Objective Weights Research Articles

Using a Flexible Risk Priority Number Method to Reinforce Abilities of Imprecise Data Assessments of Risk Assessment Problems

Risk priority number (RPN) is the most commonly used failure risk ranking method among all risk assessment methods. However, the traditional RPN method not only cannot handle incomplete information and hesitation information (such as hesitation information between the s5=Low and s6=Moderate) provided by experts, but it also does not consider the objective weights of risk assessment factors. These reasons will lead to biased conclusions, causing decision makers to make wrong judgments. To address the limitations of the traditional RPN technique, the aim of this paper is to propose the flexible RPN assessment method under an uncertain environment. The flexible RPN assessment method is an extension of the traditional RPN technique. The flexible RPN method integrates the traditional RPN method and interval-valued 2-tuple weighted average method, and, simultaneously, considers subjective weights and objective weights. In the numerical verification, this study has adopted the example of stages of treatment planning for proton beam radiation therapy to verify the correctness and validity of the proposed flexible RPN technique. The numerical results show that the proposed flexible RPN technique not only handles the hesitation and incomplete information provided by experts but also considers the subjective and objective weights of risk assessment factors, providing more reasonable ranking results in the risk analysis.

Digital transformation project risks assessment using hybrid picture fuzzy distance measure-based additive ratio assessment method

Digital transformation (DT) has become vital for companies trying to remain competitive in the recent ever-changing technological environment. DT is the integration of digital technologies into all disciplines of business from regular activities to strategic decision making. Risk management planning requires projects to assess possible risks that may negatively or positively affect a DT project. The purpose of the study is to introduce a hybridized decision support system (DSS) by combining the distance measure, ranking comparison (RANCOM) model and additive ratio assessment (ARAS) approach in the context of a picture fuzzy set (PFS). In this framework, the decision experts’ significance values are computed using a picture fuzzy score function-based formula. With the combination of objective weight using distance measure and subjective weight through the RANCOM model, a combined weight-determining approach is developed to determine the significance values of considered DT risks under picture fuzzy environment, while a hybrid ARAS model is developed to evaluate and rank DT projects from the risks perspective. To exhibit the feasibility of the introduced framework, a case study of a DT projects assessment problem is discussed in the context of picture fuzzy sets. A sensitivity study is also discussed over different values of the strategy coefficient, which confirms the strength of the proposed model. Further, a comparison with the existing picture fuzzy information-based methods is presented to prove the robustness of the developed decision-making framework.

Research on the Evaluation of Chinese Prefabricated Building Strategic Partners Based on Cloud Model and Improved Evidence Theory

In recent years, prefabricated buildings have developed rapidly in China. Compared with traditional buildings, prefabricated buildings require higher capabilities from partners in various aspects. However, due to the early stage of development of prefabricated buildings in China, the level of various enterprises varies greatly. How to evaluate partners scientifically and objectively is a realistic problem that needs to be solved urgently. In order to achieve economies of scale and promote the sustainable development of prefabricated buildings, this study proposes a novel evaluation model for strategic partner selection based on the cloud model and improved Dempster–Shafer (D-S) evidence theory. First, using a literature review and field research method, a strategic partner selection index system is developed that can reflect the characteristics of prefabricated buildings. To address the fuzziness and randomness of the traditional membership function, the cloud model is applied to calculate the membership value between the test samples and the benchmark cloud, which is subsequently transformed into basic probability distribution in the evidence theory. Furthermore, to mitigate the paradox of evidence fusion often encountered in traditional evidence theory, this model combines both the subjective and objective weights of evidence by game theory, and the conflicting evidence is corrected and fused according to the combination weight. Additionally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method is used to further optimize the strategic partners of prefabricated buildings. Finally, the optimal order obtained from the case analysis is S1 > S2 > S4 > S3 > S5, and the evaluation results are consistent with the actual situation, which verifies the effectiveness and superiority of the proposed model in resolving the evidence conflict and selecting strategic partners. The research results have certain reference significance for optimizing the selection mechanism of prefabricated building strategic partners and guiding partners to establish long-term and stable cooperative relations.

