Multi-level Evaluation Research Articles

Analysis and Application of Eco-environmental Value of Saihanba Forest Farm Based on Fuzzy Analytic Hierarchy Process and K-Means Clustering

In this paper, we aim to establish an evaluation model for the impact of Saihanba on the ecological environment to quantitatively evaluate its impact due to its significant role on maintaining ecological balance and stability. We apply the fuzzy analytic hierarchy process method on building an evaluation model of Saihanba with a multilevel evaluation factor system. In addition, we promote the model with the help of the K-means clustering analysis method after optimizing to determine which geographical locations in China and Pakistan need to establish ecological reservation.

A study on the level of digital economy development and influencing factors in Chinese coastal provinces - based on EWM and ANN algorithm

Focusing on the high-quality regional economic development of 11 coastal provinces in China in the new era, this article constructs a multi-level evaluation system of digital economy development level composed of 12 indicators and uses the EWM analysis method to measure the development level of the digital economy in the region. At the same time, 13 indicators are selected as regional economic endowment conditions, and the ANN algorithm is used to study the impact of regional economic endowment conditions on the development of the digital economy. The evaluation system of this study is relatively comprehensive, and the machine learning method is used to make the results more accurate. Taking coastal provinces as the specific research object, more concrete and targeted conclusions will be produced, providing cutting-edge theoretical support for the economically developed regions such as the Yangtze River Delta and the Pearl River.

Evaluation of the Metrological Integrated Support Capability of Launch Site Based on FUZZY-AHP

The metrological integrated support capability is an important part of the equipment support capability of the launch site. To realize the quantitative description and scientific evaluation of the metrological integrated support capability, the FUZZY-AHP method is introduced. Based on the study of the factors affecting the capability, the evaluation index system is constructed from four aspects of personnel capability, equipment capability, technical support capability, and comprehensive management capability. A multilevel evaluation model of the metrological support capability is established by using the analytic hierarchy process. Then, with the fuzzy mathematics method, the rules of the fuzzy evaluation for the quantitative and qualitative indicators are formulated. Finally, combined with a launch mission, the FUZZY-AHP method is used to evaluate the metrological integrated support capability. The method is scientific and feasible, which provides an effective reference for the subsequent evaluation of the metrological integrated support capability.

Comprehensive analysis and evaluation of ship energy efficiency practices

The IMO is working to reduce emissions from ships. One of its initiatives is the Ship Energy Efficiency Management Program (SEEMP), which is based on the Plan, Do, Check and Act (PDCA) cycle. In order to clearly describe the weight of energy efficiency improvement methods in practice, current energy efficiency practices were summarised systematically from five aspects, including optimising navigational management and operation, improving power system efficiency, reducing ship resistance, improving company management as well as the application of new energy and new technologies. Based on the Fuzzy- Analytic hierarchy process (AHP) method, a multi-level evaluation system was established by considering the importance degree of these practices. Comprehensive evaluation was carried out in accordance with the results obtained from the questionnaire survey of experienced experts in China. The evaluation results show that the management innovation has higher influence than technological alteration in achieving the energy conservation and emissions reduction of ships, and the speed optimisation is recognised as the most effective approach onboard at present. The multi-attribute decision-making architecture and comprehensive evaluation method proposed is much needed by the ship managers and crew members to determine the best practice of energy efficiency.

Temporal Events Detector for Pregnancy Care (TED-PC): A rule-based algorithm to infer gestational age and delivery date from electronic health records of pregnant women with and without COVID-19.

