Finite element analysis of shear critical prestressed SFRC beams

This study reports the details of the finite element analysis of eleven shear critical partially prestressed concrete T-beams having steel fibers over partial or full depth. Prestressed concrete T-beams having a shear span to depth ratio of 2.65 and 1.59 and failing in the shear have been analyzed Using 'ANSYS'. The 'ANSYS' model accounts for the nonlinear phenomenon, such as, bond-slip of longitudinal reinforcements, post-cracking tensile stiffness of the concrete, stress transfer across the cracked blocks of the concrete and load sustenance through the bridging of steel fibers at crack interlace. The concrete is modeled using 'SOLID65'-eight-node brick element, which is capable Of simulating the cracking and crushing behavior of brittle materials. The reinforcements such as deformed bars, prestressing wires and steel fibers have been modeled discretely Using 'LINK8' - 3D spar element. The slip between the reinforcement (rebar, fibers) and the concrete has been modeled using a 'COMBIN39'-non-linear spring element connecting the nodes of the 'LINK8' element representing the reinforcement and nodes of the 'SOLID65' elements representing the concrete. The 'ANSYS' model correctly predicted the diagonal tension failure and shear compression failure of prestressed concrete beams observed in the experiment. I-lie capability of the model to capture the critical crack regions, loads and deflections for various types Of shear failures ill prestressed concrete beam has been illustrated.

Implications of Energy Profile and Storage on Energy Harvesting Sensor Link Performance

Energy harvesting sensors (EHS), which harvest energy from the environment in order to sense and then communicate their measurements over a wireless link, provide the tantalizing possibility of perpetual lifetime operation of a sensor network. The wireless communication link design problem needs to be revisited for these sensors as the energy harvested can be random and small and not available when required. In this paper, we develop a simple model that captures the interactions between important parameters that govern the communication link performance of a EHS node, and analyze its outage probability for both slow fading and fast fading wireless channels. Our analysis brings out the critical importance of the energy profile and the energy storage capability on the EHS link performance. Our results show that properly tuning the transmission parameters of the EHS node and having even a small amount of energy storage capability improves the EHS link performance considerably.

Distributed nonlinear optimization algorithms for general network problems

There are a number of large networks which occur in many problems dealing with the flow of power, communication signals, water, gas, transportable goods, etc. Both design and planning of these networks involve optimization problems. The first part of this paper introduces the common characteristics of a nonlinear network (the network may be linear, the objective function may be non linear, or both may be nonlinear). The second part develops a mathematical model trying to put together some important constraints based on the abstraction for a general network. The third part deals with solution procedures; it converts the network to a matrix based system of equations, gives the characteristics of the matrix and suggests two solution procedures, one of them being a new one. The fourth part handles spatially distributed networks and evolves a number of decomposition techniques so that we can solve the problem with the help of a distributed computer system. Algorithms for parallel processors and spatially distributed systems have been described.There are a number of common features that pertain to networks. A network consists of a set of nodes and arcs. In addition at every node, there is a possibility of an input (like power, water, message, goods etc) or an output or none. Normally, the network equations describe the flows amoungst nodes through the arcs. These network equations couple variables associated with nodes. Invariably, variables pertaining to arcs are constants; the result required will be flows through the arcs. To solve the normal base problem, we are given input flows at nodes, output flows at nodes and certain physical constraints on other variables at nodes and we should find out the flows through the network (variables at nodes will be referred to as across variables).The optimization problem involves in selecting inputs at nodes so as to optimise an objective function; the objective may be a cost function based on the inputs to be minimised or a loss function or an efficiency function. The above mathematical model can be solved using Lagrange Multiplier technique since the equalities are strong compared to inequalities. The Lagrange multiplier technique divides the solution procedure into two stages per iteration. Stage one calculates the problem variables % and stage two the multipliers lambda. It is shown that the Jacobian matrix used in stage one (for solving a nonlinear system of necessary conditions) occurs in the stage two also.A second solution procedure has also been imbedded into the first one. This is called total residue approach. It changes the equality constraints so that we can get faster convergence of the iterations.Both solution procedures are found to coverge in 3 to 7 iterations for a sample network.The availability of distributed computer systems — both LAN and WAN — suggest the need for algorithms to solve the optimization problems. Two types of algorithms have been proposed — one based on the physics of the network and the other on the property of the Jacobian matrix. Three algorithms have been deviced, one of them for the local area case. These algorithms are called as regional distributed algorithm, hierarchical regional distributed algorithm (both using the physics properties of the network), and locally distributed algorithm (a multiprocessor based approach with a local area network configuration). The approach used was to define an algorithm that is faster and uses minimum communications. These algorithms are found to converge at the same rate as the non distributed (unitary) case.