A resilience assessment framework for slopes considering multiple performance indicators

The performance assessment of slopes during various disturbances is important to ensure the safe service of slopes. Due to the four characteristics of slope engineering, it is difficult for existing resilience evaluation models to accurately reflect slope performance. To address this problem, a resilience evaluation model applicable to slopes was developed in this study. In this model, a typical slope performance curve is proposed according to the characteristics of slope engineering and the calculation formula of slope resilience metric is derived. In order to facilitate engineering application, this study proposes a slope resilience assessment framework with the evaluation model as the core. In the resilience assessment framework, a multi-indicator system for slope performance was established to cover the slope components, and the CRITIC theory was used to combine the subjective and objective weights for each indicator. Finally, the proposed slope resilience assessment framework was applied to a slope case under the influence of continuous excavation and rainfall. The results show that the proposed resilience assessment framework can more accurately reflect the performance variations of slopes during disturbance and recovery processes than the existing model, and the resilience metric is 23.4% higher than that of the existing model. This study was the first attempt to establish a resilience assessment framework applicable to slopes, which informs subsequent research.

The transition from resistance to acceptance: Managing a marine invasive species in a changing world

Abstract Marine invasive species can transform coastal ecosystems, yet mitigating their effects can be difficult, and even impractical. Often, marine invasive species are managed at poorly matched spatial scales, and at the same time, rates of spread and establishment are increasing under climate change and can outpace resources available for population suppression. These circumstances challenge traditional conservation goals of maintaining a historic environmental state, especially for a species like the European green crab (Carcinus maenas), a formidable invader with few examples of successful long‐term removal programs. A management paradigm where decision alternatives include resisting or accepting a new ecological trajectory may be needed. We apply mathematical concepts from decision theory to develop a quantitative framework for navigating management decisions in this new resist‐accept paradigm. We develop a model of European green crab growth, removal and colonization, and we find optimal levels of removal effort that minimize both ecological change and removal cost. We establish a benchmark of colonization pressure at which green crab density becomes decoupled from a decision maker's actions, such that population control can no longer shape the invasion trajectory. For informing the decision boundary between resistance and acceptance, our results highlight that a decision maker's understanding of how removal cost scales with removal effort is more important than understanding the density‐impact relationship. We show that assuming stationary system dynamics can result in sub‐optimal levels of species removal effort, highlighting the importance of developing anticipatory management strategies by accounting for non‐stationary dynamics. Policy implications. For marine invasive species that can disperse across long distances and recolonize rapidly after removal, the focus of conservation policy should shift away from understanding how to resist change to understanding when to stop resisting change. Navigating this decision problem involves trade‐offs among competing objectives, highlighting the need for structured approaches to elicit objective weights that reflect the values of the decision maker. For natural resource managers facing possible ecosystem transformation, this decision framework can enable proactive and strategic decisions made under uncertainty in a changing world.

Factors Driving Social Capital Participation in Urban Green Development: A Case Study on Green Renovation of Old Residential Communities Under Urban Renewal in China

Urban green development is crucial for citizen well-being and serves as a key pillar of sustainable development strategies. Collaborative governance mechanisms help address management, technical, and financial challenges in urban green development. The renovation of old residential communities within the context of urban renewal is becoming a focal point in the development of Chinese cities. To promote green development in the renovation of old residential communities in China, this study introduces the collaborative governance mechanism of government–social capital cooperation and identifies the factors influencing social capital participation in green development. A hybrid approach is proposed, combining grounded theory, IGAHP subjective weighting, CRITIC objective weighting, game theory, and ISM. This approach is applied to identify, evaluate, and analyze the factors driving social capital participation in the green renovation of old residential communities. The results indicate that government incentives and constraints, policy support, the costs and benefits of implementing green renovation, the development of green renovation technologies, and construction technical abilities significantly influence the decision-making of social capital. The findings provide theoretical support for decision-making by governments and social capital in participating in the green renovation of old residential communities and offer a methodological reference for analyzing social capital participation in other urban green development projects and for further policy formulation. Future research should focus on examining social capital involvement in other types of urban green development projects.

