Weight Subjects Research Articles

Study of the Sustainability of a Forest Road Network Using GIS-MCE

An environmentally friendly and economically efficient forest road network is the key to sustainable forest management, and it is essential to evaluate the sustainability of an established forest road network and to optimize the forest road network based on sustainability criteria. This study proposes a sustainability evaluation method for forest road networks. The evaluation index system was constructed by selecting 12 indices from the social, economic, and ecological dimensions. To improve the credibility of the weighting of the evaluation indicators, the evaluation indicators were assigned subjective and objective weights by the analytic hierarchy process and the entropy weight method, respectively, and the game theory idea was used to comprehensively assign the weights. The study area was chosen to be the Taohuajiang State-Owned Forest Farm, which is a national forest park and is also an ecological public welfare forest. The development of the forest road network in the social, economic, and environmental dimensions was calculated by adopting the evaluation method proposed in this study, and the sustainability of the forest road network in each subdivision of the study area was determined, to provide a detailed theoretical basis for the management to carry out the upgrading of the sustainability of the forest road network. The results derived from the evaluation methodology show that the sustainable development of the forest road network in the study area is “excellent”. At the same time, the level of sustainability of the forest road network in different areas of the study area was also evaluated. Finally, this paper puts forward policy recommendations for optimizing the sustainable development of forest road networks. The original sustainability evaluation method in this paper provides a scientific tool for assessing the degree of sustainable development of forest road networks, which contributes to optimizing the forest road networks.

A study on risk sharing of smart city PPP projects based on EW-G1 and TOPSIS-UT: a case study in China

PurposeBuilding a smart city is a necessary path to achieve sustainable urban development. Smart city public–private partnership (PPP) project is a necessary measure to build a smart city. Since there are many participants in smart city PPP projects, there are problems such as uneven distribution of risks; therefore, in order to ensure the normal construction and operation of the project, the reasonable sharing of risks among the participants becomes an urgent problem to be solved. In order to make each participant clearly understand the risk sharing of smart city PPP projects, this paper aims to establish a scientific and practical risk sharing model.Design/methodology/approachThis paper uses the literature review method and the Delphi method to construct a risk index system for smart city PPP projects and then calculates the objective and subjective weights of each risk index through the Entropy Weight (EW) and G1 methods, respectively, and uses the combined assignment method to find the comprehensive weights. Considering the nature of the risk sharing problem, this paper constructs a risk sharing model for smart city PPP projects by initially sharing the risks of smart city PPP projects through Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to determine the independently borne risks and the jointly borne risks and then determines the sharing ratio of the jointly borne risks based on utility theory.FindingsFinally, this paper verifies the applicability and feasibility of the risk-sharing model through empirical analysis, using the smart city of Suzhou Industrial Park as a research case. It is hoped that this study can provide a useful reference for the risk sharing of PPP projects in smart cities.Originality/valueIn this paper, the authors calculate the portfolio assignment by EW-G1 and construct a risk-sharing model by TOPSIS-Utility Theory (UT), which is applied for the first time in the study of risk sharing in smart cities.

Laboratory Risk Assessment Based on SHELL-HACCP-Cloud Model

With the increasing demand and expanding scale of laboratories in colleges and universities, laboratory accidents frequently occur, seriously impacting personal health, schools, society, and the environment. Scientific and effective risk assessment is crucial to prevent accidents. Therefore, in order to effectively reduce the risk of chemical laboratories and minimize the frequency of accidents. This study employs the combination of the SHELL model and HACCP system to analyze the potential sources of hazards in hazardous chemical laboratories and establish a risk assessment index system. Based on the Cloud model, a dynamic risk assessment model for the laboratory is established to quantitatively evaluate the risk level of the evaluation results. In order to ensure the rationality of the assessment results, the subjective and objective weights are combined by the principle of minimizing information entropy. Case analysis proves the scientific validity of the evaluation results of the model, which can assist laboratory managers in formulating emergency plans and risk management mechanisms to reduce or eliminate the occurrence of experimental accidents. This approach ensures the safe and sustainable development of schools and laboratories, which is conducive to the progress of researchers’ scientific research results.

