且以环境风险权重最大;由三维云评估技术分析可知,平煤九矿的顶板事故风险级别为Ⅲ级,即修复后可接受,与该矿生产实际情况相符。组合赋权三维云评估技术的提出有助于完善深部煤矿顶板事故风险评价方法,可应用于工程实践。

基于风险评估模型的传染病预警体系的建立

目的建立一套基于风险评估模型的传染病预警体系,推动传染病管理水平的提升。方法通过搜集、整理最新的研究成果,参考相关的传染病预警评价指标体系的理论框架,构建基于风险评估模型的传染病预警体系。结果本次研究共纳入了12名专家,筛选出了三个一级指标,涉及流行前期指标、典型症状期指标、非典型症状期指标等,同时涉及7个二级指标。体系的必要性平均得分为8.27±0.24分,可获得性平均得分为7.74±0.34分。结论基于风险评估模型的传染病预警体系的建立有利于当地及时掌握传染病流行趋势,并进行调查指标分布趋势分析,然后提出相应的预警方案,可为有效预防传染病提供相关的依据。