Idea Of Partitioning Research Articles

Effect of Tempering on Microstructure and Mechanical Properties of Laser Welded and Post-Weld Treated AHSS Specimens

An advanced high strength steel (0.08 %C, 1.79 %Mn, 0.23 %Si) was subjected to different post-weld heat treatments by quenching & tempering treatments (Q&T) after laser welding to reduce the risk of martensite formation in a few seconds based on an idea of quench and partitioning (Q&P), mechanism. The thermal stability of retained austenite, microstructure development and mechanical properties have been studied at 2 tempering temperatures of 440°C (Ms) and 636°C (Bs), both for 15 minutes, by means of electron microscopy, dilatometry, hardness profile and tensile tests. Dilatometer study unveiled that redistribution of carbon atoms and precipitation of transition carbides occur around 150°C and austenite decomposition occur at 600°C. Tempering at 636°C resulted in notable effect on the mechanical properties, while no significant difference was detected at 440°C, except a slight hardness drop. The strength increased up to 12% for the different specimens without significant loss in ductility for all specimens tempered at 636°C, which may be caused by precipitation hardening and recrystallization of martensite lath boundaries during tempering around 600°C.

Modelling tumour cell proliferation from vascular structure using tissue decomposition into avascular elements

Computer models allow the mechanistically detailed study of tumour proliferation and its dependency on nutrients. However, the computational study of large vascular tumours requires detailed information on the 3-dimensional vessel network and rather high computation times due to complex geometries. This study puts forward the idea of partitioning vascularised tissue into connected avascular elements that can exchange cells and nutrients between each other. Our method is able to rapidly calculate the evolution of proliferating as well as dead and quiescent cells, and hence a proliferative index, from a given amount and distribution of vascularisation of arbitrary complexity. Applying our model, we found that a heterogeneous vessel distribution provoked a higher proliferative index, suggesting increased malignancy, and increased the amount of dead cells compared to a more static tumour environment when a homogenous vessel distribution was assumed. We subsequently demonstrated that under certain amounts of vascularisation, cell proliferation may even increase when vessel density decreases, followed by a subsequent decrease of proliferation. This effect was due to a trade-off between an increase in compensatory proliferation for replacing dead cells and a decrease of cell population due to lack of oxygen supply in lowly vascularised tumours. Findings were illustrated by an ectopic colorectal cancer mouse xenograft model. Our presented approach can be in the future applied to study the effect of cytostatic, cytotoxic and anti-angiogenic chemotherapy and is ideally suited for translational systems biology, where rapid interaction between theory and experiment is essential.

Exploratory Approaches for Studying Social Interactions, Dynamics, and Multivariate Processes in Psychological Science

ABSTRACTIn this article, I argue for the need of more use of exploratory techniques to identify dynamics in social interactions. I describe several approaches as they are applied to multivariate time series data. The first approach is an algorithm that searches for periods of variability and stability at the individual level as well as for patterns of overlap in such periods between the two individuals in a couple. These patterns describe the daily ups and downs in the couples' affect and are predictive of the state of the couples 1 to 2 years later. The second approach, hierarchical segmentation, is based on the idea of partitioning the time series in segments with distinct data patterns. In the case of data from dyads, as in the illustration, the patterns can be compared in terms of coherence between the 2 individuals in the dyad. The third approach is based on network analysis, and its use is shown as a method to examine data transitions at the individual and dyadic level as well as system-wide coherence in multivariate systems. For each approach, I provide examples of its use with empirical data. The article ends with general guidelines and recommendations for researchers interested in using exploratory methods as a way to examine psychological processes.

Emission characteristics of a model combustor for aero gas turbine application

A TeLESS concept combustor using lean staged NOx reduction technology has been proposed on the basis of previous studies. TeLESS means the Technology of Low Emissions of Stirred Swirl in this paper. As the power condition increases, the TeLESS combustor will work in low power mode, circumferentially staged mode, or high power mode. Meanwhile, each combustor dome is in either only pilot mode or two stages mode. Emission characteristics of TeLESS combustor have been investigated experimentally in a single-module rectangular model combustor. NOx emissions and combustion efficiency of the model combustor were measured under high pressures and temperatures up to 2.8MPa and 845K. Fuel stage ratio ranged from 1.5% to 34.9% in the tests. The fuel staging effects on NOx emissions, combustion efficiency and stability of the model combustor were studied under circumferentially-staged mode and high power mode conditions. Tests results showed that the optimal fuel stage ratio for the combustor varied with operating conditions. A new empirical NOx model, based on the idea of flame partition, was proposed for staged combustion. Combustor pressure, inlet temperature, and dome equivalence ratio, as well as the dome fuel stage ratio were used as input parameters in the NOx model. Finally, the empirical model was used to predict landing and take-off cycle NOx emissions for the TeLESS combustor.

