Multi-indicator System Research Articles

Transboundary Water Allocation under Water Scarcity Based on an Asymmetric Power Index Approach with Bankruptcy Theory

Cooperative and self-enforceable water allocation is a key instrument to manage geopolitical conflict induced by water scarcity, which necessitates the cooperative willingness of the agents and considers their heterogeneity in geography, climate, hydrology, environment and social economy. Based on a multi-indicator system that contains asymmetric information on water volume contribution, current water consumption, water economic efficiency and efforts for eco-environmental protection, this study proposed a water allocation framework by combining the asymmetric power index approach with bankruptcy theory for solving the transboundary water allocation problem under scarcity. The proposed method was applied to the Yellow River Basin in northern China, which is mainly shared by nine provincial districts and frequently suffers from severe water shortages, and its results were compared with six alternative methods. The results highlight the necessity of quantifying agents’ willingness to cooperate under the condition of asymmetric negotiation power when making decisions on transboundary water allocations. The proposed method allows for transboundary water allocations through simultaneous consideration of the agent’s willingness to cooperate and asymmetric negotiation power, as well as disagreement allocation points, which ensure the stability, fairness and self-enforceability of allocation results. Therefore, it can offer practical and valuable decision-making insights for transboundary water management under water scarcity.

Multi-damage index-based interfacial debonding prediction for steel-concrete composite structures with percussion method

The interfacial debonding as an invisible damage significantly undermines the bearing capacity and durability of steel-concrete composite structures (SCCS). Although the percussion method has been widely utilized in practical applications, the single damage index (DI) extracted in the analysis process tends to be invalid to abnormalities and leads to misjudgment. The accurate damage prediction requires extracting multiple indicators strongly correlated to the interfacial debonding. Accordingly, this paper proposed a novel debonding detection methodology for a multi-indicator system by integrating the percussion method with machine learning. The classification framework includes two parts: (i) 79 DIs are calculated to represent the interface characteristics comprehensively, and the fused feature selection algorithms are investigated to determine the optimal DI subset. (ii) Five classical machine learning approaches after hyperparameter tuning are evaluated with the final objective to compare the prediction accuracy of each model. Aided by the experimental measurements and correlation analyses, 20 key DIs are filtered out as model input. Then, the evaluation performance reveals that the KNN classifier achieves the superlative identification results on the experimental dataset, presenting a prediction accuracy of 99.6 %. Finally, a new dataset confirms the accuracy and effectiveness of the proposed method in the form of 2D damage imaging. The research findings of this study are beneficial for improving the detection accuracy and provide significant guidance on the percussion method-based interfacial debonding detection for the SCCS.

Remaining useful life prediction for complex systems with multiple indicators of stochastic correlation considering random shocks

The remaining useful life (RUL) prediction in complex systems is subject to complex working conditions, such as overloads, random vibration and shocks, gradual corrosion and wearing, that cause the complex and variable performance degradation of the mechanical system. Accurate prediction of RUL requires the extraction of multiple performance indicators strongly correlated to system degradation. Accordingly, we propose an RUL prediction framework for a multi-indicator system that considers the effects of random shocks and stochastic dependence. First, considering the influence of random shocks and measurement errors, a degradation state-space model is established using the stochastic correlation of multiple performance indicators. Next, the hidden state and unknown parameters of the state-space model are jointly estimated using the expectation–maximization algorithm in conjunction with the strong tracking filter algorithm based on online monitoring data. Subsequently, the probability density function expression of the RUL is derived for a multi-indicator system with three failure modes. Finally, the accuracy of the RUL prediction model is verified for a system affected by random shocks via numerical experiments. Compared with other prediction methods, the effectiveness and adaptability of the proposed method are verified using the C-MAPSS dataset and a high-temperature furnace case.

