Stable Ranking Research Articles

Next generation of electronic medical record search engines to support chart reviews: A systematic user study and future research direction

ObjectiveLittle research has been done on the user-centered document ranking approach, especially in a crowdsourcing chart review environment. As the starting point of designing and implementing the next generation of Electronic Medical Record (EMR) search engines, a systematic user study is needed to better understand the users' needs, challenges, and future research directions of EMR search engines. Materials and methodsOne primary observation during the user study is the need for a ranking method to better support the so-called "early stopping" reviewing strategy (i.e., reviewing only a subset of EMRs of one patient to make the final decision) during the clinical chart reviews. The authors proposed two novel user-centered ranking metrics: "critical documents" and "negative guarantee ratio," to better measure the power of a ranking method in supporting the “early stopping” requirements during clinical chart reviews. ResultsThe evaluation results show that i) traditional information retrieval metrics, such as the precision-at-K, have limitations in guiding the design and development of EMR search engines to better support clinical chart reviews; ii) there is not a global optimal ranking method that fits the needs of different chart reviews and different users; iii) a learning-to-rank approach cannot guarantee a stable and optimal ranking for different chart reviews and different users; and iv) A user-centered ranking metric, such as the negative guarantee ratio (NGR) metric is able to measure the “early-stopping” performance of ranking methods. ConclusionsUser-centered ranking metrics can better measure the power of ranking methods in supporting clinical chart reviews. Future research should explore more user-centered ranking metrics and evaluate their impact on real-world EMR search engines.

Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS) Method for Comparing Firm Performance in Energy Sector

The present paper introduces a new multi-criteria decision making (MCDM) model such as Evaluation based on Relative Utility and Nonlinear Standardization (ERUNS). The proposed method provides a number of unique features such as use of non-linear standardization combined with a new definition for calculating utility values of the alternatives and design flexibility. The new method is applied to compare the firm performances using Economic, Environment, Social and Governance (EESG) framework. Over the last few decades alongside economic gain, the firms have been equally emphasizing on environmental protection, social values and good governance. This paper aims to bring in the economic aspect in terms of market capitalization and enterprise value while uses rating scores for environment, social and governance to put forth an extended performance measurement framework. A set of eight leading firms belonging to the energy sector listed to NSE, India has been considered for the period FY 2022-23 as a case study. The criteria weights are calculated using the Logarithmic Percentage Change-driven Objective Weighting (LOPCOW) method. It is observed that ERUNS provides a stable and reliable ranking result free from the rank reversal issue. This paper shall be of interest to both analysts and practitioners in terms of its robust method and practical performance assessment framework.

Validation of reaction norm breeding values for robustness in Australian sheep

BackgroundThere can be variation between animals in how stable their genetic merit is across different environments due to genotype-by-environment (G×E) interactions. This variation could be used in breeding programs to select robust genotypes that combine high overall performance with stable genetic ranking across environments. There have been few attempts to validate breeding values for robustness in livestock, although this is a necessary step towards their implementation in selection decisions. The objective of this study was to validate breeding values for the robustness of body weight across different growth environments that were estimated using reaction norm models in sheep data.ResultsUsing threefold cross-validation for the progeny of 337 sires, the average correlation between single-step breeding values for the reaction norm slope and the realised robustness of progeny across different growth environments was 0.21. The correlation between breeding values for the reaction slope estimated independently in two different datasets linked by common sires was close to the expected correlation based on theory.ConclusionsSlope estimated breeding values (EBV) obtained using reaction norm models were predictive of the phenotypic robustness of progeny across different environments and were consistent for sires with progeny in two different datasets. Selection based on reaction norm EBV could be used to increase the robustness of a population to environmental variation.

Forklift Seçim Probleminin Kapsamlı Çözümü için MEREC Yöntemine Dayalı Çok Kriterli Bir Karar Verme Çerçevesi

Forklift vehicles are used for lifting, lowering, loading, and transporting loads. Aimed of this study is the selection of a forklift vehicle with a combination of multi-criteria decision-making (MCDM) methods. It was used six criteria, 13 alternatives, and the MEREC method. This study also presents a double normalization (DNMEREC) to contribute to a more convenient, and stable ranking of alternatives. 21 different methods were used to evaluate the alternatives: ARAS, CODAS, COPRAS, CoCoSo, ELECTRE, MABAC, EDAS, VIKOR, TOPSIS, SAW, WASPAS, PROMETHEE, MOORA, MOOSRA, MAIRCA, OCRA, PIV, GRA, ROV, MARCOS, PSI. The different methods rankings are integrated using the Borda, and Copeland methods.