Merkel cell carcinoma : Epidemiological study with special reference to polyomavirus and vascular factors in pathogenesis

Merkel cell carcinoma (MCC) is a rare cutaneous malignancy that occurs predominantly on sun exposed skin areas. A new polyomavirus (MCPyV) was identified in MCC tumor tissues in 2008 suggesting that a viral infection might be an etiological factor. A typical MCC is a rapidly growing painless purple nodule. In its early stage it can be misjudged by its appearance as a cyst or abscess. Recurrences are common and approximately half of the patients will develop lymph node metastases and third of the patents will have distant metastases. It affects mostly elderly persons at an average age of 70 at the time of diagnosis. MCC was first described in 1972 and the first MCC patient in Finland was identified in 1983. MCC has been poorly recognized, but increased awareness and better diagnostic accuracy has increased the incidence since the early years. In this study, all cases with a notation of MCC during 1979 2008 were obtained from the Finnish Cancer Registry. Based on this data, the incidence is 0.11 for men and 0.12 for women. It is similar than that of other Nordic countries, but lower than in the USA. For clinical series, the files of patients diagnosed with MCC during 1983 2004 were reviewed, and the tissue samples were re-evaluated, if available (n=181). Third of the patients were men, and the most common site of the primary tumor was the head and neck (53%). The majority of the patients (86%) presented with a clinically node-negative (Stage I or II) disease, but the disease recurred in 38% of them. The treatment schemes were heterogeneous. No additional benefit from a wide margin (≥2 cm) was found compared to a margin of 0.1-1.9 cm, but intralesional excision was more often associated with local recurrence. None of the patients with Stage I-II disease who had received postoperative radiotherapy had local recurrence during the follow-up period. The 5-year relative survival ratio for Stage I disease was 68%, for Stage II 67%, for Stage III 16%, and for Stage IV 0%. The relative excess risk of death was significantly lower among women than among men. Some of these tissue samples were further analyzed for vascular invasion (n=126) by immunohistochemistry using vascular endothelial markers CD-31 and D2-40. Vascular invasion was seen in 93% of the samples and it was observed already in very small, <5mm tumors. The tissue samples were also analyzed for the presence of MCPyV by using a polymerase chain reaction (PCR) and quantitative PCR. MCPyV DNA was present in 80% of 114 samples studied. The patients with virus-positive tumors had better overall survival than patients with virus-negative tumors. Immunohistochemical analyses were performed for the expression of VEGFR-2 (n=21) and endostatin (n=19), but they had no prognostic value. Our results support the concept of treating MCC with margin-negative excision and radiotherapy to the tumor bed to reduce local recurrence. The finding of a high frequency of lymphovascular invasion reduces its value as a prognostic factor, but emphasizes the role of sentinel node biopsy even in very small primary MCC.