Valuation method of intellectual property pledge financing based on income interval analysis and risk adjustment coefficient

This study has developed a new method to valuate intellectual property for pledge financing. First, based on interval theory and the relevant calculation rules, the income interval model is and then used to calculate the interval values of intellectual property. Second, based on the change structure of risk indicators, the AHP and set-valued statistics are utilized to calculate the risk adjustment coefficient. Third, the point value of intellectual property is calculated with its values at an interval scale and risk adjustment coefficient. The values of intellectual property at an interval scale provide the two parties negotiating pledge financing with a reference range of loan amounts. The risk adjustment coefficient becomes a crucial indicator for measuring value evaluation risk. The point value of intellectual property specifies how much the bank loan amount can deviate from the values of intellectual property at an interval scale. The method creates a multi-indicator system to valuate intellectual property for pledge financing, which lowers the risk of intellectual property pledge financing to a significant extent and facilitates its operation. Moreover, the method has been proven to be efficient in practice.

Assessing the decision quality of artificial intelligence and oncologists of different experience in different regions in breast cancer treatment.

AI-based clinical decision support system (CDSS) has important prospects in overcoming the current informational challenges that cancer diseases faced, promoting the homogeneous development of standardized treatment among different geographical regions, and reforming the medical model. However, there are still a lack of relevant indicators to comprehensively assess its decision-making quality and clinical impact, which greatly limits the development of its clinical research and clinical application. This study aims to develop and application an assessment system that can comprehensively assess the decision-making quality and clinical impacts of physicians and CDSS. Enrolled adjuvant treatment decision stage early breast cancer cases were randomly assigned to different decision-making physician panels (each panel consisted of three different seniority physicians in different grades hospitals), each physician made an independent "Initial Decision" and then reviewed the CDSS report online and made a "Final Decision". In addition, the CDSS and guideline expert groups independently review all cases and generate "CDSS Recommendations" and "Guideline Recommendations" respectively. Based on the design framework, a multi-level multi-indicator system including "Decision Concordance", "Calibrated Concordance", " Decision Concordance with High-level Physician", "Consensus Rate", "Decision Stability", "Guideline Conformity", and "Calibrated Conformity" were constructed. 531 cases containing 2124 decision points were enrolled; 27 different seniority physicians from 10 different grades hospitals have generated 6372 decision opinions before and after referring to the "CDSS Recommendations" report respectively. Overall, the calibrated decision concordance was significantly higher for CDSS and provincial-senior physicians (80.9%) than other physicians. At the same time, CDSS has a higher " decision concordance with high-level physician" (76.3%-91.5%) than all physicians. The CDSS had significantly higher guideline conformity than all decision-making physicians and less internal variation, with an overall guideline conformity variance of 17.5% (97.5% vs. 80.0%), a standard deviation variance of 6.6% (1.3% vs. 7.9%), and a mean difference variance of 7.8% (1.5% vs. 9.3%). In addition, provincial-middle seniority physicians had the highest decision stability (54.5%). The overall consensus rate among physicians was 64.2%. There are significant internal variation in the standardization treatment level of different seniority physicians in different geographical regions in the adjuvant treatment of early breast cancer. CDSS has a higher standardization treatment level than all physicians and has the potential to provide immediate decision support to physicians and have a positive impact on standardizing physicians' treatment behaviors.

Multi-scale energy efficiency recognition and diagnosis scheme for ethylene production based on a hierarchical multi-indicator system

It is essential to monitor and diagnose the energy efficiency level of ethylene production process to detect any abnormities in the production and adjust production parameters appropriately to ensure the energy efficiency. However, few studies have been carried out concerning the diagnosis of abnormal energy efficiency in the ethylene production with appropriate energy efficiency indicator system from multiple dimensions due to the incompatibility of the diagnosis algorithm and the complexity of this petrochemical production. Therefore, to achieve a comprehensive energy efficiency diagnosis and anomaly analysis for the ethylene production, a multi-scale energy efficiency recognition and diagnosis scheme based on a hierarchical multi-indicator system is proposed. Firstly, a novel recognition scheme for abnormal energy efficiency is conducted from a single-indicator scale to a multi-indicator scale based on a three-level indicator system with respect to the energy and materials flows in the system, process, and equipment level of the ethylene production, so that the energy efficiency of the ethylene production can be monitored in different levels and granularities. Secondly, an integration-decomposition diagnosis method to analyze the energy efficiency is proposed based on the Malmquist production index, where the overall efficiency is decomposed into production efficiency, pure production technical efficiency, and scale efficiency to form a multi-perspective in-depth diagnosis and exploration. Thirdly, a principal component analysis is converted into a preliminary abnormal cause diagnosis algorithm based on statistics so that the multi-scale and multi-level energy efficiency recognition and diagnosis for the ethylene production is achieved. Finally, the effectiveness of the diagnosis scheme is verified by applying it to a Chinese ethylene plant, and the results show that the abnormal energy efficiency can be identified effectively and the consistency of the diagnosis results with the actual situation is approximately 0.818 at least.

