Ranking Methodology Research Articles

A Methodology for Alternatives Ranking by Estimations Forming based on Values from Criteria Decomposition into Options

A Methodology for Alternatives Ranking by Estimations Forming based on Values from Criteria Decomposition into Options

Regional Analysis and Evaluation Method for Assessing Potential for Installation of Renewable Energy and Electric Vehicles

Many countries are adopting renewable energy (RE) and electric vehicles (EVs) to achieve net-zero emissions by 2050. The indicators of RE and EV potentials are different. Decision-makers want to introduce RE and EVs; however, they need a method to find suitable areas. In addition, this is required in the time-series analysis to provide a detailed resolution. In this study, we conducted a time-series analysis in Japan to evaluate suitable areas for the combined use of RE and EVs. The results showed the surplus RE areas and shortage RE urban areas. The time-series analysis has quantitatively shown that it is not enough to charge EV batteries using surplus RE. Moreover, a ranking methodology was developed for the evaluation based on electric demand and vehicle numbers. This enables the government’s prioritization of prefectures and the prefectures’ prioritization of municipalities according to their policies.

Clinical adjudication of outpatient complications reported in the U.S. News and World Report’s urology rankings

ObjectivesTo assess whether the US News and World Report (USNWR) Urology specialty ranking methodology accurately captures and classifies complications following elective outpatient urology procedures. MethodsWe conducted electronic health record chart review of n=80 elective, outpatient urology procedures with complications from 2019-2023 across four hospitals in our integrated US health system. We used the Solventum AM-PPC software and USNWR methodology to determine eligibility and measure complications. For each complication identified by the software, we assessed: 1.) Whether the procedure was performed by a urologist; 2.) Whether the adjudicator agreed with the complication type; and 3.) Whether the complication was a clinically-related sequelae of the index procedure. We reported Clavien-Dindo severity of each complication. Results: Our adjudication agreed on complication type in 62/80 (78%) complications, and 64/80 (80%) complications were clinically related to the index urology procedure. Combined, 57/80 (71%) complications were concordant on both complication type and clinical relatedness. However, 38/80 (48%) index procedures were conducted by interventional radiologists, not urologists. Furthermore, 11/80 (13.8%) complications were false positive urinary tract infections (UTIs). ConclusionsThe USNWR methodology for elective outpatient urology procedural complications showed reasonable clinical validity but detected several false positive UTIs. Further, USNWR should clarify the extent to which procedures performed by interventional radiologists belong in urology rankings.

Some New Algebraic Method Developments in the Characterization of Matrix Equalities

Algebraic expressions and equalities can be constructed arbitrarily in a given algebraic framework according to the operational rules provided, and thus it is a prominent and necessary task in mathematics and applications to construct, classify, and characterize various simple general algebraic expressions and equalities. As an update to this prominent topic in matrix algebra, this article reviews and improves upon the well-known block matrix methodology and matrix rank methodology in the construction and characterization of matrix equalities. We present a collection of fundamental and useful formulas for calculating the ranks of a wide range of block matrices and then derive from these rank formulas various valuable consequences. In particular, we present several groups of equivalent conditions in the characterizations of the Hermitian matrix, the skew-Hermitian matrix, the normal matrix, etc.

Global Ranking of Expertise about Russia

ABSTRACT Academic rankings rooted in bibliometric indicators have proliferated over the past few decades. Despite this trend, there have been few efforts to formulate rankings specifically for interdisciplinary and subdisciplinary areas. This paper introduces the Global Ranking of Expertise about Russia, focusing on organizations ranked by their contribution as a source of impactful knowledge about Russia. The ranking methodology is grounded in the quantity and impact of organizations’ publications within a sample of 29,826 articles spanning the 1990–2020 period. We present the results of an analysis that offers a list of organizations based on both the quantity and impact of their publications.