Joint Power Control, Scheduling and Routing for Multihop Energy Harvesting Sensor Networks

We study wireless multihop energy harvesting sensor networks employed for random field estimation. The sensors sense the random field and generate data that is to be sent to a fusion node for estimation. Each sensor has an energy harvesting source and can operate in two modes: Wake and Sleep. We consider the problem of obtaining jointly optimal power control, routing and scheduling policies that ensure a fair utilization of network resources. This problem has a high computational complexity. Therefore, we develop a computationally efficient suboptimal approach to obtain good solutions to this problem. We study the optimal solution and performance of the suboptimal approach through some numerical examples.

mRNA-binding protein HuR in breast carcinogenesis

Breast cancer is the most common cancer among women. Although its prognosis has improved nowadays, methods to predict the progression of the disease or to treat it are not comprehensive. This thesis work was initiated to elucidate in breast carcinogenesis the role of HuR, a ubiquitously expressed mRNA-binding protein that regulates gene expression posttranscriptionally. HuR is predominantly nuclear, but it shuttles between the nucleus and the cytoplasm, and this nucleocytoplasmic translocation is important for its function as a RNA-stabilizing and translational regulator. HuR has been associated with diverse cellular processes, for example carcinogenesis. The specific aims of my thesis work were to study the prognostic value of HuR in breast cancer and to clarify the mechanisms by which HuR contributes to breast carcinogenesis. My ultimate goal is, by better understanding the role of HuR in breast carcinogenesis, to aid in the discovery of novel targets for cancer therapies. HuR expression and localization was studied in paraffin-embedded preinvasive (atypical ductal hyperplasia, ADH, and ductal carcinoma in situ, DCIS) specimens as well in sporadic and familial breast cancer specimens. Our results show that cytoplasmic HuR expression was already elevated in ADH and remained elevated in DCIS as well as in cancer specimens. Clinicopathological analysis showed that cytoplasmic HuR expression associated with the more aggressive form of the disease in DCIS, and in cancer specimens it proved an independent marker for poor prognosis. Importantly, cytoplasmic HuR expression was significantly associated with poor outcome in the subgroups of small (2 cm) and axillary lymph node-negative breast cancers. HuR proved to be the first mRNA stability protein the expression of which is associated in breast cancer with poor outcome. To explore the mechanisms of HuR in breast carcinogenesis, lentiviral constructs were developed to inhibit and to overexpress the HuR expression in a breast epithelial cell line (184B5Me). Our results suggest that HuR mediates breast carcinogenesis by participating in processes important in cell transformation, in programmed cell death, and in cell invasion. Global gene expression analysis shows that HuR regulates genes participating in diverse cellular processes, and affects several pathways important in cancer development. In addition, we identified two novel target transcripts (connective tissue growth factor, CTGF, and Ras oncogene family member 31, RAB31) for HuR. In conclusion, because cytoplasmic HuR expression in breast cancer can predict the outcome of the disease it could serve in clinics as a prognostic marker. HuR accumulates in the cytoplasm even at its non-invasive stage (ADH and DCIS) of the carcinogenic process and supports functions essential in cell alteration. These data suggest that HuR contributes to carcinogenesis of the breast epithelium.

Design of speaker identification schemes for large number of speakers (A)

Design of speaker identification schemes for a small number of speakers (around 10) with a high degree of accuracy in controlled environment is a practical proposition today. When the number of speakers is large (say 50–100), many of these schemes cannot be directly extended, as both recognition error and computation time increase monotonically with population size. The feature selection problem is also complex for such schemes. Though there were earlier attempts to rank order features based on statistical distance measures, it has been observed only recently that the best two independent measurements are not the same as the combination in two's for pattern classification. We propose here a systematic approach to the problem using the decision tree or hierarchical classifier with the following objectives: (1) Design of optimal policy at each node of the tree given the tree structure i.e., the tree skeleton and the features to be used at each node. (2) Determination of the optimal feature measurement and decision policy given only the tree skeleton. Applicability of optimization procedures such as dynamic programming in the design of such trees is studied. The experimental results deal with the design of a 50 speaker identification scheme based on this approach.

Regenerating Codes for Distributed Storage Networks

In a storage system where individual storage nodes are prone to failure, the redundant storage of data in a distributed manner across multiple nodes is a must to ensure reliability. Reed-Solomon codes possess the reconstruction property under which the stored data can be recovered by connecting to any k of the n nodes in the network across which data is dispersed. This property can be shown to lead to vastly improved network reliability over simple replication schemes. Also of interest in such storage systems is the minimization of the repair bandwidth, i.e., the amount of data needed to be downloaded from the network in order to repair a single failed node. Reed-Solomon codes perform poorly here as they require the entire data to be downloaded. Regenerating codes are a new class of codes which minimize the repair bandwidth while retaining the reconstruction property. This paper provides an overview of regenerating codes including a discussion on the explicit construction of optimum codes.

