Aggregation Problem Research Articles

Multiple Attribute Decision-Making Based on Three-Parameter Generalized Weighted Heronian Mean

For the aggregation problem of attributes with a correlation relationship, it is often necessary to take the correlation factor into account in order to make the decision results more objective and reasonable. The Heronian mean is an aggregation operator which reflects the interaction between attributes. It is of great theoretical and practical significance to study and popularize the multiple attribute decision-making methods based on the Heronian mean operator. In this paper, we first give a new three-parameter generalized weighted Heronian mean (TPGWHM), which has a series of excellent properties such as idempotency, monotonicity and boundedness. At the same time, the relationship between the TPGWHM and the existing aggregation operators is given. Then, we propose the intuitionistic fuzzy three-parameter generalized weighted Heronian mean (IFTPGWHM) and give its idempotency, monotonicity, boundedness and limit properties. On this basis, a multiple attribute decision-making method based on the TPGWHM and a multiple attribute decision-making method based on the IFTPGWHM are given, and corresponding examples are given and analyzed.

Efficient, robust and effective rank aggregation for massive biological datasets

Massive biological datasets are available in various sources. To answer a biological question (e.g., “which are the genes involved in a given disease?”), life scientists query and mine such datasets using various techniques. Each technique provides a list of results usually ranked by importance (e.g., a list of ranked genes). Combining the results obtained by various techniques, that is, combining ranked lists of elements into one list of elements is of paramount importance to help life scientists make the most of various results and prioritize further investigations. Rank aggregation techniques are particularly well-fitted with this context as they take in a set of rankings and provide a consensus, that is, a single ranking which is the “closest” to the input rankings. However, (i) the problem of rank aggregation is NP-hard in most cases (using an exact algorithm is currently not possible for more than a few dozens of elements) and (ii) several (possibly very different) exact solutions can be obtained. As answer to (i), many heuristics and approximation algorithms have been proposed. However, heuristics cannot guarantee how far from an exact solution the consensus ranking will be, and the approximation ratio of approximation algorithms dedicated to the problem is fairly high (not less than 3/2). No solution has yet been proposed to help true-users dealing with the problem encountered in point (ii).In this paper we present a complete system able to perform rank aggregation of massive biological datasets. Our solution is efficient as it is based on an original partitioning method making it possible to compute a high-quality consensus using an exact algorithm in a large number of cases. Our solution is robust as it clearly identifies for the user groups of elements whose relative order is the same in any optimal solution. These features provide answers to points (i) and (ii) and lie in mathematical bases offering guarantees on the computed result. Also, our solution is effective as it has been implemented into a real tool, ConquR-BioV2 is used for the life science community, and evaluated at large-scale using a very large number of datasets.

Application of BiFeO3 and Au/BiFeO3 decorated kaolinite nanocomposites as efficient photocatalyst for degradation of dye and electrocatalyst for oxygen reduction reaction

To improve the photocatalytic performance and solve the aggregation problem of BiFeO3 nanoparticles, kaolinite-BiFeO3 photocatalyst was successfully prepared for the first time by decoration of ferromagnetic BiFeO3 nanoparticles onto kaolinite surface. Comparative characteristics of kaolinite, bare BiFeO3 and kaolinite-BiFeO3 photocatalyst were investigated in detail by XRD, TEM, SEM-EDX, XPS, FTIR, VSM, UV-DRS, PL, zeta potential and BET techniques. The photocatalytic activities of the samples were also evaluated by photodegradation of rhodamine B as a model reaction under different irradiations like UVA, visible and sunlight. The kaolinite-BiFeO3 exhibited more superior photocatalytic activity as compared with other samples, which is best fitted to pseudo-first-order kinetic. The reactive species trapping experiments indicated that photogenerated-holes play major roles in the degradation process whereas superoxide radicals possess a secondary effect. Enhanced photocatalytic activity of kaolinite-BiFeO3 is attributed to its kaolinite platform that reduces the recombination opportunities of the photogenerated holes and electrons as well as increases the surface area, decreases the particle size and aggregation of nanoparticles. In addition, we also designed a novel electrocatalytic platform based on gold nanoparticles loaded on kaolinite-BiFeO3 modified carbon paste electrode surface for the examination of electrocatalytic activity for oxygen reduction reaction in alkaline medium. Electrochemical characterizations of the Au/BiFeO3/kaolinite modified carbon paste electrode and other bare surfaces were comparatively carried out with the aid of cyclic voltammetry, electrochemical impedance spectroscopy and chronoamperometry. Au/BiFeO3/kaolinite modified carbon paste electrode exhibited 15-fold current enhancement together with 100 mV anodic potential shift compared with bare carbon paste electrode.