Identifying the time of SARS-CoV-2 viral infection relative to specific gestational weeks is critical for delineating the role of viral infection timing in adverse pregnancy outcomes. However, this task is difficult when it comes to Electronic Health Records (EHR). In combating the COVID-19 pandemic for maternal health, we sought to develop and validate a clinical information extraction algorithm to detect the time of clinical events relative to gestational weeks. We used EHR from the National COVID Cohort Collaborative (N3C), in which the EHR are normalized by the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). We performed EHR phenotyping, resulting in 270,897 pregnant women (June 1st, 2018 to May 31st, 2021). We developed a rule-based algorithm and performed a multi-level evaluation to test content validity and clinical validity, and extreme length of gestation (<150 or >300). The algorithm identified 296,194 pregnancies (16,659 COVID-19, 174,744 without COVID-19) in 270,897 pregnant women. For inferring gestational age, 95% cases (n = 40) have moderate-high accuracy (Cohen's Kappa = 0.62); 100% cases (n = 40) have moderate-high granularity of temporal information (Cohen's Kappa = 1). For inferring delivery dates, the accuracy is 100% (Cohen's Kappa = 1). The accuracy of gestational age detection for the extreme length of gestation is 93.3% (Cohen's Kappa = 1). Mothers with COVID-19 showed higher prevalence in obesity or overweight (35.1% vs. 29.5%), diabetes (17.8% vs. 17.0%), chronic obstructive pulmonary disease (0.2% vs. 0.1%), respiratory distress syndrome or acute respiratory failure (1.8% vs. 0.2%). We explored the characteristics of pregnant women by different gestational weeks of SARS-CoV-2 infection with our algorithm. TED-PC is the first to infer the exact gestational week linked with every clinical event from EHR and detect the timing of SARS-CoV-2 infection in pregnant women. The algorithm shows excellent clinical validity in inferring gestational age and delivery dates, which supports multiple EHR cohorts on N3C studying the impact of COVID-19 on pregnancy.

Research on Fuzzy Comprehensive Evaluation Method in Wireless Sensor Network Course Evaluation

Wireless Sensor network (WSN) is a Capstone course in engineering education certification, because the implementation of this course should reflect implementation, complexity and teamwork. It is difficult for teachers to use traditional and linear assessment criteria to evaluate students' learning effects. Aiming at the above problems, an evaluation method of wireless sensor network course based on fuzzy theory and multi-level evaluation index factor set is proposed in this paper. The method imitates the evaluation experience and intelligent behavior of education experts, and obtains an optimal course evaluation model. Through the fuzzy comprehensive evaluation of multi-level index system, students' learning effect can be evaluated accurately, objectively, fairly and scientifically.

Economic Evaluation of Drought Resistance Measures for Maize Seed Production Based on TOPSIS Model and Combination Weighting Optimization

In order to optimize the appropriate drought resistance measures in the implementation of high-efficiency and intensive production of maize seed, in 2018 and 2019, maize cultivation experiments with different drought resistance measures were carried out in the arid area of northwest China, including water retention agent (SA), white plastic film mulch (WF), black plastic film mulch (BF), straw mulch (SM), and open ground flat seed as control (CK). A total of five treatments were conducted. Ten specific indicators contained four types of attributes, namely the yield, quality, water use efficiency (WUE), and economic benefit of maize seed production, aimed at constructing a multilevel evaluation system. To improve the reliability of evaluation results, subjective and objective weights of indexes were calculated using the analytic hierarchy process (AHP) and entropy weight method (EWM), respectively. Then, based on the integrated weighting method of game theory (GT), the combined weights of subjective and objective unity were obtained. Finally, with the help of the technique for order preference by similarity to ideal solution (TOPSIS), a comprehensive benefit evaluation model was established to screen out the optimal drought resistance measures. Compared with CK, different drought resistance measures significantly improved the grain quality of seeds-production corn, and the average annual yield and WUE of black and white film treatments were improved by 49.57% and 42.97% and by 65.67% and 58.21%, respectively. This proved that black film mulching (BF) could significantly increase the yield and WUE of maize seed production and effectively improve grain quality, which could be used as the best drought-resistant cultivation mode for maize seed planting in Hexi and similar areas.

Resilience Capacity Evaluation for the Safety Management System of Power Grid Enterprise Based on AHP-MEE Model