Exact bounds for distributed graph colouring

A distributed system is a collection of networked autonomous processing units which must work in a cooperative manner. Currently, large-scale distributed systems, such as various telecommunication and computer networks, are abundant and used in a multitude of tasks. The field of distributed computing studies what can be computed efficiently in such systems. Distributed systems are usually modelled as graphs where nodes represent the processors and edges denote communication links between processors. This thesis concentrates on the computational complexity of the distributed graph colouring problem. The objective of the graph colouring problem is to assign a colour to each node in such a way that no two nodes connected by an edge share the same colour. In particular, it is often desirable to use only a small number of colours. This task is a fundamental symmetry-breaking primitive in various distributed algorithms. A graph that has been coloured in this manner using at most k different colours is said to be k-coloured. This work examines the synchronous message-passing model of distributed computation: every node runs the same algorithm, and the system operates in discrete synchronous communication rounds. During each round, a node can communicate with its neighbours and perform local computation. In this model, the time complexity of a problem is the number of synchronous communication rounds required to solve the problem. It is known that 3-colouring any k-coloured directed cycle requires at least ½(log* k - 3) communication rounds and is possible in ½(log* k + 7) communication rounds for all k ≥ 3. This work shows that for any k ≥ 3, colouring a k-coloured directed cycle with at most three colours is possible in ½(log* k + 3) rounds. In contrast, it is also shown that for some values of k, colouring a directed cycle with at most three colours requires at least ½(log* k + 1) communication rounds. Furthermore, in the case of directed rooted trees, reducing a k-colouring into a 3-colouring requires at least log* k + 1 rounds for some k and possible in log* k + 3 rounds for all k ≥ 3. The new positive and negative results are derived using computational methods, as the existence of distributed colouring algorithms corresponds to the colourability of so-called neighbourhood graphs. The colourability of these graphs is analysed using Boolean satisfiability (SAT) solvers. Finally, this thesis shows that similar methods are applicable in capturing the existence of distributed algorithms for other graph problems, such as the maximal matching problem.

Design of nonequivalent self-routing networks based on a matrix model

In earlier work, nonisomorphic graphs have been converted into networks to realize Multistage Interconnection networks, which are topologically nonequivalent to the Baseline network. The drawback of this technique is that these nonequivalent networks are not guaranteed to be self-routing, because each node in the graph model can be replaced by a (2 × 2) switch in any one of the four different configurations. Hence, the problem of routing in these networks remains unsolved. Moreover, nonisomorphic graphs were obtained by interconnecting bipartite loops in a heuristic manner; the heuristic nature of this procedure makes it difficult to guarantee full connectivity in large networks. We solve these problems through a direct approach, in which a matrix model for self-routing networks is developed. An example is given to show that this model encompases nonequivalent self-routing networks. This approach has the additional advantage in that the matrix model itself ensures full connectivity.

Distributed algorithms for edge dominating sets

An edge dominating set for a graph G is a set D of edges such that each edge of G is in D or adjacent to at least one edge in D. This work studies deterministic distributed approximation algorithms for finding minimum-size edge dominating sets. The focus is on anonymous port-numbered networks: there are no unique identifiers, but a node of degree d can refer to its neighbours by integers 1, 2, ..., d. The present work shows that in the port-numbering model, edge dominating sets can be approximated as follows: in d-regular graphs, to within 4 − 6/(d + 1) for an odd d and to within 4 − 2/d for an even d; and in graphs with maximum degree Δ, to within 4 − 2/(Δ − 1) for an odd Δ and to within 4 − 2/Δ for an even Δ. These approximation ratios are tight for all values of d and Δ: there are matching lower bounds.

