Concept Of Rank Research Articles

Application of MCDM and Taguchi super ranking concept for materials selection problem

Material selection is a topic of pivotal importance in the optimal design and development of industrial products. Increasing demand for high-performance materials with desirable and tailor-made properties is expected to drive the future market. This paper examines the application of multicriteria decision-making techniques like AHP, TOPSIS, EDAS, VIKOR, and Taguchi-based super ranking concepts for the selection of optimal aluminum alloy material for the sheet metal forming process. The criteria used to evaluate the optimal material selection were identified as yield strength, tensile strength, thermal conductivity, impact, density, specific heat, coefficient of thermal expansion, and % elongation. The most ranked aluminum material frequency is selected for sheet metal forming simulation using DEFORM, a process simulation FEM-based tool. An integrated Taguchi DOE and process simulation were carried out for the best-ranked AA2024 aluminum material. The results showed that the MCDM and Taguchi-based super ranking concept provides an intelligent and methodical assessment for solving material selection from a finite set of alternatives for the sheet metal forming process. The group decision behavior, agility, and flexibility in including multiple decision-making criteria for overall comparative analysis of materials, and manufacturing processes with uncertainty make MCDM methods a definitive tool for materials and manufacturing process selection.

One’s Fixing Method for a Distinct Symmetric Fuzzy Assignment Model

This paper hinges upon the subject of an (n × n) assignment problem and the distinct symmetric fuzzy assignment problem byassigning n machines to n jobs. One’s orientation algorithm is developed for solving the assignment problems based on the position of one’s chosen in every row as well as every column to perform allocations and obtain the assignment cost at every (n − 1) reduced matrix. We also extended the two different symmetric concept to the problem to find the optimum solution based on symmetrical data and also used the ranking concept in our fuzzy assignment problem. In this proposed algorithm, the one’s position is associated with the successor of one in each iteration toobtain the optimal schedule and assignment cost. The comparative analysis is properly considered and discussed. The proposed technique is elaborated with the help of numerical computations and it gives clarity to the idea of this concept.

Fully Nonlinear Elliptic Equations with Gradient Terms on Hermitian Manifolds

Abstract We derive a priori 2nd-order estimates for fully nonlinear elliptic equations that depend on the gradients of solutions on compact Hermitian manifolds, which is a crucial step in solving the equations. We introduce the concept of rank of the tangent cones at infinity to level hypersurfaces of the defining function of the equation to overcome difficulties caused by presence of the gradient terms. We were motivated by the fact that there had been increasing interests in fully nonlinear PDEs from complex geometry and aimed to develop general methods to cover a wide class of equations.

On universally consistent and fully distribution-free rank tests of vector independence

Rank correlations have found many innovative applications in the last decade. In particular, suitable rank correlations have been used for consistent tests of independence between pairs of random variables. Using ranks is especially appealing for continuous data as tests become distribution-free. However, the traditional concept of ranks relies on ordering data and is, thus, tied to univariate observations. As a result, it has long remained unclear how one may construct distribution-free yet consistent tests of independence between random vectors. This is the problem addressed in this paper, in which we lay out a general framework for designing dependence measures that give tests of multivariate independence that are not only consistent and distribution-free but which we also prove to be statistically efficient. Our framework leverages the recently introduced concept of center-outward ranks and signs, a multivariate generalization of traditional ranks, and adopts a common standard form for dependence measures that encompasses many popular examples. In a unified study, we derive a general asymptotic representation of center-outward rank-based test statistics under independence, extending to the multivariate setting the classical Hájek asymptotic representation results. This representation permits direct calculation of limiting null distributions and facilitates a local power analysis that provides strong support for the center-outward approach by establishing, for the first time, the nontrivial power of center-outward rank-based tests over root-n neighborhoods within the class of quadratic mean differentiable alternatives.