With the rapid development of urbanization and modernization, people’s requirements for electric power are also consequently increasing. It is thus essential to ensure the sustainable and stable supply of power and maintain the safety performance of the power grid. Constructing a modern safety management system (SMS) can effectively improve power enterprises’ ability to respond to emergencies. To effectively evaluate the ability of electric power enterprises to resist risk impact, and the recovery and optimization ability after impact, this paper makes a systematic investigation of the SMS of power enterprises. It evaluates the safety resilience capabilities of the SMS from four basic elements of stability, redundancy, efficiency, and adaptability. A multilevel evaluation system of electric power enterprises’ safety resilience capacity consists of four first-level and 25 second-level indices. Then, an analytic hierarchy process (AHP) and matter-element extension (MEE) joint model—the AHP-MEE evaluation model of the resilience capacity of power enterprises—is proposed. A case study of electric power enterprise safety resilience evaluation is conducted using the proposed resilience evaluation index system and assessment model. The results showed that the provided model agrees with the actual situation and verified the index system and model for electric power enterprise safety reliability and applicability. Improved enterprise safety resilience recommendations are proposed accordingly based on the evaluation results. This work proposes a robust safety resilience capacity evaluation method in the face of upcoming new risks, thus building a good working foundation to construct a new power system with new energy sources.

A Transfer Learning Framework for Deep Learning-Based CT-to-Perfusion Mapping on Lung Cancer Patients.

PurposeDeep learning model has shown the feasibility of providing spatial lung perfusion information based on CT images. However, the performance of this method on lung cancer patients is yet to be investigated. This study aims to develop a transfer learning framework to evaluate the deep learning based CT-to-perfusion mapping method specifically on lung cancer patients.MethodsSPECT/CT perfusion scans of 33 lung cancer patients and 137 non-cancer patients were retrospectively collected from two hospitals. To adapt the deep learning model on lung cancer patients, a transfer learning framework was developed to utilize the features learned from the non-cancer patients. These images were processed to extract features from three-dimensional CT images and synthesize the corresponding CT-based perfusion images. A pre-trained model was first developed using a dataset of patients with lung diseases other than lung cancer, and subsequently fine-tuned specifically on lung cancer patients under three-fold cross-validation. A multi-level evaluation was performed between the CT-based perfusion images and ground-truth SPECT perfusion images in aspects of voxel-wise correlation using Spearman’s correlation coefficient (R), function-wise similarity using Dice Similarity Coefficient (DSC), and lobe-wise agreement using mean perfusion value for each lobe of the lungs.ResultsThe fine-tuned model yielded a high voxel-wise correlation (0.8142 ± 0.0669) and outperformed the pre-trained model by approximately 8%. Evaluation of function-wise similarity indicated an average DSC value of 0.8112 ± 0.0484 (range: 0.6460-0.8984) for high-functional lungs and 0.8137 ± 0.0414 (range: 0.6743-0.8902) for low-functional lungs. Among the 33 lung cancer patients, high DSC values of greater than 0.7 were achieved for high functional volumes in 32 patients and low functional volumes in all patients. The correlations of the mean perfusion value on the left upper lobe, left lower lobe, right upper lobe, right middle lobe, and right lower lobe were 0.7314, 0.7134, 0.5108, 0.4765, and 0.7618, respectively.ConclusionFor lung cancer patients, the CT-based perfusion images synthesized by the transfer learning framework indicated a strong voxel-wise correlation and function-wise similarity with the SPECT perfusion images. This suggests the great potential of the deep learning method in providing regional-based functional information for functional lung avoidance radiation therapy.

Alzheimer’s disease beyond the amyloid accumulation

Abstract Alzheimer’s disease represents one of the unsolved problems of modern medicine, which assumes a significant financial burden in the research, medical, and social fields as well. Treatments intended for preventing and slowing neurodegenerative processes are the subject of a considerable amount of research and clinical trials, but the results are still insignificant regarding the clinical applicability. Initially, we believed that slow neurodegeneration can be traced back entirely to the accumulation of β-amyloid proteins with atypical structure and behavior in certain areas of the brain. This theory was constantly supplemented by the observation of intracellularly aggregating neurofibrillary tangles and the formulation of neuroinflammatory mechanisms. In this regard Alzheimer’s disease-associated neuroinflammation is an immunological response to the presence of β-amyloid oligomers, which initially leads to microglial activation and inflammatory cytokine release. Over time, this process becomes chronic, extending to astrocytes, neurons, and brain microvascularization, and leading to functional impairment, which is clinically manifested in cognitive and memory deficits. The defined central role of neuroinflammation was overturned by the therapeutic failure of drugs with anti-inflammatory capacity. Subsequently, our knowledge was completed by the recognition of how oxidative stress and the altered brain insulin signaling influences metabolic processes, opening new perspectives for drug development. Despite this, new drug candidates are successively failing, as complex regulatory mechanisms have been identified that question the initial triggering role of the β-amyloid and the final impairing effect of the inflammation. The aim of this study is to summarize and present the biochemical and pathophysiological knowledge that contributed to the currently available more comprehensive picture and a more detailed understanding of the processes of Alzheimer’s disease. Properly planned and executed preclinical experiments are essential to establish further clinical conclusions. By the comparison of the currently used rodent models, we tried to draw attention to the complexity of animal experiments and the importance of their multi-level (behavioral, biochemical, histological) evaluation.