Research on programmatic multi-attribute decision-making problem: An example of bridge pile foundation project in karst area.

The selection of construction plans for adverse geological conditions frequently encountered during the construction of bridge pile foundations will have a significant impact on the project's progress, quality, and cost. There is a need for the optimization of multi-attribute decision-making methods, considering the subjectivity in in weight allocation and the practical implementation obstacles. In this study, an evaluation framework for pile foundation construction schemes in karst areas was established. The directed graph and Bellman-Ford algorithm are employed to improve the Analytic Network Process (ANP) in the systematic structure, thereby calculating the subjective weights of various indicators. Simultaneously, based on the concept of dynamic weighting, a multiple linear regression is introduced for analyzing the weights of similar projects, resulting in the derivation of universal weights for the primary indicators within the evaluation system. The combination weights are subsequently determined through the weighted average of the two types of weights. Finally, the comprehensive scores of alternative schemes are computed using the grey-fuzzy evaluation method to enable decision-making in scheme selection. Cloud model, ELECTRE-II, and VIKOR methodologies were utilized for the comparison of results. Combining with a case study of a bridge project in karst development area in southern China, the findings indicate that the improved ANP method possesses practical applicability and yields effective computational results. The introduction of universal weights serves to ameliorate the inherent subjectivity in weight allocation. The pile foundation quality achieved using the optimal construction plan is classified as Class I, which prove the feasibility of the model.

Spatiotemporal variations of eco-environmental vulnerability in Shiyang River Basin, China

Eco-environmental vulnerability reflects the unfavorable degree of the ecological environment presented by external disruptions. The Shiyang River Basin, as a typical desert interior river basin in Northwest China, plays crucial ecological roles. Exploring eco-environmental vulnerability's spatial and temporal evolution is vital to maintaining ecosystem stability. We utilized the ecological sensitivity-resilience-press (SRP) model to create a complete evaluation index system of eco-environmental vulnerability using meteorological, remote sensing, and statistical data. The cloud theory improved analytic hierarchy process (CM-AHP) was used to calculate the subjective weights, the entropy weight method (EWM) was used to estimate the objective weights, and the comprehensive weights of the indicators were determined using the least square method, which combined the subjective and objective weights. From 2000 to 2020, the spatial-temporal features of eco-environmental vulnerability in the research area were analyzed. The results indicate that surface temperature, land use, and monthly precipitation are key factors impacting the state of eco-environmental vulnerability. The basin's potential-vulnerable and light-vulnerable areas are predominantly located in the south and southwest, whereas the northern and eastern areas are classified as severe-vulnerable and extreme-vulnerable. Extreme-vulnerable areas declined from 8.59% in 2000 to 4.3% in 2020, while potential-vulnerable areas climbed from 2.11% in 2000 to 8.08% in 2020. The eco-environmental vulnerability as a whole has evolved in a benign direction. The research results can serve as a theoretical groundwork for the restoration of the Shiyang River Basin's ecological environment and long-term development.

Node priority evaluation modeling of the substation to be restored based on weight fusion

Severe weather is likely to lead to a large area of power outage accidents, and the emergency repair manpower, flood control materials reserve, and transportation capacity are often limited. This paper studies the node priority evaluation method of substations to be restored in disaster areas and establishes a node priority evaluation model of substations to be restored based on weight fusion. Firstly, the priority evaluation index system is constructed, then subjective weight is assigned to each evaluation index through hierarchical analysis, and objective weight is assigned by the entropy weight method. Finally, the subjective and objective weight fusion optimization strategy based on game theory is adopted, which not only retains the objectivity of the original object but also considers the subjectivity of expert evaluation opinions. The example analysis shows that the combined weights of each priority evaluation index are [0.1835, 0.2432, 0.2631] and [0.0376, 0.0694, 0.0806, 0.1226], and the emergency response needs urgency score of disaster site A is finally 0.6024. We set the recovery priority to Level 2.