Analysis of the Educational Application Users Satisfaction Levels by Using the Integrated Entropy Weight VIKOR Method

A product review is an essential metric to understand the acceptability level of users. This rating process is often implemented to measure the quality and excellence of a product. It also helps the decision-making processes of potential users regarding the implementation of applications, due to the observation of many options. Furthermore, a product review enables the comparison of applications before imminent subscription. In this case, potential users often analyze various rating levels and perform selection within the Google Play Store based on the experiences of other subscribers. The review is also found to benefit students and software developers, concerning the strengths and weaknesses of the applications. For developers, subsequent modifications of products are mostly supported by this rating process during development. From this context, users and developers need to incorporate Multi-Criteria Decision-Making (MCDM) models, to understand the best and worst applications. Therefore, this study aims to assess the important criteria expected by users of seven educational service applications, by using the integrated Entropy Weight-VIKOR method. In this report, the service applications were selected based on the highest number of users in the Google Play Store.All criterion’s mass was also calculated through the entropy weight method. To rank the application through eight selected criteria, the VIKOR method was subsequently used. The results showed that the best educational service application qualities were Khan Academy and Udemy, with Socratic being the lowest. Some criteria such as download (Cr 1 ), video (Cr 2 ), cost (Cr 3 ), and teacher (Cr 8 ) were also suggested for improvement. From these results, thev-value did not affect the rankings of Khan Academy, Udemy, or Socratic, although it influenced the positions of other applications. This indicated that Khan Academy and Udemy had excellent customer satisfaction levels, regarding functionality and few specific complaints. However, Socratic had high complaints from individuals and low users satisfaction. Based on these results, a comparative analysis was carried out with the baseline study, for analytical validation. In this case, better outputs were observed for the present study than the baseline analysis, due to having three stable ranking positions.

Individual attitudes towards moral costs and benefits drive responses to moral dilemmas

AbstractWe review some processing assumptions that underlie the currently used measures of moral judgement with moral dilemmas, contrasting them with the overlooked possibility that the primary mechanism consists in assessing a net balance of the costs versus benefits of the sacrificial action. Different dilemmas scenarios present different net balances of cost versus benefits, and participants usually change between them from disapproval to approval motivated by what appears to be a larger positive balance of moral benefits. The thresholds for such change are personal and vary. For the instrument to reliably estimate the individual differences in approval thresholds, there can be no disagreement on the facts affecting the balances and the ranking order of the items. We designed an instrument targeting three different balances with five indicators each and ran an exploratory factor analysis to prove that the different balances operate cleanly as separate factors. The expected three‐factor solution and a stable ranking of the balance points were obtained across three samples from three different continents in three languages, suggesting the activation of cross‐culturally stable cognitive processes. The strength ratio of utilitarian (U) to deontological (D) sensitivity was calculated for each participant and corrected using the scores of a balance‐point with no net benefits. This instrument overcomes validation difficulties burdening the existing measures and offers good prospects for further development.

Robust morris screening method (RMSM) for complex physiological models

Background and objectives: Morris screening sensitivity analysis (MSM) comes forth as the method needing the minimum number of model simulations to qualify the impact of input parameter variations on outputs of complex, nonlinear and overparametrized models. However, the reliability of MSM indices (mean and standard deviation) and the reproducibility of their results are rarely explored despite the input parameter tuning/identification needs. In fact, these models, such those used in medical applications as digital twins, often lie in this category and need efficient and robust tools to assess both sensitivity and reliability of the outputs to numerous input model parameters.Methods: In this study, a new Robust Morris Screening Method (RMSM) is proposed and based on new indices: the absolute median (χ*) and the median absolute deviation (ρ). The proposed RMSM approach is evaluated on a complex multi-scales neuromuscular electrophysiological model simulating HD-sEMG (high density surface electromyography) signals at the skin surface. The reliability and stability of new RMSM indicators are evaluated at different trajectories within the parameter space and compared to classical MSM results. For this purpose, We propose a new methodology for parameter screening based on the ratio ρ/χ* as a graphic indicator of (non)linearity and (non)monotonicity of parameter effects.Results: Firstly, the results demonstrated that the computed elementary effects (EE) of inputs are not normally distributed using MSM indices contrary to the proposed RMSM indices. Secondly, the ranking stability of RMSM indices was earlier obtained from 20 trajectories (T=20), while MSM ranking remained unstable until T = 100. Thirdly, The screening separation between influential and negligible input model parameters was more distinct and interpretable with RMSM than MSM.Conclusion: The proposed RMSM approach ensures a fast, reliable and stable ranking of parameters for complex and overparametrized models compared to classical MSM. this allows a more precise exploration of the model parameter influence space for future application in parameter tuning and identification.