Fabrication and mechanical properties of WC nanoparticle dispersion-strengthened copper

Cu composites strengthened through carbide nanoparticle dispersions (CDS–Cu) usually present a problematic agglomeration of nanoparticles that deteriorate their mechanical properties. To address this issue, in this study, CDS–Cu composites were prepared via spark plasma sintering (SPS) of reduced Cu-coated WC core–shell precursor powders, which were obtained through intermittent electrodeposition. This novel preparation method overcomes the aggregation problem of tungsten carbide (WC) nanoparticles, thereby improving the interface-bonding strength between the reinforcing-phase WC nanoparticles and the Cu matrix and further enhancing the load-transfer efficiency. The yield strength of the Cu-2.1 wt% WC composite prepared via pressureless sintering is up to 243 ± 3 MPa, which is significantly higher than that of pure Cu (130 MPa). Orowan strengthening and grain refinement strengthening induced by dispersed nano-WC particles are the main strengthening mechanisms. Due to the load-transfer strengthening, the yield strength of the SPS composite is improved to 271 ± 2 MPa. After hot rolling, the yield strength is further increased to 315 ± 2 MPa, owing to the additional contribution of dislocation strengthening originating from the plastic deformation process. The proposed method in this study provides an advanced pathway for the preparation of ceramic nanoparticle dispersion-strengthened metals.

Anion species-triggered antibody separation system utilizing a thermo-responsive polymer column under optimized constant temperature

Although the field of antibody drugs has grown larger, the antibody production still faces several challenges. Effective antibody purification is required, but the conventional purification method for antibodies is cost intensive and often causes aggregation problems, indicating the need for new alternative antibody purification methods. In the present study, a constant temperature antibody purification system for use with a thermo-responsive polymer column was developed based on switching of anion species in eluents. By adjusting the temperature for each antibody, the developed column enabled separation of the therapeutic monoclonal antibodies, rituximab and trastuzumab, from contaminants without changing salt concentration or pH of the eluents. The thermo-responsive hydrogel-modified column packing material was synthesized by introducing n-butyl methacrylate, acrylic acid, N,N′-methylenebisacrylamide and N-isopropylacrylamide to the surface of silica beads with an initiator by a graft-from approach. Elution behavior of antibodies with three types of anions, such as citrate, phosphate, and chloride were tested under three different temperature conditions. It was demonstrated that the thermo-responsive hydrogel grafted column showed a switchable antibody retention behavior at constant temperature and salt concentration, with antibody adsorption by NaCl eluent and desorption by citric acid buffer eluent.

EDUCATIONAL LANDSCAPES IN THE CONTEXT OF THE DIGITAL EDUCATIONAL ENVIRONMENT

The concept of "educational map", as well as a number of related concepts such as "concept map", have been the subject of numerous studies in the field of pedagogy over the years. They are a tool for describing and analyzing the knowledge of a specific student at a specific point in time. Recently, there has been interest in the problem of generalization and aggregation of information contained in separate individual maps. The concept of "Concept Landscape" was proposed as an approach to solving this problem (Mühling, 2017). However, this concept is focused exclusively on the analysis of the knowledge structure of a group of students. Accordingly, the problem arises of finding a more general and more universal tool that allows us to organically fit aggregated educational maps into the structure of the digital educational environment. We propose to consider this environment as a context that unites all possible educational maps. In accordance with the proposed approach, by the educational landscape we mean the result of the aggregation of several educational maps, united by the digital educational environment as a common context. We also set ourselves the task of identifying technologies for the aggregation of educational maps. We consider superposition, inclusion and absorption as such technologies and disclose the content of these operations in our article. We also establish the main form of the formal description of the educational landscape - a graph with colored vertices and edges. We propose a five-component algorithm for constructing and processing an educational landscape and describe the content of all its stages in the article. In our study, we also give an answer to the question about the role of the educational landscape within the digital educational environment. This role is, in our opinion, multifactorial. The main factors include: the formation of a space of possible individual learning trajectories; analysis and forecasting of the group dynamics of knowledge and skills of students; creation of a tool for supporting the design of an digital educational environment.

