New Approach to Parton Shower MC's for Precision QCD Theory: HERWIRI1.0(31)

By implementing the new IR-improved Dokshitzer-Gribov-Lipatov-Altarelli-Parisi-Callan-Symanzik (DGLAP-CS) kernels recently developed by one of us in the HERWIG6.5 environment we generate a new MC, HERWIRI1.0(31), for hadron-hadron scattering at high energies. We use MC data to illustrate the comparison between the parton shower generated by the standard DGLAP-CS kernels and that generated by the new IR-improved DGLAP-CS kernels. The interface to MC@NLO, MC@NLO/HERWIRI, is illustrated. Comparisons with FNAL data and some discussion of possible implications for LHC phenomenology are also presented.Comment: 24 pages, 10 figures; published versio

Analysis of the Malaysian Toll Road Public-Private Partnership Program and Recommendations for Policy Improvements

Malaysia has relied on toll road public-private partnerships (PPPs) for over twenty years to provide important highway infrastructure. The program has been active with nearly 1800 kilometers either constructed or concessions agreed to. The public has been less supportive of the program due to low transparency and little public involvement. Public protests are common, which may lead to long-term program instability. The CLIOS Process, developed at MIT, is applied to Malaysia’s toll road PPP program to develop new policies that can better meet these public concerns while maintaining the financial viability of the sector. With increases in transparency and public involvement, the political risks of the program should be reduced and long-term stability for the government and concessionaires improved. We argue that the focus should be at the regional transportation planning level where toll road PPPs can be compared with alternatives for meeting transportation needs rather than at the national level where Malaysian toll road PPPs are currently handled

Analysis of Statistical Impact of Steroids in Professional Major League Baseball Players

Rampant steroid usage tainted Major League Baseball (MLB) in the late 1980s, and decades later, steroid usage is still a serious issue. Steroids, along with other illegal substances, have heavily impacted various statistics in professional baseball (Petersen, Jung, & Eugene Stanley, 2008). Many records—a more notable one being Barry Bond’s 73 homerun season—have been broken during this timeframe, which has been coined as the “Steroid Era” of baseball (Rymer, 2012). In a sport with more statistics than any other, the impact steroid usage has on baseball statistics becomes fascinating, and this impact can be mapped in a variety of ways. In fact, there is an entire branch of statistical modeling specifically for baseball formally known as “sabermetrics”(SABR). The core of this thesis is an attempt to analyze the statistical impact steroids have on baseball statistics at a professional level. By utilizing various baseball sabermetrics to collect data, this study examines how steroids have a careerewide impact on the statistical distribution of a MLB player who used steroids with respect to a player who refrained from usage of such illegal substances. By applying various analyses on said data, potential differences can be made quantifiable.These results could be telling enough to portray suggestive anomalies in a MLB player’s statistics. On a larger level, these results could be telling enough to discourage steroid usage among professional baseball players entirely

Graphene formation on SiC substrates

Graphene layers were created on both C and Si faces of semi-insulating, on-axis, 4H- and 6H-SiC substrates. The process was performed under high vacuum (<10-4 mbar) in a commercial chemical vapor deposition SiC reactor. A method for H2 etching the on-axis sub-strates was developed to produce surface steps with heights of 0.5 nm on the Si-face and 1.0 to 1.5 nm on the C-face for each polytype. A process was developed to form graphene on the substrates immediately after H2 etching and Raman spectroscopy of these samples confirmed the formation of graphene. The morphology of the graphene is described. For both faces, the underlying substrate morphology was significantly modified during graphene formation; sur-face steps were up to 15 nm high and the uniform step morphology was sometimes lost. Mo-bilities and sheet carrier concentrations derived from Hall Effect measurements on large area (16 mm square) and small area (2 and 10 um square) samples are presented and shown to compare favorably to recent reports.Comment: European Conference on Silicon Carbide and Related Materials 2008 (ECSCRM '08), 4 pages, 4 figure

The Effect of Perennial Ryegrass Cultivar Lock up Length and Nitrogen on Forage Quality for Silage

A study was undertaken to determine the effects of three perennial ryegrass (Lolium perenne L.) cultivars (Vedette, Impact and Nevis) with differing maturities, lock up length and nitrogen (N) application upon the dry matter (DM) yield and nutritive characteristics of pasture for silage. The addition of N at 50 kgN/ha significantly (P\u3c 0.05) increased DM yield for all cultivars. Metabolisable energy (ME) (MJ/kgDM) of the ryegrass declined with time for all treatments, although by week 8 of lock up the ME content of Vedette was significantly (P\u3c 0.05) lower than for other cultivars. When the DM yield and ME content of ryegrass at early ear emergence for each cultivar was compared, the harvestable metabolisable energy (MJ/ha) was highest for Nevis followed by Impact and Vedette. In conclusion, there is potential to use later maturing cultivars of ryegrass in south east Australia to allow for later harvesting of forage for silage, whilst maintaining ME and maximising DM yields. Furthermore the use of N fertiliser can also increase DM yields without impinging on pasture quality

A Model for Circuit Execution Runtime And Its Implications for Quantum Kernels At Practical Data Set Sizes

Quantum machine learning (QML) is a fast-growing discipline within quantum computing. One popular QML algorithm, quantum kernel estimation, uses quantum circuits to estimate a similarity measure (kernel) between two classical feature vectors. Given a set of such circuits, we give a heuristic, predictive model for the total circuit execution time required, based on a recently-introduced measure of the speed of quantum computers. In doing so, we also introduce the notion of an "effective number of quantum volume layers of a circuit", which may be of independent interest. We validate the performance of this model using synthetic and real data by comparing the model's predictions to empirical runtime data collected from IBM Quantum computers through the use of the Qiskit Runtime service. At current speeds of today's quantum computers, our model predicts data sets consisting of on the order of hundreds of feature vectors can be processed in order a few hours. For a large-data workflow, our model's predictions for runtime imply further improvements in the speed of circuit execution -- as well as the algorithm itself -- are necessary.Comment: 8.5 pages of main text + 1.5 pages of appendices. 7 figures & 3 table