Resilient Multi-Source Integrated Navigation Method for Aerospace Vehicles Based on On-Line Evaluation of Redundant Information

Aerospace vehicle navigation systems are equipped with multi-source redundant navigation sensors. According to the characteristics of the above navigation system configuration, building a resilient navigation framework to improve the accuracy and robustness of the navigation system has become an urgent problem to be solved. In the existing integrated navigation methods, redundant information is only used for backup. So, it cannot use the redundant navigation information to improve the accuracy of the navigation system. In this paper, a resilient multi-source fusion integrated navigation method based on comprehensive information evaluation has been proposed by combining of qualitative analysis and quantitative analysis in information theory. Firstly, this paper proposes a multi-layer evaluation framework of redundant information and carries out quantitative analysis of redundant information with the information disorder analysis theory to improve the reliability of the navigation system. Secondly, a navigation output effectiveness evaluation system has been established to analyze the output of heterogeneous navigation subsystems qualitatively to improve the fusion accuracy. Finally, through the mutual correction of multi-level information evaluation results, the error decoupling between the output parameters of heterogeneous navigation sensors has been realized to improve the robustness of the system. The experimental results show that the method proposed in this paper can adaptively allocate and adjust the weight of navigation information at all levels, realize the “non-stop” work of the navigation system and enhance the resilient of the navigation architecture. The navigation accuracy is improved compared with the existing multi-source fusion algorithm, which reflects the reliability and robustness of this algorithm.

Study on the extension evaluation of smart logistics distribution of fresh agricultural products based on the perspective of customers

The rapid development of the Internet has accelerated the expansion of e-commerce sacle of fresh agricultural products. The actual audience of smart logistics distribution of fresh agricultural products is customers, and customers enjoy the process and results of distribution services. However, the current research mainly selects indicators from the aspects of enterprise performance, cost and technical level based on the perspective of managers and technicians, which make it difficult to truly reflect customers’ feelings in the evaluation results. At the same time, the evaluation methods mainly focus on the comprehensive evaluation method and fuzzy evaluation method. These evaluation methods are greatly affected by subjective factors in the evaluation grade distribution, and the assignment is often relatively complete and inaccurate. To solve these problems, this paper constructs the evaluation index system of intelligent logistics distribution of fresh agricultural products from the perspective of customers, so that the selection of indicators is more in line with the real wishes of customers. And we use the extension function to construct the correlation function for multi-level extension evaluation to ensure the accuracy of the evaluation results. Taking X logistics enterprise as an example, this paper verifies the scientificity of the evaluation index system of intelligent logistics distribution of fresh agricultural products through empirical research, which has reference significance for further improving the intelligent logistics distribution of fresh agricultural products.

FAHP-Based Reliability Evaluation of Distributed IoT Devices in a Distribution Power Grid

Reliable operation of distributed internet of things (IoT) devices is essential for supporting two-way interaction between energy flow and information flow in a distribution power grid. Traditional reliability evaluation methods suffer from several challenges including complex evaluation indicator selection and inadaptability of fixed weights with actual reliability requirements. In this paper, we first establish a multilevel reliability evaluation indicator system, where the indicators are selected through joint consideration of comprehensiveness and effectiveness. Then, we propose a fuzzy analytic hierarchy process- (FAHP-) based reliability evaluation method, where a three-layer reliability evaluation architecture is established. Specifically, a 0.1-0.9 scaling method is adopted to establish the fuzzy judgment matrix and scale the important relationship among state-layer elements, which can be dynamically adjusted to generate the weights adapting with actual operation state. The scores of distributed IoT devices and corresponding reliability levels can be obtained through the weighted summation of the scores of each layer. Simulation results verify the effectiveness and accuracy of the proposed method through comparing with actual reliability value and two existing evaluation methods.