The Bradley-Terry Model in Binary Outcome Driven Rankings: An Application in Amateur Hockey

Ranking systems serve critical roles in sport settings, most notably in determining playoff participants and seeding. Many ranking methodologies exist that are flexible enough to incorporate many input measures and produce models that are highly predictive of game outcomes. However, there are circumstances—especially for amateur sport leagues—in which more complex inputs are either unavailable or not desirable, as they may lead to adverse performance incentives. Therefore, the goal of this paper is to highlight a ranking methodology that only considers binary game outcomes, i.e., wins and losses. Specifically, we consider the efficacy of the Bradley-Terry Model to efficiently rank sport teams for playoff consideration. We apply this method as a case study to the New England Prep School Ice Hockey Association (NEPSIHA), and compare the accuracy of their current ranking system to the Bradley-Terry model using simulation methods. We show that Bradley-Terry significantly outperforms NEPSIHA’s current method, especially when teams face unbalanced strengths of schedule. This result holds under various league competitive balance distributions.

Anything but Representative Democracy: Explaining Conspiracy Believers’ Support for Direct Democracy and Technocracy

Conspiracy theories gained considerable attention during the Covid-19 pandemic. Although studies have extensively explored their (mostly) negative impacts on various political and social aspects, like participation, health-related behavior, and violence, their influence on support for democracy remains relatively unexplored. The few existing studies offer conflicting findings, prompting my focus to shift from assessing generic support for democracy to examining preferences for alternative decision-making models. To address some limitations of prior research on alternative models of decision-making, I combine a trade-off item with a ranking methodology: respondents were prompted to indicate their first and second preferences for different democratic and non-democratic models over representative democracy. The study is based on data from a representative survey in Germany (July/August 2022; N = 2,536). My findings confirm that the belief in conspiracy theories is positively associated with a preference for direct democratic decision-making. However, conspiracy believers also favor expert-based decision-making over elected politicians—but direct democracy would be their primary choice. Although the evidence for a preference for autocracy over representative democracy is associated with a higher degree of uncertainty, it does suggest that conspiracy believers tend to favor “anything but” representative democracy. These findings contribute to the broader discourse on the impact of conspiracy beliefs on democratic systems.

Defining optimal left ventricular assist device short-term outcomes may provide insight into programmatic quality assessment

BackgroundPatients have substantial variability in perioperative outcomes after left ventricular assist device (LVAD) implant. A perioperative multidimensional tool integrating mortality, adverse events (AEs), and patient-reported outcomes to assist in quality improvement initiatives is needed. MethodsPatients undergoing HeartMate 3 LVAD implant (1/1/2017 and 1/31/2024) in the Society of Thoracic Surgeons’ Intermacs registry were studied. A Cox proportional hazard multivariable analysis incorporating AEs as time-varying covariates for mortality out to 180 days was used to generate the Intermacs Short-Term Composite Quality (INSITE) score, reflecting the adjusted hazard ratio (HR) for mortality contributed by each AE, applying global ranking methodology. In those alive and on support at 6 months, multivariable logistic regression (odds ratio, OR) was used to examine the impact of AEs on health-related quality of life (QOL) at 180 days, captured through the INSITE-QOL score. Failure to achieve ≥1 point increase in visual analog scale (VAS) from baseline was the event in the QOL analysis. ResultsOf 13,148 patients, 4,389 (33.4%) suffered at least one AE or death through 180 days. Stroke (survival: HR 13.1; QOL: HR 1.7), dialysis (survival: HR 31.4; QOL: HR 4.2), prolonged respiratory failure (survival: HR 5.7; QOL: HR 2.3), reoperation (Survival: HR 3.4; QOL: HR 1.6) and right heart failure (survival: 5.0; QOL: HR 1.4), contributed to both mortality and failure to improve QOL at 180 days (all p<0.05). The median INSITE and INSITE-QOL scores were 0.0 [0.0,1.6] and 0.0 [0.0,0.0], respectively. At 9.4% (n=17) of centers, a high INSITE score (≥13) was present in 15% of patients while the top 25% of centers had perfect INSITE-QOL scores in at least 75% of patients. ConclusionsAEs after LVAD confer differential impact on mortality and QOL, enabling development of global rank outcome scores. Given the high mortality hazard conferred by 180-day AEs, center-specific quality interventions aimed at reducing early complications provide the greatest opportunity to improve long-term survival and QOL.