Muslim Zion: Pakistan as a Political Idea

Muslim Zion: Pakistan as a Political Idea

Muslim Zion: Pakistan as a Political Idea

Muslim Zion: Pakistan as a Political Idea

Blind adaptive carrier phase recovery for QAM systems

This paper provides a study of adaptive phase recovery in quadrature amplitude modulation (QAM) based communication systems. Here, we modify the traditional fourth-power phase recovery algorithm (FP-PRA) to propose three improved algorithms, and analyze their performances in the presence of additive white Gaussian noise, phase noise, frequency-offset, and inter-symbol interference. We demonstrate that it is possible to obtain the optimal values of step-sizes (or loop-gains) in closed-form in the presence of phase noise and/or frequency-offset. In particular, we discuss two methods to improve FP-PRA. The first method involves utilizing the idea of partitioning the QAM constellation into QPSK-like and not-QPSK-like annular regions. The phase synchronizer is allowed to update only when a derotated QAM symbol lies in QPSK-like region; otherwise, the update process is stopped. The second method exploits an evolving idea of QAM-to-QPSK transformation, and uses transformed symbols to estimate phase mismatch. We provide a new interpretation of this transformation method and relate it to the quadrant-wise centroid of the rotated constellation. Furthermore, we discuss the feasibility of this method for both square and cross-QAM, and, identify and verify numerically the existence of false-locks in the case of cross-QAM. To the best of our knowledge, the ideas of constellation partitioning and constellation transformation have not appeared earlier in the context of adaptive phase recovery. We discuss adaptive blind estimation of optimal step-sizes in the presence of phase noise and frequency-offset. Finally, we discuss the modification of the proposed stochastic gradient methods to transform them into batch processing algorithms so as to make them more suitable for higher data rate systems. Numerical experiments indicate that the proposed algorithms can outperform the traditional FP-PRA algorithm for a number of practical QAM sizes under different mismatch conditions, and that our analytical findings are in close agreement with the simulation results.

Lower bounds and exact algorithms for the quadratic minimum spanning tree problem

We address the quadratic minimum spanning tree problem (QMSTP), the problem of finding a spanning tree of a connected and undirected graph such that a quadratic cost function is minimized. We first propose an integer programming formulation based on the reformulation–linearization technique (RLT). We then use the idea of partitioning spanning trees into forests of a given fixed size and obtain a QMSTP reformulation that generalizes the RLT model. The reformulation is such that the larger the size of the forests, the stronger lower bounds provided. Thus, a hierarchy of formulations is obtained. At the lowest hierarchy level, one has precisely the RLT formulation, which is already stronger than previous formulations in the literature. The highest hierarchy level provides the convex hull of integer feasible solutions for the problem. The formulations introduced here are not compact, so the direct evaluation of their linear programming relaxation bounds is not practical. To overcome that, we introduce two lower bounding procedures based on Lagrangian relaxation. These procedures are embedded into two parallel branch-and-bound algorithms. As a result of our study, several instances in the literature were solved to optimality for the first time.

Perturbation Methods for Markov-Switching DSGE Models

This paper develops a general perturbation methodology for constructing high-order approximations to the solutions of Markov-switching DSGE models. We introduce an important and practical idea of partitioning the Markov-switching parameter space so that a steady state is well defined. With this definition, we show that the problem of finding an approximation of any order can be reduced to solving a system of quadratic equations. We propose using the theory of Grobner bases in searching all the solutions to the quadratic system. This approach allows us to obtain all the approximations and ascertain how many of them are stable. Our methodology is applied to three models to illustrate its feasibility and practicality.

A Framework for Ranking and KNN Queries in a Probabilistic Skyline Model

Skyline computation has gained a lot of attention in recent years. According to the definition of skyline, objects that belong to skyline cannot be ranked among themselves because they are incomparable. This constraint limits the application of skyline. Fortunately, due to the recently proposed probabilistic skyline model, skyline objects which contain multiple elements, can now be compared with each others. Different from the traditional skyline model where each object can either be a skyline object or not, in the probabilistic skyline model, each object is assigned a skyline probability to denote its likelihood of being a skyline object. Under this model, two simple but important questions will naturally be asked: (1) Given an object, which of the objects are the K nearest neighbors to it based on their skyline probabilities? (2) Given an object, what is the ranking of the objects which have skyline probabilities greater than the given object? To the best of our knowledge, no existing work can effectively answer these two questions. Yet, answering them is not trivial. For a medium-size dataset (e.g. 10,000 objects), it may take more than an hour to compute the skyline probabilities of all objects. In this paper, we propose a novel framework to answering the above two questions on the fly efficiently. Our proposed work is based on the idea of bounding-pruning-refining strategy. We first compute the skyline probabilities of the target object and all its elements. For the rest of the objects, instead of computing their accurate skyline probabilities, we compute the upper bound and lower bound skyline probabilities using the elements of the target object. Based on lower bound and upper bound of their skyline probabilities, some objects, which cannot be in the result, will be pruned. For those objects, which we are unknown whether they are in the results or not, we need to refine their bounds. The refinement strategy is based on the idea of space partition. Specifically, we first partition the whole dataspace into several subspaces based on the distribution of elements in the target object. When we iteratively do the the refinement of the bounds, we will do the partitioning strategy in each subspace. In order to implement this framework, a novel tree, called Space Partition Tree (SPTree) is proposed to index the objects and their elements. We evaluate our proposed work using three synthetic datasets and one real-life dataset. We report all our findings in this paper.