Stable variable ranking and selection in regularized logistic regression for severely imbalanced big binary data.

We develop a novel covariate ranking and selection algorithm for regularized ordinary logistic regression (OLR) models in the presence of severe class-imbalance in high dimensional datasets with correlated signal and noise covariates. Class-imbalance is resolved using response-based subsampling which we also employ to achieve stability in variable selection by creating an ensemble of regularized OLR models fitted to subsampled (and balanced) datasets. The regularization methods considered in our study include Lasso, adaptive Lasso (adaLasso) and ridge regression. Our methodology is versatile in the sense that it works effectively for regularization techniques involving both hard- (e.g. Lasso) and soft-shrinkage (e.g. ridge) of the regression coefficients. We assess selection performance by conducting a detailed simulation experiment involving varying moderate-to-severe class-imbalance ratios and highly correlated continuous and discrete signal and noise covariates. Simulation results show that our algorithm is robust against severe class-imbalance under the presence of highly correlated covariates, and consistently achieves stable and accurate variable selection with very low false discovery rate. We illustrate our methodology using a case study involving a severely imbalanced high-dimensional wildland fire occurrence dataset comprising 13 million instances. The case study and simulation results demonstrate that our framework provides a robust approach to variable selection in severely imbalanced big binary data.

Topo-heuristic reconfiguration of algebraic LSTM components to optimize temporal modulation in long-range dependencies

In this paper, the authors present a modified LambdaRank ranking algorithm based on the mathematical apparatus of the basic machine learning model (LSTM). LambdaRank is an effective method for ranking objects according to their importance, so it is often used as a mandatory component in search engines and recommendation systems. In this paper, an improvement of the algorithm is proposed by using optimisation techniques and introducing additional parameters for more accurate and stable ranking. The effectiveness of the proposed approach is verified on experimental real application data. The obtained accuracy results of the upgraded algorithm have also been analysed and compared with the classical variation of LambdaRank.

News and Networks: Using Financial News Coverage to Measure Bank Interconnectedness

We study the 'interconnectedness' of stress-tested banks by exploiting how they are mentioned together in the context of financial news. We start by constructing weekly co-occurrence network matrices following Ronnqvist and Sarlin (2015) text-to-network approach. Using the COVID-19 pandemic as an external shock, we examine how bank networks behave during high stress periods. We find that banks become more interconnected during peaks of COVID-19 induced stress. We put forth a new measure of systemic risk that utilizes text-based eigenvector centrality. This measure provides a more stable ranking system than the traditional SRISK measure during both high and low stress periods.

DEA efficiency in agriculture: Measurement unit issues

The selection of inputs and outputs in DEA models represents a vibrant methodological topic. At the same time; however, the problem of the impact of different measurement units of selected inputs is understated in empirical literature. Using the example of Czech farms, we show that the DEA method does not provide consistent score estimates, neither a stable ranking for different popular measurements of labour and capital factors of production. For this reason, studies based on DEA efficiency results for differently measured inputs should be compared only with great caution.

Quantifying interconnectedness and centrality ranking among financial institutions with TVP-VAR framework.

Financial risk is spread and amplified through the interconnectedness among financial institutions. We apply a time-varying parameter vector autoregression model to analyze the dynamic spillover effects in the Chinese financial system. We find that the 2017 house price control policies have significantly increased the risk of China's financial system. Before 2017, with the prosperity of the real estate market, the interconnectedness of the Chinese financial system continued to decline, while after 2017, with the slowdown of house price growth and the downturn of the real estate market, the interconnectedness turned to increase. For different sectors, the trends and the magnitudes of the spillover effects are diverse, and any sector can contribute to systemic risk in a dynamic way. Finally, we rank 20 systemically important financial institutions according to two centrality measures. The stable institution ranking provides less noisy information for regulators to formulate a policy and intervene in the market effectively.