Agarose hydrogel doped with gold nanobipyramids(AuNBPs@AG)as colorful height readout device for sensing hydrogen peroxide in complex sample matrix

The portable and instrument-free sensing device for quantitative detection is of significant applications in resource constrained countries and regions. Here, a colorful height readout device formed by agarose hydrogel doped with gold nanobipyramids (AuNBPs@AG) is developed for the determination of hydrogen peroxide (H2O2) content. The responsive mechanism is based on H2O2 guided etching of AuNBPs, which causes the plasmon-mediated color change of the AuNBPs@AG hydrogel filled in plexiglass tube from top to bottom. We demonstrate the height of color-changed hydrogel is quantitatively related to the concentration of H2O2. The agarose hydrogel, as the carrier material for loading AuNBPs, guarantees the stability of AuNBPs and promotes the separation of complex samples. The method shows a good linear relationship in four ranges of H2O2 concentrations of 20 − 100 μM, 200 − 1000 μM, 1 − 10 mM and 10 − 100 mM. Compared with previous methods, using this inexpensive and easy to operate device for H2O2 detection can effectively reduce the interference of the color background, overcome the problem of the nanoparticles aggregation, and eliminate the need for expensive and sophisticated detection instruments. The practicality of the method is demonstrated via the analysis of commercial products such as contact lens solution and hair dye with satisfactory results. Our work provides a novel path for developing the height-readout quantitative devices with integrating hydrogels and plasmonic nanomaterials.

Using Unsupervised Learning for Data-driven Procurement Demand Aggregation

Procurement is an essential operation of every organization regardless of its size or domain. As such, aggregating the demands could lead to better value-for-money due to: (1) lower bulk prices; (2) larger vendor tendering; (3) lower shipping and handling fees; and (4) reduced legal and administration overheads. This paper describes our experience in developing an AI solution for demand aggregation and deploying it in A*STAR, a large governmental research organization in Singapore with procurement expenditure to the scale of hundreds of millions of dollars annually. We formulate the demand aggregation problem using a bipartite graph model depicting the relationship between procured items and target vendors, and show that identifying maximal edge bicliques within that graph would reveal potential demand aggregation patterns. We propose an unsupervised learning methodology for efficiently mining such bicliques using a novel Monte Carlo subspace clustering approach. Based on this, a proof-of-concept prototype was developed and tested with the end users during 2017, and later trialed and iteratively refined, before being rolled out in 2019. The final performance was 71% of past cases transformed into bulk tenders correctly detected by the engine; for new opportunities pointed out by the engine 81% were deemed useful for potential bulk tender contracts in the future. Additionally, per each valid pattern identified, the engine achieved 100% precision (all aggregated purchase orders were correct), and 79% recall (the engine correctly identified 79% of orders that should have been put into the bulk tenders). Overall, the cost savings from the true positive contracts spotted so far are estimated to be S$7 million annually.

Reconstruction of low-rank aggregation kernels in univariate population balance equations

The dynamics of particle processes can be described by population balance equations which are governed by phenomena including growth, nucleation, breakage and aggregation. Estimating the kinetics of the aggregation phenomena from measured density data constitutes an ill-conditioned inverse problem. In this work, we focus on the aggregation problem and present an approach to estimate the aggregation kernel in discrete, low rank form from given (measured or simulated) data. The low-rank assumption for the kernel allows the application of fast techniques for the evaluation of the aggregation integral (mathcal {O}(nlog n) instead of mathcal {O}(n^{2}) where n denotes the number of unknowns in the discretization) and reduces the dimension of the optimization problem, allowing for efficient and accurate kernel reconstructions. We provide and compare two approaches which we will illustrate in numerical tests.