The skip-octree: a dynamic cloud storage index framework for multidimensional big data systems

Nowadays the information explosion have generated a large amount of data. To utilise these data, there is a trend of setup efficient data index, especially for multidimensional data indexing. However, most of the current cloud storage systems build data indexing based on the distributed hash DHT, where data are stored by the key-value. This module is not very suitable for range queries in multidimensional data indexing. To solve these problems, this paper builds a multidimensional data index based on skip lists and octree structure. Firstly, this architecture adopts the structure of octree to store data and establish the corresponding index mechanism, it can make use of the idea of different dimensional space partitioning to achieve a multi-dimensional index. Secondly, the relevant algorithms are designed to sustain the characteristics of this framework in this paper. Finally, the simulation experiment shows that the skip-octree architecture is feasible and efficient.

A correlated overflow model with a view towards applications in credit risk

In this paper we present and explicitly solve a specific multi-dimensional, correlated overflow problem. Due to the ordering of the underlying components in our model, explicit results are obtained for the probabilities under consideration; importantly, the results are not in terms of Laplace transforms. In our setting, each component behaves as a compound Poisson process with unit-sized upward jumps, decreased by a linear drift. The approach relies on a Benes (1963) type argumentation, that is, the idea of partitioning the overflow event with respect to the last 'exceedance epoch'. This type of problems arises naturally in various branches of applied probability, and therefore has several applications. In this paper we point out two specific application areas: one in mathematical finance, and one in queueing. In the former, several 'obligors' (whose 'distances-to-default' are correlated, as is the case in practice) are considered, and it is quantified how likely it is that there are defaults before a given time T. In the latter, one is often interested in the workload or storage process; the results of this paper provide expressions for overflow probabilities in the context of specific coupled queuing systems.

Modelling on microblog posts clustering based on iteration feature selection and abstractive summarisation

With the coming of big data era, data mining and intelligent processing become more and more important, and modelling on novel big data processing is necessary. As micro-blog posts' properties on short texts and the linguistic unreliable features, it is necessary to analyse and cluster these similar posts together for further data mining and recommendation. This paper uses the classical clustering algorithm of k-means, and then presents a novel modelling approach to partition the micro-blog posts into the corresponding k similar groups. Furthermore, a feature selection model based on 2-phase iteration is proposed. Based on this model, a clustering algorithm is presented. The proposed algorithm takes use of the partition idea and avoids the influence of the outliers or noise data. Lastly, a proposed cluster abstractive summarisation approach is presented to summarise every individual cluster. On the basis of this, it is easy for users to know the main content about a cluster. Experiment shows the feasibility of the approach, and some existing problems and further works are also presented in the end.

Experimental Study of Smooth Channel Flow Division Based on Velocity Distribution

AbstractThe idea of partitioning a flow has been widely used by hydraulic researchers and engineers as a mathematical treatment to simplify complex flows. Although the flow partitioning argument is intensive, no specially designed experiments have been conducted to verify them or the models, or to determine whether the existence of division lines can be inferred from mean velocity profiles. The aim of this paper is to examine the existence of division lines in smooth channel flow by analyzing the mean velocity distribution in a flume with a smooth flat or curved bed. The newly discovered division lines from experiments were compared with the existing models. It is found that some proposed models proposed have good agreement with the derived division lines obtained from the mean velocity distributions.

Greedy immunization strategy in weighted scale-free networks

Purpose – The purpose of this paper is to present a new immunization strategy for effectively solving the control of the spread of the virus. Design/methodology/approach – Inspired by the idea of network partition, taking two optimization targets which are the scale of sub-network and the sum of the strengths of the sub-network's nodes into account at the same time, a new immunization strategy based on greedy algorithm in the scale-free network is presented. After specifying the number of nodes through the immunization, the network is divided into the scale of sub-network and the sum of the strength of the sub-network's nodes as small as possible. Findings – The experimental results show that the proposed algorithm has the better performance than targeted immunization which is supposed to be highly efficient at present. Originality/value – This paper proposes a new immunization strategy based on greedy algorithm in the scale-free network for effectively solving the control of the spread of the virus.