Sparse kernel models provide optimization of training set design for genomic prediction in multiyear wheat breeding data.

The success of genomic selection (GS) in breeding schemes relies on its ability to provide accurate predictions of unobserved lines at early stages. Multigeneration data provides opportunities to increase the training data size and thus, the likelihood of extracting useful information from ancestors to improve prediction accuracy. The genomic best linear unbiased predictions (GBLUPs) are performed by borrowing information through kinship relationships between individuals. Multigeneration data usually becomes heterogeneous with complex family relationship patterns that are increasingly entangled with each generation. Under these conditions, historical data may not be optimal for model training as the accuracy could be compromised. The sparse selection index (SSI) is a method for training set (TRN) optimization, in which training individuals provide predictions to some but not all predicted subjects. We added an additional trimming process to the original SSI (trimmed SSI) to remove less important training individuals for prediction. Using a large multigeneration (8 yr) wheat (Triticum aestivum L.) grain yield dataset (n = 68,836), we found increases in accuracy as more years are included in the TRN, with improvements of ∼0.05 in the GBLUP accuracy when using 5 yr of historical data relative to when using only 1 yr. The SSI method showed a small gain over the GBLUP accuracy but with an important reduction on the TRN size. These reduced TRNs were formed with a similar number of subjects from each training generation. Our results suggest that the SSI provides a more stable ranking of genotypes than the GBLUP as the TRN becomes larger.

Evaluating the distributional fairness of alternative adaptation policies: a case study in Vietnam’s upper Mekong Delta

To support equitable adaptation planning, quantitative assessments should consider the fairness of the distribution of outcomes to different people. What constitutes a fair distribution, however, is a normative question. In this study, we explore the use of different moral principles drawn from theories of distributive justice to evaluate fairness. We use adaptation planning in Vietnam Mekong Delta as a case study. We evaluate the preference ranking of six alternative policies for seven moral principles across an ensemble of scenarios. Under the baseline scenario, each principle yields distinctive preference rankings, though most principles identify the same policy as the most preferred one. Across the ensemble of scenarios, the commonly used utilitarian principle yields the most stable ranking, while rankings from other principles are more sensitive to uncertainty. The sufficientarian and the envy-free principles yield the most distinctive ranking of policies, with a median ranking correlation of only 0.07 across all scenarios. Finally, we identify scenarios under which using these two principles results in reversed policy preference rankings. Our study highlights the importance of considering multiple moral principles in evaluating the fairness of adaptation policies, as this would reduce the possibility of maladaptation.

A stable multi-criteria decision model based on Markov chain

This paper aims to generate a stable solution to a multi-criteria decision-making (MCDM) problem with unknown decision parameters. To this end, it proposes a multi-criteria decision model with interval numbers based on the Markov chain. For the construction of the proposed model, MCDM is demonstrated from the perspective of the Markov chain. Under these conditions, optimization problems are constructed for each alternative with a specified ranking based on unknown criterion weights, which are representative decision parameters. By using the optimized criterion weight matrix of each alternative derived from the constructed optimization problems, the transition probability of each alternative between two rankings is designed to form the transition probability matrix of each alternative. The relevant properties are theoretically analyzed. From the eigenvector of such a matrix, the stable ranking value distribution of each alternative is obtained, whose existence and uniqueness are theoretically proven. The stable solution is generated from the stable ranking distributions of alternatives. The proposed model is then used to analyze a management platform supplier selection problem, in which its applicability and validity are demonstrated. The meaningfulness of the proposed model is validated by simulation experiments and case comparison.

Banishing the inner Econ and justifying paternalistic nudges

Abstract Paternalistic nudging and framing aim to correct flaws in deliberation by relying on the same cognitive mechanisms that create those flaws. Regarding some choices as flawed and in need of correction requires some standard of correctness. In their well-known book, Nudge, Thaler and Sunstein take the individual's own “purified” preferences to be that standard, which is inconsistent with the finding of behavioral economics that individuals do not have a stable preference ranking of alternatives, but instead construct their preferences when faced with a choice. This essay defends an alternative, readily usable standard to judge whether individuals are choosing badly and whether nudges can help them to choose better.

