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With the growing scale of power grids, integrated power grids often contain multiple areas. When the control centre of each regional grid conducts an assessment of local voltage stability, the calculation is always based on the local regional power grid model. However, less consideration is given to a detailed model of the entire network, which may lead to a large calculation error. Under the premise of ensuring the data and information security of Supervisory Control and Data Acquisition between different regional power grid operation control centres, the purpose of this paper is to reduce calculation error simply by using the data of a local power network.

According to the calculation methodology of “decomposition and coordination” and the power balance equation of an interconnected power grid, an improved radial equivalent independent (REI) equivalent method, which can reflect the dynamic characteristics of interconnected power grids to a certain extent, is proposed in this paper. A mathematical model of multi-area-grid L indicator synergic computing is derived as well.

With the calculation of Institute of Electrical and Electronics Engineers (IEEE) standard grids and an actual grid model, it is proven that the method proposed in this paper can significantly improve the accuracy of the regional power grid L indicator calculation and achieve the synergic computing of a multi-area power system L indicator, without an increase in data interaction among the regional power grids.

The indicator of voltage stability among multi-area was obtained by using the improved REI equivalent method with the change of the load participation factor. Particularly, the coordinated calculation method can be implemented on a local power grid without knowledge of all the parameters of its interconnection, which can avoid possible leakage of confidential data and information of the system owners.

As supply chain excellence matters, designing an appropriate health-care supply chain is a great consideration to the health-care providers worldwide. Therefore, the purpose of this paper is to benchmark several potential health-care supply chains to design an efficient and effective one in the presence of mixed data.

To achieve this objective, this research illustrates a hybrid algorithm based on data envelopment analysis (DEA) and goal programming (GP) for designing real-world health-care supply chains with mixed data. A DEA model along with a data aggregation is suggested to evaluate the performance of several potential configurations of the health-care supply chains. As part of the proposed approach, a GP model is conducted for dimensioning the supply chains under assessment by finding the level of the original variables (inputs and outputs) that characterize these supply chains.

This paper presents an algorithm for modeling health-care supply chains exclusively designed to handle crisp and interval data simultaneously.

The outcome of this study will assist the health-care decision-makers in comparing their supply chains against peers and dimensioning their resources to achieve a given level of productions.

A real application to design a real-life pharmaceutical supply chain for the public ministry of health in Morocco is given to support the usefulness of the proposed algorithm.

The novelty of this paper comes from the development of a hybrid approach based on DEA and GP to design an appropriate real-life health-care supply chain in the presence of mixed data. This approach definitely contributes to assist health-care decision-makers design an efficient and effective supply chain in today’s competitive word.

The purposes of this research are to study the theory and method of multi-attribute index system design and establish a set of systematic, standardized, scientific index systems for the design optimization and inspection process. The research may form the basis for a rational, comprehensive evaluation and provide the most effective way of improving the quality of management decision-making. It is of practical significance to improve the rationality and reliability of the index system and provide standardized, scientific reference standards and theoretical guidance for the design and construction of the index system.

Using modern methods such as complex networks and machine learning, a system for the quality diagnosis of index data and the classification and stratification of index systems is designed. This guarantees the quality of the index data, realizes the scientific classification and stratification of the index system, reduces the subjectivity and randomness of the design of the index system, enhances its objectivity and rationality and lays a solid foundation for the optimal design of the index system.

Based on the ideas of statistics, system theory, machine learning and data mining, the focus in the present research is on “data quality diagnosis” and “index classification and stratification” and clarifying the classification standards and data quality characteristics of index data; a data-quality diagnosis system of “data review – data cleaning – data conversion – data inspection” is established. Using a decision tree, explanatory structural model, cluster analysis, K-means clustering and other methods, classification and hierarchical method system of indicators is designed to reduce the redundancy of indicator data and improve the quality of the data used. Finally, the scientific and standardized classification and hierarchical design of the index system can be realized.

The innovative contributions and research value of the paper are reflected in three aspects. First, a method system for index data quality diagnosis is designed, and multi-source data fusion technology is adopted to ensure the quality of multi-source, heterogeneous and mixed-frequency data of the index system. The second is to design a systematic quality-inspection process for missing data based on the systematic thinking of the whole and the individual. Aiming at the accuracy, reliability, and feasibility of the patched data, a quality-inspection method of patched data based on inversion thought and a unified representation method of data fusion based on a tensor model are proposed. The third is to use the modern method of unsupervised learning to classify and stratify the index system, which reduces the subjectivity and randomness of the design of the index system and enhances its objectivity and rationality.

