Remote sensing of vegetation and land-cover change in Arctic Tundra Ecosystems

The objective of this paper is to review research conducted over the past decade on the application of multi-temporal remote sensing for monitoring changes of Arctic tundra lands. Emphasis is placed on results from the National Science Foundation Land-Air-Ice Interactions (LAII) program and on optical remote sensing techniques. Case studies demonstrate that ground-level sensors on stationary or moving track platforms and wide-swath imaging sensors on polar orbiting satellites are particularly useful for capturing optical remote sensing data at sufficient frequency to study tundra vegetation dynamics and changes for the cloud prone Arctic. Less frequent imaging with high spatial resolution instruments on aircraft and lower orbiting satellites enable more detailed analyses of land cover change and calibration/validation of coarser resolution observations.The strongest signals of ecosystem change detected thus far appear to correspond to expansion of tundra shrubs and changes in the amount and extent of thaw lakes and ponds. Changes in shrub cover and extent have been documented by modern repeat imaging that matches archived historical aerial photography. NOAA Advanced Very High Resolution Radiometer (AVHRR) time series provide a 20-year record for determining changes in greenness that relates to photosynthetic activity, net primary production, and growing season length. The strong contrast between land materials and surface waters enables changes in lake and pond extent to be readily measured and monitored.     

Utilizing the Explicit Finite Element Method for Studying Granular Flows

Granular flows are systems of complex dry particulates whose behavior is difficult to predict during sliding contact. Existing computational tools used to simulate granular flows are particle dynamics, cellular automata (CA), and continuum modeling. In the present investigation, another numerical tool—the explicit finite element method (FEM)—is analyzed as a potential technique for simulating granular flow. For this purpose, explicit dynamic finite element models of parallel shear cells were developed. These models contained 52 particles and consisted of granules that are both round and multi-shaped (diamond, triangle, and rectangle). Each parallel shear cell consisted of a smooth stationary top wall and a rough bottom surface that was given a prescribed velocity of U = 0.7 in/sec (1.78 cm/s). The coefficient of friction (COF) between the particle–particle and particle–wall collisions was varied between 0.0 and 0.75. Utilizing the output of the simulations, results are presented for the shear behavior, particle kinetic energy, and particle stresses within the shear cell as a function of time. As a means of validating the explicit technique for granular flow, a 75 particle, zero roughness, couette shear cell model (solid fraction of 0.50) is subsequently presented for which direct comparisons are made to the results published by Lun. [Lun, C.K. et al.: Phys. Fluids 8, 2868–2883 (1996)] Overall, the results indicate that the explicit FEM is a powerful tool for simulating granular flow phenomena in sliding contacts. In fact, the explicit method demonstrated several advantages over existing numerical techniques while providing equivalent accuracy to the molecular dynamics (MD) approach. These advantages included being able to monitor the collision (sub-surface and surface) stresses and kinetic energies of individual particles over time, the ability to analyze any particle shape, and the ability to capture force chains during granular flow.     

Statistical modeling of dissimilarity increments for d-dimensional data: Application in partitional clustering

This paper addresses the use of high order dissimilarity models in data mining problems. We explore dissimilarities between triplets of nearest neighbors, called dissimilarity increments (DIs). We derive a statistical model of DIs for d-dimensional data (d-DID) assuming that the objects follow a multivariate Gaussian distribution. Empirical evidence shows that the d-DID is well approximated by the particular case d=2. We propose the application of this model in clustering, with a partitional algorithm that uses a merge strategy on Gaussian components. Experimental results, in synthetic and real datasets, show that clustering algorithms using DID usually outperform well known clustering algorithms.     

The impact of NERICA adoption on productivity and poverty of the small-scale rice farmers in the Gambia

The study assessed the causal effects of NERICA adoption on rice yields and income, using country-wide data of 600 rice farmers in The Gambia. To control for differences in socio-demographic and environmental characteristics of adopters and non-adopters and enable a causal interpretation of NERICA adoption on the variables of interest, the study used the counterfactual outcome framework to control for such differences. The results, based on observed sample estimates, showed significant differences in rice yields and income between the NERICA adopters and non-adopters. Further, the results of the framework, based on the Local Average Treatment Effect (LATE) estimates, which allowed a causal interpretation, showed that NERICA adoption significantly increased average rice yields and annual income of small-scale rice farmers by 157 kg per hectare and $148, respectively.     

Statistical Programs for High Speed Computers

Eight-run two level factorial and fractional factorial designs are examined from two points of view: (1) the number of levels which must be changed in performing the design, (2) the effect of a first order time trend on the main effects. It is found that only a few run orders are desirable from these viewpoints and thus randomization of the runs is likely, in general, to lead to an unfavorable sequence.     

Statistical Programs for High Speed Computers

It is pointed out that the inferential theory of the segmented regression model depends strongly on whether or not continuity at the change-point is assumed. In particular, if it is not, the likelihood ratio test for the presence of two segments tends to infinity.     

Statistical Programs for High Speed Computers

Two objectives of much experimentation in science and engineering are (i) to establish the form of an adequate mathematical model for the system being investigated and (ii) to obtain precise estimates of the model parameters. In the past, statistical design procedures have been proposed for tackling either one of these problems separately. Investigators, however, frequently want to perform experiments which will shed light on both questions simultaneously. In this paper, therefore, we present a design criterion which takes both objectives into account. The basic design strategy is to emphasize model discrimination when there is considerable doubt as to which model is best and gradually shifting the emphasis to parameter estimation as experimentation progresses and discrimination is accomplished. It is assumed that experiments can be performed sequentially. The use of the design criterion is illustrated with an example.     

Design and analysis adaptivity in multiresolution topology optimization

Multiresolution topology optimization (MTO) methods involve decoupling of the design and analysis discretizations, such that a high-resolution design can be obtained at relatively low analysis costs. Recent studies have shown that the MTO method can be approximately 3 and 30 times faster than the traditional topology optimization method for two-dimensional (2D) and three-dimensional (3D) problems, respectively. To further exploit the potential of decoupling analysis and design, we propose a dp-adaptive MTO method, which involves locally increasing/decreasing the polynomial degree of the shape functions (p) and the design resolution (d). The adaptive refinement/coarsening is performed using a composite refinement indicator that includes criteria based on analysis error, presence of intermediate densities, as well as the occurrence of design artifacts referred to as QR-patterns. While standard MTO must rely on filtering to suppress QR-patterns, the proposed adaptive method ensures efficiently that these artifacts are suppressed in the final design, without sacrificing the design resolution. The applicability of the dp-adaptive MTO method is demonstrated on several 2D mechanical design problems. For all the cases, significant speedups in computational time are obtained. In particular for design problems involving low material volume fractions, speedups of up to a factor of 10 can be obtained over the conventional MTO method.     

Development and management of geomembrane liner hippos

The paper describes the authors experience with the formation of geomembrane liner hippos, which is the spherical deformation of the liner within a liquid or viscous medium. The paper presents a summary of six case histories, describing the key features of the hippo and how it formed. The paper also describes the management measures introduced to address the hippo feature.     

The phenomenon of all-elements-sleeping-beauties in scientific literature

The phenomenon of all-elements-sleeping-beauties in science is revealed by four special cases. The ??sleeping beauties?? prick their fingers on the ??spindles?? so that they fall into sleep then are awakened by their ??princes??. The authors speculate that the phenomenon could happen in scientific literatures with high quality.