Aplikasi Metode Proses Hirarki Analitik dan Pemrograman Integer 0-1 Dalam Menentukan Komposisi Pemain Sepak Bola pada Football Manager 2019

Football has became a favorite sport of the world community. Every supporter of a football team would want their team to win the competition they participated in. Formation, strategy, and composition of teams are factors that influence the team's victory in a match. These three factors are the responsibility of a football coach in concocting his team in winning. This paper will discuss the application of the Analytical Hierarchy Process and the Integer 0-1 Program to assist football coaches in composing the composition of football players in a match. The AHP is used in this case to calculate the priority weights of each soccer player criteria while the Integer 0-1 Program is used to get eleven players to be deployed in a match. The results of both methods are simulated using the game Football Manager 2019 with team Manchester United in the English Premier League. Based on simulations conducted during the two season matches, Manchester United was able to finish in a fairly stable ranking in the English Premier League standings for two seasons.

On sustainable improvements of agricultural practices in the Bairrada region (Portugal)

The objective of this paper is to develop a framework to overcome problems of soil nutrient legacies and poor quality of water bodies raised by agricultural practices. A multicriteria decision analysis (MCDA) approach is proposed to evaluate different alternatives to help to define sustainable vineyard agricultural solutions. The methodology developed was applied to a basin in the north-central part of Portugal, the São Lourenço basin. The land cover of the basin consists predominantly of vines. A number of alternatives based on different vineyard management practices and land use are evaluated against environmental and socioeconomic criteria by means of the soil and water assessment tool (SWAT) model and considering information included in some databases. The preference ranking organization method for enrichment evaluation (PROMETHEE) outranking method is used to rank the alternatives and to evaluate weight stability intervals assigned to criteria. A supplementary analysis is performed by a different MCDA method (technique for order of preference by similarity to ideal solution (TOPSIS)), belonging to the family of compensatory methods, to provide additional insights to the decision makers. The results show the best and the worst alternatives for vineyard agricultural practices according to the weights assigned to the criteria. The sensitivity analysis of the weights indicated most stable ranking cases and the criterion that presents the narrow stability intervals. This work offers the opportunity to discuss comparisons between different types of agricultural practices and give insights for future developments of other areas where intensive vineyard cultivation is implemented.

Nonspecificity, strife and total uncertainty in supervised feature selection

In this paper we propose three novel feature ranking methods for supervised feature selection in the context of classification which are based on possibility theory. All three methods – nonspecificity, strife and total uncertainty – are tested on eight artificial data sets and ten medical real-world data sets and benchmarked against ReliefF, the Fisher score, the fuzzy entropy and similarity (FES), the Fuzzy similarity and entropy (FSAE) filter, symmetrical uncertainty as well as using no feature selection. The feature ranking methods were applied following two approaches: (1) using a fixed threshold for the number of highest-ranking features selected and (2) using a hybrid feature selection approach with a classifier (k-nearest neighbor classifier, decision tree, similarity classifier, SVM) to select the optimal number of features to select. The results indicate that strife and the Fisher score are the two feature ranking methods that for both approaches are on average ranked the highest in terms of the test set accuracy on the real-world data sets. Besides that, for the hybrid approach, strife uses most of the time a considerably smaller number of features than nonspecificity and total uncertainty. In terms of stability, which was measured with the adjusted stability measure (ASM), the Fisher score and strife were among the most stable feature ranking methods in this study. Additionally, strife’s feature subsets were diverse compared to those of the remaining feature selection methods, making it a good candidate to be included in a feature selection ensemble.

Dynamic Green Growth Assessment of China’s Industrial System with an Improved SBM Model and Global Malmquist Index

This study proposes a method for resource management and optimisation in the industrial sector of China. Differing from previous research on the green assessment of industrial systems focusing on “black box” evaluation, our approach contributes to the two-stage structure of an industrial system that consists of an industrial production process and a pollution treatment process. The corresponding network slack-based model (SBM) is proposed to analyse the performance of China’s provincial industry sector. Based on our network SBM, the global Malmquist index is built to analyse the total factor productivity changes of system and individual processes to evaluate the consistency of sustainable development where dynamic green growth assessment is realized. The results show that the whole system and its pollution treatment process performance are poor and disorganised, while the industrial production process maintains a stable ranking for the 30 regions in China. We find that the main cause of this phenomenon is the variable technical efficiency change in the 30 regions, which reflects the immaturity of the management of the pollution treatment process. System performance is also highly related to regionalism.