A tale of two solvers: Eager and lazy approaches to bit-vectors

The standard method for deciding bit-vector constraints is via eager reduction to propositional logic. This is usually done after first applying powerful rewrite techniques. While often efficient in practice, this method does not scale on problems for which top-level rewrites cannot reduce the problem size sufficiently. A lazy solver can target such problems by doing many satisfiability checks, each of which only reasons about a small subset of the problem. In addition, the lazy approach enables a wide range of optimization techniques that are not available to the eager approach. In this paper we describe the architecture and features of our lazy solver (LBV). We provide a comparative analysis of the eager and lazy approaches, and show how they are complementary in terms of the types of problems they can efficiently solve. For this reason, we propose a portfolio approach that runs a lazy and eager solver in parallel. Our empirical evaluation shows that the lazy solver can solve problems none of the eager solvers can and that the portfolio solver outperforms other solvers both in terms of total number of problems solved and the time taken to solve them.

Characterization and applications of two protease enzymes obtained by culture dependent and independent approaches from mangrove sediments

Soil community genomics or metagenomics is employed in this study to analyze the evolutionary related - ness of mangrove microbial community. The metagenomic DNA was isolated from mangrove sediment and 16SrDNA was amplified using universal primers. The amplicons were ligated into pTZ57R/T cloning vector and transformed onto E. coli JM109 host cells. The recombinant plasmids were isolated from positive clones and the insert was confirmed by its reamplification. The amplicons were subjected to Amplified Ribosomal DNA Restriction Analysis (ARDRA) using three different tetra cutter restriction enzymes namely Sau3A1, Hha1 and HpaII. The 16SrDNA insert were sequenced and their identity was determined. The sequences were submitted to NCBI database and accession numbers obtained. The phylo - genetic tree was constructed based on Neighbor-Joining technique. Clones belonged to two major phyla of the bacterial domain, namely Firmicutes and Proteobacteria, with members of Firmicutes predominating. The microbial diversity of the mangrove sediment was explored in this manner.

Max Weber in the thought of Edward Shils (1910–1995) and Ernest Gellner (1925–1995): the paradox of two Weberian approaches to the understanding of nations and nationalism?

This article traces the paradoxical impact of Weber's oeuvre on two major scholars of nationalism, Ernest Gellner and Edward Shils. Both these scholars died in 1995, leaving behind a rich corpus of writings on the nation and nationalism, much of which was inspired by Max Weber. The paradox is that although neither scholar accepted Weber's sceptical attitude to the concept of ‘nation’, they both used his other major concepts, such as ‘rationality’, ‘disenchantment’, ‘unintended consequences’, the ‘ethic of responsibility’ and ‘charisma’, in their very analyses of the nation and nationalism. And they both saw, each in his own way, the nation and nationalism as constitutive elements of modern societies. However, the paradox ceases being a paradox if one sees the integration, by Shils and Gellner, of concepts of the nation and of nationalism in the analysis of modernity, as a development of Weber's ideas.

Latent or manifest observers : two dichotomous approaches of surveillance in mental health nursing

Background. Surveillance is a central activity among mental health nursing, but it is also questioned for its therapeutic value and considered to be custodial. Aim. The aim of this study was to describe how mental health nurses use different approaches to observe patients in relation to the practice of surveillance in psychiatric nursing care. Methods. In this study, Spradley's twelve-step ethnographic method was used. Results. Mental health nurses use their cultural knowing to observe patients in psychiatric care in various ways. Two dichotomous approaches were identified: the latent and the manifest approach. Discussion. Different strategies and techniques for observing patients are structured along two dichotomies. The underlying relationships between these two different dichotomous positions transform the act of observing into surveillance. This is further developed in a theoretical model called the powerful scheme of observation and surveillance (PSOS).

Effect of pelleting and pellet size of a concentrate for intensively reared beef cattle on in vitro fermentation by two different approaches

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Two Surgical Approaches to Fracture Malunion Repair

Two birds were presented with malunion fractures. The first was a young toco toucan (Ramphastos toco) with malunion of the tarsometatarsus that was treated by an opening-corrective osteotomy and an acrylic-pin external skeletal fixator (type II) to stabilize the osteotomy. The second bird was m adult southern caracara (Caracara plancus) with radial and ulnar malunion that was treated by closing-wedge osteotomies. Stabilization of the osteotomy sites was accomplished through 1 bone plate fixed cranially on the ulna with 6 cortical screws and an interfragmentary single wire in radius. In both cases, the malunion was corrected, but the manus of the southern caracara was amputated because of carpal joint luxation that induced malposition of the feathers.

TWO DECOMPOSITION APPROACHES APPLIED TO THE MULTI-AREA OPTIMAL REACTIVE POWER FLOW PROBLEM

This paper applies two methods of mathematical decomposition to carry out an optimal reactive power flow (ORPF) in a coordinated decentralized way in the context of an interconnected multi-area power system. The first method is based on an augmented Lagrangian approach using the auxiliary problem principle (APP). The second method uses a decomposition technique based on the Karush-Kuhn-Tucker (KKT) first-order optimality conditions. The viability of each method to be used in the decomposition of multi-area ORPF is studied and the corresponding mathematical models are presented. The IEEE RTS-96, the IEEE 118-bus test systems and a 9-bus didactic system are used in order to show the operation and effectiveness of the decomposition methods.

