918 resultados para Subsistence Minimum


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Since the implementation of Ghana's national Structural Adjustment Programme (SAP), policies associated with the programme have been criticized for perpetuating poverty within the country's subsistence economy. This article brings new evidence to bear on the contention that the SAP has both fuelled the uncontrolled growth of informal, poverty-driven artisanal gold mining and further marginalized its impoverished participants. Throughout the adjustment period, it has been a central goal of the government to promote the expansion of large-scale gold mining through foreign investment. Confronted with the challenge of resuscitating a deteriorating gold mining industry, the government introduced a number of tax breaks and policies in an effort to create an attractive investment climate for foreign multinational mining companies. The rapid rise in exploration and excavation activities that has since taken place has displaced thousands of previously-undisturbed subsistence artisanal gold miners. This, along with a laissez faire land concession allocation procedure, has exacerbated conflicts between mining parties. Despite legalizing small-scale mining in 1989, the Ghanaian government continues to implement procedurally complex and bureaucratically unwieldy regulations and policies for artisanal operators which have the effect of favouring the interests of established large-scale miners.

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We study the regularization problem for linear, constant coefficient descriptor systems Ex' = Ax+Bu, y1 = Cx, y2 = Γx' by proportional and derivative mixed output feedback. Necessary and sufficient conditions are given, which guarantee that there exist output feedbacks such that the closed-loop system is regular, has index at most one and E+BGΓ has a desired rank, i.e., there is a desired number of differential and algebraic equations. To resolve the freedom in the choice of the feedback matrices we then discuss how to obtain the desired regularizing feedback of minimum norm and show that this approach leads to useful results in the sense of robustness only if the rank of E is decreased. Numerical procedures are derived to construct the desired feedback gains. These numerical procedures are based on orthogonal matrix transformations which can be implemented in a numerically stable way.

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Open solar flux (OSF) variations can be described by the imbalance between source and loss terms. We use spacecraft and geomagnetic observations of OSF from 1868 to present and assume the OSF source, S, varies with the observed sunspot number, R. Computing the required fractional OSF loss, χ, reveals a clear solar cycle variation, in approximate phase with R. While peak R varies significantly from cycle to cycle, χ is surprisingly constant in both amplitude and waveform. Comparisons of χ with measures of heliospheric current sheet (HCS) orientation reveal a strong correlation. The cyclic nature of χ is exploited to reconstruct OSF back to the start of sunspot records in 1610. This agrees well with the available spacecraft, geomagnetic, and cosmogenic isotope observations. Assuming S is proportional to R yields near-zero OSF throughout the Maunder Minimum. However, χ becomes negative during periods of low R, particularly the most recent solar minimum, meaning OSF production is underestimated. This is related to continued coronal mass ejection (CME) activity, and therefore OSF production, throughout solar minimum, despite R falling to zero. Correcting S for this produces a better match to the recent solar minimum OSF observations. It also results in a cycling, nonzero OSF during the Maunder Minimum, in agreement with cosmogenic isotope observations. These results suggest that during the Maunder Minimum, HCS tilt cycled as over recent solar cycles, and the CME rate was roughly constant at the levels measured during the most recent two solar minima.

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We present a model that describes features common to many famines: (i) a famine may occur without a substantial decline in aggregate food availability; (ii) famines often have a very uneven impact on different groups of population; and (iii) expectations about future food markets affect current market behaviour and result in starvation for certain groups of population. We consider an exchange economy with two types of agents, food producers and non-food producers. An agent starves if his consumption of food falls below the minimum subsistence level. We show that non-food producers are more vulnerable to starvation than food producers, and may fail to survive even when the aggregate amount of food in the economy is enough to guarantee survival of all agents. In an economy with government procurement and public distribution, we show that the government policy may become unsustainable if the food producers condition their expectations about future public sales on current public stock level.

