Weights Of Different Indicators Research Articles

Construction of the Index System for the Integration of Professional Education and Innovation Entrepreneurship Education in Applied Universities

In applied universities, innovation and entrepreneurship education (IEE) has entered a new era of comprehensive integration of professional education. However, as a novel educational activity, the evaluation of professional innovation integration still lacks reasonable appraisal standards. This article is based on the basic theory of the Kirkpatrick evaluation model, combined with the innovation and entrepreneurship practice of China's applied universities, this study initially constructs an index system for professional innovation integration in applied universities, estimates and ascertains the weights of different indicators using the Delphi and analytic hierarchy process approach, and thereafter, builds an index system for professional innovation integration in applied universities, which comprises of 4 primary indicators, namely: reaction layer, learning layer, behavior layer, and achievement layer, supported by 9 secondary indicators and 23 tertiary indicators.

Analysis and Evaluation of Ecotourism Resources in China Based on RIAM Model

Abstract A comprehensive assessment of ecotourism resources is needed for the sustainable development of ecotourism in China. In this paper, based on the scientific classification of ecological resources, the RIAM model for the environmental impact assessment of ecotourism resources is established, and the indicator sets are obtained based on a fast matrix calculation combining semi-quantitative and qualitative. Then, the hierarchical analysis method is used to calculate the weights of different indicators for the established indicator sets. Finally, an empirical analysis of the resources of Huangtengxia Ecotourism Area was conducted based on the comprehensive assessment model. It was found that the bio-ecological impact index of Huangtengxia was [6,8,-2,15,7], and the economic return impact index was [4,7,10,-5,8]. Taken together, the overall evaluation of ecological tourism resources in Huangtengxia has a total score of 72, which belongs to the middle to the upper level of similar tourism resource regions in China. This study establishes a scientific evaluation system of ecotourism resources in Huangtengxia, which provides a valuable reference template for the evaluation of ecotourism resources in China.

Grading surface urban heat island and investigating factor weight based on interpretable deep learning model across global cities

Significant urbanization resulted in increasing surface urban heat island (SUHI) that caused negative impacts on urban ecological environment, and residential comfort. Accurately monitoring the spatiotemporal variations and understanding controls of SUHI were essential to propose effective mitigation measurements. However, SUHI grades across global cities remained unknown, which cloud greatly support for global mitigations. Additionally, quantitative evaluating factor weights for different SUHI indicators and grades worldwide remained further investigations. Therefore, this paper proposed SUHI grading based on agglomerative hierarchical clustering, and further quantified factor weights for different indicators and grades based on an interoperable machine learning named TabNet. There were three major findings. (1) Global cities were grouped into five grades, including SUCI (surface urban cool island), insignificant, low-value, medium-value, and high-value SUHI grades, indicating significant differences among different grades. SUHI grades showed significant climate-based variations, wherein the arid climate was dominated by the SUCI grade at daytime but the high-value grade at nighttime. (2) Vegetation difference was an important factor for daytime SUHII accounting for 27%. Daytime frequency of SUHI was controlled by vegetation difference, temperature, evaporation and nighttime light, accounting for 78%. The major factors for nighttime frequency were albedo differences and nighttime light, accounting for 45%. (3) Related factors contributed differently to various SUHI grades. The weight of △EVI for daytime SUHII gradually increased with grades, while it for daytime frequency and maximum duration of SUHI decreased with grades. The nighttime SUHII of the low-value grade was greatly affected by the background climate, while that of the medium-value and high-value grades were strongly impacted by anthropogenic heat flux. The diurnal contrast of grades and coupling effects with heat wave were further discussed. This paper aimed to provide information on grades and controls of SUHI for further mitigation proposal.

Random Forest-TOPSIS Based Light Pollution Evaluation System

Economic development inevitably brings many negative problems, among which the negative impact of light pollution is growing and attracting the attention of more and more scholars. To establish a light pollution evaluation system, in this paper, through the three main of light pollution, i.e., glare, light spillover, and light infringement, the formulae for calculating the three were obtained through research papers in this field and evolution of advanced scholars, and the weights of different indicators were obtained by applying Random Forest-TOPSIS, which were 0.74,0.14,0.12, and thus a comprehensive evaluation system of light pollution was established. The light pollution evaluation system established in this paper is important for measuring the degree of light pollution.