In Situ Preparation and Immobilization of Semiconducting Polymer Dots on Microbeads for Efficient and Stable Photocatalytic Hydrogen Evolution

Organic semiconducting polymers dots (Pdots) have recently shown efficient photocatalytic activity for hydrogen evolution in an aqueous phase. However, colloidal Pdots face problems of aggregation and precipitation during the photocatalytic reaction due to unavoidable collisions between particles, thus resulting in a short catalytic lifetime. In this work, in situ preparation of PFBT Pdots on the surface of microbeads is reported. Results indicate that, with this facile method and support of a template, the photocatalytic properties of PFBT Pdots can be highly enhanced.

Fabrication of a TiC-Ti Matrix Composite Coating Using Ultrasonic Vibration-Assisted Laser Directed Energy Deposition: The Effects of Ultrasonic Vibration and TiC Content

Titanium and its alloys exhibit superior properties of high corrosion resistance, an excellent strength to weight ratio and outstanding stiffness among other things. However, their relatively low hardness and wear resistance limit their service life in high-performance applications of structure parts, gears and bearings, for example. The fabrication of a ceramic reinforced titanium matrix composite (TMC) coating could be one of the solutions to enhance the microhardness and wear resistance. Titanium carbide (TiC) is a preferable candidate due to the advantages of self-lubrication, low cost and a similar density and thermal expansion coefficient with titanium. The fabrication of TiC-TMC coatings onto titanium using a laser directed energy deposition (LDED) process has been conducted. The problems of TiC aggregation, low bonding quality and the generation of fabrication defects still exist. Considering ultrasonic vibration could generate acoustic steaming and transient cavitation actions in melted materials, which could homogenize the distribution of reinforcement materials and promote the dissolution of TiC into liquid titanium. In this study, for the first time, we investigate the ultrasonic vibration-assisted LDED of TiC-TMC coatings. The effects of ultrasonic vibration and reinforcement content on the phase compositions, reinforcement aggregation, bonding quality, fabrication defects and mechanical properties (including microhardness and wear resistance) of LDED deposited TiC-TMC coatings have been investigated. With the assistance of ultrasonic vibration, the aggregation of TiC was reduced, the porosity was decreased, the defects in the bonding interface were reduced and the mechanical properties including microhardness and wear resistance were increased. However, the excessive TiC content could significantly increase the TiC aggregation and manufacturing defects, resulting in the reduction of the mechanical properties.

A highly scalable algorithm for weak rankings aggregation

• Fast and scalable algorithms to deal with optimal bucket order problem. • First ordering then append (FOTA) approach. • Comparison with counterpart approach (first clustering then ordering). • Sound experimental evaluation including benchmarks with very large datasets. The Optimal Bucket Order Problem (OBOP) is a rank aggregation problem which consists in finding a consensus ranking (with ties) that generalizes a set of input rankings. In this paper, with the aim of solving the OBOP in an efficient and scalable way, we propose several greedy algorithms based on different sort-first and cluster-second strategies. More specifically, the sorting step is based on the Borda method, whereas in the cluster step, pairs of adjacent buckets are suitably joined. The proposed methods are experimentally compared with the state-of-the-art greedy algorithms for solving the OBOP by using a large benchmark of real-world databases. Furthermore, we provide a complete statistical analysis of the experimental study, which shows that several of the proposed algorithms outperform the current state-of-the-art greedy algorithms. We also analyze the trade-off between accuracy and execution time of the algorithms to guide the users regarding the selection of the best option for each particular case. The study carried out shows that our proposal is not only competitive in terms of accuracy with the state-of-the-art evolutionary strategy for dealing with the OBOP, but is also fast and scalable.