An Integrated Two-Step Optimization Model and Aggregative Multi-Criteria Approach for Establishing Sustainable Tram Transportation Plan

The choice of the most appropriate sustainable scheme for the organization of tram transportation in cities is of great importance for tram operators, for users of transportation services, and for the protection of the environment from harmful emissions. This study aims to propose a methodology for formulating a tram transportation plan considering technological, environmental, economic, and social indicators. The variant schemes represent the routes of a tram in the tram network. The methodology includes four stages. The first stage involves the determination of variant schemes for a transportation plan of service with trams. In the second stage, a two-step optimization model is proposed to determine the number and trams by types for each tram route for each variant scheme, and also to establish the distribution of trams by depots. The third stage includes ranking the variant schemes by applying the sequential interactive model for urban systems (SIMUS) multi-criteria method. Eleven quantitative and qualitative criteria for evaluating the tram transportation plan were introduced. A verification of the results is performed in the fourth stage. For this purpose, a comparison of the preference ranking organization method for enrichment of evaluations (PROMETHEE) method and the technique for order of preference by similarity to ideal solution (TOPSIS) method is made. Both methods have different approaches for decision making and differ from the SIMUS method. Two strategies were proposed to determine the criteria weights. One is based on the Shannon entropy method and the other uses the objective weights obtained through the SIMUS method. Finally, in the fifth stage, the results obtained through the SIMUS, PROMEHEE and TOPSIS methods are combined using the expected value obtained by applying the program evaluation and review technique method (PERT). The proposed methodology was applied to study tram transportation in Sofia, Bulgaria. Five variant schemes were considered. The schemes are optimized through the criterion of minimum energy consumption. The number of trams by routes and by type was determined. An improved scheme for tram transportation in Sofia was proposed. The scheme makes it possible to increase the frequency of the trams by 13%, to reduce the zero mileage of rolling stock, and to reduce carbon dioxide pollution by 11%.

Vulnerability analysis of power grid structure based on complex network theory

IntroductionThe key nodes of an intelligent distribution network significantly impact the reliability and stability of the distribution network’s operation. The failure of these key nodes can severely affect the safe operation of the distribution network. Therefore, vulnerability analysis of key nodes is particularly important.MethodsThis article proposes a comprehensive weighting method for evaluating indicators,combining the analytic hierarchy process (AHP) and entropy weighting method, while considering the structure and operational status of the power system grid. Key node structural evaluation indicators, such as node degree, node shrinkage centrality, and electric mediator, are established considering “significance” and “destructiveness.” State indicators are established based on the degree of impact of current, voltage, and load changes on the grid, as well as the uniformity of their distribution, including the improved current distribution entropy, voltage terre entropy, and three-phase state indicators of lost load. Subsequently, based on the AHP and entropy weighting method, a comprehensive weighting method is proposed to assign subjective and objective weights to the comprehensive evaluation indicators, obtaining the comprehensive weights of the indicators. Finally, the gray correlation degree is introduced to improve the ideal solution of the multi-objective decision making method, obtaining the criticality of the grid nodes and then identifying the critical nodes.Results and DiscussionThe example analysis presented in this article shows that the identified critical nodes of the power grid have a high degree of overlap with the identification results of different methods, can better identify edge nodes, and validate the effectiveness of the proposed evaluation indicators and key node identification methods.

A novel MADM model integrating hybrid information for evaluating the development prospects of urban new energy vehicles.