Efficient Fully Distribution-Free Center-Outward Rank Tests for Multiple-Output Regression and MANOVA

Extending rank-based inference to a multivariate setting such as multiple-output regression or MANOVA with unspecified d-dimensional error density has remained an open problem for more than half a century. None of the many solutions proposed so far is enjoying the combination of distribution-freeness and efficiency that makes rank-based inference a successful tool in the univariate setting. A concept of center-outward multivariate ranks and signs based on measure transportation ideas has been introduced recently. Center-outward ranks and signs are not only distribution-free but achieve in dimension d > 1 the (essential) maximal ancillarity property of traditional univariate ranks. In the present case, we show that fully distribution-free testing procedures based on center-outward ranks can achieve parametric efficiency. We establish the Hájek representation and asymptotic normality results required in the construction of such tests in multiple-output regression and MANOVA models. Simulations and an empirical study demonstrate the excellent performance of the proposed procedures.

Multivariate ranks based on randomized lift-interdirections

Every multivariate sign and rank test needs a workable concept of ranks for multivariate data. Unfortunately, multidimensional spaces lack natural ordering and, consequently, there are no universally accepted ways how to rank vector observations. Existing proposals usable beyond small dimensions are very few in number, and each of them has its own advantages and drawbacks. Therefore, new multivariate ranks based on randomized lift-interdirections are presented, discussed and investigated. These naturally robust and invariant hyperplane-based ranks can be computed quickly and easily even in relatively high-dimensional spaces, and they can be used for nonparametric statistical inference in some existing optimal statistical procedures without altering their asymptotic behavior under null hypotheses or changing their performance under local alternatives. This is not only proved theoretically in case of the canonical sign and rank one-sample test for elliptically distributed observations, but also illustrated empirically in a small simulation study.

The GLD-plot: a depth-based graphical tool to investigate unimodality of directional data

A graphical tool for investigating the unimodality of hyperspherical data is proposed. It is based on the notion of statistical data depth function for directional data. A data depth extends the univariate concept of rank and leads to a natural centre-outward ordering of the sample points. First, a local version of distance-based depths for directional data is introduced. Then, the orderings induced by a local depth and its corresponding global version are compared by means of a two-dimensionalscatterplot, i.e., the GLD-plot (global-local depth plot). The graph can be easily visualized and interpreted. The proposal is evaluated on simulated and real data examples.

Optimizations for Computing Relatedness in Biomedical Heterogeneous Information Networks: SemNet 2.0.

Literature-based discovery (LBD) summarizes information and generates insight from large text corpuses. The SemNet framework utilizes a large heterogeneous information network or “knowledge graph” of nodes and edges to compute relatedness and rank concepts pertinent to a user-specified target. SemNet provides a way to perform multi-factorial and multi-scalar analysis of complex disease etiology and therapeutic identification using the 33+ million articles in PubMed. The present work improves the efficacy and efficiency of LBD for end users by augmenting SemNet to create SemNet 2.0. A custom Python data structure replaced reliance on Neo4j to improve knowledge graph query times by several orders of magnitude. Additionally, two randomized algorithms were built to optimize the HeteSim metric calculation for computing metapath similarity. The unsupervised learning algorithm for rank aggregation (ULARA), which ranks concepts with respect to the user-specified target, was reconstructed using derived mathematical proofs of correctness and probabilistic performance guarantees for optimization. The upgraded ULARA is generalizable to other rank aggregation problems outside of SemNet. In summary, SemNet 2.0 is a comprehensive open-source software for significantly faster, more effective, and user-friendly means of automated biomedical LBD. An example case is performed to rank relationships between Alzheimer’s disease and metabolic co-morbidities.

Dynamics of severe accidents in the oil & gas energy sector derived from the authoritative ENergy-related severe accident database.

Organized into a global network of critical infrastructures, the oil & gas industry remains to this day the main energy contributor to the world’s economy. Severe accidents occasionally occur resulting in fatalities and disruption. We build an oil & gas accident graph based on more than a thousand severe accidents for the period 1970–2016 recorded for refineries, tankers, and gas networks in the authoritative ENergy-related Severe Accident Database (ENSAD). We explore the distribution of potential chains-of-events leading to severe accidents by combining graph theory, Markov analysis and catastrophe dynamics. Using centrality measures, we first verify that human error is consistently the main source of accidents and that explosion, fire, toxic release, and element rupture are the principal sinks, but also the main catalysts for accident amplification. Second, we quantify the space of possible chains-of-events using the concept of fundamental matrix and rank them by defining a likelihood-based importance measure γ. We find that chains of up to five events can play a significant role in severe accidents, consisting of feedback loops of the aforementioned events but also of secondary events not directly identifiable from graph topology and yet participating in the most likely chains-of-events.

