Examination of the similarity between a new Sigmoid function-based consensus ranking method and four commonly-used algorithms

High-centrality nodes have disproportionate influence on the behavior of a network; therefore controlling such nodes can efficiently steer the system to a desired state. Existing multiplex centrality measures typically rank nodes assuming the layers are qualitatively similar. Many real systems, however, are comprised of networks heterogeneous in nature, for example, social networks may have both agnostic and affiliative layers. Here, we use rank aggregation methods to identify intervention targets in multiplex networks when the structure, the dynamics, and our intervention goals are qualitatively different for each layer. Our approach is to rank the nodes separately in each layer considering their different function and desired outcome, and then we use Borda count or Kemeny aggregation to identify a consensus ranking—top nodes in the consensus ranking are expected to effectively balance the competing goals simultaneously among all layers. To demonstrate the effectiveness of consensus ranking, we apply our method to a degree-based node removal procedure such that we aim to destroy the largest component in some layers, while maintaining large-scale connectivity in others. For any multi-objective intervention, optimal targets only exist in the Pareto-sense; we, therefore, use a weighted generalization of consensus ranking to investigate the trade-off between the competing objectives. We use a collection of model and real networks to systematically investigate how this trade-off is affected by multiplex network structure. We use the copula representation of the multiplex centrality distributions to generate model multiplex networks with given rank correlations. This allows us to separately manipulate the marginal centrality distribution of each layer and the interdependence between the layers, and to investigate the role of the two using both analytical and numerical methods.

In this paper we explore the possibility of deriving consensus rankings by solving consensus optimization problems, characterizing consensus rankings as suitable complete order relations minimizing the average Kemeny-Snell distance to the individual rankings. This optimization problem can be expressed as a binary programming (BP) problem which can typically be solved reasonably efficiently. The underlying theory is discussed in Sect. 1. Applications of the proposed method given in Sect. 2 include a comparison to other mathematical programming (MP) approaches using the data set of Tse [9] and establishing a consensus ranking of marketing journals identified by domain experts from a subset of the Harzing journal quality list [2]. In Sect. 3 we discuss computational details and present the results of a benchmark experiment comparing the performance of the commercial solver CPLEX to three open source mixed integer linear programming (MILP) solvers

ABSTRACTIn many different applications of group decision-making, individual ranking agents or judges are able to rank only a small subset of all available candidates. However, as we argue in this article, the aggregation of these incomplete ordinal rankings into a group consensus has not been adequately addressed. We propose an axiomatic method to aggregate a set of incomplete rankings into a consensus ranking; the method is a generalization of an existing approach to aggregate complete rankings. More specifically, we introduce a set of natural axioms that must be satisfied by a distance between two incomplete rankings; prove the uniqueness and existence of a distance satisfying such axioms; formulate the aggregation of incomplete rankings as an optimization problem; propose and test a specific algorithm to solve a variation of this problem where the consensus ranking does not contain ties; and show that the consensus ranking obtained by our axiomatic approach is more intuitive than the consensus ranking obtained by other approaches.

Aggregating multiple rankings in a database is an important task well studied by the database community. High-stakes application domains include hiring, lending, and education where multiple decision makers rank candidates and their input is then combined into an overall consensus ranking. While state-of-art aggregation methods succeed in giving equal say to each decision maker, to date no methods ensure fair representation for groups of candidates being ranked, who risk being impacted by discriminatory bias. We present the first solution to this open problem of guaranteeing fairness for disadvantaged groups of candidates, while still producing a good consensus of the base rankings. We design a family of exact fair algorithms achieving optimality for fair rank aggregation. We also develop approximate methods achieving fairness with guaranteed minimal approximation error scaling to millions of candidates in the rankings. A comparative study evaluates our proposed methods, revealing trade-offs between aggregation accuracy and different degrees of unfair bias in a rich variety of rank aggregation scenarios. Our real-world case study demonstrates that our solutions mitigate unfair bias using real-world data.

Consensus temporal order of amino acids and evolution of the triplet code

A framework for group decision support systems: An application in the evaluation of information technology for logistics firms

Prioritizing missense mutations with respect to their functional impact, especially those found in well-known cancer-associated genes within tumor tissue, remains a major task that is still not fully characterized. Even though a large number of bioinformatic algorithms exist that computationally estimate mutational pathogenicity, they showed significant inconsistency in their predictions. Only a few of these tools demonstrate better accuracy in separating drivers from nondriver mutations in cancer. The objective of this study is to rank cancer driver gene mutations based on their in-silico predictive pathogenicity by employing a deep learning method. We developed DeeprankCDM, a deep learning method to identify consensus ranking by aggregating different ranks. Our model is implemented based on pairwise learn-to-rank, an approach designed to determine preferences between items and aggregate multiple individual ranks into a single, consensus ranking. The method shows good consistency during training, with an accuracy of 0.7802, ranging tightly between 0.7791 and 0.7804. Notably, along with well-known gene PTEN, missense mutations in the well-established and frequently mutated gene TP53 emerged as top-rankers in the final aggregated list, indicating the good and reliable performance of our developed method.