Fast-convergent federated learning with class-weighted aggregation

Recently, federated learning has attracted great attention due to its advantage of enabling model training in a distributed manner. Instead of uploading data for centralized training, it allows devices to keep local data private and only send parameters to server. Then the server aggregates local models to derive a global model. In this paper, we study the aggregation problem in federated learning, especially with non-independently and identically distributed data. Since existing scheme may degrade the representative of local models after aggregation, we propose to reallocate weights of local models based on contributions to each class. Then two class-weighted aggregation strategies are developed to improve the communication efficiency in federated learning. Evaluation shows that the proposed schemes reduce 30.49% and 23.59% of communication costs compared with FedAvg.

Synthesis of Nanosilica and its application as Water Based Mud Rheology Enhancer

This study synthesized nanosilica from sodium silicate solution using precipitation method for 3:1 ammonia/ethanol ratio, and examines the effectiveness of the nanosilica in formulating high-performance water based mud system. Sodium silicate solution (SSS) is a low-cost precursor than tetraethyl orthosilicate. Three (3) solutions of sodium silicate with concentrations 0.5 N, 1 N and 1.5 N were used to precipitate nanosilica. The use of sodium silicate solution in alkaline medium produces less agglomerated nanosilica. The results gave Similarly, three (3) different samples of silica nanoparticles (Sample A, Sample B, and Sample C) with each concentration respectively. The silica nanoparticles from the Sample A had the smallest in size and quantity, and also the most dispersed. For Sample B, the nanosilica produced was apparently bigger than that of sample A in size and quantity produced. Sample C was produced using two procedures: Sample C1 (with ultra Sonicator) and Sample C2. (ultra Sonicator). Samples C1 and C2 produced the biggest size and quantity of nanosilica with less the dispersion. But, there were aggregation problems, due to the higher concentration of sodium silicate solution. There was no much difference between samples C1 and C2; just that with the former, the nanosilica was better dispersed and the latter was little more aggregated. The precipitated nanosilica, Sample B with 1 N concentration of SSS gave the optimum size and quantity, and was used to formulate the examined high-performance water based mud system. The silica nanoparticles had a positive effect on the drilling fluid systems flow properties. This due to their ultrafine size and high surface area. Also, nanosilica production is cheap and feasible, so it could easily replace other expensive additives.

Two-Aggregator Topology Optimization Using Single Paths in Data Center Networks

This paper focuses on the data aggregation problem to two aggregators in a data center network under the constraint that each source rack must use a single path to each aggregator. We derive bounds on the approximation ratios of two classes of aggregation algorithms- Restricted 1-Round (R1R) and Restricted 2-Round (R2R). We achieve tighter bounds for the problem with additional constraints on the inputs. We propose another strategy using the 2Chain topology for k = 2, where k denotes the maximum degree of each top-of-rack(ToR) switch (number of uplinks in a ToR switch) in the data center and show that the optimal 2Chain cannot have an aggregation time greater than that of the optimal R1R and R2R topologies. For k ≥ 4, we propose a 1-round aggregation algorithm (1R) that uses trees for aggregation. Experimental results illustrate that 1R, R2R and R1R reduce the aggregation time by up to 85, 67 and 67 percent respectively for k = 4 relative to the two-round aggregation algorithm proposed by Wang et al. Moreover, for k = 2, the 2Chain can reduce the aggregation time up to 42 and 24 percent respectively relative to R1R and R2R.

The median rule in judgement aggregation

A judgement aggregation rule takes the views of a collection of voters over a set of interconnected issues and yields a logically consistent collective view. The median rule is a judgement aggregation rule that selects the logically consistent view which minimizes the average distance to the views of the voters (where the “distance” between two views is the number of issues on which they disagree). In the special case of preference aggregation, this is called the Kemeny rule. We show that, under appropriate regularity conditions, the median rule is the unique judgement aggregation rule which satisfies three axioms: Ensemble Supermajority Efficiency, Reinforcement, and Continuity. Our analysis covers aggregation problems in which the consistency restrictions on input and output judgements may differ. We also allow for issues to be weighted, and provide numerous examples in which issue weights arise naturally.