Maternity research priorities in country Western Australia: a Delphi study.

Objective Health research priorities are commonly identified and resourced by strategic leaders. The importance of recognising the expertise of clinician-researchers is being prioritised by a national funding shift towards applied research. There is a dearth of evidence regarding research priorities for maternity care in rural and remote health in Australia. This study aimed to develop an evidence-based consensus of maternity research priorities in regional, rural, and remote areas of Australia's largest rural health service (by land area) in Western Australia. Methods A three-phased Delphi method was selected to achieve an interdisciplinary, evidence-based consensus on maternity research priorities within Western Australian Country Health Service. Results Across three study phases, 432 participants responded. Representation was from seven regions and all stakeholder roles within the regions. Phase 1 included 173 responses yielding 53 concepts categorised under five domains. Phase 2 involved 161 participants who prioritised concepts under domains of (i) workforce and education; (ii) health equity; (iii) Aboriginal health; (iv) logistics and health systems; and (v) clinical. Phase 3 included 96 participants revealing 15 maternity research priorities with the top four ranked concepts: 'recruitment and retention of staff'; 'care for women and families with vulnerabilities', 'models of care offering continuity' and 'systems efficiencies'. Conclusions The novel evidence provided in this study, in conjunction with a strong consensus on research priorities and an interdisciplinary approach, strengthens the findings of this study and amplifies the mandate of action without delay.

Regularized high dimension low tubal-rank tensor regression

Tensor regression models are of emerging interest in diverse fields of social and behavioral sciences, including neuroimaging analysis, neural networks, image processing and so on. Recent theoretical advancements of tensor decomposition have facilitated significant development of various tensor regression models. The focus of most of the available literature has been on the Canonical Polyadic (CP) decomposition and its variants for the regression coefficient tensor. A CP decomposed coefficient tensor enables estimation with relatively small sample size, but it may not always capture the underlying complex structure in the data. In this work, we leverage the recently developed concept of tubal rank and develop a tensor regression model, wherein the coefficient tensor is decomposed into two components: a low tubal rank tensor and a structured sparse one. We first address the issue of identifiability of the two components comprising the coefficient tensor and subsequently develop a fast and scalable Alternating Minimization algorithm to solve the convex regularized program. Further, we provide finite sample error bounds under high dimensional scaling for the model parameters. The performance of the model is assessed on synthetic data and is also used in an application involving data from an intelligent tutoring platform.

Defining the Critical Components of Informed Consent for Genetic Testing.

Purpose: Informed consent for genetic testing has historically been acquired during pretest genetic counseling, without specific guidance defining which core concepts are required. Methods: The Clinical Genome Resource (ClinGen) Consent and Disclosure Recommendations Workgroup (CADRe) used an expert consensus process to identify the core concepts essential to consent for clinical genetic testing. A literature review identified 77 concepts that are included in informed consent for genetic tests. Twenty-five experts (9 medical geneticists, 8 genetic counselors, and 9 bioethicists) completed two rounds of surveys ranking concepts’ importance to informed consent. Results: The most highly ranked concepts included: (1) genetic testing is voluntary; (2) why is the test recommended and what does it test for?; (3) what results will be returned and to whom?; (4) are there other types of potential results, and what choices exist?; (5) how will the prognosis and management be impacted by results?; (6) what is the potential family impact?; (7) what are the test limitations and next steps?; and (8) potential risk of genetic discrimination and legal protections. Conclusion: Defining the core concepts necessary for informed consent for genetic testing provides a foundation for quality patient care across a variety of healthcare providers and clinical indications.

