The 2018 Midterm Election: Nevada and the Nation Post-Election Analysis

Brookings Mountain West, in partnership with CSUN, was pleased to present part two of a two-part analysis on the 2018 Midterm elections. The 2018 Midterms included elections for all 435 members of the House of Representatives, including four seats in Nevada. In the U.S. Senate, 34 seats were up for election, including one seat in Nevada. Across the United States, 36 states elected governors, including the State of Nevada. The Democratic Party sought to flip a minimum of 24 seats to become the majority party in House and 2 seats to become the majority party in the Senate. Two Mountain West states, Nevada and Arizona, presented the best opportunity for the Democratic Party to flip seats in the U.S. Senate. Republicans looked to flip seats in 10 states that Donald Trump won in 2016. Panelists reacted to the policy issues and voting trends that resulted from the 2018 Midterm elections

A Timed IO monad

Programming with explicit timing information is often tedious and error prone. This is especially visible in music programming where, when played, the specified durations of notes and rests must be shortened in order to compensate the actual duration of all surrounding processing. In this paper, we develop the notion of timed extension of a monad that aims at relieving programmers from such a burden. We show how, under simple conditions, such extensions can be built, and how useful features of monad programming such as asynchronous concurrency with promises or data-flow programming with monadic streams can be uniformly lifted to the resulting timed programming framework. Even though presented and developed in the abstract, the notion of timed extension of a monad is nevertheless illustrated by two concrete instances: a default timed IO monad where programmers specify durations in mi-croseconds, and a musically timed IO monad, where programmers specify durations in number of beats, the underlying tempo, that is, the speed of the music in beats per minute, possibly changed whenever needed

Most Asked Agronomic Questions

Exact date of bulletin unknown.PDF pages: 4

Adult onset lung disease following transient disruption of fetal stretch-induced differentiation

One of the mechanisms by which adult disease can arise from a fetal origin is by in utero disruption of organogenesis. These studies were designed to examine respiratory function changes in aging rats following transient disruption of lung growth at 16 days gestation. Fetuses were treated in utero with a replication deficient adenovirus containing the cystic fibrosis conductance transmembrane regulator (CFTR) gene fragment cloned in the anti-sense direction. The in utero-treated rats demonstrated abnormal lung function beginning as early as 30 days of age and the pathology progressed as the animals aged. The pulmonary function abnormalities included decreased static compliance as well as increased conducting airway resistance, tissue damping, and elastance. Pressure volume (PV) curves demonstrated a slower early rise to volume and air trapping at end-expiration. The alterations of pulmonary function correlated with lung structural changes determined by morphometric analysis. These studies demonstrate how transient disruption of lung organogensis by single gene interference can result in progressive change in lung function and structure. They illustrate how an adult onset disease can arise from subtle changes in gene expression during fetal development

Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy \u3e95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment

Compile-time optimization of near-neighbor communication for scalable shared-memory multiprocessors

Scalable shared-memory multiprocessor systems are typically NUMA (nonuniform memory access) machines, where the exploitation of the memory hierarchy is critical to achieving high performance. Iterative data parallel loops with near-neighbor communication account for many important numerical applications. In such loops, the communication of partial results stresses the memory system performance. In this paper, we develop data placement schemes that minimize communication time where the near-neighbor interaction is determined by a stencil. Under a given loop partition, our compile-time algorithm partitions global data into four classes for each processor, with each class requiring specific consistency maintenance requirements. The ADAPT (Automatic Data Allocation and Partitioning Tool) system was implemented to automatically partition parallel code segments for the BBN TC2000, a scalable shared-memory multiprocessor. ADAPT caches global arrays and maintains data consistency in software through instructions that flush data from private caches. Restructuring of a fluid flow code segment by ADAPT improved performance by a factor of more than 3 on the BBN TC2000. Features in current generation pipelined processors with multiple functional units permit the overlap of memory accesses with computation. Our experiments on the BBN TC2000 show that the degree of overlap is limited by architectural parameters, such as the number of CPU registers.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/30342/1/0000744.pd

Characterizing forest succession with lidar data: An evaluation for the Inland Northwest, USA

Quantifying forest structure is important for sustainable forest management, as it relates to a wide variety of ecosystem processes and services. Lidar data have proven particularly useful for measuring or estimating a suite of forest structural attributes such as canopy height, basal area, and LAI. However, the potential of this technology to characterize forest succession remains largely untested. The objective of this study was to evaluate the use of lidar data for characterizing forest successional stages across a structurally diverse, mixed-species forest in Northern Idaho. We used a variety of lidar-derived metrics in conjunction with an algorithmic modeling procedure (Random Forests) to classify six stages of three-dimensional forest development and achieved an overall accuracy \u3e95%. The algorithmic model presented herein developed ecologically meaningful classifications based upon lidar metrics quantifying mean vegetation height and canopy cover, among others. This study highlights the utility of lidar data for accurately classifying forest succession in complex, mixed coniferous forests; but further research should be conducted to classify forest successional stages across different forests types. The techniques presented herein can be easily applied to other areas. Furthermore, the final classification map represents a significant advancement for forest succession modeling and wildlife habitat assessment

Investigating the influence of LiDAR ground surface errors on the utility of derived forest inventories

Light detection and ranging, or LiDAR, effectively produces products spatially characterizing both terrain and vegetation structure; however, development and use of those products has outpaced our understanding of the errors within them. LiDAR’s ability to capture three-dimensional structure has led to interest in conducting or augmenting forest inventories with LiDAR data. Prior to applying LiDAR in operational management, it is necessary to understand the errors in Li- DAR-derived estimates of forest inventory metrics (i.e., tree height). Most LiDAR-based forest inventory metrics require creation of digital elevation models (DEM), and because metrics are calculated relative to the DEM surface, errors within the DEMs propagate into delivered metrics. This study combines LiDAR DEMs and 54 ground survey plots to investigate how surface morphology and vegetation structure influence DEM errors. The study further compared two LiDAR classification algorithms and found no significant difference in their performance. Vegetation structure was found to have no influence, whereas increased variability in the vertical error was observed on slopes exceeding 30°, illustrating that these algorithms are not limited by high-biomass western coniferous forests, but that slope and sensor accuracy both play important roles. The observed vertical DEM error translated into ±1%–3% error range in derived timber volumes, highlighting the potential of LiDAR-derived inventories in forest management