A Light Pollution Risk Assessment Model Based on TOPSIS and EWM-AHP Methods

With the development of industrialization and urbanization, light pollution has become increasingly serious in various countries. Assessing the risk level of light pollution (LPRL) is the foundation and the most important step to study the light pollution problem. In this paper, five primary indicators and 13 secondary indicators are selected, and the indicator weights are determined by EMW and AHP. The light pollution risk levels of different regions are calculated by the Topsis method. The output results show that the LPRL is highest in urban areas, followed by suburban and rural areas, and the LPRL in protected land is lowest, and the model results are consistent with the actual situation. Finally, this paper analyzes the weights of different indicators in the LRPL model, and proposes corresponding intervention policies for the three factors with the highest weights.

Study on the effectiveness evaluation of medical consortia based on AHP

Objective: To effectively evaluate the timeliness of China's medical union policy, and to help the medical management department provide a reference basis for the implementation of the medical union policy, this paper, by combing the relevant research on the medical union policy at home and abroad, constructs a comprehensive evaluation index system of the timeliness of the medical union in four dimensions: the construction of basic medical services, the sharing of regional information, the two-way referral within the medical union, and the level of medical service. The weights of different indicators are calculated by using the analytic hierarchy process, and the results of different indicators are compared and analyzed. The results show that the level of medical service and regional information sharing are the most important indicators for evaluating the effectiveness of the medical consortium.

Assessing climate change vulnerability of smallholder farmers in northwest Ethiopia: application of a household intrinsic vulnerability index

PurposeThe purpose of this study is to identify the most vulnerable households and districts in Northwest Ethiopia and help decision-makers in developing and prioritising effective adaptive strategies and actions.Design/methodology/approachA multi-scale analytical tool and hazard-generic socio-economic indicators were developed to identify and prioritise the most vulnerable households and districts in Northwest Ethiopia. Categorical principal component analysis with 36 indicators was used to develop weights for different indicators and construct a household intrinsic vulnerability index. Data were collected through key information interviews, focus group discussions and a household survey with 1,602 randomly selected households in three districts of Northwest Ethiopia.FindingsDrawing on intrinsic vulnerability index computation, this study highlights that low levels of education, low access to climate information and credit services, long distance travelled to fetch water and frequent food shortages are the dominant factors contributing to high levels of intrinsic vulnerability at district level, while lack of livelihood support and income diversification are the key drivers of vulnerability at household level. The findings of this study further show that the majority of households (78.01%) falls within the very high to moderately high vulnerable category. Disaggregating the data according to agro-climatic zones highlights that the prevalence of high intrinsic vulnerability is most widespread in the lowland agro-climatic zone (82.64%), followed by the highland (81.97%) and midland zones (69.40%).Practical implicationsFrom a policy intervention vantage point, addressing the drivers of vulnerability provides a reliable approach to reduce the current vulnerability level and manage potential climate change-induced risks of a system. Specifically, reliable information on inherent vulnerability will assist policymakers in developing policies and prioritising actions aimed at reducing vulnerability and assisting in the rational distribution of resources among households at a local level.Originality/valueThis study contributes to the existing vulnerability literature by showing how hazard-generic socio-economic indicators in the vulnerability assessment adopted by the IPCC (2014) are important to identify drives of vulnerability which ultimately may feed into a more fundamental treatment of vulnerability.

Study on zoning of livestock carrying potential based on spatial distribution of soil organic matter on the Bashang Plateau of northern China

An understanding of the existing patterns of soil spatial heterogeneity is needed to clarify the potential of carrying livestock in different grassland regions, so as to provide an important reference and data support for the calculation of stock capacity and the establishment of reasonable grazing prohibition areas and times. Under the same vegetation coverage state, soils with different resilience capabilities will show different livestock carrying capacity. Thus, this study aimed to analyze the effect of soil organic matter as an indicator of soil resilience capabilities on the zoning results of livestock carrying potential, and to discuss the vegetation changes in different potential zoning over the years. By combining qualitative and quantitative methods, the weights of different indicators influencing livestock carrying potential were calculated. The results of this study show that, except for the unique effects of drought, vegetation coverage improved from 2000 to 2017 and that the most significant improvements were observed in Fengning and Guyuan Counties after grazing prohibition. The degraded area in Weichang County, which had good basic vegetation coverage, was the largest in the past many years, accounting for 5.3% of the local area. However, the vegetation coverage affected by the natural environment in Kangbao County has remained basically unchanged for many years. Soil organic matter participates in the zoning, which reflects the spatial variability in the livestock carrying potential. The calculation results of this work show that the study area can be divided into three levels of carrying potential (high, medium and low) and conservation area. Among the various regions, the high-potential area of Fengning County accounts for the largest proportion, followed by Weichang County, with proportions of 14.1% and 13.6%, respectively. Considering the proportions of high- and moderate- potential areas, the livestock carrying potential of the six counties decreased in the following order: Weichang, Fengning, Guyuan, Zhangbei, Shangyi and Kangbao. According to the multi-year changes in vegetation and the characteristics of the degraded spatial distribution in various regions, the high-potential areas should be normal grazing to increase grassland biodiversity, while the medium-potential areas and low-potential areas can be grazed by restricting the number of livestock and sporadically, respectively, to enhance grassland utilization.