In this context, this study aims to obtain information about the noise levels emitted to the environment by modeling the noise map of Dalaman Airport and correlating these noise values according to the Environmental Noise Directive (END) and World Health Organization (WHO) limits.

Dalaman Airport Aeronautical Information Publication and 2022 flight data from the airport were analyzed. The noise levels exposed to schools and health institutions were determined using the Cnossos–Eu calculation method.

Maximum noise levels were obtained as L_den 92.29 dB(A), L_day 85.24 dB(A), L_evening 89.00 dB(A) and L_night 85.23 dB(A) according to the noise indicators. Limit values recommended by the END and WHO according to noise indicator types and measurement results were correlated and evaluated.

In the noise modeling of Dalaman Airport, there has previously been no evaluation of the noise limits recommended according to END or WHO in the literature.

This paper investigates the effect of the global financial crisis (GFC) on the level of corporate social responsibility disclosures (CSRD) in the annual report and/or CSR report of 36 major listed Portuguese companies in each of the years 2005, 2008 and 2011.

The analysis is framed principally by stakeholder theory. Data were explored using thematic content analysis and an index of disclosure calculated by year, industry type (consumer proximity versus environment sensitivity) and category of information.

Before the GFC, Portuguese listed companies increased their CSRD practices significantly. During the crisis, there was a slight decrease in CSRD. However, this was not as pronounced, as it would otherwise have been because it was counteracted by increased disclosures of company interactions with society, particularly in matters of corruption prevention and community engagement. CSRD was higher for companies with high consumer proximity but did not appear to be influenced by companies’ level of environmental sensitivity.

The results reveal a strong concern by companies for stakeholder management (particularly in respect of community relations) in a period of financial crisis. This study highlights the effect of a company’s proximity to consumers on levels of CSRD.

The purpose of this paper is to analyse the level of transparency of the libraries of Catalan universities that are members of the Consortium of University Services of Catalonia (CSUC).

The analysis involved reviewing the information available on the websites of the ten libraries belonging to CSUC. For each library’s website, the presence of 18 indicators was explored. These indicators had been defined by the Commitment and Transparency Foundation (Spain).

There is a notable difference between the level of transparency of Catalan universities and their libraries. Moreover, the universities’ culture of transparency is not reflected in that of the libraries. Four of the libraries can be considered transparent because they have accomplished from 12 to 18 indicators; three can be considered “translucent”, due to their indicators ranging from 11 to 7 points; and three are considered opaque as a result of obtaining from 1 to 5 indicators.

There is a need to review the volume and quality of information that can be consulted on library websites, considering that all services and entities funded with public money must inform citizens of the principles that govern their management and the results obtained.

The information regarding transparency is incomplete and should be organised with parameters that make it easier to find. Libraries that occupy the lowest positions in the ranking all belong to private universities. Their poor results are due to the lack of information justifying their management or providing information on how the service is organised. Some conclusions of this study are very similar to those of the study on Madrid’s Madroño Consortium (Pacios Lozano, 2016) which has been taken into account in this paper.

The purpose of this paper is to develop a new approach to market analysis, and test how the China‐US soybean market integration and arbitrage efficiency have changed across 1995/1‐/2004/1 – a period covering strengthened government efforts to join WTO, and the institutional changes following WTO accession.

The authors employ a regime‐switching model with parametric, semi‐parametric, and nonparametric measures to discern different market conditions in the sample period, as well as detect the structural shifts over time. Three types of data – expected prices, transaction costs, and trade flow – are used for the analysis.

Results of the analysis indicate better integration over time, but deteriorating efficiency. The markets often departed from efficient arbitrage throughout the study periods, and counter‐intuitively worsened after China's elimination of quota in 1999, and accession to WTO in 2001. One other interesting finding is that the state monopoly practice in soybean trading during the early periods produced seemingly competitive equilibrium price relationships.

The paper is an original work that provides policy implications regarding the impacts and effectiveness of government policies on China's international soybean markets, the remaining bottlenecks, and the challenge to both Chinese soybean farmers and the US exporters. The method of identifying trade variation in a price model and the combination of parametric and semi‐parametric analyses with comprehensive data further permits more accurate and intuitive interpretations previously unavailable.