Multi-indicator system for evaluating the effectiveness of management of enterprise development financing

The article establishes to assess the effectiveness of financial security management is necessary to take into account the relationship between indicators of enterprise development and indicators of efficiency of financial security management in dynamics and adjust the assessment for positive or negative external influences. Insufficient research has focused on assessing the effectiveness of financial management of development, especially on the formation of an appropriate system that can guide practitioners. Therefore, the purpose of the article is to develop a multi-indicator system for assessing the effectiveness of management of financing the development of enterprises. To solve this problem, the study was aimed at forming comprehensive recommendations that provide appropriate steps and actions. The manual is developed using the conceptual research method, the method covers three phases: collection of steps and measures from existing recommendations on the evaluation system (1), classification of indicators and definition of system structure (2), definition of criteria for assessing financial compliance systems for evaluating the effectiveness of financial security management (3). The proposed recommendations allow to analyze the dynamics of both the global development of the enterprise and the management of financial security necessary for development, which allows to develop measures to improve the management of financial security of the enterprise. According to the results of the research, the developed multi-indicator system for assessing the effectiveness of management of financing the development of agricultural machinery of PJSC "Harvesters" was tested.

Do compact cities have higher efficiencies of agglomeration economies? A dynamic panel model with compactness indicators

Compact development is proposed to preserve land resources as well as promote agglomeration economies. This paper adopts a multi-indicator system of compact cities, including population density, boundary limitation, and road density, to examine the relationship between the compact city and urban efficiency through agglomeration economies. We empirically examine the relationships between compact city indicators and comprehensive/technical efficiency measures of the cities through GMM regression with panel data of 226 prefectural and upper-level cities in China during 2001–2015. We find the answers heterogeneous for each indicator-efficiency pair for each city type. Higher population density and compact urban form are beneficial to the urban economic efficiencies of large cities, but not for the technical efficiency of small cities. Road density is conducive efficiencies for small and medium cities, but not technical efficiencies for large cities. We confer that the heterogeneity may be explained by the economic structure compositions of cities of different sizes. Based on our findings, we propose tailor-made policy suggestions regarding urban compactness and economic efficiencies for cities of different sizes.

Inequalities in the European Union—A Partial Order Analysis of the Main Indicators

The inequality within the 27 European member states has been studied. Six indicators proclaimed by Eurostat to be the main indicators charactere the countries: (i) the relative median at-risk-of-poverty gap, (ii) the income distribution, (iii) the income share of the bottom 40% of the population, (iv) the purchasing power adjusted GDP per capita, (v) the adjusted gross disposable income of households per capita and (vi) the asylum applications by state of procedure. The resulting multi-indicator system was analyzed applying partial ordering methodology, i.e., including all indicators simultaneously without any pretreatment. The degree of inequality was studied for the years 2010, 2015 and 2019. The EU member states were partially ordered and ranked. For all three years Luxembourg, The Netherlands, Austria, and Finland are found to be highly ranked, i.e., having rather low inequality. Bulgaria and Romania are, on the other hand, for all three years ranked low, with the highest degree of inequality. Excluding the asylum indicator, the risk-poverty-gap and the adjusted gross disposable income were found as the most important indicators. If, however, the asylum application is included, this indicator turns out as the most important for the mutual ranking of the countries. A set of additional indicators was studied disclosing the educational aspect as of major importance to achieve equality. Special partial ordering tools were applied to study the role of the single indicators, e.g., in relation to elucidate the incomparability of some countries to all other countries within the union.

