61 resultados para Peruvian Corporation, limited.
Resumo:
Multiuser diversity (MUDiv) is one of the central concepts in multiuser (MU) systems. In particular, MUDiv allows for scheduling among users in order to eliminate the negative effects of unfavorable channel fading conditions of some users on the system performance. Scheduling, however, consumes energy (e.g., for making users' channel state information available to the scheduler). This extra usage of energy, which could potentially be used for data transmission, can be very wasteful, especially if the number of users is large. In this paper, we answer the question of how much MUDiv is required for energy limited MU systems. Focusing on uplink MU wireless systems, we develop MU scheduling algorithms which aim at maximizing the MUDiv gain. Toward this end, we introduce a new realistic energy model which accounts for scheduling energy and describes the distribution of the total energy between scheduling and data transmission stages. Using the fact that such energy distribution can be controlled by varying the number of active users, we optimize this number by either i) minimizing the overall system bit error rate (BER) for a fixed total energy of all users in the system or ii) minimizing the total energy of all users for fixed BER requirements. We find that for a fixed number of available users, the achievable MUDiv gain can be improved by activating only a subset of users. Using asymptotic analysis and numerical simulations, we show that our approach benefits from MUDiv gains higher than that achievable by generic greedy access algorithm, which is the optimal scheduling method for energy unlimited systems. © 2010 IEEE.
Resumo:
Larsen and Toubro (L&T) Limited is India’s largest construction conglomerate. L&T’s expertise is harnessed to execute high value projects that demand adherence to stringent timelines in a scenario where disparate disciplines of engineering are required to be coordinated on a critical path. However, no company can acquire such a feat without systematic management of its human resource. An investigation on the human resource management practices in orienting L&T’s success can help to identify some of the ethical human resource practices, especially in the context of Indian market. Accordingly, a well-designed employee satisfaction survey was conducted for assessment of the HRM practices being followed in L&T. Unlike other companies, L&T aims to meet the long-term needs of its employees rather than short-term needs. There were however few areas of concerns, such as yearly appraisal system and equality to treat the employees. It is postulated that the inequality to treat the male and female employees is primarily a typical stereotype due to the fact that construction is conventionally believed to be a male dominant activity. A periodic survey intended to provide 360° feedback system can help to avoid such irregularities. This study is thus expected to provide healthy practices of HRM to nurture the young talents of India. This may help them to evaluate their decisions by analyzing the complex relationship between HRM practices and output of an organization.
Resumo:
Forearm skin biopsies were obtained from diabetic subjects with and without limited joint mobility, and from non-diabetic control subjects. Collagen purified from these samples was assayed for non-enzymatic glycosylation. The level in all diabetic patients was significantly greater than that in control subjects (p less than 0.001), but those diabetic patients with limited joint mobility had a level of collagen glycosylation similar to that in those with normal joints (15.3 +/- 1.3 and 16.5 +/- 1.3 nmol fructose/10 mg protein, respectively; mean +/- SEM). Glycosylation of collagen in the diabetic patients correlated with glycosylated haemoglobin measured at the time of skin biopsy (r = 0.60). These results do not support the hypothesis that non-enzymatic glycosylation of collagen, as reflected by the ketoamine link, plays an important role in the development of limited joint mobility in diabetes.
Resumo:
Pseudomonas aeruginosa is an opportunistic pathogen and an important cause of infection, particularly amongst cystic fibrosis (CF) patients. While specific strains capable of patient-to-patient transmission are known, many infections appear to be caused by unique and unrelated strains. There is a need to understand the relationship between strains capable of colonising the CF lung and the broader set of P. aeruginosa isolates found in natural environments. Here we report the results of a multilocus sequence typing (MLST)-based study designed to understand the genetic diversity and population structure of an extensive regional sample of P. aeruginosa isolates from South East Queensland, Australia. The analysis is based on 501 P. aeruginosa isolates obtained from environmental, animal and human (CF and non-CF) sources with particular emphasis on isolates from the Lower Brisbane River and isolates from CF patients obtained from the same geographical region. Overall, MLST identified 274 different sequence types, of which 53 were shared between one or more ecological settings. Our analysis revealed a limited association between genotype and environment and evidence of frequent recombination. We also found that genetic diversity of P. aeruginosa in Queensland, Australia was indistinguishable from that of the global P. aeruginosa population. Several CF strains were encountered frequently in multiple ecological settings; however, the most frequently encountered CF strains were confined to CF patients. Overall, our data confirm a non-clonal epidemic structure and indicate that most CF strains are a random sample of the broader P. aeruginosa population. The increased abundance of some CF strains in different geographical regions is a likely product of chance colonisation events followed by adaptation to the CF lung and horizontal transmission among patients.