The existing concept in the building rating scheme especially in developing countries was more focused on the environment, economic, social and culture. The new approach of the classroom condition index (CCI) assessment scheme has its uniqueness in environmental and social aspects because of high building performance in secondary school buildings. The requirements set by the Ministry of Education Malaysia include providing a conducive learning environment, especially for students who are considered as the main users of classrooms in school buildings. Currently, the school administration needs to manually record the condition of the classroom to increase its comfort level. The lack of a structured scheme for classroom assessment makes it difficult for school administration to focus on the overall classroom condition (physical environmental aspect) in the school building. The purpose of this study is to develop a framework for classroom conditions by proposing a CCI assessment scheme for a secondary school building in Malaysia.

Mixed methods were used to carry out the study. The first stage of this study concentrates on developing a system for CCI that relates to physical elements in the classroom. This is done by reviewing the literature on the classroom physical performance, as well as a comparison between several building rating systems locally and abroad. The structure of the proposed CCI scheme is grouped into four main themes, namely, space management quality (SMQ), building condition (BC), indoor environmental quality (IEQ) and teaching and learning quality (TLQ). In addition, there are 12 categories and 23 indicators listed under this theme. The second stage focuses on formulating assessment categories with their relevant performance indicators. This phase undergoes a validation process by conducting a survey (questionnaire) toward the classroom’s main users, which are students and teachers. This is to ensure the accuracy of classroom conditions in the school building. A semi-structured interview was also conducted among building experts. They are building surveyors, building engineers, building designers and building performance experts to support the main findings in the second stage. Relative importance (RI) index approach has been applied to show the indicators weighting and ranking are used as data collections method by using Statistical Package of Social Science software to examine the RI of each category and indicator, respectively.

The findings show that prominent RI and balanced weights are formed from these four main themes. They are SMQ (19.9%), BC (26.6%), IEQ (33.2%) and TLQ (20.3%). The outcome of this study will contribute to a detailed assessment scheme for CCI at the secondary school building. The contribution of the CCI Assessment Scheme is more comprehensive and holistic than the conventional assessment process for BCs. It focuses specifically on classroom space as it is the most important area to achieve a high level of comfort comparing to other spaces in the school building. This holistic approach encompasses all types of classrooms. The concept of one tool fits all is seen as no longer a relevant adaptation in this context. This proposed tool is to be used only for the classroom (as the name CCI implies) and it cannot be used for the other types of spaces, for instance, teacher room, library, meeting room, toilet, canteen and, etc. This is because different spaces represent different physical indicators to be classified. This, in turn, contributes to a conducive learning environment for students in the school.

This paper provides the current information, knowledge and findings related to the classroom physical indicators in developing the assessment scheme for the classroom environment. It will assist both technical and non-technical experts to clarify the current condition of classroom physical performance that ideally may affect the students’ learning environment. The novelty of CCI development is not only on the adopted method but it also includes the ideas on next generation model of rating system that ideally need specific indicators and weighting to be generated into an intelligent computerized system.

Selecting suitable and competent partners is an important prerequisite to improve the performance of collaborative product innovation (CPI). The purpose of this paper is to propose an integrated multi-criteria approach and a decision optimization model of partner selection for CPI from the perspective of knowledge collaboration.

First, the criteria for partner selection are presented, considering comprehensively the knowledge matching degree of the candidates, the knowledge collaborative performance among the candidates, and the overall expected revenue of the CPI alliance. Then, a quantitative method based on the vector space model and the synergetic matrix method is proposed to obtain a comprehensive performance of candidates. Furthermore, a multi-objective optimization model is developed to select desirable partners. Considering the model is a NP-hard problem, a non-dominated sorting genetic algorithm II is developed to solve the multi-objective optimization model of partner selection.

A real case is analyzed to verify the feasibility and validity of the proposed model. The findings show that the proposed model can efficiently select excellent partners with the desired comprehensive attributes for the formation of a CPI alliance.

Theoretically, a novel method and approach to partner selection for CPI alliances from a knowledge collaboration perspective is proposed in this study. In practice, this paper also provides companies with a decision support and reference for partner selection in CPI alliances establishment.