Data Dissemination With Side Information and Feedback

Index coding (IC), which can be regarded as a special class of network coding, deals with the problem of sending a number of packets to a group of receivers, each of which requests one packet and may have some other packets in its cache. This paper generalizes the IC problem in that both the packet requested by a receiver and the packets in its cache can be linear combinations of the packets. To minimize the number of transmissions required, a heuristic algorithm based on the idea of partitioning the users into coding groups is designed. To realize this idea, a polynomial time algorithm to determine whether a set of users form a coding group over the binary field or a field with a size larger than the number of users is constructed. For users that form a coding group, the corresponding encoding vector can be also found. A lower bound is derived in order to evaluate the performance of the heuristic algorithm. Numerical results show that the number of transmissions required by the heuristic algorithm and the lower bound both grow roughly linearly with the number of users, and the heuristic algorithm outperforms some benchmark algorithms.

Energy/Lifetime Cooptimization by Cache Partitioning With Graceful Performance Degradation

Aging of transistors can adversely impact the long-term reliability of devices in subnanometric technologies. Without any countermeasure, the first component that becomes unreliable will determine the life span of an entire device. The effect is more susceptible in memory arrays, where failure of a single SRAM cell would cause the failure of the whole system. In this paper, we propose a reliability management technique based on the idea of cache partitioning, which deals with cell failures by gracefully degrading its performance. By this partitioning-based strategy, various subblocks will become unreliable at different times, and the cache will keep functioning with reduced efficiency. A coarse-grain implementation of this approach, with the use of a smart aging-driven partitioning algorithm, provides a lifetime extension of more than 2× . On the other hand, a fine-grain strategy with a single cache line as a unit of power management, stretch the lifetime to its maximum limits with an addition of small hardware overhead.

Genetic Algorithm based Bi level Histogram Modification Framework for Image Contrast Enhancement

Histogram equalization is a well known technique used for contrast enhancement. However, it changes the brightness of an image and hence, not suitable for consumer electronic products. This paper proposes a new technique, Bi Level Histogram Modification using Genetic Algorithm (BLHM) for the purpose of enhancing the contrast as well as preserving details for any given input image. The basic idea of this technique is partition the input image histogram into two sub-histograms based on its mean. Then, a bi-criteria optimization problem is formulated to satisfy the aforementioned requirements and the two sub-histograms are modified by selecting the optimal contrast enhancement parameters. Finally, the union of two modified sub-histograms produces a contrast enhanced and details preserved output image. While formulating the optimization problem, Genetic Algorithm is employed to control the degree of enhancement. This mechanism enhances the contrast of the input image better than its existing contemporary HE methods. The performance of the proposed method is measured in terms of Discrete Entropy and Contrast Improvement Index.

ANALISIS KEMAMPUAN MAHASISWA PENDIDIKAN GURU SEKOLAH DASAR FKIP UNIVERSITAS RIAU DALAM PENGUKURAN KELILING DAN LUAS BANGUN DATAR

The purpose of this study is to analyze student teachers at Primary School Teacher Education of RiauUniversity in understanding the concepts of measurement perimeter and area. The reason in doingthis research is that many student teachers usually use formal formula in solving measurementperimeter and area. They will do the same thing when they teach Primary School Students, so thestudents will not know how the formula works. Design research was chosen as a method in gettingdata. The data were collected from 48 student teachers at Primary School Teacher Education of RiauUniversity. The result showed that six out of ten group of student teachers did not use partitioning inthe beginning of the lesson. They also made a mistake in measuring the perimeter of unstructuredshapes. After classroom discussion, 96% student teachers came to the idea of partitioning. It meansthat they realized the important of partitioning in measure the area of unstructured shapes.Key words: measurement perimeter and area, design research, partitioning, and unstructured shape.

New Stability Condition for Linear Systems with Time-Varying Delay

Generally, the obtained results on delayed systems can be classified into two types: delay-independent ones and delay-dependent ones. Delay-dependent stabilization problem for a class linear system with interval time-varying delay is studied. Early first proposed stability analysis method for systems with time-varying delay in a range, but the method therein still leaves much room for improvement. A sufficient condition in terms of linear matrix inequalities (LMIs) is achieved by constructing a novel Lyapunov-Krasovskii functional with the idea of partitioning the time delay, and then using free-weighting matrix approach and adopting inequalities, the interval delay is dealt with successfully. Compared with former stability analysis approaches, this approach can overcome the defect of finding a common positive definite matrix, and reduce conservative greatly. Finally, one simulation example is given to illustrate the effectiveness of the methods.