Geospatial modeling of the tropical cyclone risk in the Guangdong Province, China

Tropical cyclones (TCs) are prominent natural hazards in the Guangdong Province, China; hence, comprehensive risk assessment is vital to reduce potential losses due to TC hazards. A multi-indicator system was developed by incorporating 19 criteria under 3 risk components and a combination of the analytic hierarchy process and index of entropy was adopted to generate a TC risk map. Spatial statistics were applied to determine the spatial association and significant TC risk hotspots. The TC risk map showed that ∼40% of the study area is classified as a high or extremely high TC risk. Areas with the highest risk are mainly concentrated in the eastern region (ER), western region (WR), and Zhuhai and Shenzhen in the Pearl River Delta (PRD), whereas other areas in the PRD and mountainous region (MR) in the inland are classified as zones with the lowest risk. The results of the spatial autocorrelation analysis showed a significant positive spatial clustering in the spatial pattern of the TC risk, suggesting a varying spatial distribution and clustering pattern of the TC risk level that can be used as preliminary planning framework for tropical cyclone risk management. The results of this study provide theoretical reference for the design of disaster mitigation measures.

Synthesis of Multi-indicator System Over Time: A Poset-based Approach

In recent years, sustainable development has become one of the main issues of scientific and institutional debate. The literature on this concept is wide and often presents conflicting positions. This leads to considering sustainable development as a contested concept. The growing interest and importance of this topic has also led to an increasing focus on the aspect of its measurement. Dealing with the measurement of complex phenomena, like sustainable development, means dealing with synthesis. The traditional and dominant approach to the synthesis of multi-indicator systems of cardinal variables is the use of the aggregative-compensative approach. Despite its success, this approach presents a series of critical issues. In this paper, we propose a method of synthesizing multi-indicator systems over time based on the Partial Order Theory. Applying and comparing the method we proposed and an aggregative method, the Adjusted Mazziotta–Pareto Index, to one of the 15 sustainable development goals, we highlight the strengths of the new methodological proposal.

Measuring Equitable and Sustainable Well-Being in Italian Regions: The Non-aggregative Approach

The official Italian well-being measuring system (“Equitable and Sustainable Well-being—BES”) is probably the worldwide most advanced attempt to pursue the beyond GDP perspective effectively. In it, well-being is described in terms of 12 domains and a complex multi-indicator system of around 130 indicators, drawn mainly from Istat (official Italian statistical bureau) surveys and administrative archives. In order to get a more synthetic view of well-being, in the last four BES reports Istat employed aggregative procedures providing composite indicators for each well-being domain. The aggregative road to synthesis is however problematic, when complex and non-highly correlated indicator systems are to be summarized, mainly due to its compensative nature and interpretational difficulties. As a valuable alternative, in this paper we adopt a non-aggregative approach to synthesis, based on Partially Ordered Set Theory (Poset Theory) and show how it can be used to provide more “complexity-preserving”insights into well-being. In particular, we describe each well-being domain as a partially ordered set and compute synthetic indicators for well-being rankings at regional level for year 2017, getting more robust and interpretable results than with mainstream aggregative procedures.

Community and individual iodine status assessment in premenopausal women in Shanxi, China: Repeated spot urine versus 24-hour urine.

