'Trusting the numbers': mineral prospecting, raising finance and the governance of knowledge

Mineral prospecting and raising finance for ‘junior’ mining firms has historically been regarded as a speculative activity. For the regulators of securities markets upon which ‘junior’ mining companies seek to raise capital, a perennial problem has been handling not only the indeterminacy of scientific claims, but also the social basis of epistemic practices. This paper examines the production of a system of public warrant and associated knowledge practices intended to enable investors to differentiate between ‘destructive’ and ‘productive’ varieties of financial speculation. It traces the use of the notion of ‘disclosure’ in constructing and legitimizing the ‘juniors’ market in Canada. It argues that though the work of ‘economics’ may be necessary in the construction of markets, it is by no means sufficient. Attention must also be given to the ways in which legal models of ‘the free-market’ can be translated and constantly re-worked across the sites and spaces of regulatory practice, animating the geographies of markets.

Adaptive Measurement-Based Policy-Driven QoS Management with Fuzzy-Rule-based Resource Allocation

Fixed and wireless networks are increasingly converging towards common connectivity with IP-based core networks. Providing effective end-to-end resource and QoS management in such complex heterogeneous converged network scenarios requires unified, adaptive and scalable solutions to integrate and co-ordinate diverse QoS mechanisms of different access technologies with IP-based QoS. Policy-Based Network Management (PBNM) is one approach that could be employed to address this challenge. Hence, a policy-based framework for end-to-end QoS management in converged networks, CNQF (Converged Networks QoS Management Framework) has been proposed within our project. In this paper, the CNQF architecture, a Java implementation of its prototype and experimental validation of key elements are discussed. We then present a fuzzy-based CNQF resource management approach and study the performance of our implementation with real traffic flows on an experimental testbed. The results demonstrate the efficacy of our resource-adaptive approach for practical PBNM systems

Worked to Death? A census-based longitudinal study of the relationship between the numbers of hours spent working and mortality risk

Background There has been an increasing interest in the health effects of long
working hours, but little empirical evidence to substantiate early
10 case series suggesting an increased mortality risk. The aim of the
current study is to quantify the mortality risk associated with long
working hours and to see if this varies by employment relations and
conditions of occupation.
Methods A census-based longitudinal study of 414 949 people aged 20-59/64
15 years, working at least 35 h/week, subdivided into four occupational
classes (managerial/professional, intermediate, own account workers,
workers in routine occupations) with linkage to deaths records
over the following 8.7 years. Cox proportional hazards models were
used to examine all-cause and cause-specific mortality risk.
20 Results Overall 9.4% of the cohort worked 55 or more h/week, but this
proportion was greater in the senior management and professional
occupations and in those who were self-employed. Analysis of 4447
male and 1143 female deaths showed that hours worked were
associated with an increased risk of all-cause mortality only for
25 men working for more than 55 or more h/week in routine/semiroutine
occupations [adjusted hazard ratios (adjHR) 1.31: 95%
confidence intervals (CIs) 1.11, 1.55)] compared with their peers
working 35–40 h/week. Their equivalent risk of death from cardiovascular
disease was (adjHR 1.49: 95% CIs 1.10, 2.00).
30 Conclusions These findings substantiate and add to the earlier studies indicating
the deleterious impact of long working hours but also suggest that
the effects are moderated by employment relations or conditions of
occupation. The policy implications of these findings are discussed.

Parental leave regulations and the persistence of the male breadwinner model: Using fuzzy-set ideal type analysis to assess gender equality in an enlarged Europe

his paper uses fuzzy-set ideal type analysis to assess the conformity of European leave regulations to four theoretical ideal typical divisions of labour: male breadwinner, caregiver parity, universal breadwinner and universal caregiver. In contrast to the majority of previous studies, the focus of this analysis is on the extent to which leave regulations promote gender equality in the family and the transformation of traditional gender roles. The results of this analysis demonstrate that European countries cluster into five models that only partly coincide with countries’ geographical proximity. Second, none of the countries considered constitutes a universal caregiver model, while the male breadwinner ideal continues to provide the normative reference point for parental leave regulations in a large number of European states. Finally, we witness a growing emphasis at the national and EU levels concerning the universal breadwinner ideal, which leaves gender inequality in unpaid work unproblematized.

BIIL 284 reduces neutrophil numbers but increases P. aeruginosa bacteremia and inflammation in mouse lungs

BACKGROUND: A clinical study to investigate the leukotriene B(4) (LTB(4))-receptor antagonist BIIL 284 in cystic fibrosis (CF) patients was prematurely terminated due to a significantly increased risk of adverse pulmonary events. We aimed to establish the effect of BIIL284 in models of Pseudomonas aeruginosa lung infection, thereby contributing to a better understanding of what could have led to adverse pulmonary events in CF patients.

METHODS: P. aeruginosa DNA in the blood of CF patients during and after acute pulmonary exacerbations and in stable patients with non-CF bronchiectasis (NCFB) and healthy individuals was assessed by PCR. The effect of BIIL 284 treatment was tested in an agar bead murine model of P. aeruginosa lung infection. Bacterial count and inflammation were evaluated in lung and other organs.

RESULTS: Most CF patients (98%) and all patients with NCFB and healthy individuals had negative P. aeruginosa DNA in their blood. Similarly, the P. aeruginosa-infected mice showed bacterial counts in the lung but not in the blood or spleen. BIIL 284 treatment decreased pulmonary neutrophils and increased P. aeruginosa numbers in mouse lungs leading to significantly higher bacteremia rates and lung inflammation compared to placebo treated animals.

CONCLUSIONS: Decreased airway neutrophils induced lung proliferation and severe bacteremia in a murine model of P. aeruginosa lung infection. These data suggest that caution should be taken when administering anti-inflammatory compounds to patients with bacterial infections.

Quantum Chromatic Numbers via Operator Systems

We define several new types of quantum chromatic numbers of a graph and characterize them in terms of operator system tensor products. We establish inequalities between these chromatic numbers and other parameters of graphs studied in the literature and exhibit a link between them and non-signalling correlation boxes.

An Efficient LS-SVM-Based Method for Fuzzy System Construction

This paper proposes an efficient learning mechanism to build fuzzy rule-based systems through the construction of sparse least-squares support vector machines (LS-SVMs). In addition to the significantly reduced computational complexity in model training, the resultant LS-SVM-based fuzzy system is sparser while offers satisfactory generalization capability over unseen data. It is well known that the LS-SVMs have their computational advantage over conventional SVMs in the model training process; however, the model sparseness is lost, which is the main drawback of LS-SVMs. This is an open problem for the LS-SVMs. To tackle the nonsparseness issue, a new regression alternative to the Lagrangian solution for the LS-SVM is first presented. A novel efficient learning mechanism is then proposed in this paper to extract a sparse set of support vectors for generating fuzzy IF-THEN rules. This novel mechanism works in a stepwise subset selection manner, including a forward expansion phase and a backward exclusion phase in each selection step. The implementation of the algorithm is computationally very efficient due to the introduction of a few key techniques to avoid the matrix inverse operations to accelerate the training process. The computational efficiency is also confirmed by detailed computational complexity analysis. As a result, the proposed approach is not only able to achieve the sparseness of the resultant LS-SVM-based fuzzy systems but significantly reduces the amount of computational effort in model training as well. Three experimental examples are presented to demonstrate the effectiveness and efficiency of the proposed learning mechanism and the sparseness of the obtained LS-SVM-based fuzzy systems, in comparison with other SVM-based learning techniques.