Texas A&M University

About: Texas A&M University is a education organization based out in College Station, Texas, United States. It is known for research contribution in the topics: Population & Finite element method. The organization has 72169 authors who have published 164372 publications receiving 5764236 citations.

Sequencing of allotetraploid cotton ( Gossypium hirsutum L. acc. TM-1) provides a resource for fiber improvement

Abstract: Upland cotton is a model for polyploid crop domestication and transgenic improvement. Here we sequenced the allotetraploid Gossypium hirsutum L. acc. TM-1 genome by integrating whole-genome shotgun reads, bacterial artificial chromosome (BAC)-end sequences and genotype-by-sequencing genetic maps. We assembled and annotated 32,032 A-subgenome genes and 34,402 D-subgenome genes. Structural rearrangements, gene loss, disrupted genes and sequence divergence were more common in the A subgenome than in the D subgenome, suggesting asymmetric evolution. However, no genome-wide expression dominance was found between the subgenomes. Genomic signatures of selection and domestication are associated with positively selected genes (PSGs) for fiber improvement in the A subgenome and for stress tolerance in the D subgenome. This draft genome sequence provides a resource for engineering superior cotton lines.

Modern electric, hybrid electric, and fuel cell vehicles : fundamentals, theory, and design

Abstract: Environmental Impact and History of Modern Transportation Air Pollution Global Warming Petroleum Resources Induced Costs Importance of Different Transportation Development Strategies to Future Oil Supply History of EVs History of HEVs History of Fuel Cell Vehicles Fundamentals of Vehicle Propulsion and Brake General Description of Vehicle Movement Vehicle Resistance Dynamic Equation Tire-Ground Adhesion and Maximum Tractive Effort Power Train Tractive Effort and Vehicle Speed Vehicle Power Plant and Transmission Characteristics Vehicle Performance Operating Fuel Economy Brake Performance Internal Combustion Engines 4S, Spark-Ignited IC Engines 4S, Compression-Ignition IC Engines 2S Engines Wankel Rotary Engines Stirling Engines Gas Turbine Engines Quasi-Isothermal Brayton Cycle Engines Electric Vehicles Configurations of EVs Performance of EVs Tractive Effort in Normal Driving Energy Consumption Hybrid Electric Vehicles Concept of Hybrid Electric Drive Trains Architectures of Hybrid Electric Drive Trains Electric Propulsion Systems DC Motor Drives Induction Motor Drives Permanent Magnetic BLDC Motor Drives SRM Drives Design Principle of Series (Electrical Coupling) Hybrid Electric Drive Train Operation Patterns Control Strategies Design Principles of a Series (Electrical Coupling) Hybrid Drive Train Design Example Parallel (Mechanically Coupled) Hybrid Electric Drive Train Design Drive Train Configuration and Design Objectives Control Strategies Parametric Design of a Drive Train Simulations Design and Control Methodology of Series-Parallel (Torque and Speed Coupling) Hybrid Drive Train Drive Train Configuration Drive Train Control Methodology Drive Train Parameters Design Simulation of an Example Vehicle Design and Control Principles of Plug-In Hybrid Electric Vehicles Statistics of Daily Driving Distance Energy Management Strategy Energy Storage Design Mild Hybrid Electric Drive Train Design Energy Consumed in Braking and Transmission Parallel Mild Hybrid Electric Drive Train Series-Parallel Mild Hybrid Electric Drive Train Peaking Power Sources and Energy Storages Electrochemical Batteries Ultracapacitors Ultra-High-Speed Flywheels Hybridization of Energy Storages Fundamentals of Regenerative Breaking Braking Energy Consumed in Urban Driving Braking Energy versus Vehicle Speed Braking Energy versus Braking Power Braking Power versus Vehicle Speed Braking Energy versus Vehicle Deceleration Rate Braking Energy on Front and Rear Axles Brake System of EV, HEV, and FCV Fuel Cells Operating Principles of Fuel Cells Electrode Potential and Current-Voltage Curve Fuel and Oxidant Consumption Fuel Cell System Characteristics Fuel Cell Technologies Fuel Supply Non-Hydrogen Fuel Cells Fuel Cell Hybrid Electric Drive Train Design Configuration Control Strategy Parametric Design Design Example Design of Series Hybrid Drive Train for Off-Road Vehicles Motion Resistance Tracked Series Hybrid Vehicle Drive Train Architecture Parametric Design of the Drive Train Engine/Generator Power Design Power and Energy Design of Energy Storage Appendices Index

Catalysis Research of Relevance to Carbon Management: Progress, Challenges, and Opportunities

Abstract: There is increased recognition by the world’s scientific, industrial, and political communities that the concentrations of greenhouse gases in the earth’s atmosphere, particularly CO_2, are increasing. For example, recent studies of Antarctic ice cores to depths of over 3600 m, spanning over 420 000 years, indicate an 80 ppm increase in atmospheric CO_2 in the past 200 years (with most of this increase occurring in the past 50 years) compared to the previous 80 ppm increase that required 10 000 years.2 The 160 nation Framework Convention for Climate Change (FCCC) in Kyoto focused world attention on possible links between CO2 and future climate change and active discussion of these issues continues.3 In the United States, the PCAST report4 “Federal Energy Research and Development for the Challenges of the Twenty First Century” focused attention on the growing worldwide demand for energy and the need to move away from current fossil fuel utilization. According to the U.S. DOE Energy Information Administration,5 carbon emission from the transportation (air, ground, sea), industrial (heavy manufacturing, agriculture, construction, mining, chemicals, petroleum), buildings (internal heating, cooling, lighting), and electrical (power generation) sectors of the World economy amounted to ca. 1823 million metric tons (MMT) in 1990, with an estimated increase to 2466 MMT in 2008-2012 (Table 1).

You are where you tweet: a content-based approach to geo-locating twitter users

Abstract: We propose and evaluate a probabilistic framework for estimating a Twitter user's city-level location based purely on the content of the user's tweets, even in the absence of any other geospatial cues By augmenting the massive human-powered sensing capabilities of Twitter and related microblogging services with content-derived location information, this framework can overcome the sparsity of geo-enabled features in these services and enable new location-based personalized information services, the targeting of regional advertisements, and so on Three of the key features of the proposed approach are: (i) its reliance purely on tweet content, meaning no need for user IP information, private login information, or external knowledge bases; (ii) a classification component for automatically identifying words in tweets with a strong local geo-scope; and (iii) a lattice-based neighborhood smoothing model for refining a user's location estimate The system estimates k possible locations for each user in descending order of confidence On average we find that the location estimates converge quickly (needing just 100s of tweets), placing 51% of Twitter users within 100 miles of their actual location