Mitigating subjectivity and bias in AI development indices: A robust approach to redefining country rankings

Artificial Intelligence (AI) indices have emerged to assess countries’ progress in AI development, aiding decision-making on investments and policy choices. Typically, these indices are derived from a linear weighted sum of various criteria, employing deterministic weights. However, this approach fails to capture interactions among criteria, and the use of deterministic weights is susceptible to debate. To mitigate these issues, we conduct a methodological analysis based on Choquet integral (CI) and Stochastic Multicriteria Acceptability Analysis 2 (SMAA-2). We assess correlations between different AI dimensions and employ CI to model them. Additionally, we apply SMAA-2 to conduct a sensitivity analysis using both weighted sum and CI in order to evaluate the stability of the indices with regard to the weights. Finally, we introduce a ranking methodology based on SMAA-2, which considers several sets of weights to derive the ranking of countries. In the computational analysis, we evaluate our approach using the dataset employed in The Global AI Index, as proposed by the British news website Tortoise. The results reveal that our approach effectively mitigates bias. Furthermore, we scrutinize changes in the ranking resulting from weight adjustments and demonstrate that our proposed rankings closely align with those derived from variations in weights, indicating robustness.

A methodology for ranking of critical links in transportation networks based on criticality score distributions

Criticality of transportation networks links is calculated through utilizing criticality metric(s) for various disruption scenarios that generally include one link removal/degradation at a time or simultaneous removal/degradation of multiple links. As also shown in this paper, single link removals do not reveal the actual criticality of link(s) due to network dependencies. Multiple simultaneous link removal scenarios can capture the network interactions; however, the results relate to the criticality ranking of scenarios than the links, e.g., the links in scenario-1 are more critical than the links in scenario-2. This paper addressed the need for a methodology that provides individual link criticality rankings based on failure scenarios. The proposed approach utilizes the distribution of the criticality scores for each link based on running traffic assignment for each failure scenario, e.g., the distribution for link #X is composed of the criticality scores of all link failure combinations that include link #X. The criticality ranking for each link is identified based on its criticality distribution's mean, coefficient of variation and skewness. The use of the proposed approach is illustrated on test networks to show that it provides robust link criticality rankings that accounts for network interactions across varying scales of link failure scenarios.

Using Convolutional Neural Network to Determine Time Window for Analyzing Local Shear-Wave Splitting Measurements

Abstract The time window for analyzing local shear-wave splitting (SWS) phases significantly affects the quality of measurements, revealing a noteworthy domain influence. In this study, an approach using convolutional neural network (CNN) is applied to determine the end of time window (e), which has a similar idea of the phase-picking CNNs. The start of time window is 0.5 s before e. Our data set contains 803 human-labeled measurements, recorded from three stations located in Ridgecrest, California. These measurements are foreshocks and aftershocks of an M 7.1 earthquake on 6 July 2019. After 21 times shifting on each measurement, 90% of the data set is applied as the training data set, with the remaining 10% as the testing data set. The performance of CNN with the testing data set is compared with a nonmachine learning method, multiple filter automatic splitting technique (MFAST). The results reveal that the CNN yields more similar results with human-labeled outcomes than MFAST, as evidenced by lower absolute error and standard deviation for e, SWS time, the orientation of fast-wave polarization, and more consistent results on the map. The CNN also performs well when applied to data recorded by a station in Parkfield, California. This study shows the outstanding performance of CNN in picking the time window and the reliable automatic determination of this time window, and it is also a crucial step for future development of automatic ranking methodologies.