Urinary iodine is an essential index of iodine nutrition evaluation. To establish the number of repeated spot urine collections necessary to reflect individual iodine status over 2 months and assess its feasibility to serve as an independent indicator of individual iodine status. We performed a longitudinal, 2-months study from May to June in 2015 of 23 apparently healthy Chinese women aged 18 to 44 (32±9) y. Spot urine samples were collected on any two days of the week, and 24-h urine samples were collected once every 6 days. 368 spot urine and 230 24-h urine samples were analysed. The median urinary iodine concentration (UIC) was 140.5 (75.2, 246.9) μg/L. The estimated 24-h urinary iodine excretion (24-h UIE) values from spot urine samples and measured 24-h UIE values from 24-h urine samples were 348±240 μg/24h and 330±216 μg/24h (p=0.003), respectively. Irrespective of the urinary iodine method, the intra- individual coefficient of variation (CV%) was lower than the inter-individual CV%. Bland-Altman analysis revealed differences between spot urine and 24-h urine. When the precision ranges with 95% confidence were ±15%, ±20%, ±25% or 30%, the number of an individual's spot urine samples required were 30, 16, 11 or 8, respectively. Repeated spot urine is not a feasible way to assess recent individual iodine intake. The development of a multi-indicator system could provide an acceptable individual evaluation index of iodine status.

Measuring regional effects of model inputs with random Forest

In many disciplines involving high-dimensional data, permutation variable importance measure (PVIM) based on random forest is widely used for importance ranking of model inputs. This work extends the traditional PVIM to investigate the regional effects of the internal value range of model inputs. The PVIM function is firstly defined to measure the residual PVIM when the distribution range of one input variable is reduced to its subranges. An efficient computational algorithm is developed, and clear mathematic meaning for the proposed computational procedure is interpreted. The proposed method is further demonstrated with Ishigami function and a multi-indicator system model.

Assessing and Grouping Chemicals Applying Partial Ordering Alkyl Anilines as an Illustrative Example.

In chemistry, there is a long tradition in classification. Usually, methods are adopted from the wide field of cluster analysis. The present study focusses on the application of partial ordering methodology for the classification of 21 alkyl substituted anilines. The analyses are based on the concepts from partial order methodology and cluster analyses. Here, with the example of 21 alkyl anilines, we show that concepts taken out from the mathematical discipline of partially ordered sets may be applied for classification. The chemical compounds are described by a multi-indicator system. For the present study four indicators, mainly taken from the field of environmental chemistry were applied and a graph of the ordering (Hasse diagram) was constructed. A Hasse diagram is an acyclic, transitively reduced, triangle-free graph that may have several graph-theoretical components. The Hasse diagram has been directed from a structural chemical point of view. Two cluster analysis methods are applied (K-means and a hierarchical cluster method) and compared with the results from the Hasse diagram. In both cases, the partitioning of the set of 21 compounds by the component structure of the Hasse diagram appears to be better interpretable. It is shown that the partial ordering approach indeed can be used for classification in the present case. However, it must be clearly stated that a guarantee for meaningful results, in general, cannot be given. For that, further theoretical work is needed.

Human-machine cooperation in planning and scheduling: a case study on an unstable environment

In an industrial environment, manufacturing systems may be subject to considerable uncertainties which may lead to numerous schedule disturbances. These disturbances prevent the execution of the planned production schedule. 'groups of permutable jobs' is one of the most studied methods that deal with this drawback. It constructs a flexible solution characterised by a set of schedules, allowing a human operator to execute, in real-time, the schedule that best fits the state of the shop. However, because of the limited complexity that one human being can handle, he/she needs to cooperate with the machine in order to take efficient decisions. This paper focuses on investigating the human-machine cooperation for planning and scheduling. A new human-machine interface model assisted with a multi-indicator decision support system has been proposed and evaluated. The results show the usefulness/limits of the proposed model and provide insights into the practice of production planning and scheduling. [Received 27 October 2017; Revised 13 December 2017, 7 June 2018; Accepted 8 June 2018]

Partial ordering as decision support to evaluate remediation technologies

When facing necessary remediation actions a series of potential technologies may be considered. Typically the eventual selection of the more appropriate remediation technology cannot be made based on a single indicator. Thus, the analysis turns into a multi-criteria decision analysis and an initial step is consequently the development of a multi-indicator system (MIS). A one-dimensional metric serving as an ordering index can easily be obtained by combining the component indicators via aggregation techniques, which unambiguously will lead to loss of information and possibly to more or less severe compensation effects. The present study proposes an alternative to aggregation based on simple concepts of partial order methodology. Hence, for illustrative and explanatory purposes an exemplary MIS corresponding of 5 possible remediation options, ROi, i = 1-5, in addition to the non-remedied situation, RO0, and the complete remediation, ROmax, for 3 chemicals was set up and subsequently analyzed. The results are shown to be distinctly different from an ordering based on an aggregated indicator. In contrast to the total order that is constructed from an aggregated indicator partial ordering allow only for a weak ordering, as e.g., based on average orders. It is shown how the more appropriate remediation technology may be selected and further how the results obtained may serve as a basis for selective improvement of specific remediation options. The methods described here is not limited to, e.g., chemicals but have a more universal applicability.