As an effective approach to mitigating urban environmental issues, New Energy Vehicles (NEVs) have become a focal point of research regarding their current development status and future prospects in China. Addressing the significant disparities in the development of the NEVs industry across different cities, this study focuses on ten typical Chinese cities and develops a novel multi-attribute decision-making (MADM) framework to evaluate the prospects of NEVs promotion in these cities. The study first establishes a comprehensive indicator system that covers key dimensions such as economy, policy support, infrastructure, technological innovation, and environment, encompassing five different types of evaluation information. This system incorporates five different types of evaluation information: exact numbers, interval numbers, triangular fuzzy numbers, hesitant fuzzy numbers, and probabilistic linguistic term sets (PLTS), enhancing the framework's ability to handle diverse data types. Subsequently, the improved entropy (IEntropy) weight method is employed to determine the objective weights of the evaluation indicators. These objective weights are then integrated with the Vlsekriterijumska Optimizacija I Kompromisno Resenje (VIKOR) method, facilitating a structured group decision-making approach that synthesizes hybrid evaluation information. Based on modular thinking, hybrid evaluation information is synthesized to evaluate and rank the NEVs development prospects of each city. Sensitivity analysis and comparative analysis further demonstrate the robustness and reliability of the proposed MADM framework. The ranking results indicate that Shanghai and Guangzhou lead in NEVs promotion, while cities like Harbin and Zhengzhou lag behind. Based on these findings, the study proposes targeted policy recommendations to promote the sustainable development of the NEVs industry in major Chinese cities.

Dynamic Routing and Knowledge Re-Learning for Data-Free Black-Box Attack.

Deep learning models have emerged as strong and efficient tools that can be applied to a broad spectrum of complex learning problems and many real-world applications. However, more and more works show that deep models are vulnerable to adversarial examples. Compared to vanilla attack settings, this paper advocates a more practical setting of data-free black-box attack, for which the attackers can completely not access the structures and parameters of the target model, as well as the intermediate features and any training data associated with the model. To tackle this task, previous methods generate transferable adversarial examples from a transparent substitute model to the target model. However, we found that these works have the limitations of taking static substitute model structure for different targets, only using hard synthesized examples once, and still relying on data statistics of the target model. This may potentially harm the performance of attacking the target model. To this end, we propose a novel Dynamic Routing and Knowledge Re-Learning framework (DraKe) to effectively learn a dynamic substitute model from the target model. Specifically, given synthesized training samples, a dynamic substitute structure learning strategy is proposed to adaptively generate optimal substitute model structure via a policy network according to different target models and tasks. To facilitate the substitute training, we present a graph-based structure information learning to capture the structural knowledge learned from the target model. For the inherent limitation that online data generation can only be learned once, a dynamic knowledge re-learning strategy is proposed to adjust the weights of optimization objectives and re-learn hard samples. Extensive experiments on four public image classification datasets and one face recognition benchmark are conducted to evaluate the efficacy of our Drake. We can obtain significant improvement compared with state-of-the-art competitors. More importantly, our DraKe consistently achieves attack superiority for different target models (e.g., residual networks, and vision transformers), showing great potential for complex real-world applications.

Natural or man-made disaster? Lessons from the extreme rain and flood disaster in Zhengzhou, China on "2021.7.20"

Driven by extreme weather events, precipitation-induced flood disasters are occurring more frequently, posing substantial threats to both economic development and public safety. Increased human production activities have further intensified the impact of flood disasters. However, understanding of the factors influencing flood disasters and their associated risk assessments remains limited. To better formulate flood prevention and disaster reduction strategies suited to local conditions, this paper takes the "7.20" extreme rainfall event in the main urban area of Zhengzhou, Henan Province, China, on July 20, 2021, as a case study and uses geographic detectors to incorporate human activities into the quantitative analysis of influencing factor contributions. Based on the three aspects of disaster incubation, disaster causation, and disaster bearing, this study proposes using the influence values obtained from geographic detectors as objective weight assignments for risk assessment, thereby conducting a locally adapted risk assessment of Zhengzhou's main urban area. Research results indicate that the "7.20" heavy rainstorm in Zhengzhou's main urban area involved a superposition of natural and human factors. Impervious surfaces had the highest explanatory power, followed by population density, confirming that this flood disaster arose from the interaction of multiple factors. The flood disaster risk assessment for Zhengzhou's main urban area shows higher risk levels in the central area and relatively lower levels in peripheral areas. The central area features flat terrain, with high population density and a large proportion of impervious surfaces. Among these, 22 subdistricts are located in extremely high-risk flood zones, while 6 subdistricts, classified as low-risk, account for over 60% of the low-risk area, making them relatively lower-risk communities. Comparison with flood waterlogging point data verifies the accuracy of the risk assessment through statistical analysis, allowing for further analysis of the spatial differentiation of flood risk. The findings of this study offer valuable insights for preventing and managing urban extreme rainfall disasters.