Integrating Social Drivers of Health into Hospital Ratings with Application to the 100 Top Hospitals Study.

The objective was to investigate the relationship between social drivers of health (SDOH) and hospital performance within the 100 Top Hospitals study, exploring methods to recognize hospitals serving marginalized communities. Publicly available data sourced from the Centers for Medicare and Medicaid Services and the 2023 100 Top Hospitals study was used. The study employed multivariable hierarchical generalized linear regression models to assess the association between an SDOH composite variable derived using principal component analysis and overall hospital performance measures within the 100 Top Hospitals study. The analysis revealed a statistically significant association between SDOH factors and study ranking results. The SDOH composite variable is a significant predictor of performance within the 100 Top Hospitals study. Accounting for SDOH is essential to recognize high-performing hospitals serving marginalized communities. The findings suggest a need for broader considerations of SDOH in hospital ranking methodologies across various industry programs.

Lists of Journals and Ratings of Russian Publications: Inconsistencies and Possible Ways to Eliminate Them

When Web of Science and Scopus ceased all commercial activity in Russia, the issue of maximum separation from global scientometric systems became acute and appeared on the agenda. Under the present-day conditions of sanctions, the role of Russian academic journals in information support of science has increased significantly. The purpose of this study was to compare the contents of various lists of journals used to evaluate research activity in Russia and to develop a most balanced approach to ranking scholarly publications. The object of the study was Russian periodicals indexed in information systems, including the White List, as well as the main and additional Lists of the Higher Attestation Commission (HAC). A collection of Russian periodicals was compiled using databases such as the Web of Science Core Collection, Scopus and the Russian Science Citation Index (RSCI). Using the overlay method, we compared information on publications from these databases with details taken from the White List and the Lists of the HAC (the main and additional ones). Information on the overlapping of Russian journals in these lists was collected. This made it possible to compare the positions of the same journals in the rankings in accordance with their levels in the White List and categories of the HAC. The levels and categories of journals were supplemented with information about the percentiles of the Science Index rating (RSCI). The lists of journals were analyzed from the informative and qualitative points of view. As a result of this study, it has been found that none of the lists meets the following basic requirements: 1) the completeness of coverage of the most significant journals in a wide range of academic fields without prejudice to any of them; 2) a careful examination of the methodology of ranking by categories or levels. A pretty accurate understanding of quality levels of academic journals can be achieved by using a new indicator of scholarly periodicals – percentiles of journals in the Science Index rating (RSCI).

Changes in characteristics and risk of freshwater microplastics under global warming

Microplastics present a significant threat to freshwater ecosystems. However, the impact of global warming on their characteristics and associated risks remains uncertain. This study collected 2793 sample sites from literature and datasets to create a new risk assessment and rank methodology, known as the Multi-characteristics Potential Ecological Risk Index (MPERI), which incorporates various microplastic characteristics, such as concentration, size distribution, color, shape, and polymer diversity. Using regression random forest models (RRF), this study predicted that a 10 °C increase would raise microplastic concentration from 12,465.34 ± 68,603.87 to 13,387.17 ± 60,692.96 particles/m3. The percentage of small-size microplastics initially decreased (from 69.10 % to 68.72 %) and then increased (from 68.72 % to 68.78 %), while the diversity of color, shape, and polymer decreased by 0.29 %, 3.24 %, and 0.17 %, respectively. Furthermore, global warming could increase the rank of microplastic risks from high (405.25 ± 528.9) to dangerous (535.37 ± 582.03) based on the MPERI method. Most countries would experience an increase in risk values, with Indonesia and Vietnam transitioning from low to medium risk, and China and Malaysia transitioning from high to dangerous risk. The feature importance assessment of the RRF model indicated that concentration was the most influential variable in determining the change in risk values. While other microplastic characteristics had a lesser impact compared to concentration, they still influenced the risk ranking. This study highlights the role of global warming in shaping microplastic risks.