Using ranked weights and ideal point concept to measure energy architecture performance: an empirical study in emerging markets

Using ranked weights and ideal point concept to measure energy architecture performance: an empirical study in emerging markets

Experimental investigation of selective laser melting parameters for higher surface quality and microhardness properties: taguchi and super ranking concept approaches

In the current study, near net-shaped selective laser melting (SLM) technology was employed to build nickel-based superalloy Inconel 625 (IN625) parts with good quality. Taguchi method was employed to formulate a systematical study, analyze, and optimize the influencing factors, i.e., laser power (LP), scan speed (SS) and hatch distance (HD) on the resulting micro-hardness (MH) and surface roughness (SR) of the build samples. Scanning electron microscope (SEM) and X-ray diffraction analysis were carried out to characterize the powder morphology (spherical shaped particle possessing the size of 35 ± 6 μm) and the surface of the build samples. Laser power was the most contributing factor on the analyzed parameters (MH and SR), followed by the scanning speed and hatch distance. Taguchi determined optimal condition (MH: LP = 270 W, SS = 800 mm/s, HD = 0.08 mm; SR: LP = 270 W, SS = 800 mm/s, HD = 0.08 mm) which resulted in higher microhardness of 416 HV and lower surface roughness of 2.82 μm. Higher MH was attributed to the minimal porosity, while the uniform smooth surface of the build samples resulted in low SR as evident from the SEM images and surface texture analysis. Super ranking concept (SRC) was used to optimize the MH and SR simultaneously, by determining a single optimal condition (LP = 300 W, SS = 600 mm/s, HD = 0.10 mm). The obtained optimal condition resulted in a MH of 382 HV, and a SR of 3.92 μm. The results of optimal conditions are validated subjected to SEM morphologies.

Concept selection with hesitant fuzzy ANP-PROMETHEE II

ABSTRACT An integrated approach is given for the assessment and ranking of concepts as part of the new product development (NPD) process. Selection of the best concept in NPD involves multiple criteria decision-making (MCDM) since there are numerous possibly competing criteria to consider. Here, “hesitant fuzzy Analytic Network Process” (H-F-ANP) and “hesitant fuzzy Preference Ranking Organization Method for Enriching Evaluations II” (H-F-PROMETHEE II) are integrated to assess the concepts and determine the best. In the hesitant fuzzy ANP-PROMETHEE II (H-F-ANP-PROMETHEE II), H-F-ANP is applied to calculate criteria weights, taking into consideration inner and outer dependence and feedback interactions between criteria and H-F-PROMETHEE II is implemented to rank concept alternatives, employing the calculated criteria weights. A demonstrative example is given where five concepts are evaluated by three decision makers (DMs) to exhibit the applicability. For comparison, H-F-ANP-VIKOR is utilized.

On the Communication Complexity of AND Functions

Log-rank conjecture is one of challenging problems in communication complexity. It is known to hold for some special function classes such as XOR functions f(x ⊕ y) when the outer function f is monotone, symmetric, AC <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">0</sup> , low F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -degree, linear threshold or read-k. However, for AND functions f(x ∧ y), Log-rank conjecture is only known to hold when the outer function is a monotone function or a function with small alternating numbers. In this paper, we show that Log-rank conjecture holds for AND functions whose outer functions are read-once F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -degree polynomials, constant-F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -degree polynomials, and symmetric functions. For read-once and symmetric AND functions, their proof are based on the elementary analysis of Möbius sparsity of the outer functions and designing efficient protocols for computing the corresponding AND functions in terms of the Möbius sparsity of their outer functions. For a constant- F <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> -degree AND function f(x ∧ y), we use the concept of the variable rank to design a decision tree for computing f where the tree depth is bounded above by a polynomial in the logarithm of the Möbius sparsity of f. The communication protocol can be obtained easily from the constructed decision tree. Our results deepen the understanding of the log-rank conjecture for AND functions.