Synergy of machine learning and the Einstein Choquet integral with LOPCOW and fuzzy measures for sustainable solid waste management

<p>Solid waste management (SWM) protects public health, the environment, and limited resources in densely populated and urbanized countries such as Singapore. This work presents an advanced framework for optimizing SWM using advanced mathematical models and decision-making techniques, including the circular $ q $-rung orthopair fuzzy set (C$ q $-ROFS) for data, combined with the Choquet integral (CI) and logarithmic percentage change-driven objective weighting (LOPCOW) methods, enhanced by the aggregation operators (AOs) circular $ q $-rung orthopair fuzzy Einstein Choquet integral weighted averaging (C$ q $-ROFECIWA) and circular $ q $-rung orthopair fuzzy Einstein Choquet integral weighted geometric (C$ q $-ROFECIWG) aggregation operators. By conducting a systematic evaluation, these methods classified different alternatives to SWM, evaluating them according to criteria such as their environmental impact, cost-effectiveness, waste reduction efficiency, feasibility of implementation, health safety, and public acceptance. The operators C$ q $-ROFECIWA and C$ q $-ROFECIWG perform better than previous approaches in the effective management of multifaceted and dynamic SWM scenarios. The comparison study demonstrates that the integration of these operators with LOPCOW and the Choquet integral offers decision-making conclusions that are more reliable and sustainable. The study conducted in Singapore successfully finds the most feasible SWM alternatives and emphasizes the possibility of implementing more environmentally sustainable practices in the urban environment. This research offers practical insights for policymakers and emphasizes the need to improve and enhance these approaches to improve SWM in various urban environments.</p>

A New Integrated Criteria Weighting Approach for Determining Production Priority in Demand Management

In the demand management process; it is tried to balance the customers' needs with the company's supply possibilities. Effective demand management starts with effective stock control and tracking of customer orders and ends with customer satisfaction. Correctly determining production priorities is one of the ways to increase customer satisfaction. In this context, a new approach has been developed to determine the production priority needed in the demand management process. A new integrated approach based on Multi-Criteria Decision Making (MCDM) is proposed to determine production priorities. In the first stage of this three-stage approach, customer alternatives are determined. In the second stage, production priority criteria are determined depending on customer characteristics. In the third stage; with LBWA (Level Based Weight Assessment) and CRITIC (The Criteria Importance Through Intercriteria Correlation) methods, objective and subjective criterion weights are calculated by considering expert opinions and real data. Then, integrated criterion weights are determined with the SOWIA (Subjective and Objective Weight Integrated Approach) method using objective and subjective criterion weights. In the literature; few studies determine production priority using the MCDM method. Additionally, no study has been found in the literature in which quantitative and qualitative methods are used integrated in the ceramic industry. Therefore, it is thought that this study will contribute to the literature.

PROJECT PERFORMANCE ANALYSIS OF TURKISH UNIVERSITIES BY LOPCOW-CRADIS METHODS

The encouraging regulations put forward by the countries in the fields of science and technology make quite positive contributions to the efforts of the relevant stakeholders. The effort to benefit from these financial supports, which has a motivational role for scientists and industrial organizations focusing on research and development activities, has become a competitive issue and performance indicator today. In this study, the performance of 200 state and foundation universities operating in Turkiye is examined in terms of project submission and execution capacities within the scope of Research Support Programs Directorate (ARDEB). In order to weight the evaluation criteria, the LOgarithmic Percentage Change-driven Objective Weighting (LOPCOW) method was used and the criterion "Number of Project Submissions" is determined as the prominent one. The performance order of the universities is determined by the Compromise Ranking of Alternatives from Distance to Ideal Solution (CRADIS) method where Middle East Technical University, Ege University, Istanbul Technical University, Hacettepe University, Ankara University, Istanbul University, Koç University, İhsan Doğramacı Bilkent University, İzmir Technology Institute and Erciyes University are determined in the first ten order. It has also been determined that especially the universities established in recent are far from a competitive position.