Assessment of safety risk in airline operations based on constant-sum game

Safety is the key to airline operations. The premise for ensuring safety is valid classification and scientific assessment of hazardous sources. The airlines, however, are deficient in an overall mastery and effective analysis of the hazardous sources currently. Here, we propose a model for a safety risk assessment in airline operations based on constant-sum games, with a comprehensive analysis of hazardous sources identified by airlines. The subjective and objective weights of the hazardous source indicators are balanced depending on the optimal coefficients, which are determined by deriving the countermeasure model through the constant-sum game theory. The comparative analysis of safety risks and weaknesses in management for airlines or secondary departments is then conducted. Meanwhile, a long-term reward mechanism is introduced into the model to eliminate the negative effect of a sudden increase in safety risk indices caused by the newly added hazardous sources. This method provides airlines with an accurate assessment of the safety status and an estimation of safety development which trends to make targeted decisions and realize prevention beforehand.

The establishment of evaluation systems and an index for energy superpower

Building an energy superpower is a major development strategy for China at present and long-term goal for other countries. This study aims to propose an ideal evaluation system, and construct a quantifiable index for energy superpower. 7 weight allocation schemes and 16 weight sets were employed to test the impacts of different weights on energy superpower index (ESPI), ensuring authenticity, rationality, and accuracy, and finally the subjective entropy weight method was adopted to determine the weights. The ESPI of 11 economies, including China, the United States, Canada, Russia, Australia, Germany, Japan, the United Kingdom, South Korea, Argentina, and the European Union, was computed for a period of 21 years (2000−2020). China's index demonstrated a remarkable increase from the second-to-last place in 2000 to second place in 2020, surpassing the other 10 countries. The United States and Argentina's energy superpower indices consistently ranked first and last respectively from 2000 to 2020, while the early European Union's index ranked second, but was later surpassed by China, falling to third. The energy superpower indices of Canada, Russia, Australia, Germany, the United Kingdom, Japan, and South Korea share similar values with slow growth rates, roughly ranking in between the European Union and Argentina.

Heuristic Path Search and Multi-Attribute Decision-Making-Based Routing Method for Vehicular Safety Messages

Efficient routing in urban vehicular networks is essential for timely and reliable safety message transmission, and the selection of paths and relays greatly affects the quality of routing. However, existing routing methods usually face difficulty in finding the globally optimal transmission path due to their greedy search strategies or the lack of effective ways to accurately evaluate relay performance in intricate traffic scenarios. Therefore, we present a vehicular safety message routing method based on heuristic path search and multi-attribute decision-making (HMDR). Initially, HMDR utilizes a heuristic path search, focusing on road section connectivity, to pinpoint the most favorable routing path. Subsequently, it employs a multi-attribute decision-making (MADM) technique to evaluate candidate relay performance. The subjective and objective weights of the candidate relays are determined using ordinal relationship analysis and the Criteria Importance Through Intercriteria Correlation (CRITIC) weighting methods, respectively. Finally, the comprehensive utility values of the candidate relays are calculated in combination with the link time and the optimal relay is selected. In summary, the proposed HMDR method is capable of selecting the globally optimal transmission path, and it comprehensively considers multiple metrics and their relationships when evaluating relays, which is conducive to finding the optimal relay. The experimental results show that even if the path length is long, the proposed HMDR method gives preference to the path with better connectivity, resulting in a shorter total transmission delay for safety messages; in addition, HMDR demonstrates faster propagation speed than the other evaluated methods while ensuring better one-hop distance and one-hop delay. Therefore, it helps to improve the performance of vehicular safety message transmission in intricate traffic scenarios, thus providing timely data support for secure driving.