PurposeThe purpose of this paper is to examine the influence of a number of factors such as high school major, high school score, gender, Stat105, Maths I, Maths II grades, and grade point average (GPA) on students' academic performance in an introductory operations research (OR) course at the department of Business Administration – College of Business and Economics at the UAE University.Design/methodology/approachAll students who completed the introductory OR course during the fall and spring semesters of the academic years 2005/2006 and 2006/2007 were chosen. Collected data were analyzed using correlation and stepwise multiple regression.FindingsThe results indicate that significant correlations exist between OR grade and GPA, maths I grade, maths II grade, and stat 105 grade. Results also show that there is statistically significant difference between the performance, measured by grade, in OR of males and females. There is also statistically significant difference in performance in OR between students majoring in art and those majoring in science in high school. Regression analysis indicates that GPA is the most significant predictor of students' performance in the OR course.Originality/valueThis paper is first study to examine the influence of a number of factors on students' performance in the OR course in the developing countries in general and the UAE in particular. It has implications for both decision makers and academicians.

Distance-based and ad hoc consensus models in ordinal preference ranking

Africa Centres for Disease Control and Prevention (CDC), which supports all 55 member states (MS) in Africa, was established to strengthen the capacity and capability of Africa's public health institutions to detect and respond quickly and effectively to disease threats and outbreaks based on science, policy, and data-driven interventions and programmes. This function is drawn from the African Union (AU) Assembly of Heads of State and Government, which adopted the statute of the Africa CDC in January 2016 and implemented it in mid-2017. Africa desperately needs an operational research (OR) framework for all 55 MS. The OR framework's strategic aim is to strengthen the capacity to initiate, design and implement OR during disease outbreaks and emergencies. The framework, with support from the Africa CDC, will therefore provide a concise guide to institutional structures, procedures and standards, as well as an outline of methodologies and tools for conducting OR that is context-specific, while promoting the use of generated evidence for policy and decision makers. Drawing on expert reviews, we present here the need for the OR framework and the process of its development for research during public health emergencies in Africa.

Evaluating the performance of suppliers based on using the R'AMATEL-MAIRCA method for green supply chain implementation in electronics industry

Contributors

The insurance industry, which is an important component of the financial channel, is an essential part of the Turkish economy, and assessing the financial performance is critical for insurance companies to improve efficiency and productivity, increase competitiveness, and enhance fiscal health. The study presented a technique for assessing the financial performance of all insurance companies registered in Borsa Istanbul by implementing an integrated method that combines the Criteria Importance Through Intercriteria Correlation (CRITIC) and Proximity Indexed Value (PIV) methods. The rationale for implementing the PIV method is the lack of adequate financial studies available on the insurance companies that employed this specific model. Initially, 18 evaluation criteria were defined. The CRITIC method was applied for the criteria weights, and insurance companies were ranked using PIV. Subsequently, the COPRAS, VIKOR, ARAS, and SAW Multi-Criteria Decision-Making (MCDM) methodologies were applied. Performance rankings derived from PIV were compared with those obtained from other MCDM models employed. Finally, Spearman’s Rank Correlation and Kendall’s Rank Correlation Coefficient methods were applied to analyze the extent of correlations and interactions between ranking outcomes. The PIV assessment results pointed out that AGESA received the highest rank for financial performance, and AKGRT had the lowest rank. AGESA consistently received high rankings compared to all other methods examined. Nevertheless, RAYSG and AKGRT constantly ranked poorly. All deployed methods ranked AKGRT and RAYSG in the final two positions. The study’s findings underscore that ranking outcomes of PIV largely align with alternate MCDM methodologies utilized.

The process of rapid development in modern cities has led to a significant number of playgrounds being located in confined areas, near roads with heavy vehicle traffic (including tram tracks), next to short-term parking spaces and refuse containers. Particular attention is paid to unauthorised parking which is caused by a lack of designated parking spaces. In addition, in residential areas of modern megacities there is a lot of infill development, which is an additional source of noise and dust. Thus, a significant number of playgrounds are located in areas with excessive anthropogenic load.
 The materials presented in the article describe the current condition of playgrounds in most big cities of Ukraine. Particular importance is attached to the factors which are connected with vehicle traffic, for example noise pollution, air dustiness and pollutant emissions.
 At the first stage, a list of factors affecting the environmental safety of children's playgrounds in urbanised areas was compiled and analysed.
 The next stage involves the development of a questionnaire and the direct assessment of the influencing factors by the experts involved. The article considers the factors that are rarely considered in the study of the impact of individual anthropogenic factors of the environment on public health, however, in modern conditions they cannot be neglected.
 The final stage consists of data processing and formation of conclusions. Using the method of rank correlation, the main factors of influence on the objects of research are identified.
 The results of the research stated in the article can be used in further study of the subject and in the development of methods to eliminate the influence of the most significant factors affecting the ecological safety of children's playgrounds.

Influence of phystcochemical interactions on the properties of suppositories IV. Factors influencing the in vivo release of ketoprofen and metronidazole from fatty suppository bases