Assessment of the environmental comfort of lactating sows via improved analytic hierarchy process and fuzzy comprehensive evaluation

Since there are many interacting influence factors of the comfortable degree of lactating sows, a method that combines improved analytic hierarchy process (IAHP) and fuzzy comprehensive evaluation (FCE) was introduced to conduct a quantitative evaluation of the comfortable degree. Besides, an evaluation index system was established, and the weights of different indicators were determined by using IAHP method, including temperature, relative humidity, concentrations of carbon dioxide (CO2), ammonia (NH3), hydrogen sulfide (H2S), and air speed. The construction method of fuzzy membership function and the calculation method of the parameters were proposed following the principle that the summation of membership degrees is equal to 1. Three basic types of membership functions (MFs), i.e., ridgemf, gaussmf, and trimf were used to build an evaluation model which fitted IAHP-FCE well. The proposed method was verified and applied based on the environmental data in different seasons obtained from a pig farm in Zhenjiang City, Jiangsu Province, China. It is demonstrated that the proposed IAHP-FCE model with various types of MFs has drawn a unique and consistent conclusion. Moreover, the IAHP-FCE model has a higher correlation coefficient of 0.874 compared with the single-factor evaluation (SFE) model. The IAHP-FCE model could be served as a beneficial strategy for the precise regulation and early warning of environmental conditions to improve sow welfare. Keywords: lactating sow house, environmental comfort, analytic hierarchy process, fuzzy comprehensive evaluation, assessment DOI: 10.25165/j.ijabe.20221502.6149 Citation: Chen C, Liu X Q, Duan W Y, Liu C J. Assessment of the environmental comfort of lactating sows via improved analytic hierarchy process and fuzzy comprehensive evaluation. Int J Agric & Biol Eng, 2022; 15(2): 58–67.

Research on the Interaction of Environmental Factors in the Three Gorges Reservoir Area Based on the Two-layer Network System Dynamics Model

This paper takes the ecological security of the Chongqing Three Gorges Reservoir area as the research object, establishes five subsystems of policy, population, water environment, economy, and biology, and uses the analytic network process to calculate the impact weights of different indicators in each layer of the system. The results show that the weight of the water environment indicator layer is the largest, and other indicators have varying degrees of impact on ecological security. On this basis, construct a system dynamics model, select the main constraints of environmental factors in the Three Gorges Reservoir area, set three different development scenarios, and observe their impact on CODcr And determine the range of ecological safety thresholds.

Modeling the Impact of Agricultural Production on Food Security Taking Into Account Import Substitution

The assessment of such a multidimensional concept as food security involves the analysis of a number of indicators characterizing the food production sector, the food market, food availability for the population, the influence of external factors, in particular import substitution. The use of integral or aggregated indicators when carrying out such an assessment is rather difficult, since it involves the determination of relative weights for different indicators and the interpretation of the values of the composite indicator. In modern studies on the topic of food security, attempts are made to analyze the various categories of the population, distinguished by social and demographic, territorial criteria or type of agricultural activity. As an effective assessment methodology, the use of cognitive economic and mathematical modeling is proposed, which allows for analyzing groups of statistical indicators and assessing the impact of various factors on changes in food security. The recommended method can also be used to predict the level of food security.

Decision support tools for wind and solar farm site selection in Isfahan Province, Iran

Optimizing the location of wind and photovoltaic solar power plants is a significant environmental management problem. The effectiveness of the site selection process for renewable energy systems (RES) could be strengthened by flexible spatial and environmental planning strategies using decision support systems (DSS) to critically identify the most productive, environmentally friendly and acceptable sites for the production of sustainable and reliable wind and solar energy. This study discusses hybrid DSS, using multi-criteria evaluation based on the analytical hierarchy process (AHP), a geographical information system (GIS), fuzzy logic, and a weighted linear combination (WLC) approach to determine optimal locations for renewable energy generation infrastructure. In the first stage, the most decisive factors for evaluating the site suitability were identified, based on experts’ opinions. Next, raster layers of ecological and socioeconomic sub-criteria were prepared GIS software. After incorporating the raster maps of each parameter, fuzzy membership functions were applied to normalize each raster layer between 0 and 1. The relative weights of different indicators were calculated using super decision software. Prioritizing vital elements were performed using AHP. In the final stage, the WLC approach was utilized to amalgamate layers in the GIS environment, which afforded the final site suitability maps. In Isfahan Province, Iran, 26% of the land area was found to be highly suitable for solar farms with 18% being highly suitable for wind farms. The results illustrate that using and comparing the results from combinations of computer-based DSS are more likely to result in better decisions than using individual DSS tools for the determination of the most suitable sites for RES location.