Magnet selection for permanent magnet synchronous machines by decision science

This work investigates magnet selection for permanent magnet synchronous machines (PMSMs) by decision science. 20 multiple-attribute decision-making (MADM) methods ranked a list of 45 industrial-grade permanent magnets based on eight property requirements―high energy density (BHMAX), high coercivity (HC), high magnetic remanence (BR), high electrical resistivity (ρ), high Curie temperature (TC), high maximum service temperature (TS), low linear thermal expansion coefficient (α), and low density (d)―targeted to selecting magnets for PMSMs. The Shannon's entropy method evaluated the objective attribute weights of ρ, BHMAX, and HC, as ∼46%, ∼24%, and ∼23%, respectively, and the rest—BR, TC, TS, d, and α―combined as ∼7%; equal weights (∼12.5% each) to all the attributes were the subjective weights. Principal component analysis (PCA) consolidated ranks from various MADM methods, and hierarchical clustering (HC) grouped magnets based on their similarities. For PMSMs in applications where ρ, BHMAX, and HC, are more critical compared to the rest of the properties, the eight top-ranked magnets are N35 (Neodymium® 35), N35H (Neodymium® 35H), N30 (Neodymium® 30), N30H (Neodymium® 30H), N27H (Neodymium® 27H), N27 (Neodymium® 27), F5–S (Ferrite 5, Sintered Iron Strontium Oxide), and F3–S (Ferrite 3, Sintered Iron Barium Oxide) in that order. On the other hand, for PMSMs in applications where all the attributes are equally important, the eight top-ranked magnets are N35 (Neodymium® 35), N35H (Neodymium® 35H), N30H (Neodymium® 30H), N30 (Neodymium® 30), N27H (Neodymium® 27H), N27 (Neodymium® 27), CS4 (Samarium Cobalt 4), and F5–S (Ferrite 5, Sintered Iron Strontium Oxide) in that order. The consolidated ranks of the magnets obtained by assigning objective and subjective attribute weights showed a strong correlation of ∼0.94. This investigation targeted magnet selection for PMSMs from a comprehensive dataset of industrial-grade permanent magnets, utilizing several property requirements, applying both objective and subjective attribute weights identified Neodymium® 35 as the top-ranked magnet.

A Comprehensive Evaluation Method for Planning and Design of Self-Sufficient Wind Power Energy Systems at Ports

This study proposes a scientific method to assess the rationality of planning and design of self-sufficient wind power systems (SS-WPSs) at ports. The evaluation method proposed is based on the concept of integrated development of water transportation and a clean energy system, taking into account the demand of the former and the availability of the latter. First, the factors representing the property of the system from the perspectives of economy, environment, energy efficiency, self-sufficiency, and reliability are calculated. The evaluation method is developed by using a combined Analytical Hierarchy Process and Entropy Weight Method (AHP-EWM) to calculate the subjective and objective weights of these factors. Subsequently, a combined weighting method based on game theory is employed with the aim of minimizing deviations between the subjective and objective weights and the combination weights, thereby determining the optimal combination of weights of the evaluation factors. These weights will be used to calculate evaluation results under different planning schemes. To validate the effectiveness of the method, a case study is developed based on a wind power energy demonstration project at a dock of a container port. Study results reveal that Option C, considered in this study, outperforms other options with the highest evaluation score of 0.737. Sensitivity analysis further underscores the reliability of Option C, showing a robust 96.15% probability of achieving the maximum final score within a 20% variation range. This indicates that the case study demonstrates that intuitive and realistic evaluation results can be obtained from the proposed method. These results affirm the practicality of our approach, providing invaluable insights for planners, policymakers, and stakeholders involved in sustainable energy initiatives.

Efficient hybrid conjugate gradient techniques for vector optimization

Scalarization approaches transform vector optimization problems (VOPs) into single-objective optimization but have trade-offs: information loss, subjective weight assignments, and limited representation of the Pareto front. To address these limitations, alternative strategies like conjugate gradient (CG) techniques are valuable for their simplicity and less memory usage. The paper introduces three CG techniques for VOPs, including two CG techniques that satisfy sufficient descent conditions (SDC) without a line search. These two CG techniques are combined with the third CG technique, a variant of the Polak–Ribiére–Polyak (PRP) technique, resulting in two hybrid CG techniques. Global convergence of these hybrids is achieved, without convexity assumptions, under standard assumptions and Wolfe line search. Numerical analysis and comparisons with nonnegative PRP and Liu–Storey (LS) CG techniques showcase the implementation and effectiveness of our hybrid CG techniques. The results demonstrate the promise of our hybrids techniques.