Analysis Of Elementary School Students' Difficulties In Solve Mathematical Questions On Object Weight Measurement Material

The ability to measure the weight of objects is a skill needed in various fields, such as trade, industry and health. However, various studies mention that many students have difficulty in understanding and solving math problems related to measuring the weight of objects effectively. This study aims to analyze the difficulties faced by grade 6 students of UPT SDN 025 JANJI, Doloksanggul in solving mathematics problems measuring the weight of objects. Researchers made questions in the form of measuring the weight of objects and found various student errors in answering each question. The method used in this research is quantitative research, which is a research method that focuses on the collection, analysis, and interpretation of numerical data. Based on the results of research and analysis conducted by researchers on 13 sixth grade students at UPT SDN 025 JANJI above, students often face various errors in solving weight measurement problems, namely (1) errors in reading the problem, (2) understanding information, (3) choosing the right mathematical operation, and (4) performing calculations. Students often face various errors in solving weight measurement problems, including errors in reading the problem, understanding the information, choosing the right mathematical operation, and performing calculations. These errors can be categorized into several types, such as reading errors, comprehension errors, transformation errors, process skill errors, and other errors.

DETERMINING THE HUMAN CAPITAL PERFORMANCE OF LATIN AMERICAN COUNTRIES AND TÜRKİYE WITH CRITIC-BASED ARAS METHOD

The aim of this study is to determine the human capital performance of Latin American Countries and Türkiye using the CRITIC (Criteria Importance Through Intercriteria Correlation) and ARAS (Additive Ratio Assessment) techniques. In the analysis, CRITIC-based ARAS method has been used. This method is a combination two methods. The CRITIC method has provided the objective weights of the criteria, and human capital performances and ranking of the countries are obtained by ARAS method. In order to evaluate the human capital stock of the countries the following criteria are utilized: the infant mortality rate (per 1,000 live births), unemployment rate (percentage of the total labor force), average life expectancy at birth, total (years), labor force participation rate (percentage of the total population aged 15-64), current health expenditure (percentage of GDP), internet users (percentage of the total population), and population aged 15-64 (percentage of the total population). The analysis covers the years of 2019, 2020 and 2021. According to the CRITIC method results, the infant mortality rate has been the least significant criterion for every year under study, whereas the unemployment rate is the most significant. The ARAS method suggests that in every year examined, Cuba has done the best in terms of human capital. Chile and Uruguay rank second and third, respectively, after Cuba. Throughout the years under study, Guinea has performed the worst, followed by Haiti. Venezuela in 2021 and Honduras in 2019 and 2020 rank third-worst. Among the 22 nations, Türkiye ranks 15th in 2019, 12th in 2020, and 13th in 2021.

Weight illusions explained by efficient coding based on correlated natural statistics

In our everyday experience, the sizes and weights of objects we encounter are strongly correlated. When objects are lifted, visual information about size can be combined with haptic feedback about weight, and a naive application of Bayes’ rule predicts that the perceived weight of larger objects should be exaggerated and smaller objects underestimated. Instead, it is the smaller of two objects of equal weight that is perceived as heavier, a phenomenon termed the Size-Weight Illusion (SWI). Here we provide a normative explanation of the SWI based on principles of efficient coding, which dictate that stimulus properties should be encoded with a fidelity that depends on how frequently those properties are encountered in the environment. We show that the precision with which human observers estimate object weight varies as a function of both mass and volume in a manner consistent with the estimated joint distribution of those properties among everyday objects. We further show that participants’ seemingly “anti-Bayesian” biases (the SWI) are quantitatively predicted by Bayesian estimation when taking into account the gradient of discriminability induced by efficient encoding. The related Material-Weight Illusion (MWI) can also be accounted for on these principles, with surface material providing a visual cue that changes expectations about object density. The efficient coding model is further compatible with a wide range of previous observations, including the adaptability of weight illusions and properties of “non-illusory” objects. The framework is general and predicts perceptual biases and variability in any sensory properties that are correlated in the natural environment.