Revisiting the National Ranking Methodology of Higher Education Institution in Pakistan: A Societal and Entrepreneurial Perspective

The notion of third mission (3M) activities is becoming increasingly important to exploit universities’ knowledge and capabilities in society. Besides, there is an emerging need of reflection of these activities in ranking methodologies of universities. In Pakistan, national ranking of universities is conducted by HEI statutory body. The purpose of this study is to assess the methodology of national ranking of HEIs in Pakistan for the reflection of third mission activities i.e., entrepreneurial and societal engagement. Content analysis was performed on the national ranking methodology based on Third Mission Model by Molas-Gallart, Salter, Patel, Scott and Duran (2002). The findings of this study revealed that only 1.03% of the third mission activities are reflected in the methodology of the national ranking system of HEI statutory body i.e., Patents (0.52%), ORICs (0.17%), Grant (0.17%) and Exchange (0.17%).This study will provide a critical base to policy makers, to incorporate third mission activities in ranking methodology to address the emerging economic and social challenges faced by the country.

Correlations and impact of anaerobic digestion operating parameters on the start-up duration: Database construction for robust start-up guidelines

Anaerobic digestion (AD) has become a popular technique for organic waste management while offering economic and environmental advantages. As AD becomes increasingly prevalent worldwide, research efforts are primarily focused on optimizing its processes. During the operation of AD systems, the occurrence of unstable events is inevitable. So far, numerous conclusions have been drawn from full and lab-scale studies regarding the driving factors of start-up perturbations. However, the lack of standardized practices reported in start-up studies raises concerns about the comparability and reliability of obtained data. This study aims to develop a knowledge database and investigate the possibility of applying machine learning techniques on experimentation-extracted data to assist start-up planning and monitoring. Thus, a standardized database referencing 75 cases of start-up of one-stage wet continuously-stirred tank reactors (CSTR) processing agricultural, industrial, or municipal organic effluent in mono-digestion from 31 studies was constructed. 10 % of the total observations included in this database concern failed start-up experiments. Then, correlations between the parameters and their impacts on the start-up duration were studied using multivariate analysis and a model-based ranking methodology. Insights into trends of choices were highlighted through the correlation analysis of the database. As such, scenarios favoring short start-up duration were found to involve relatively low retention times (average initial and final hydraulic retention times, (HRTi) and (HRTf) of 26.25 and 20.6 days, respectively), high mean organic loading rates (average OLRmean of 5.24 g VS·d−1·L −1) and the processing of highly fermentable substrates (average feed volatile solids (VSfeed) of 81.35 g L−1). The model-based ranking of AD parameters demonstrated that the HRTf, the VSfeed, and the target temperature (Tf) have the strongest impact on the start-up duration, receiving the highest relative scores among the evaluated AD parameters. The database could serve as a reference for comparison purposes of future start-up studies allowing the identification of factors that should be closely controlled.

Automatic Literature Mapping Selection: Classification of Papers on Industry Productivity

The academic community has witnessed a notable increase in paper publications, whereby the rapid pace at which modern society seeks information underscores the critical need for literature mapping. This study introduces an innovative automatic model for categorizing articles by subject matter using Machine Learning (ML) algorithms for classification and category labeling, alongside a proposed ranking method called SSS (Scientific Significance Score) and using Z-score to select the finest papers. This paper’s use case concerns industry productivity. The key findings include the following: (1) The Decision Tree model demonstrated superior performance with an accuracy rate of 75% in classifying articles within the productivity and industry theme. (2) Through a ranking methodology based on citation count and publication date, it identified the finest papers. (3) Recent publications with higher citation counts achieved better scores. (4) The model’s sensitivity to outliers underscores the importance of addressing database imbalances, necessitating caution during training by excluding biased categories. These findings not only advance the utilization of ML models for paper classification but also lay a foundation for further research into productivity within the industry, exploring themes such as artificial intelligence, efficiency, industry 4.0, innovation, and sustainability.