Partial order methodology: a valuable tool in chemometrics

Often from multi‐indicator system (MIS), a composite indicator is derived by applying weighted sums of the single indicators. The eventual ranking being based on composite indicator yields a total (linear) ranking. In contrast, partial order ranking constitutes an alternative approach where assumptions about linearity or distribution of the indicators are not needed and indicators are in general used without quantification of mutual preferences. The present paper describes analyses of MIS based on a series of partial order techniques as offered by the PyHasse software. The paper describes how important information on an MIS can be obtained without lengthy and often controversial and possible subjective discussions about indicator weights and thus avoiding both covering up valuable information contained in the single indicators as well as unwanted compensation effects. The application of partial order for obtaining chemometric information is illustrated by a study of 12 chloro‐organic pesticides. All calculation has been carried out applying the experimental software PyHasse. Copyright © 2013 John Wiley & Sons, Ltd.

Testing wastewater to detect illicit drugs: State of the art, potential and research needs

Illicit drug use is a global phenomenon involving millions of individuals, which results in serious health and social costs. The chemical analysis of urban wastewater for the excretion products of illicit drugs is a potent approach for monitoring patterns and trends of illicit drug use in a community. The first international and multidisciplinary conference on this topic was recently organized to present the epidemiological knowledge of patterns in drug use and the information obtained from wastewater analysis. This paper gives an overview of the main issues that emerged during the conference, focusing on the identified research gaps and requirements and on the future challenges and opportunities from bringing together wastewater analysis and drug epidemiology. The European Monitoring Centre for Drugs and Drug Addiction (EMCDDA) uses an established multi-indicator system to monitor illicit drug use and to identify the emergence of new psychoactive substances. The methodological challenges of monitoring a hidden and stigmatized behavior like drug use include the limitations of self-report data and reporting delays. An increasing evidence base suggests that wastewater analysis can address some of these problems. Specifically this technique can: monitor temporal and spatial trends in drug use at different scales, provide updated estimates of drug use, and identify changing habits and the use of new substances. A best practice protocol developed by a Europe-wide network of experts is available to produce homogeneous and comparable data at different sites. The systematic evaluation of uncertainties related to wastewater analysis has highlighted which areas require careful control and those that need further investigation to generally improve the approach. Wastewater analysis has considerable potential to complement existing approaches for monitoring drug use due to its ability to produce objective, real-time estimates of drug use and to give timely information of any change in the patterns of use.

Application of selected partial order tools to analyze fate and toxicity indicators of environmentally hazardous chemicals

Studies on fate and toxicity are important aspects in environmental impact assessments of chemicals. In order to elucidate “what is worst” a ranking of the chemicals under investigation is of significant interest, which obviously constitutes a multi-indicator system (MIS). Partial order methodologies turn out to be advantageous tools for analyses of such systems.In the present paper we focus on QSAR generated fate and toxicity indicators of 30 chemicals, comprising of a variety of chemical structures. In many risk assessment studies the level of a multi-indicator system is abandoned and the data are aggregated in order to obtain a composite index. However, such aggregation techniques are far from being trivial. The present paper describes an alternative strategy to handle MIS, i.e., applying partial order methodology leading to useful information. The paper demonstrate how partial order techniques lead to a weak order of the 30 chemicals as well as to the relative importance of the single indicators included in the study. Specifically, the paper addresses the topics on the “relations between the indicators” and “do chemical structures constitute a valid basis for ranking chemicals according to their environmental impact?”.