Implementation of the Additive Ratio Assessment (ARAS) Method for Employee Performance Assessment in the Office of Perum Bulog

Employee Performance Appraisal is very important, especially for employees in the scope of Perum BULOG Divre Bengkulu to encourage motivation and professionalism of employees in improving work quality. Constraints faced in conducting performance appraisals are due to the diversity of educational backgrounds, experiences, competencies and employee portfolios, so we need a system that can assist in the performance appraisal process so that the Employee Performance Appraisal does not become slow and experience difficulties. In the process of designing an employee performance appraisal decision support system application design uses the ARAS approach. The ARAS (Additive Ratio Assessment) method is one of the multicriteria decision-making methods based on the ranking concept using the utility degree, namely by comparing the overall index value of each alternative to the overall index value of the optimal alternative. The system implementation uses the Visual Basic 2010 programming language and the method used in this research is the waterfall method. From the results of the tests carried out, it can be concluded that this decision support system can provide selected employee decisions which later can be used as a reference for determining the quality of employee performance.

Experimental Investigation and Optimisation of Mechanical and Microstructure Behaviour of Stir Cast and Hot-Pressed Al-12.5%Si-ZrO2 Composites: Taguchi and Super Ranking Concept

ABSTRACT Stir cast processing route was employed to prepare the composite. Taguchi experimental plan with a set of parameters such as stir speed, stir time, ZrO2 % reinforcement, and casting temperature were studied to know their influence on the composite properties. ZrO2 % reinforcement, followed by stir speed, is the most dominant factor influence on composite properties. Super ranking concept determined stir casting optimal condition resulted in 25.02% and 5.64% increase in ultimate tensile strength and hardness, whereas 37.68% reduced wear loss of composites compared to initial stir casting conditions. The hot-pressing parameters (pressure, temperature, and dwell time) were analyzed to know the process insights on composite properties. The pressure is the most dominating factor followed by temperature on all the properties of composites. Super ranking concept determined optimal hot-pressing conditions showed a 39.3% reduction in wear loss, 11.54% and 4.88% increase in ultimate tensile strength and hardness values compared to initial hot-pressing condition, respectively. Applying a hot-pressing technique on the optimized conditions of stir casted parts resulted in 40.7% reduction in wear loss, 19.71% and 9.5% increase in ultimate tensile strength and hardness values. The difference in properties obtained for samples fabricated correspond to initial, and optimal conditions of stir casting and hot-pressing technique was strongly justified with resulted microstructures and worn surface morphologies.

Coarse median algebras: the intrinsic geometry of coarse median spaces and their intervals

This paper establishes a new combinatorial framework for the study of coarse median spaces, bridging the worlds of asymptotic geometry, algebra and combinatorics. We introduce a simple and entirely algebraic notion of coarse median algebra which simultaneously generalises the concepts of bounded geometry coarse median spaces and classical discrete median algebras. We study the coarse median universe from the perspective of intervals, with a particular focus on cardinality as a proxy for distance. In particular we prove that the metric on a quasi-geodesic coarse median space of bounded geometry can be constructed up to quasi-isometry using only the coarse median operator. Finally we develop a concept of rank for coarse median algebras in terms of the geometry of intervals and show that the notion of finite rank coarse median algebra provides a natural higher dimensional analogue of Gromov’s concept of delta -hyperbolicity.

Implementation of TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) in Determining Poverty Ranking of Village Communities as Recommendations for Government Assistance

Since the monetary crisis that hit Indonesia in 1998 it has become a scourge tense for the State of Indonesia. Every year it increases, this thing one of the reasons is the increasing price of food, clothing and food. In response to this, the government has provided several economic assistance, such asFamily Hope Program, KIP (Smart Indonesian Family) and assistance provided by the government. However, there is a lot of assistance the community, especially in the village, admitted to being poor to get this assistance. So that aid was not on target, causing unequal distribution of aid to the community who are indeed poor in accordance with government criteria, as happened, around 1,443 assistance was leveraged or double-checked because it was not right on target. Therefore, we need a ranking concept that is right on target and according to the criteria who are entitled to get assistance from the government by using the TOPSIS method as a recommendation who is entitled to assistance from the government. The fundamental reason for choosing this method is because of the level of complexity so that it has high accuracy when compared to others. And the method will be created using a website, so that it is easily accessible in all places. So, calculating the TOPSIS aims to see the poor in accordance with the criteria set by the government as a recommendation for providing assistance to truly poor people