JUST Capital

The case examines the development and launch of an exchange-traded fund (ETF) based on JUST Capital's socially responsible corporate ranking methodologies. The case provides a market overview of Environment, Social, and Corporate Governance (ESG) and socially responsible investing (SRI), what has driven growth in those areas worldwide, and several best-practice investment approaches. Following the overview, the case describes the founding and development of JUST Capital, explores JUST Capital's ranking methodologies, and presents the decision point faced by the CEO: requisite selection of one of three strategies in order for JUST Capital to generate “self-sustaining” revenue.

Road infrastructure maintenance: Operative method for interventions’ ranking

Planned maintenance of transportation infrastructure is a milestone in maintaining the functionality and safety of road networks. This paper is part of this process and aims to propose an easily applicable and versatile operational methodology for prioritizing maintenance interventions generally scheduled on road networks of considerable extent. An operational methodology for ranking and prioritizing maintenance interventions is proposed; this method is applicable to road networks of such size that direct inspection of each situation is impossible. The core of the paper is the analytical description of this methodology, based on a weighted sum function of three blocks: i) Category − is a score related to the purpose of the intervention; ii) Asset − is a score due to the road on which the intervention is located (considering Average Daily Traffic, Accidentality, Social Cost, Road Type, Routes); iii) Typology − is a score related to the element on which the intervention is being carried out. After describing the parameters and the implementation procedure for each block, the calibration of the relative weights is described, and a sensitivity analysis is performed. In the case study, the proposed methodology is applied to the ANAS corporate network in Italy. This case study will highlight the usefulness, versatility, and operability of the methodology with GIS (Geographic Information System) tools for implementing thematic maps. A concluding section concerns the addition of a scoring block − Spatial Context − as an additional differentiating factor due to the spatial context of each road.

A Natural Language Processing-Based Classification and Mode-Based Ranking of Musculoskeletal Disorder Risk Factors

This research explores the intricate landscape of Musculoskeletal Disorder (MSD) risk factors, employing a novel fusion of Natural Language Processing (NLP) techniques and mode-based ranking methodologies. Enhancing knowledge of MSD risk factors, their classification, and their relative severity is the main goal of enabling more focused preventative and treatment efforts. The study benchmarks eight NLP models, integrating pre-trained transformers, cosine similarity, and various distance metrics to categorize risk factors into personal, biomechanical, workplace, psychological, and organizational classes. Key findings reveal that the Bidirectional Encoder Representations from Transformers (BERT) model with cosine similarity attains an overall accuracy of 28%, while the sentence transformer, coupled with Euclidean, Bray–Curtis, and Minkowski distances, achieves a flawless accuracy score of 100%. Using a 10-fold cross-validation strategy and performing rigorous statistical paired t-tests and Cohen’s d tests (with a 5% significance level assumed), the study provides the results with greater validity. To determine the severity hierarchy of MSD risk variables, the research uses survey data and a mode-based ranking technique parallel to the classification efforts. Intriguingly, the rankings align precisely with the previous literature, reaffirming the consistency and reliability of the approach. “Working posture” emerges as the most severe risk factor, emphasizing the critical role of proper posture in preventing MSD. The collective perceptions of survey participants underscore the significance of factors like “Job insecurity”, “Effort reward imbalance”, and “Poor employee facility” in contributing to MSD risks. The convergence of rankings provides actionable insights for organizations aiming to reduce the prevalence of MSD. The study concludes with implications for targeted interventions, recommendations for improving workplace conditions, and avenues for future research. This holistic approach, integrating NLP and mode-based ranking, contributes to a more sophisticated comprehension of MSD risk factors and opens the door for more effective strategies in occupational health.