On the complexity of solving polytree-shaped limited memory influence diagrams with binary variables
Resumo:
Influence diagrams are intuitive and concise representations of structured decision problems. When the problem is non-Markovian, an optimal strategy can be exponentially large in the size of the diagram. We can avoid the inherent intractability by constraining the size of admissible strategies, giving rise to limited memory influence diagrams. A valuable question is then how small do strategies need to be to enable efficient optimal planning. Arguably, the smallest strategies one can conceive simply prescribe an action for each time step, without considering past decisions or observations. Previous work has shown that finding such optimal strategies even for polytree-shaped diagrams with ternary variables and a single value node is NP-hard, but the case of binary variables was left open. In this paper we address such a case, by first noting that optimal strategies can be obtained in polynomial time for polytree-shaped diagrams with binary variables and a single value node. We then show that the same problem is NP-hard if the diagram has multiple value nodes. These two results close the fixed-parameter complexity analysis of optimal strategy selection in influence diagrams parametrized by the shape of the diagram, the number of value nodes and the maximum variable cardinality.
Resumo:
We present a new algorithm for exactly solving decision-making problems represented as an influence diagram. We do not require the usual assumptions of no forgetting and regularity, which allows us to solve problems with limited information. The algorithm, which implements a sophisticated variable elimination procedure, is empirically shown to outperform a state-of-the-art algorithm in randomly generated problems of up to 150 variables and 10^64 strategies.
Resumo:
We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that these problems are NP-hard even if the underlying graph structure of the problem has low treewidth and the variables take on a bounded number of states, and that they admit no provably good approximation if variables can take on an arbitrary number of states.
Resumo:
We present a new algorithm for exactly solving decision making problems represented as influence diagrams. We do not require the usual assumptions of no forgetting and regularity; this allows us to solve problems with simultaneous decisions and limited information. The algorithm is empirically shown to outperform a state-of-the-art algorithm on randomly generated problems of up to 150 variables and 10^64 solutions. We show that the problem is NP-hard even if the underlying graph structure of the problem has small treewidth and the variables take on a bounded number of states, but that a fully polynomial time approximation scheme exists for these cases. Moreover, we show that the bound on the number of states is a necessary condition for any efficient approximation scheme.
Resumo:
We describe some unsolved problems of current interest; these involve quantum critical points in
ferroelectrics and problems which are not amenable to the usual density functional theory, nor to
classical Landau free energy approaches (they are kinetically limited), nor even to the Landau–
Kittel relationship for domain size (they do not satisfy the assumption of infinite lateral diameter)
because they are dominated by finite aperiodic boundary conditions.
Resumo:
This paper presents a novel method of audio-visual fusion for person identification where both the speech and facial modalities may be corrupted, and there is a lack of prior knowledge about the corruption. Furthermore, we assume there is a limited amount of training data for each modality (e.g., a short training speech segment and a single training facial image for each person). A new representation and a modified cosine similarity are introduced for combining and comparing bimodal features with limited training data as well as vastly differing data rates and feature sizes. Optimal feature selection and multicondition training are used to reduce the mismatch between training and testing, thereby making the system robust to unknown bimodal corruption. Experiments have been carried out on a bimodal data set created from the SPIDRE and AR databases with variable noise corruption of speech and occlusion in the face images. The new method has demonstrated improved recognition accuracy.
Resumo:
High levels of genetic diversity and high propagule pressure are favoured by conservation biologists as the basis for successful reintroductions and ensuring the persistence of populations. However, invasion ecologists recognize the ‘paradox of invasion’, as successful species introductions may often be characterized by limited numbers of individuals and associated genetic bottlenecks. In the present study, we used a combination of high-resolution nuclear and mitochondrial genetic markers to investigate the invasion history of Reeves' muntjac deer in the British Isles. This invasion has caused severe economic and ecological damage, with secondary spread currently a concern throughout Europe and potentially globally. Microsatellite analysis based on eight loci grouped all 176 introduced individuals studied from across the species' range in the UK into one genetic cluster, and seven mitochondrial D-loop haplotypes were recovered, two of which were present at very low frequency and were related to more common haplotypes. Our results indicate that the entire invasion can be traced to a single founding event involving a low number of females. These findings highlight the fact that even small releases of species may, if ignored, result in irreversible and costly invasion, regardless of initial genetic diversity or continual genetic influx.
Resumo:
his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.
Resumo:
Antimicrobial peptides from amphibian skin secretion display remarkable broad-spectrum antimicrobial activity and are thus promising for the discovery of new antibiotics. In this study, we report a novel peptide belonging to the phylloseptin family of antimicrobial peptides, from the skin secretion of the purple-sided leaf frog, Phyllomedusa baltea, which was named Phylloseptin-PBa. Degenerate primers complementary to putative signal peptide sites of frog skin peptide precursor-encoding cDNAs were designed to interrogate a skin secretion-derived cDNA library from this frog. Subsequently, the peptide was isolated and identified using reverse phase HPLC and MS/MS fragmentation. The synthetic replicate was demonstrated to have activity against S. aureus, E. coli and C. albicans at concentrations of 8, 128 and 8 mg/L, respectively. In addition, it exhibited anti-proliferative activity against the human cancer cell lines, H460, PC3 and U251MG, but was less active against a normal human cell line (HMEC). Furthermore, a haemolysis assay was performed to assess mammalian cell cytotoxicity of Phylloseptin-PBa. This peptide contained a large proportion of α-helical domain, which may explain its antimicrobial and anticancer activities.