Indians in British Guiana, 1919 -1929: a study in effort and achievement

From the 1830s to 1917, despair in India drove a small minority into indentureship overseas. These were probably men and women of considerable initiative and extraordinary courage. Their achievements in British Guiana suggest this. Men, women, and children toiled relentlessly on the sugar plantations, while exploiting every conceivable niche to supplement meagre wages. They built a stable family life. They adapted rice and cattle to the plantation environment, thus adumbrating the character of future Indian villages; but they also resisted the injustices of the system. Indians founded villages throughout coastal Guiana, from the late nineteenth-century. In spite of endemic malaria, a hazardous environment requiring elaborate drainage and irrigation, poor sanitation, an undercurrent of Black envy, and the remorseless hostility of the plantocracy and the State to Indian enterpise in rice and cattle, they progressed. Indians adapted their rich material and religious culture, recreating aspects of their ancestral villages. At the hub of their tradition was the family: although most migrated alone, a modified joint-family structure evolved. Their thrift, industry, judicious delegation of family labour, and an exemplary commitment to their families, sustained them in activities which others considered unremunerative. The practice of Hinduism and Islam was costly; it encouraged saving. Cultural security strengthened their self-confidence and sustained effort; it bred a sense of purpose. By the 1920s, rice, cattle, commerce, etc., had spawned an Indian middle class. These set standards for the community: they established an entrepreneurial tradition; their professional achievements undermined Indian indifference to education; some promoted intellectual curiosity; and facilitated Indian participation in organised cricket, the most eloquent manifestation of arrival. The middle class expanded conceptions of attainable goals. But Indian adaptation was shaped profoundly by a resurgence of pride in the achievements of ancient India and the rise of Gandhi. A separate Indian community, differing significantly in their basic assumptions from those of the Blacks, developed in British Guiana. The implications for race relations were already ominous in the 1920s

Probabilistic Robust Design For Dynamic Systems Using Metamodelling

Designers use simulations to observe the behaviour of a system and to make design decisions to improve dynamic performance. However, for complex dynamic systems, these simulations are often time-consuming and, for robust design purposes, numerous simulations are required as a range of design variables is investigated. Furthermore, the optimum set is desired to meet specifications at particular instances in time. In this thesis, the dynamic response of a system is broken into discrete time instances and recorded into a matrix. Each column of this matrix corresponds to a discrete time instance and each row corresponds to the response at a particular design variable set. Singular Value Decomposition (SVD) is then used to separate this matrix into two matrices: one that consists of information in parameter-space and the other containing information in time-space. Metamodels are then used to efficiently and accurately calculate the response at some arbitrary set of design variables at any time. This efficiency is especially useful in Monte Carlo simulation where the responses are required at a very large sample of design variable sets. This work is then extended where the normalized sensitivities along with the first and second moments of the response are required at specific times. Later, the procedure of calculating the metamodel at specific times and how this metamodel is used in parameter design or integrated design for finding the optimum parameters given specifications at specific time steps is shown. In conclusion, this research shows that SVD and metamodelling can be used to apply probabilistic robust design tools where specifications at certain times are required for the optimum performance of a system

Metamodel-Based Probabilistic Design for Dynamic Systems with Degrading Components

The probabilistic design of dynamic systems with degrading components is difficult. Design of dynamic systems typically involves the optimization of a time-invariant performance measure, such as Energy, that is estimated using a dynamic response, such as angular speed. The mechanistic models developed to approximate this performance measure are too complicated to be used with simple design calculations and lead to lengthy simulations. When degradation of the components is assumed, in order to determine suitable service times, estimation of the failure probability over the product lifetime is required. Again, complex mechanistic models lead to lengthy lifetime simulations when the Monte Carlo method is used to evaluate probability. Based on these problems, an efficient methodology is presented for probabilistic design of dynamic systems and to estimate the cumulative distribution function of the time to failure of a performance measure when degradation of the components is assumed. The four main steps include; 1) transforming the dynamic response into a set of static responses at discrete cycle-time steps and using Singular Value Decomposition to efficiently estimate a time-invariant performance measure that is based upon a dynamic response, 2) replacing the mechanistic model with an approximating function, known as a “metamodel” 3) searching for the best design parameters using fast integration methods such as the First Order Reliability Method and 4) building the cumulative distribution function using the summation of the incremental failure probabilities, that are estimated using the set-theory method, over the planned lifetime. The first step of the methodology uses design of experiments or sampling techniques to select a sample of training sets of the design variables. These training sets are then input to the computer-based simulation of the mechanistic model to produce a matrix of corresponding responses at discrete cycle-times. Although metamodels can be built at each time-specific column of this matrix, this method is slow especially if the number of time steps is large. An efficient alternative uses Singular Value Decomposition to split the response matrix into two matrices containing only design-variable-specific and time-specific information. The second step of the methodology fits metamodels only for the significant columns of the matrix containing the design variable-specific information. Using the time-specific matrix, a metamodel is quickly developed at any cycle-time step or for any time-invariant performance measure such as energy consumed over the cycle-lifetime. In the third step, design variables are treated as random variables and the First Order Reliability Method is used to search for the best design parameters. Finally, the components most likely to degrade are modelled using either a degradation path or a marginal distribution model and, using the First Order Reliability Method or a Monte Carlo Simulation to estimate probability, the cumulative failure probability is plotted. The speed and accuracy of the methodology using three metamodels, the Regression model, Kriging and the Radial Basis Function, is investigated. This thesis shows that the metamodel offers a significantly faster and accurate alternative to using mechanistic models for both probabilistic design optimization and for estimating the cumulative distribution function. For design using the First-Order Reliability Method to estimate probability, the Regression Model is the fastest and the Radial Basis Function is the slowest. Kriging is shown to be accurate and faster than the Radial Basis Function but its computation time is still slower than the Regression Model. When estimating the cumulative distribution function, metamodels are more than 100 times faster than the mechanistic model and the error is less than ten percent when compared with the mechanistic model. Kriging and the Radial Basis Function are more accurate than the Regression Model and computation time is faster using the Monte Carlo Simulation to estimate probability than using the First-Order Reliability Method