Quantifying Medium-Sized City Flood Vulnerability Due to Climate Change Using Multi-Criteria Decision-Making Techniques: Case of Republic of Korea

This study proposed a systematic approach to quantifying city flood vulnerability (CFV) related to climate change using several multi-criteria decision-making methods in medium-sized cities and investigated the sources of uncertainty in this assessment. In addition, this study was intended to explore ways for quantifying flood vulnerability and mitigating the impact of data uncertainty on flood vulnerability through multi-criteria decision-making (MCDM) methods. The MCDM method was applied as a representative method to quantify flood vulnerability, which considers regional priorities. This study used the weighted summation method, TOPSIS, and VIKOR to calculate all CFVs for medium-sized cities. Furthermore, fuzzy- and grey-TOPSIS were included to account for the uncertainty inherent in the MCDM methods, such as the usage of average values and varying weighting values for all CFV indicators across stakeholders. This study incorporated expert surveys and the entropy approach to derive subjective and objective weights for all conceivable indicators. As a result, we looked at the proposed grey-TOPSIS technique, which can minimize the uncertainty. Finally, grey-TOPSIS can notably provide robust and sustainable prioritizing since it actively reflects the views of multiple stakeholders and takes uncertainty in the data into account.

Rock Burst Intensity-Grade Prediction Based on Comprehensive Weighting Method and Bayesian Optimization Algorithm–Improved-Support Vector Machine Model

In order to accurately judge the tendency of rock burst disaster and effectively guide the prevention and control of rock burst disaster, a rock burst intensity-grade prediction model based on the comprehensive weighting of prediction indicators and Bayesian optimization algorithm–improved-support vector machine (BOA-SVM) is proposed for the first time. According to the main factors affecting the occurrence and intensity of rock burst, the rock stress coefficient (σθ/σc), brittleness coefficient (σc/σt) and elastic energy index (Wet) are selected to construct the rock burst prediction indicator system. On the basis of the research of other scholars, according to the main performance and characteristics of rock burst, rock burst is divided into four intensity levels. The collected and sorted 120 sets of rock burst case data at home and abroad are taken as learning samples, and the T-SNE algorithm is used to perform dimensionality-reduction visualization processing on the sample data, visually display the distribution of samples of different grades, evaluate the representativeness of the sample data and prejudge the feasibility of the machine learning algorithm to distinguish different rock burst intensity levels. The combined improved analytic hierarchy process (IAHP) and Delphi method determine the subjective weight of the indicators; the combined entropy weight method and CRITIC method determine the objective weight of the indicator, and use the harmonic mean criterion of information theory to synthesize the subjective weight and objective weight of the indicator to obtain the comprehensive weight of the indicators. After weighted prediction indicators, a rock burst intensity-grade prediction model is constructed based on the support vector machine, and the hyperparameters of three types of support vector machines are improved by using the Bayesian optimization algorithm. Then, the prediction accuracy of different models is calculated by the random cross-validation method, and the feasibility and effectiveness of the rock burst intensity-grade prediction model is verified. In order to evaluate the generalization and engineering applicability of the proposed model, 20 groups of rock burst case data from the Maluping mine and Daxiangling tunnel are introduced to predict the rock burst intensity grade. The results show that the accuracy of the rock burst intensity-grade prediction model based on comprehensive weighting and BOA-SVM is as high as 93.30%, which is of higher accuracy and better effect than the ordinary model, and can provide warning information with a higher fault tolerance rate, which provides a new way of thinking for rock burst intensity-grade prediction.

