Using combined XRD-XRF analysis to identify meteorite ablation debris

In this research paper, X-ray diffraction (XRD) and X-ray fluorescence (XRF) spectrometry has been used to determine the mineralogical and elemental composition of a stone sample recovered from a location near village Lehri in district Jhelum, Pakistan. The test data is compared with previous findings (as reported in literature and included in references) to identify this sample stone as part of a meteorite ablation debris

Combustion Analysis of a Meteorite Debris

In this research paper, nature and origins of a meteorite debris identified near village Lehri in district Jhelum, Pakistan have been determined. Total carbon content of a specimen of the meteorite debris is determined using combustion analysis and this abundance has been compared with values reported in literature to establish likely origins of the debris

Symmetry enhancement of extremal horizons in D=5 supergravity

We consider the near-horizon geometry of supersymmetric extremal black holes in un-gauaged and gauged 5-dimensional supergravity, coupled to abelian vector multiplets. By analyzing the global properties of the Killing spinors, we prove that the near-horizon geometries undergo a supersymmetry enhancement. This follows from a set of generalized Lichnerowicz-type theorems we establish, together with an index theory argument. As a consequence, these solutions always admit a $\mathfrak{sl}(2,\mathbb{R})$ symmetry group.Comment: 29 pages, late

Law Done Backwards: The Tightening Of Civil And Loosening Of Criminal Protections

This article shows how protections for civil defendants arguably exceed protections for criminal defendants, and considers some constitutional and practical implications of this ongoing shift

Shape based classification and functional forecast of traffic flow profiles

This dissertation proposes a methodology for traffic flow pattern analysis, its validation, and forecasting. The shape of the daily traffic flows are directly related to the commuter’s traffic behavior which merit analysis based on their shape characteristics. As a departure from the traditional approaches, this research proposed a methodology based on shape for traffic flow analysis. Specifically, Granulometric Size Distributions (GSDs) were used to achieve classification of daily traffic flow patterns. A mathematical morphology method was used that allows the clustering of shapes. The proposed methodology leads to discovery of interesting daily traffic phenomena such as five normal daily traffic shapes beside abnormal shapes representing accidents, congestion behavior, peak time fluctuations, and malfunctioning sensors. To ascertain the significance of shape in traffic analysis, the proposed methodology was validated through a comparative classification analysis of the original data and GSD transformed data using the Back Prorogation Neural Network (BPNN). Results demonstrated that through shape based clustering more appropriate grouping can be accomplished that can result in better estimates of model parameters. Lastly, a functional time series approach was proposed to forecast traffic flow for short and medium-term horizons. It is based on functional principal components decomposition to forecast three different traffic scenarios. Real-time forecast scenarios of partially observed traffic profiles through Penalized Least squares (PLS) technique were also demonstrated. Functional methods outperform the conventional ARIMA model in both short and medium-term forecast horizons. In addition, performance of functional methods in forecasting beyond one hour was also found to be robust and consistent. --Abstract, page iii

The Effect of Robotic Technology on Perioperative Outcomes in Total Knee Arthroplasty

Introduction Robotic technology has recently regained momentum in total knee arthroplasty (TKA) but the effects of this technology on accuracy of implant positioning, intraoperative soft tissue injury and postoperative functional rehabilitation remain unknown. The objectives of this research thesis were to compare a comprehensive range of radiological objectives and perioperative outcomes in conventional jig-based TKA versus robotic-arm assisted TKA, and use optical motion capture technology to quantify the effects of anterior cruciate ligament (ACL) and posterior cruciate ligament (PCL) resection on knee biomechanics. Methods A series of prospective cohort studies were undertaken in patients with established knee osteoarthritis undergoing primary conventional jig-based TKA versus robotic-arm assisted TKA. Predefined radiological and perioperative study outcomes were recorded by independent observers. Optical motion capture technology during robotic TKA was used to quantify the effects of ACL and PCL resection on knee biomechanics. Results Robotic-arm assisted TKA was associated with improved accuracy of implant positioning, reduced periarticular soft tissue injury, decreased bone trauma, improved postoperative functional rehabilitation, and reduced early systemic inflammatory response compared to conventional jig-based TKA. The Macroscopic Soft Tissue Injury (MASTI) classification system was developed and validated for grading intraoperative periarticular soft tissue injury and bone trauma during TKA. ACL resection created flexion-extension mismatch by increasing the extension gap more than the flexion gap, whilst PCL resection increased the flexion gap proportionally more than the extension gap and created mediolateral laxity in knee flexion but not in extension. Conclusion Robotic-arm assisted TKA was associated with increased accuracy of implant positioning, reduced iatrogenic soft tissue injury, and improved functional rehabilitation compared to conventional jig-based TKA. ACL and PCL resections created unique changes in knee biomechanics that affected flexion-extension gaps and mediolateral soft tissue tension during TKA. On the basis of this thesis, further clinical trials have been established to determine the long-term clinical significance of these findings

Examination For Independent Predictors Of Seasonality Of Birth Across Forty-Nine Low- And Middle-Income Countries: Analyses Of The United States Agency For International Development Demographic And Health Survey Data

Although seasonal patterns in the distribution of live births have been well described across distinct populations for centuries, little is known about determinants of birth seasonality, particularly among low- and middle-income countries. Knowledge of determinants of birth seasonality may assist in the timely implementation and distribution of maternal and child health resources that may morbidity and mortality. In this study we examined for maternal and household sociodemographic determinants of birth seasonality in a sample of 3,260,238 live births across 49 nations that are included in the United States Agency for International Development Demographic and Health Survey Program. Our findings demonstrate that birth seasonality is independently associated with maternal and household sociodemographic characteristics including maternal age at birth, maternal body mass index, maternal education, and household wealth. Additionally, we demonstrate that temperature at time of conception is an independent predictor of birth seasonality. We also document trends in the directionality and magnitude of independent predictors of birth seasonality between the Northern and Southern Hemispheres as well as across maternal religious affiliations. The knowledge of seasonal trends in births along with its sociodemographic predictors offers meaningful insights that can be directed towards public health resources that have the potential to improve maternal and child health outcomes and more targeted public health campaigns in low- and middle-income countries