Anatomical origins of ocular dominance in mouse primary visual cortex

Ocular dominance (OD) plasticity is a classic paradigm for studying the effect of experience and deprivation on cortical development, and is manifested as shifts in the relative strength of binocular inputs to primary visual cortex (V1). The mouse has become an increasingly popular model for mechanistic studies of OD plasticity and, consequently, it is important that we understand how binocularity is constructed in this species. One puzzling feature of the mouse visual system is the gross disparity between the physiological strength of each eye in V1 and their anatomical representation in the projection from retina to the dorsal lateral geniculate nucleus (dLGN). While the contralateral-to-ipsilateral (C/I) ratio of visually evoked responses in binocular V1 is approximately 2:1, the ipsilateral retinal projection is weakly represented in terms of retinal ganglion cell (RGC) density where the C/I ratio is approximately 9:1. The structural basis for this relative amplification of ipsilateral eye responses between retina and V1 is not known. Here we employed neuroanatomical tracing and morphometric techniques to quantify the relative magnitude of each eye's input to and output from the binocular segment of dLGN. Our data are consistent with the previous suggestion that a point in space viewed by both eyes will activate 9 times as many RGCs in the contralateral retina as in the ipsilateral retina. Nonetheless, the volume of the dLGN binocular segment occupied by contralateral retinogeniculate inputs is only 2.4 times larger than the volume occupied by ipsilateral retinogeniculate inputs and recipient relay cells are evenly distributed among the input layers. The results from our morphometric analyses show that this reduction in input volume can be accounted for by a three-to-one convergence of contralateral eye RGC inputs to dLGN neurons. Together, our findings establish that the relative density of feed-forward dLGN inputs determines the C/I response ratio of mouse binocular V1

Combined Analysis of Variation in Core, Accessory and Regulatory Genome Regions Provides a Super-Resolution View into the Evolution of Bacterial Populations

The use of whole-genome phylogenetic analysis has revolutionized our understanding of the evolution and spread of many important bacterial pathogens due to the high resolution view it provides. However, the majority of such analyses do not consider the potential role of accessory genes when inferring evolutionary trajectories. Moreover, the recently discovered importance of the switching of gene regulatory elements suggests that an exhaustive analysis, combining information from core and accessory genes with regulatory elements could provide unparalleled detail of the evolution of a bacterial population. Here we demonstrate this principle by applying it to a worldwide multi-host sample of the important pathogenic E. coli lineage ST131. Our approach reveals the existence of multiple circulating subtypes of the major drug-resistant clade of ST131 and provides the first ever population level evidence of core genome substitutions in gene regulatory regions associated with the acquisition and maintenance of different accessory genome elements.Peer reviewe

A New World for Kaila

It was my illiterate maternal great-grandmother, Kaila (1889–1956) who kindled my curiosity in the antecedence of the girmitiyas (indentured labourers from India). When she died on 6 December 1956 I was only six years old; but she still possesses a niche in my memory—a revered mythical presence—though dimmed by time. I think this idealised image of Kaila is compounded of the adulatory recollections of my extended family, and my own faded snapshots and later embellishments of her. But there’s ..

Exploring the Impetus of R2 Universities that Attain R1 Status

Approximately 10% of post-secondary institutions are classified as doctoral universities. The Carnegie Classification model identifies doctoral universities as either research or professional/doctoral universities. Research universities are further classified as R1, considered the most organizationally complex and prestigious, with very high research activity; or R2, next-tier research universities with high research activity. This study examined the increasing number of institutions shifting from R2 classification to R1, and the early resultant institutional and public policy tradeoffs arising with reclassification. Research universities have evolved over time and are presently recognized as having tripartite missions of teaching, service, and research. As research universities aspire to increase their prestige and reputation, they do so in a system that incentivizes research productivity and output. Although public research universities enjoy strong societal interest, they continuously navigate financial stress. Governed by publicly appointed boards, they are often directly regulated by or indirectly impacted by state agencies. Public research universities aspiring to transition from R2 to R1 status do so while competing for limited market resources and funding from state legislatures. In an effort increase their prestige and competitiveness, many public research universities emulate the prestigious post-secondary models that attract top faculty and students, shifting to R1 status over time. Yet pursuing R1 status may introduce unintended consequences and tradeoffs during and after the process of achieving R1 status. This study revealed R2 institutions seeking to attain R1 status undergo institutional isomorphism, defined as a phenomenon in which organizations in a similar field grow more and more alike as they evolve. The study determined that R2 institutions face external and societal pressures, adopt models from successful research institutions, and experience change throughout their faculty, administration, and professional networks as they attain R1 status. Specifically, this study examines the 2010–2018 increase in the number of R2 public research universities pursuing R1 status, the strategies those institutions implemented to attain R1 status, and the early resultant institutional and public policy tradeoffs arising with R1 status