The Influence of Stability in New Power Systems with the Addition of Phase Modulation Functions in Thermal Power Units

The addition of phase modulation function technology to thermal power units is one of the most effective measures to solve dynamic reactive power shortages in the construction process of new power systems. In this paper, the influence of the phase modulation function transformation of thermal power units on the stability of a new power system is studied. Firstly, the new power system stability index is deeply analyzed, and an evaluation system for power system transient stability is constructed from five key dimensions: transient voltage, static voltage, power angle stability, power flow characteristics, and grid support. Secondly, a fuzzy comprehensive evaluation method considering the subjective and objective comprehensive weights is proposed, and the influence of the phase modulation transformation of the thermal power unit on the stability of the receiving-end power grid is quantitatively analyzed. Finally, a CEPRI36 node example model was built based on the PSASP v.7.91.04.9258 (China Electric Power Research Institute, Beijing, China) platform to verify the accuracy and effectiveness of the proposed method. The results show that the proposed method can quantitatively analyze the impact of adding a phase modulation function to thermal power units on the stability of the power system. At the point of renewable energy connection, the static voltage stability index improved by 42.9%, the transient power angle stability index improved by 32.1%, the multi-feed effective short-circuit ratio index improved by 33.9%, and the comprehensive evaluation score improved by 14.7%. These results further indicate that adding a phase modulation function to thermal power units can provide a large amount of dynamic reactive power support and improve the voltage stability and operational flexibility of the system.

Measurement of Synergy Management Performance in Prefabricated Building Project Supply Chain

Prefabricated building (PB) involves many participating enterprises. Its implementation faces many challenges, mainly in the areas of technology, cost, construction management, management of supply chain (SC), and sustainability. The purpose of synergy management of a prefabricated building project supply chain (PBPSC) is to help the whole SC obtain better benefits. This study establishes a measurement index system from the perspective of green sustainability in terms of cost control, technology, information, reliability of SC, and environmental protection. The COWA-CRITIC ideal point method is used to calculate the subjective and objective combination weights, and a cloud model based on this combination weight is constructed for measurement, which is validated by taking the project of a residential building as an example. The results are compared with those of other measurement models to verify the applicability of the measurement model of this study in the synergy management performance of the PBPSC. The sensitivity of the indexes within the five subsystems is analyzed using the one-way rotation OAT method to allow decision makers to identify the most sensitive indexes. This study shows that the synergy management performance of SC in this project is better. The measurement model used in this study is consistent with the results obtained from other measurement models and is in line with the actual situation. Sensitivity analysis using the one-factor rotation OAT method shows that among the secondary indexes, specialized technical staff is the most sensitive to changes in weights; among the primary indexes, cost control is the most sensitive to changes in weights. This provides a new method for measuring the synergy management performance of the PBPSC. Based on the results of this study, corresponding countermeasures are proposed for the synergy management of the PBPSC, which will provide a reference for the synergy management of SCs with similar projects in the future.

Comparative Analysis of the Flight Characteristics and Energy Efficiency of a Quadcopter and an Octacopter

This publication is part of a study aimed at finding the optimal configuration (tricopter, quadcopter, octacopter, etc.) in terms of flight stability, payload and energy efficiency. For this purpose, full non-linear quadcopter and octacopter models have been created. Data from real objects (linear and weight characteristics, parameters of the engine-propeller system) were used to create them. Quadcopter and Octacopter use the same motors and propellers. In the simulations, the mass of the two objects is the same. Both models are controlled with PID controllers in four channels (Yaw, pitch, roll and height). The initial adjustment of the regulators was carried out according to the second method of Ziegler-Nichols. A secondary adjustment of the regulators was carried out, with the goal of minimal over-adjustment, speed and accuracy. A parallel simulation of the nonlinear models in the Simulink environment was performed, on the basis of which the conclusions about the stability and energy efficiency of the quadcopter and the octacopter were made.