Modeling Analysis of Improved Minimum Safe Following Distance under Internet of Vehicles

The development of the Internet of vehicles technology can improve the communication between vehicles, thereby changing the driving behavior of drivers. Therefore, the traditional safe-following model cannot accurately describe the driving behavior and needs to be improved accordingly. First, two key parameters (i.e., drivers’ reaction sensitivity and road friction coefficient) are obtained through a comprehensive comparative analysis of influencing factors on the Internet of vehicles environment. And the calculation methods of these two parameters are proposed by using the multilevel comprehensive weighted evaluation method and the BP neural network. Then, these two key parameters are used to improve the traditional minimum safety distance model for adapting to driving behavior under the Internet of vehicles environment. Finally, through setting up simulation experiments and comparative analysis, the relationship between different influencing factors and the minimum safe following distance is obtained, and the influence degree of different influencing factors is sorted. The most important factor affecting car-following safety is the drivers’ characteristics. It can provide strong theoretical support for the safe driving assistance system of vehicles.

506 Connections IN Health: A Model for Coalition Building?

OBJECTIVES/GOALS: Participants will learn about the Connections IN Health partnership. Participants will learn about the mixed-methods, multi-level evaluation model used to evaluate health coalitions. Participants will learn about how the Connections IN Health partnership promoted coalition growth among Indiana chronic disease coalitions. METHODS/STUDY POPULATION: Chronic disease coalitions under the Connections IN Health (CINH) partnership were evaluated using a novel mixed-methods, multi-level evaluation framework that triangulates coalition functioning and effectiveness (F/E) surveys, social network analysis (SNA), and health data. F/E and SNA surveys were distributed to active coalition members of the chronic disease coalitions before, 1 year, and 2 years after CINH was implemented. F/E data was analyzed to detect significant changes in coalition members’ perceptions of coalition functioning and effectiveness using unpaired, two sample t-tests in R. SNA data was analyzed to detect changes in partnership networks over time, such as membership growth and strength of connections using R software. County-level health data will be evaluated at year 5. RESULTS/ANTICIPATED RESULTS: The results of our longitudinal analysis of perceptions of functioning and effectiveness demonstrated significant increases in perceived leadership, satisfaction with the coalition, outcome efficacy, and members’ commitment to the coalition after Connections IN Health (CINH) was implemented. Social network analysis (SNA) demonstrated growth of coalition membership and strength of connections over time, including health information sharing, formal relationships, trust, and communication. SNA results also demonstrated increases in centralization and clustering among local vs. statewide organizations in coalition networks over time. Results were shared back with coalition members to make data-driven decisions about future coalition processes and functioning. DISCUSSION/SIGNIFICANCE: Health coalitions are a nationally recognized vehicle to promote and translate evidence-based practices to communities; however, evidence to support their effectiveness is limited at best. Our results provide evidence of CINH’s efficacy and identifies factors required to build effective and sustainable health coalitions.

A Public Value-Based, Multilevel Evaluation Framework to Examine Public Bike-Sharing Systems. Implications for Cities’ Sustainable Transport Policies

Abstract This article proposes a multilevel bike-sharing assessment framework based on the concept of public value. This approach makes it possible to combine customer satisfaction with the transport service system with determinants of demand for bicycle services in the form of value. The framework aims to evaluate the parameters of public bike systems (PBS) that determine user value, and that co-create user value, system value, and social and ecological value, to identify the characteristics of the bicycle that need improvement in order to meet users’ needs and optimize quality. The framework uses empirical verification through satisfaction surveys of PBS users in Lodz, Poland. The results of the study were subjected to factor analysis, which revealed four groups of factors that satisfy public bike users: (1) impact on the health, environment, mobility and traffic in the city, (2) reliability, and comfort, (3) intramodality, (4) price and technical availability.