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We study the empirical performance of the classical minimum-variance hedging strategy, comparing several econometric models for estimating hedge ratios of crude oil, gasoline and heating oil crack spreads. Given the great variability and large jumps in both spot and futures prices, considerable care is required when processing the relevant data and accounting for the costs of maintaining and re-balancing the hedge position. We find that the variance reduction produced by all models is statistically and economically indistinguishable from the one-for-one “naïve” hedge. However, minimum-variance hedging models, especially those based on GARCH, generate much greater margin and transaction costs than the naïve hedge. Therefore we encourage hedgers to use a naïve hedging strategy on the crack spread bundles now offered by the exchange; this strategy is the cheapest and easiest to implement. Our conclusion contradicts the majority of the existing literature, which favours the implementation of GARCH-based hedging strategies.

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We develop a new sparse kernel density estimator using a forward constrained regression framework, within which the nonnegative and summing-to-unity constraints of the mixing weights can easily be satisfied. Our main contribution is to derive a recursive algorithm to select significant kernels one at time based on the minimum integrated square error (MISE) criterion for both the selection of kernels and the estimation of mixing weights. The proposed approach is simple to implement and the associated computational cost is very low. Specifically, the complexity of our algorithm is in the order of the number of training data N, which is much lower than the order of N2 offered by the best existing sparse kernel density estimators. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with comparable accuracy to those of the classical Parzen window estimate and other existing sparse kernel density estimators.

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Recent years have seen increased interest in skeletal populations from the Imperial Roman Age in Italy, but much less is known about diet and standards of living in the subsequent medieval period. To fill this gap, we conducted a morphological analysis of human remains from Albano, an Italian town near Rome, as well as a stable isotope analysis of bone collagen to reconstruct diet. The sample was recovered from a Medieval cemetery (1040–1220 cal. yr. BP) located in the gardens of the historical Palazzo Doria Pamphili in Albano. A minimum number of 40 individuals (31 adults and 9 sub-adults) were examined using standard methods. Though the general health status of the population was good, signs of cribra orbitalia and diffuse enthesopathies were noted during the morphological examination. Stable carbon and nitrogen isotope analyses of the bone collagen from 24 adult humans and three faunal bones indicate that the diet of the population may be described as predominantly terrestrial and C3-plant based although the data for some of the individuals suggest a modest consumption of C4-(millet) based or aquatic proteins. No evidence of significant dietary differences between the sexes was found. The comparison of the isotope data from Albano with those from populations recovered in the same region is consistent with a shift from a terrestrial, possibly plant foods-dominated subsistence in the Early Middle Ages to a diet with a higher contribution from animal proteins, both terrestrial and aquatic, in the Later Middle Ages.

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The behavior of the Sun and near-Earth space during grand solar minima is not understood; however, the recent long and low minimum of the decadal-scale solar cycle gives some important clues, with implications for understanding the solar dynamo and predicting space weather conditions. The speed of the near-Earth solar wind and the strength of the interplanetary magnetic field (IMF) embedded within it can be reliably reconstructed for before the advent of spacecraft monitoring using observations of geomagnetic activity that extend back to the mid-19th century. We show that during the solar cycle minima around 1879 and 1901 the average solar wind speed was exceptionally low, implying the Earth remained within the streamer belt of slow solar wind flow for extended periods. This is consistent with a broader streamer belt, which was also a feature of the recent low minimum (2009), and yields a prediction that the low near-Earth IMF during the Maunder minimum (1640-1700), as derived from models and deduced from cosmogenic isotopes, was accompanied by a persistent and relatively constant solar wind of speed roughly half the average for the modern era.

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This paper demonstrates that the use of GARCH-type models for the calculation of minimum capital risk requirements (MCRRs) may lead to the production of inaccurate and therefore inefficient capital requirements. We show that this inaccuracy stems from the fact that GARCH models typically overstate the degree of persistence in return volatility. A simple modification to the model is found to improve the accuracy of MCRR estimates in both back- and out-of-sample tests. Given that internal risk management models are currently in widespread usage in some parts of the world (most notably the USA), and will soon be permitted for EC banks and investment firms, we believe that our paper should serve as a valuable caution to risk management practitioners who are using, or intend to use this popular class of models.

