Numerical Algorithms for finding Black Hole solutions of Einstein's Equations

Einstein's Theory of General Relativity has proven remarkably successful at modelling a wide range of gravitational phenomena. Amongst some of the novel features in this description is the existence of black holes; regions of space-time where gravity is so strong that light cannot escape. The properties of black holes have been extensively studied within General Relativity, culminating in the result that the few known space-times are the only allowed stationary black hole solutions in four dimensions. In the past half century, research has focused on how to unify the distinct theories of gravity and quantum mechanics. A common theme amongst several strong candidates is that space-time, the backdrop for gravity, is fundamentally higher dimensional. In these theories, the structure of black hole solutions is relatively unknown and expected to be much richer; finding such solutions is, however, a very hard task. In this thesis, we introduce new numerical methods to study higher dimensional black holes. The methods, based on refinements of existing work and the novel application of standard techniques, are then used to study a number of black hole space-times. Namely the structure of black holes on a Kaluza-Klein background, and rotating Kerr black holes. We demonstrate that these algorithms can be applied in a wide class of situations and yield good quality results with comparative ease. New results are presented in both cases studied. We examine the predicted merger between non-uniform black strings and localised black holes on a Kaluza-Klein background. We find evidence for a new type of non-uniform black string with one Euclidean negative mode and lower entropy than the uniform strings. We discover a window of localised black holes with one Euclidean negative mode but positive specific heat. We also look at the local structure of the merger point and find consistency with Kol's cone prediction

Deriving Equations from Sensor Data Using Dimensional Function Synthesis

We present a new method for deriving functions that model the relationship between multiple signals in a physical system. The method, which we call dimensional function synthesis, applies to data streams where the dimensions of the signals (e.g., length, mass, etc.) are known. The method comprises two phases: a compile-time synthesis phase and a subsequent calibration using sensor data. We implement dimensional function synthesis and use the implementation to demonstrate efficiently summarizing multi-modal sensor data for two physical systems using 90 laboratory experiments and 10,000 synthetic idealized measurements. The results show that our technique can generate models in less than 300\,ms on average across all the physical systems we evaluated. This is a marked improvement when compared to an average of 16 seconds for training neural networks of comparable accuracy on the same computing platform. When calibrated with sensor data, our models outperform traditional regression and neural network models in inference accuracy in all the cases we evaluated. In addition, our models perform better in training latency (over 1096x improvement) and required arithmetic operations in inference (over 34x improvement). These significant gains are largely the result of exploiting information on the physics of signals that has hitherto been ignored

Study of Safer Storage and Handling of Graphite Oxide

PresentationDue to the immense potential of graphene for energy storage and composite filler applications the large-scale production of graphene is of increasing commercial and academic interest. The existing direct methods of large-scale graphene production are not economical using current technology. Therefore, an alternate synthesis route to produce graphene-like material involving graphite oxide (GO) is pre-dominantly used. This method involves the oxidation of graphite to GO and its subsequent reduction to reduced graphene oxide (rGO). The proposed method has shown potential for bulk production at high yield. However, prior studies have shown that GO can undergo explosive decomposition under certain conditions. There is no documented process safety incident specifically related to GO so far but GO is an energetic material that can undergo explosive thermal reduction, A number of unanticipated process incidents have occurred due to inadequate study and understanding of energetic materials stored in large quantities. As research is moving towards large scale manufacturing of GO, the motivation of this research is to investigate potential process safety issues with bulk GO storage and handling. Specifically, we examine the underlying causes of explosive behavior of bulk GO and propose safer storage and handling conditions. Additional studies are conducted in an Advanced Reactive System Screen Tool (ARSST) calorimeter to understand the effect of storage temperature, impurities, pH, and process conditions. This research will be beneficial in assessing the hazards of GO and enhancing safety of rGO production processes over their life cycles

Deriving Equations from Sensor Data Using Dimensional Function Synthesis

We present a new method for deriving functions that model the relationship between multiple signals in a physical system. The method, which we call dimensional function synthesis, applies to data streams where the dimensions of the signals are known. The method comprises two phases: a compile-time synthesis phase and a subsequent calibration using sensor data. We implement dimensional function synthesis and use the implementation to demonstrate efficiently summarizing multi-modal sensor data for two physical systems using 90 laboratory experiments and 10 000 synthetic idealized measurements. We evaluate the performance of the compile-time phase of dimensional function synthesis as well as the calibration phase overhead, inference latency, and accuracy of the models our method generates. The results show that our technique can generate models in less than 300 ms on average across all the physical systems we evaluated. When calibrated with sensor data, our models outperform traditional regression and neural network models in inference accuracy in all the cases we evaluated. In addition, our models perform better in training latency (over 8660× improvement) and required arithmetic operations in inference (over 34× improvement). These significant gains are largely the result of exploiting information on the physics of signals that has hitherto been ignored.This research is supported by an Alan Turing Institute award TU/B/000096 under EPSRC grant EP/N510129/1, by Royal Society grant RG170136, and by EPSRC grants EP/P001246/1 and EP/R022534/1