Green Supplier Selection Based on Sequential Group Three-Way Decision Making

Environmental protection and sustainable development have become the consensus of all countries in the world. Enterprises must pay attention to the impact on the environment in their operations. Therefore, the selection of green suppliers has become a crucial issue for companies. Supplier selection is a dynamic and complex multi-attribute group decision-making process. The decision results have the tripartite characteristics of “accepted”, “rejected” and “pending further investigation”, and experts need to constantly negotiate in the decision-making process to achieve consensus. In view of the above characteristics, this study constructs a sequential group three-way decision making (TWDM) method to support green supplier selection. Firstly, we review the existing literature on the evaluation criteria and selection methods of green suppliers. In this process, we construct an evaluation attribution system including the following four aspects: product formation, service level, development capability, and green level. Secondly, combining with the sequential and group characteristics of decision making, we propose a multiple-attribute sequential group TWDM method based on a multi-level granularity structure. The weight of each decision maker is determined by his influence weight and interval-valued intuitionistic fuzzy entropy weight. The attribute weight is determined by entropy weight and subjective weight. By using the VIsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method improved by grey relation analysis (GRA), we calculate conditional probabilities. Since the cumulative prospect theory can reflect the risk attitude of decision makers, we apply it to calculate the decision thresholds. Finally, we apply a case of Q automobile manufacturer to verify the effectiveness, applicability and feasibility of the method. The innovation of this study is to construct social networks at each granularity level and introduce an expert information exchange model to promote group consensus. The sequential group TWDM method provides a new reference and idea for the selection of green suppliers.

Evaluation Method for Green Construction Demonstration Projects in China Based on G-TOPSIS

Although the construction industry has played an important role in promoting national economic growth, over the past decades, construction activities have caused serious negative impacts on the ecological environment. Faced with this challenge, many countries have made promoting the greening of the construction industry one of their development goals. As a high-level demonstration project for green construction, Green Construction Demonstration Projects (GC-DPs) play a significant role in improving the level of green construction and promoting the green development of the construction industry. This study aims to establish a reasonable GC-DP evaluation method to promote the development of green construction in China. An evaluation index system is constructed, including five criterion layers, 20 main factors, and 60 sub-factors. Sixty sub-factors correspond to 60 specific indicators. The combination optimization of subjective and objective weights of indicators is conducted using game theory, and the comprehensive weights of indicators are calculated. Furthermore, a GC-DP evaluation model based on the gray TOPSIS method with game theory combination weights (hereinafter referred to as G-TOPSIS) is established. Finally, a case study is carried out to verify the feasibility of the proposed method. Expert scoring and actual engineering data are used to calculate indicator weights, and game theory is utilized to balance the influence of subjective and objective factors. Results show that the evaluation results obtained using the proposed G-TOPSIS method are consistent with the actual situation of the project.

Weight Optimization Decision Algorithm in (p,q)-Rung Probabilistic Hesitant Orthopair Fuzzy Environments

Aiming at the fuzzification of a decision environment and the challenge of determining the weights associated with the interaction among decision-makers, this study offers an original method for (p,q)-rung probabilistic hesitant orthopair fuzzy multi-objective group decision-making, which is founded on the weight optimization principle. Firstly, the notion of a probabilistic hesitant fuzzy set is expanded to a (p,q)-rung. Secondly, the determination of subjective and objective weights is accomplished through the utilization of the Analytic Network Process (ANP) and the Entropy Method. According to the degree of deviation and dispersion of each weight, an optimal objective function is constructed, and the neural network is used to iteratively solve for the best scheme of the comprehensive weight. Subsequently, the Elimination Et Choice Translating Reality (ELECTRE) approach was refined and applied to decision-making in the (p,q)-rung probabilistic hesitant orthopair fuzzy environment. Finally, comparative analysis was used to demonstrate the new method’s effectiveness and superiority.

The Significance of the Factors Increasing the Asphalt Pavement Recycling Rate in the Country, Determined Using Multiple-Criteria Decision-Making Methods