Multilevel Evaluation of Teaching Quality in Higher Education Using Single-Valued Neutrosophic Set

Evaluation of teaching quality is essential for teachers’ promotion, students’ course selection, and institutes’ standing. A multilevel evaluation framework for teaching quality in higher education was investigated by combining the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) with the single-valued neutrosophic set (SVNS). An indicator system was constructed, including the teaching performance and the students’ learning outcomes. For the qualitative indicator values, an SVNS representation method was proposed, aiming to describe the uncertainty and improve the credibility and validity of the evaluation. Then, both the qualitative data and quantitative data were applied to the TOPSIS-based multilevel evaluation framework, which consisted of an overall assessment and five specific evaluations. The former assessment would provide a final rank and determine the best lecturers to be given priority in awards and promotion. The latter would focus on identifying areas where the lecturers could do better and would give them tips to overcome their challenges. Finally, a descriptive example was provided to verify the proposed framework and demonstrate its practicality.

A multi-level evaluation of bioclimatic design in Mediterranean climates

Bioclimatic design strategies are an efficient architectural approach to improve thermal comfort, save energy, and reduce buildings’ carbon footprint. In this study, the effect of five bioclimatic design strategies on energy demands of an office building, considering window-to-wall ratio, solar heat gain coefficient, sun shading with overhangs, thermal insulation and natural ventilation, is investigated along with their associated economic and environmental benefits in the Mediterranean region using EnergyPlus software. For this purpose, six locations were identified to represent different climate’s types. The results revealed that the highest energy savings shares were achieved by incorporating thermal insulation and natural ventilation designs in the hot desert climate with 90.69% and 20.21%, respectively. Furthermore, sun shading and small glazing area with low solar heat gain coefficients are recommended for hot climates and vice versa for cold ones. Moreover, the investigated designs exhibited high economic benefits, which translated by lower life cycle costs and payback periods, and higher savings-to-investment ratios, especially thermal insulation and natural ventilation designs. Additionally, significant CO2 emissions are avoided using these designs. The findings of this work could serve as a guideline for building stakeholders to choose the appropriate designs according to the building climatic conditions.

Re-Fracturing Wells Selection by Fuzzy Comprehensive Evaluation Based on Analytic Hierarchy Process—Taking Mahu Oilfield as An Example

Re-fracturing treatments of horizontal wells are increasingly gaining popularity to address the issue of rapid production decline and low recovery ratio for the conglomerate reservoir of the Mahu Oilfield. How to effectively select the horizontal wells with potentiality for re-fracturing and conduct the re-fracturing operation to achieve the purpose is the key problem that needs to be investigated urgently. However, the conventional methods for vertical wells are not in our consideration, and some methods for horizontal wells have their limits for the Mahu reservoir. To cope with problems mentioned above, fourteen factors from geology parameters, engineering parameters, and production performance parameters are considered to establish a multi-level evaluation model to quantify the potentiality of each horizontal well for re-fracturing in the Mahu Oilfield. First, the analytic hierarchy process (AHP) is used to obtain the weights of various factors affecting the productivity of horizontal wells, and on this basis, the subordination degree and evaluation matrix are then calculated, and finally, the fuzzy synthetic determination is obtained to determine the candidate wells for re-fracturing. The results have shown that the weights corresponding to engineering parameters obtained by the AHP method are the largest, followed by geology parameters, and the weights of production performance parameters are the minimal relatively; the number of fractures and the sand quantity of single cluster are the main controlling factors in engineering factors, and the initial formation pressure is the main controlling factor in geological factors; there is obvious correlation between the cumulative oil production after 90 days of primary fracturing with final cumulative production. Wells M15, M13, and M7 rank top three among the candidate wells. Through re-fracturing treatment by temporary plugging, the daily oil production of well M15 has increased significantly and is even higher than that of the primary hydraulic fracturing stimulation, confirming the reliability of the proposed selection method.

Post Evaluation of Power Grid Project Based on Rough Set and Improved Multi-Level Extension Assessment

Post evaluation of power grid investment and construction project is an important part of power grid construction project. The effect of post evaluation will directly affect the quality level of project construction. Therefore, this paper proposes an improved post evaluation system based on rough set and fuzzy multi-level extension. Firstly, the main factors affecting the function effect of power grid construction project are analyzed. Then, the improved fuzzy multi-level extension evaluation model is constructed by combining fuzzy comprehensive evaluation theory with multi-level extension evaluation method. Finally, a 220 kV substation construction project is taken as an example to verify the validity and rationality of the model.