Inferences on earthquake kinematic properties from data inversion: two different approaches

During my PhD, starting from the original formulations proposed by Bertrand et al., 2000 and Emolo & Zollo 2005, I developed inversion methods and applied then at different earthquakes. In particular large efforts have been devoted to the study of the model resolution and to the estimation of the model parameter errors. To study the source kinematic characteristics of the Christchurch earthquake we performed a joint inversion of strong-motion, GPS and InSAR data using a non-linear inversion method. Considering the complexity highlighted by superficial deformation data, we adopted a fault model consisting of two partially overlapping segments, with dimensions 15x11 and 7x7 km2, having different faulting styles. This two-fault model allows to better reconstruct the complex shape of the superficial deformation data. The total seismic moment resulting from the joint inversion is 3.0x1025 dyne.cm (Mw = 6.2) with an average rupture velocity of 2.0 km/s. Errors associated with the kinematic model have been estimated of around 20-30 %. The 2009 Aquila sequence was characterized by an intense aftershocks sequence that lasted several months. In this study we applied an inversion method that assumes as data the apparent Source Time Functions (aSTFs), to a Mw 4.0 aftershock of the Aquila sequence. The estimation of aSTFs was obtained using the deconvolution method proposed by Vallée et al., 2004. The inversion results show a heterogeneous slip distribution, characterized by two main slip patches located NW of the hypocenter, and a variable rupture velocity distribution (mean value of 2.5 km/s), showing a rupture front acceleration in between the two high slip zones. Errors of about 20% characterize the final estimated parameters.

Effects of two different memory training approaches in very preterm-born children

Background: Little research has been conducted to assess the effect of using memory training with school-aged children who were born very preterm. This study aimed to determine whether two types of memory training approaches resulted in an improvement of trained functions and/or a generalization of the training effect to non-trained cognitive domains. Methods: Sixty-eight children born very preterm (7¬-12 years) were randomly allocated to a group undertaking memory strategy training (n=23), working memory training (n=22), or a waiting control group (n=23). Neuropsychological assessment was performed before and immediately after the training or waiting period, and at a six-month follow-up. Results: In both training groups, significant improvement of different memory domains occurred immediately after training (near transfer). Improvement of non-trained arithmetic performance was observed after strategy training (far transfer). At a six-month follow-up assessment, children in both training groups demonstrated better working memory, and their parents rated their memory functions to be better than controls. Performance level before the training was negatively associated with the training gain. Conclusions: These results highlight the importance of cognitive interventions, in particular the teaching of memory strategies, in very preterm-born children at early school age to strengthen cognitive performance and prevent problems at school.

Effects of two different memory training approaches in very pretermborn children

Background: Little research has been conducted to assess the effect of using memory training with school aged children who were born very preterm. This study aimed to determine whether two types of memory training approaches resulted in an improvement of trained functions and/or a generalization of the training effect to non-trained cognitive domains. Methods: Sixty-eight children born very preterm (7-12 years) were randomly allocated to a group undertaking memory strategy training (n=23), working memory training (n=22), or a waiting control group (n=23). Neuropsychological assessment was performed before and immediately after the training or waiting period, and at a six-month follow-up. Results: In both training groups, significant improvement of different memory domains occurred immediately after training (near transfer). Improvement of non-trained arithmetic performance was observed after strategy training (far transfer). At a six-month follow-up assessment, children in both training groups demonstrated better working memory, and their parents rated their memory functions to be better than controls. Performance level before the training was negatively associated with the training gain. Conclusions: These results highlight the importance of cognitive interventions, in particular the teaching of memory strategies, in very preterm-born children at early school age to strengthen cognitive performance and prevent problems at school.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The electroencephalograph (EEG) signal is one of the most widely used signals in the biomedicine field due to its rich information about human tasks. This research study describes a new approach based on i) build reference models from a set of time series, based on the analysis of the events that they contain, is suitable for domains where the relevant information is concentrated in specific regions of the time series, known as events. In order to deal with events, each event is characterized by a set of attributes. ii) Discrete wavelet transform to the EEG data in order to extract temporal information in the form of changes in the frequency domain over time- that is they are able to extract non-stationary signals embedded in the noisy background of the human brain. The performance of the model was evaluated in terms of training performance and classification accuracies and the results confirmed that the proposed scheme has potential in classifying the EEG signals.

Two Different Approaches of Feature Extraction for Classifying the EEG Signals

The focus of this chapter is to study feature extraction and pattern classification methods from two medical areas, Stabilometry and Electroencephalography (EEG). Stabilometry is the branch of medicine responsible for examining balance in human beings. Balance and dizziness disorders are probably two of the most common illnesses that physicians have to deal with. In Stabilometry, the key nuggets of information in a time series signal are concentrated within definite time periods are known as events. In this chapter, two feature extraction schemes have been developed to identify and characterise the events in Stabilometry and EEG signals. Based on these extracted features, an Adaptive Fuzzy Inference Neural network has been applied for classification of Stabilometry and EEG signals.