Ranking recovery from limited pairwise comparisons using low-rank matrix completion

This paper proposes a new methodology for solving the well-known rank aggregation problem from pairwise comparisons using low-rank matrix completion. Partial and noisy data of pairwise comparisons is first transformed into a matrix form. We then use tools from matrix completion, which has served as a major component in the low-rank based completion solution for the Netflix challenge, to construct the preference of different objects. In our approach, the data from multiple comparisons is used to create an estimate of the probability of object i winning (or be chosen) over object j, where only a partial set of comparisons between the N objects is known. These probabilities can be transformed to take the form of a rank-one matrix. An alternating minimization algorithm, in which the target matrix takes a bilinear form, is used in combination with maximum likelihood estimation for both factors. The reconstructed matrix is used to obtain the true underlying preference intensity. We start by exploring the asymptotic case of an infinitely large number of comparisons (“noiseless case”). We then extend our solution to the case of a finite number of comparisons for a subset of pairs (“noisy case”). This work demonstrates the improvement of our proposed algorithm over the state-of-the-art techniques in both simulated scenarios and real data.

A Workspace-Analysis-Based Genetic Algorithm for Solving Inverse Kinematics of a Multi-Fingered Anthropomorphic Hand

Although the solution of inverse kinematics for a serial redundant manipulator has been widely researched, many algorithms still seem limited in dealing with complex geometries, including multi-finger anthropomorphic hands. In this paper, the inverse kinematic problems of multiple fingers are an aggregate problem when the target points of fingers are given. The fingers are concatenated to the same wrist and the objective is to find a solution for the wrist and two fingers simultaneously. To achieve this goal, a modified immigration genetic algorithm based on workspace analysis is developed and validated. To reduce unnecessary computation of the immigration genetic algorithm, which arises from an inappropriate inverse kinematic request, a database of the two fingers’ workspace is generated using the Monte Carlo method to examine the feasibility of inverse kinematic request. Furthermore, the estimation algorithm provides an optimal set of wrist angles for the immigration genetic algorithm to complete the remaining computation. The results reveal that the algorithm can be terminated immediately even when the inverse kinematic request is out of the workspace. In addition, a distribution of population in each generation illustrates that the optimized wrist angles provide a better initial condition, which significantly improves the convergence of the immigration genetic algorithm.

Pythagoreanm-Polar Fuzzy Weighted Aggregation Operators and Algorithm for the Investment Strategic Decision Making

The role of multipolar uncertain statistics cannot be unheeded while confronting daily life problems on well-founded basis. Fusion (aggregation) of a number of input values in multipolar form into a sole multipolar output value is an essential tool not merely of physics or mathematics but also of widely held problems of economics, commerce and trade, engineering, social sciences, decision-making problems, life sciences, and many more. The problem of aggregation is very wide-ranging and fascinating, in general. We use, in this article, Pythagorean fuzzy numbers (PFNs) in multipolar form to contrive imprecise information. We introduce Pythagoreanm-polar fuzzy weighted averaging (PmFWA), Pythagoreanm-polar fuzzy weighted geometric (PmFWG), symmetric Pythagoreanm-polar fuzzy weighted averaging (SPmFWA), and symmetric Pythagoreanm-polar fuzzy weighted geometric (SPmFWG) operators for aggregating uncertain data. Finally, we present a practical example to illustrate the application of the proposed operators and to demonstrate its practicality and effectiveness towards investment strategic decision making.

Tell me who your co-worker is and I will tell you how much you earn: human capital spillovers in the Greek health sector

ABSTRACT Using cross-sectional data, collected via a survey from the Greek healthcare sector, we estimate both the private (direct) and public/social (indirect) returns to education from an increase in an employee’s educational level. By utilising a spatial econometrics approach, we control for the aggregation problems that plague the majority of the existing literature. The results demonstrate the existence of statistically and economically significant spillover effects, irrespective of whether the educational ‘distance’ between employees is increased or decreased, and indicate that traditional estimates of the rate of return to education are significantly biased when not accounting for spillover effects.