Optimisation problems in wireless sensor networks

This thesis studies optimisation problems related to modern large-scale distributed systems, such as wireless sensor networks and wireless ad-hoc networks. The concrete tasks that we use as motivating examples are the following: (i) maximising the lifetime of a battery-powered wireless sensor network, (ii) maximising the capacity of a wireless communication network, and (iii) minimising the number of sensors in a surveillance application. A sensor node consumes energy both when it is transmitting or forwarding data, and when it is performing measurements. Hence task (i), lifetime maximisation, can be approached from two different perspectives. First, we can seek for optimal data flows that make the most out of the energy resources available in the network; such optimisation problems are examples of so-called max-min linear programs. Second, we can conserve energy by putting redundant sensors into sleep mode; we arrive at the sleep scheduling problem, in which the objective is to find an optimal schedule that determines when each sensor node is asleep and when it is awake. In a wireless network simultaneous radio transmissions may interfere with each other. Task (ii), capacity maximisation, therefore gives rise to another scheduling problem, the activity scheduling problem, in which the objective is to find a minimum-length conflict-free schedule that satisfies the data transmission requirements of all wireless communication links. Task (iii), minimising the number of sensors, is related to the classical graph problem of finding a minimum dominating set. However, if we are not only interested in detecting an intruder but also locating the intruder, it is not sufficient to solve the dominating set problem; formulations such as minimum-size identifying codes and locating–dominating codes are more appropriate. This thesis presents approximation algorithms for each of these optimisation problems, i.e., for max-min linear programs, sleep scheduling, activity scheduling, identifying codes, and locating–dominating codes. Two complementary approaches are taken. The main focus is on local algorithms, which are constant-time distributed algorithms. The contributions include local approximation algorithms for max-min linear programs, sleep scheduling, and activity scheduling. In the case of max-min linear programs, tight upper and lower bounds are proved for the best possible approximation ratio that can be achieved by any local algorithm. The second approach is the study of centralised polynomial-time algorithms in local graphs – these are geometric graphs whose structure exhibits spatial locality. Among other contributions, it is shown that while identifying codes and locating–dominating codes are hard to approximate in general graphs, they admit a polynomial-time approximation scheme in local graphs.

Analysing local algorithms in location-aware quasi-unit-disk graphs

A local algorithm with local horizon r is a distributed algorithm that runs in r synchronous communication rounds; here r is a constant that does not depend on the size of the network. As a consequence, the output of a node in a local algorithm only depends on the input within r hops from the node. We give tight bounds on the local horizon for a class of local algorithms for combinatorial problems on unit-disk graphs (UDGs). Most of our bounds are due to a refined analysis of existing approaches, while others are obtained by suggesting new algorithms. The algorithms we consider are based on network decompositions guided by a rectangular tiling of the plane. The algorithms are applied to matching, independent set, graph colouring, vertex cover, and dominating set. We also study local algorithms on quasi-UDGs, which are a popular generalisation of UDGs, aimed at more realistic modelling of communication between the network nodes. Analysing the local algorithms on quasi-UDGs allows one to assume that the nodes know their coordinates only approximately, up to an additive error. Despite the localisation error, the quality of the solution to problems on quasi-UDGs remains the same as for the case of UDGs with perfect location awareness. We analyse the increase in the local horizon that comes along with moving from UDGs to quasi-UDGs.

Development of a New Finite Element for the Analysis of Sandwich Beams with Soft Core

A new super convergent sandwich beam finite element formulation is presented in this article. This element is a two-nodded, six degrees of freedom (dof) per node (3 dof u(0), w, phi for top and bottom face sheets each), which assumes that all the axial and flexural loads are taken by face sheets, while the core takes only the shear loads. The beam element is formulated based on first-order shear deformation theory for the face sheets and the core displacements are assumed to vary linearly across the thickness. A number of numerical experiments involving static, free vibration, and wave propagation analysis examples are solved with an aim to show the super convergent property of the formulated element. The examples presented in this article consider both metallic and composite face sheets. The formulated element is verified in most cases with the results available in the published literature.