Evaluation of Interior Design Schemes Based on Artificial Intelligence Processing Technology

This article analyzes the necessity of evaluating interior design schemes. The content of this article includes timely discovering the shortcomings of the plan, improving the comprehensive benefits brought by the plan, and reducing the appearance of construction changes. The composition combines the basic concepts and development of artificial intelligence processing technology, and studies the construction principles of artificial intelligence processing technology in a clear system. The author studied how to evaluate the content of the indicators, the integration of energy-saving function indicators, the calculation of the weights of different indicators, and the objective evaluation of specific application functions in the test plan. The author’s purpose is to improve the reliability of the evaluation results of the interior design plan and speed up the progress of the later plan implementation.

An Entropy-Based Self-Adaptive Node Importance Evaluation Method for Complex Networks

Identifying important nodes in complex networks is essential in disease transmission control, network attack protection, and valuable information detection. Many evaluation indicators, such as degree centrality, betweenness centrality, and closeness centrality, have been proposed to identify important nodes. Some researchers assign different weight to different indicator and combine them together to obtain the final evaluation results. However, the weight is usually subjectively assigned based on the researcher’s experience, which may lead to inaccurate results. In this paper, we propose an entropy-based self-adaptive node importance evaluation method to evaluate node importance objectively. Firstly, based on complex network theory, we select four indicators to reflect different characteristics of the network structure. Secondly, we calculate the weights of different indicators based on information entropy theory. Finally, based on aforesaid steps, the node importance is obtained by weighted average method. The experimental results show that our method performs better than the existing methods.

Development of a Matrix Based Statistical Framework to Compute Weight for Composite Hazards, Vulnerability and Risk Assessments

Selection of relative weights for different indicators is a critical step during assessment of composite hazards, vulnerability, and risk. While assigning weight to an indicator, it is important to consider the influence of an individual indicator on a particular composite index. In general, the larger the weight of the indicator, the higher the importance of that indicator compared to other indicators. In this study, a new matrix based statistical framework (MSF) for weight assignment is developed that can be considered as the simplest and most accurate method for assigning weights for a large number of indicators. This method (MSF) is based on the valuation of the correlation matrix and Eigenvector associated with Eigenvalue. Relying on the inter build up methodology, MSF can fulfill some built-in gaps among other weightage methods. It can also directly give the ‘decision’ to select the relative weights that are found from the Eigenvector corresponding to the largest Eigenvalue. The new method is applied by assigning weights to 15 socio-economic indicators and assessed vulnerability and risk in the Bangladesh coast. While comparing with other weight methods, it is found that MSF gives the most acceptable physical explanation about the relative values of weights of indicators. In terms of accuracy, MSF is found to be most accurate compared to other weight methods. When large numbers of indicators are involved in an application, MSF is found to be relatively simple and easy to apply compared to other methods.

Evaluating China’s Air Pollution Control Policy with Extended AQI Indicator System: Example of the Beijing-Tianjin-Hebei Region

This paper calculated and evaluated the air quality of 13 cities in China’s Beijing-Tianjin-Hebei (BTH) region from February 2015 to January 2018 based on the extended AQI (Air Quality Index) Indicator System. By capturing the heterogeneous information in major pollutant indicators and the standardization process, we depicted the important effect of other relevant features of pollutant indicators beyond single-point data. Based on that, we further calculated the assessment value of the air quality of different cities in the BTH region by using the Collaborative Filtering Backward Cloud Model to construct differentiated weights of different indicators. With help of the Back Propagation (BP) Neutral Network, we simulated the effect of the pollution control policies of the Chinese government targeting air pollution since March 2016. Our conclusion is: the pollution control policies have improved the air quality of Beijing by 55.74%, and improved the air quality of Tianjin by 34.38%; while the migration of polluting enterprises from Beijing and Tianjin has caused different changes in air quality in different cities of Hebei province—we saw air quality deterioration by 58.60% and 38.68% in Shijiazhuang and Handan city respectively.

Effect of fermentation on water mobility and distribution in fermented cornmeal using LF-NMR and its correlation with substrate.