Management of Intracranial Meningiomas Using Keyhole Techniques

BACKGROUND: Keyhole craniotomies are increasingly being used for lesions of the skull base. Here we review our recent experience with these approaches for resection of intracranial meningiomas. METHODS: Clinical and operative data were gathered on all patients treated with keyhole approaches by the senior author from January 2012 to June 2013. Thirty-one meningiomas were resected in 27 patients, including 9 supratentorial, 5 anterior fossa, 7 middle fossa, 6 posterior fossa, and 4 complex skull base tumors. Twenty-nine tumors were WHO Grade I, and 2 were Grade II. RESULTS: The mean operative time was 8 hours, 22 minutes (range, 2:55-16:14) for skull-base tumors, and 4 hours, 27 minutes (range, 1:45-7:13) for supratentorial tumors. Simpson Resection grades were as follows: Grade I = 8, II = 8, III = 1, IV = 15, V = 0. The median postoperative hospital stay was 4 days (range, 1-20 days). In the 9 patients presenting with some degree of visual loss, 7 saw improvement or complete resolution. In the 6 patients presenting with cranial nerve palsies, 4 experienced improvement or resolution of the deficit postoperatively. Four patients experienced new neurologic deficits, all of which were improved or resolved at the time of the last follow-up. Technical aspects and surgical nuances of these approaches for management of intracranial meningiomas are discussed. CONCLUSIONS: With careful preoperative evaluation, keyhole approaches can be utilized singly or in combination to manage meningiomas in a wide variety of locations with satisfactory results

Quality of life three years after diagnosis of localised prostate cancer: population based cohort study

Objective To quantify the risk and severity of negative effects of treatment for localised prostate cancer on long term quality of life

Immune Evasion by Murine Melanoma Mediated through CC Chemokine Receptor-10

Human melanoma cells frequently express CC chemokine receptor (CCR)10, a receptor whose ligand (CCL27) is constitutively produced by keratinocytes. Compared with B16 murine melanoma, cells rendered more immunogenic via overexpression of luciferase, B16 cells that overexpressed both luciferase and CCR10 resisted host immune responses and readily formed tumors. In vitro, exposure of tumor cells to CCL27 led to rapid activation of Akt, resistance to cell death induced by melanoma antigen-specific cytotoxic T cells, and phosphatidylinositol-3-kinase (PI3K)–dependent protection from apoptosis induced by Fas cross-linking. In vivo, cutaneous injection of neutralizing antibodies to endogenous CCL27 blocked growth of CCR10-expressing melanoma cells. We propose that CCR10 engagement by locally produced CCL27 allows melanoma cells to escape host immune antitumor killing mechanisms (possibly through activation of PI3K/Akt), thereby providing a means for tumor progression

Variability in modern pollen rain from moist and wet tropical forest plots in Ghana, West Africa

How pollen moves within and between ecosystems affects factors such as the genetic structure of populations, how resilient they are to environmental change, and the amount and nature of pollen preserved in the sedimentary record. We set artificial pollen traps in two 100 m by 100 m vegetation plots, one in a wet evergreen forest, and one in a moist semi-deciduous forest in Ghana, West Africa. Five traps from each plot were counted annually from 2011 to 2014, to examine spatial and temporal variation in the pollen rain of the most abundant taxa shared between pollen and vegetation assemblages. Samples from the wet evergreen plot exhibited high variability within years, with the dominant pollen types changing between samples, and many pollen taxa being over-represented relative to their parent plant abundance in some traps whilst being entirely absent from others. The most abundant plant taxa of the wet evergreen plot (Drypetes and Cynometra) do, however, constitute major components of the pollen rain. There is less variation between samples from the moist semi-deciduous plot spatially, as it is dominated by Celtis, which typically comprises >70% of the pollen assemblages. We conclude that pollen rain in these tropical ecosystems is highly heterogeneous, and suggest that pollen assemblages obtained by trapping are susceptible to small-scale variations in forest structure. Conversely, this may mean that current recommendations of more than three years of trapping in tropical systems may be too high, and that space could substitute for time in modern tropical pollen trapping