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A method of automatically identifying and tracking polar-cap plasma patches, utilising data inversion and feature-tracking methods, is presented. A well-established and widely used 4-D ionospheric imaging algorithm, the Multi-Instrument Data Assimilation System (MIDAS), inverts slant total electron content (TEC) data from ground-based Global Navigation Satellite System (GNSS) receivers to produce images of the free electron distribution in the polar-cap ionosphere. These are integrated to form vertical TEC maps. A flexible feature-tracking algorithm, TRACK, previously used extensively in meteorological storm-tracking studies is used to identify and track maxima in the resulting 2-D data fields. Various criteria are used to discriminate between genuine patches and "false-positive" maxima such as the continuously moving day-side maximum, which results from the Earth's rotation rather than plasma motion. Results for a 12-month period at solar minimum, when extensive validation data are available, are presented. The method identifies 71 separate structures consistent with patch motion during this time. The limitations of solar minimum and the consequent small number of patches make climatological inferences difficult, but the feasibility of the method for patches larger than approximately 500 km in scale is demonstrated and a larger study incorporating other parts of the solar cycle is warranted. Possible further optimisation of discrimination criteria, particularly regarding the definition of a patch in terms of its plasma concentration enhancement over the surrounding background, may improve results.

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The area of Arctic September sea ice has diminished from about 7 million km2 in the 1990s to less than 5 million km2 in five of the past seven years, with a record minimum of 3.6 million km2 in 2012 (ref. 1). The strength of this decrease is greater than expected by the scientific community, the reasons for this are not fully understood, and its simulation is an on-going challenge for existing climate models2, 3. With growing Arctic marine activity there is an urgent demand for forecasting Arctic summer sea ice4. Previous attempts at seasonal forecasts of ice extent were of limited skill5, 6, 7, 8, 9. However, here we show that the Arctic sea-ice minimum can be accurately forecasted from melt-pond area in spring. We find a strong correlation between the spring pond fraction and September sea-ice extent. This is explained by a positive feedback mechanism: more ponds reduce the albedo; a lower albedo causes more melting; more melting increases pond fraction. Our results help explain the acceleration of Arctic sea-ice decrease during the past decade. The inclusion of our new melt-pond model10 promises to improve the skill of future forecast and climate models in Arctic regions and beyond.

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It is estimated that globally over 2 billion people do not have a bank account, with many more in the developed and developing worlds ‘under-banked’, meaning they have limited access to financial services. Reaching the unbanked and underbanked with appropriate financial services is widely recognised as critical for future global economic growth and prosperity. Drawing upon multidimensional understandings of poverty, and framed by literature on poverty pools, traps and cycles, this paper explores the use of financial products and services in the developing world and critically reflects on their potential role in poverty alleviation and wider sustainable development. Discussions are illustrated with reference to qualitative empirical research undertaken in East and Southern Africa, and a sense-making of the lived financial experiences of low income individuals, households and communities.

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We develop an on-line Gaussian mixture density estimator (OGMDE) in the complex-valued domain to facilitate adaptive minimum bit-error-rate (MBER) beamforming receiver for multiple antenna based space-division multiple access systems. Specifically, the novel OGMDE is proposed to adaptively model the probability density function of the beamformer’s output by tracking the incoming data sample by sample. With the aid of the proposed OGMDE, our adaptive beamformer is capable of updating the beamformer’s weights sample by sample to directly minimize the achievable bit error rate (BER). We show that this OGMDE based MBER beamformer outperforms the existing on-line MBER beamformer, known as the least BER beamformer, in terms of both the convergence speed and the achievable BER.