Cordyceps militaris (C. militaris) was utilized to ferment cornmeal by solid state fermentation. The main objective of this study was to investigate effect of fermentation on the dynamic state of water and microstructure distribution of water within cornmeal with Low-field nuclear magnetic resonance, as well as the effect on composition and microstructure properties. The spin-spin relaxation time (T2) showed significant changes in solid-state fermented cornmeal. Principal component analysis further revealed that the variations within different fermentation stage could be discriminated by the T2 parameters. Bulk water (T22) was the main form of water present and lost in substrates. The weights of different indicators, as assessed by multiple regression analysis, demonstrated that there was a strong correlation between starch and T2 relaxation. Scanning electron microscopy demonstrated that fermentation can cause the appearance of micropores. The longer relaxation time of T22 during logarithmic period can be interpreted as a loosening of the structure at the starch hydrolysis, introducing more water into the structure. Thus, the differences in composition and structure of the substrate at different fermentation time produce different T2 values.

Public blockchain evaluation using entropy and TOPSIS

Blockchains have attracted worldwide attention in recent years. With the increasing number of public blockchains, the evaluation of public blockchains becomes meaningful. This paper aims to make a comprehensive evaluation of public blockchains from multiple dimensions. Three first-level indicators and eleven second-level indicators are designed to evaluate the public blockchains. The technique for order preferences by similarity to an ideal solution (TOPSIS) method is used to rank public blockchains. The entropy method is used to calculate the weights of different indicators objectively. Since Bitcoin has an absolute advantage, a let-the-first-out (LFO) strategy is proposed to reduce the criteria of the positive ideal solution and make a more reasonable evaluation. The evaluation indicators and the ranking results of public blockchains can provide related researchers and managers with references and support their decisions. The three first-level indicators are technology, recognition, and activity. Their weights are 16.80%, 59.00%, and 24.20% respectively. The evaluation result shows that Bitcoin, Ethereum, and EOS are ranked in the top three public blockchains.

A structure–efficiency based performance evaluation of the urban water cycle in northern China and its policy implications

Understanding the urban water cycle is critical, as the watershed water cycle is greatly influenced by cities under the processes of urbanization with problems of high frequency of water shortage, environment deterioration and ecological degradation. Different from traditional evaluation method which focuses on one or some component of the whole system, this research aims to propose an innovative framework to evaluate the performance of the urban water cycle by means of the structure–efficiency index (SE_index). This index aggregates key indicators from structure and efficiency aspects (i.e. S_index and E_index), based on the water balance principle (which considers a multi-source water distribution process, water consumption process, and the wastewater treatment and discharge process). An entropy weight method is used to determine the weights of different indicators in an objective way. The performance levels of the urban water cycle of case study cities in Haihe River basin are evaluated, and key indicators are identified by sensitivity analysis. Four city categories with different structures or efficiency characteristics are presented by cluster analysis technique and their associated policy implications are discussed. For cities with relatively low S_index and E_index values (such as Xingtai, Hebi, Shjiazhuang and Hebi), it is meaningful to both optimize their urban water cycle structure and improve the efficiency of the water supply and different water use sectors. Although cities such as Beijing and Tianjin have relatively high S_index and E_index values, there is still room for them to make structure optimization compared with other cities in the basin. The proposed SE_index and its application can help provide quantitative information on the urban water cycle performance status to promote sustainable urban water policy making and related infrastructure planning.

Measuring household vulnerability to climate-induced stresses in pastoral rangelands of Kenya: Implications for resilience programming

This study uses statistical and econometric tools to measure households’ vulnerability in pastoral rangelands of Kenya. It considered 27 socio-economic and biophysical indicators obtained from 302 households’ in-depth interviews to reflect climate vulnerability components: adaptive capacity, exposure and sensitivity. The theoretical framework used combines exposure and sensitivity to produce potential impact, which was then compared with adaptive capacity in order to generate an overall measure of vulnerability. Principal component analysis (PCA) was used to develop weights for different indicators and produce a household vulnerability index (HVI) so as to classify households according to their level of vulnerability. In order to understand the determinants of vulnerability to climate-induced stresses, an ordered probit model was employed with predictor variables. The results show that 27% of households were highly vulnerable, 44% were moderately vulnerable and 29% of households were less vulnerable to climate-induced stresses. Factor estimates of the probit model further revealed that the main determinants of pastoral vulnerability are sex of household head, age of household head, number of dependents, marital status, social linkages, access to extension services and early warning information, complementary source of income, herd size and diversity, herd structure, herd mobility, distance to markets, employment status, coping strategies and access to credit. Therefore, policies that address these determinants of vulnerability with emphasis on women's empowerment, education and income diversifications are likely to enhance resilience of pastoral households.