Asphalt pavement, a component of transport infrastructure, deteriorates under the influence of destructive factors. The damages which have occurred during its period of use are repaired and the asphalt pavement is recycled when its further rehabilitation is economically and technically irrational. The material from the asphalt pavement layer that has reached its limit state is milled out or broken and crushed and then is repeatedly used in the production of hot-mix asphalt (HMA) or warm-mix asphalt (WMA) mixtures. In this paper, the dynamics of the percentage recycling ratio (RR) of old asphalt pavement material was investigated. RR represents the quantity of reclaimed asphalt pavement (RAP) used in the production of HMA and WMA mixtures in Europe and the USA, divided by the total amount of RAP prepared in the country. Factors and goals affecting it are analyzed. An original system of 10 criteria that increase the RR country has been created. Decision makers do not know which factors have the maximum influence in increasing the use of RAP and this should go in connection with environmental pollution reduction and achieving economic utility. By applying different multiple-criteria decision-making (MCDM) methods and using the importance given to these criteria by 14 experts, the normalized subjective weights of the criteria were determined. Analytic hierarchy process (AHP), rank correlation, average rank transformation into weight—linear (ARTIW-L) and non-linear (ARTIW-N), and direct percentage weight (DPW) methods were used in the study. The results show that the RAP recycle rate is close to 100% in countries with a sustainable economic background. In the Baltic countries, it is mostly increased by the adequacy of regulatory documents, the strategy promoting asphalt recycling in the country, and the homogeneity and classifying of RAP. The number and capacity of RAP stocks, the number and productivity of asphalt milling equipment and the wear and tear of the asphalt pavement have the least influence on the increase in RR. The opinions of experts in assessing the significance of all criteria are consistent. The averages of the weights of criteria determined by four MCDM methods (AHP, ARTIW-L, ARTIW-N, DPW) made it possible to obtain more reliable results. These results can be used to make strategic decisions and to create a plan of practical actions to increase the RAP recycling rate in the developing countries.

Comparison of ticagrelor and prasugrel just before PCI in NSTE acute coronary syndromes, LIKE-ACCOAST analysis

Abstract Background Recommendations for the preference of prasugrel over ticagrelor just before PCI in NSTE ACS are based on limited and controversial evidence. Furthermore, prasugrel can’t be used in patients after a stroke/TIA, and the risk of bleeding in older and low-weight patients annuls its benefit. Purpose We aimed to add to the current evidence comparing the benefit of prasugrel and ticagrelor just before PCI (without pretreatment) in NSTE ACS patients. Methods The prasugrel population comprised the ACCOAST study non-pretreatment arm undergoing PCI (N 1372) (ref 1). The ticagrelor study population was selected from the long-term all-comers National Registry of Cardiovascular Surgery and Interventions: Module of Cardiovascular interventions of Institute of Health Information and Statistics of the Czech Republic (N 15126)who were 1) undergoing PCI for NSTE ACS, 2) received ticagrelor loading dose 180 mg just before the procedure, 3) fulfilled the inclusion and exclusion criteria of the ACCOAST trial (N 1370). The endpoint (identically to the ACCOAST primary EP) was a composite of cardiovascular death, myocardial infarction, stroke, urgent revascularization, or glycoprotein IIb/IIIa bailout seven days after PCI. The data were matched to the structure of published ACCOAST no pre-treatment group using R with maic package, which provides a generalized workflow for the generation of subject weights to be used in Matching-Adjusted Indirect Comparison (MAIC) (Ref 3,4). In MAIC, unbiased comparison between outcomes of two trials is facilitated by weighting the subject-level outcomes of one trial with weights derived such that the weighted aggregate measures of the prognostic or effect modifying variables are equal to those of the sample in the comparator trial. Results The study population characteristics are presented in Table. We did not find any difference in the efficacy endpoint at seven and 30 days after PCI between the groups treated with prasugrel and ticagrelor (Figure). Conclusion Prasugrel and ticagrelor given just before PCI for NSTE ACS are equally effective in reducing ischemic risk.Table.Efficacy endpoint occurence.

Collaborative Development Mechanism of Vocational Education and Open Education Based on Advantageous Correlation in the Context of High-Quality Development

Abstract High-quality development is the goal of the collaborative development of vocational education and open education, and this paper constructs a quality evaluation index system for collaborative development starting from the characteristics of vocational education and open education. The subjective and objective weights of each index are obtained by using the G1 method and the CRITIC method, respectively. The combined weights are solved by multiplicative normalization, and the synergistic development of vocational education and open education is comprehensively evaluated based on the gray correlation degree. To verify the effectiveness of the evaluation index system and explore the mechanism of synergistic development, an empirical analysis was conducted. The results show that the weight of the open education index is 0.3491, the correlation coefficient of the evaluation indexes is more than 0.7, and the factor with the strongest influence is the influence of education with a correlation value of 0.973, which indicates that the mechanism of collaborative development between vocational education and open education needs to pay attention to the influence of education, so as to promote the high-quality development of both of them.