Empty Rows Research Articles

Win-Win Policy For Developing and Industrial Nations: Six Examples

Win-win policy analysis in this context can be defined as handing policy problems by finding solutions that exceed the best initial expectations of conservatives, liberals, Republicans, Democrats, or whoever are the major groups, sides, or viewpoints in the policy dispute. Win-win is also called super-optimizing or doing better than the previous best of all major groups. There are basically five steps to win-win policy analysis: 1. What are the major goals of conservatives, liberals, or other major groups who are disputing what policy should be adopted for a given policy problem? 2. What are the major alternatives of those groups for dealing with the policy problem? 3. What are the relations between each major alternative and each major goal? In their simplest form, these relations can be expressed in terms of a minus sign (relatively adverse relation) and plus sign (relative conducive relation), and a zero (neither adverse nor conducive relation). 4. What new alternative is there that might be capable of: a. achieving the conservative goals even better than the conservative alternative, and b. simultaneously capable of achieving the liberal goals even more than the liberal alternative? Whatever new alternative meets these two criteria is a win-win alternative or a super-optimum solution. 5. Is the proposed win-win alternative capable of getting over various hurdles that frequently exist. These hurdles may be political, administative, technological, legal, psychological, and economic in random order. Win-win solutions should also consider how to upgrade workers and firms that may be displaced by downsizing due to increased productivity, free trade, defense conversion, immigration, merit treatment, labor utilization, creativity, and related factors. There are various ways of facilitating ideas for win-win solutions. Some of the ways include: 1. Expand the resources available so that both liberal and conservative goals can be achieved. 2. Modify the liberal alternatives so they also achieve the conservative goals, and/or modify the conservative alternatives so they also achieve the liberal goals. 3. Redefine the problem to emphasize goals, rather than alternatives. 4. Fully combine alternatives that are not mutually exclusive. 5. Make use of a mind-stimulating matrix that shows goals on the columns, alternatives on the rows, and scores or directions of relations in the cells. Have an empty row at the bottom of the matrix for inserting words and data on possible win-win solutions.

Null Model Analysis of Species Co-Occurrence Patterns

The analysis of presence–absence matrices with “null model” randomization tests has been a major source of controversy in community ecology for over two decades. In this paper, I systematically compare the performance of nine null model algorithms and four co-occurrence indices with respect to Type I and Type II errors. The nine algorithms differ in whether rows and columns are treated as fixed sums, equiprobable, or proportional. The three models that maintain fixed row sums are invulnerable to Type I errors (false positives). One of these three is a modified version of the original algorithm of E. F. Connor and D. Simberloff. Of the four co-occurrence indices, the number of checkerboard combinations and the number of species combinations may be prone to Type II errors (false negatives), and may not reveal significant patterns in noisy data sets. L. Stone and A. Robert's checkerboard score has good power for detecting species pairs that do not co-occur together frequently, whereas D. Schluter's V ratio reveals nonrandom patterns in the row and column totals of the matrix. Degenerate matrices (matrices with empty rows or columns) do not greatly alter the outcome of null model analyses. The choice of an appropriate null model and index may depend on whether the data represent classic “island lists” of species in an archipelago or standardized “sample lists” of species collected with equal sampling effort. Systematic examination of a set of related null models can pinpoint how violation of the assumptions of the model contributes to nonrandom patterns.

NULL MODEL ANALYSIS OF SPECIES CO-OCCURRENCE PATTERNS

The analysis of presence-absence matrices with ''null model'' randomization tests has been a major source of controversy in community ecology for over two decades. In this paper, I systematically compare the performance of nine null model algorithms and four co-occurrence indices with respect to Type I and Type II errors. The nine algorithms differ in whether rows and columns are treated as fixed sums, equiprobable, or proportional. The three models that maintain fixed row sums are invulnerable to Type I errors (false positives). One of these three is a modified version of the original algorithm of E. F. Connor and D. Simberloff. Of the four co-occurrence indices, the number of checkerboard com- binations and the number of species combinations may be prone to Type II errors (false negatives), and may not reveal significant patterns in noisy data sets. L. Stone and A. Robert's checkerboard score has good power for detecting species pairs that do not co- occur together frequently, whereas D. Schluter's V ratio reveals nonrandom patterns in the row and column totals of the matrix. Degenerate matrices (matrices with empty rows or columns) do not greatly alter the outcome of null model analyses. The choice of an ap- propriate null model and index may depend on whether the data represent classic ''island lists'' of species in an archipelago or standardized ''sample lists'' of species collected with equal sampling effort. Systematic examination of a set of related null models can pinpoint how violation of the assumptions of the model contributes to nonrandom patterns.

The diffusion of oxygen on W(110) The influence of the p(2 × 1) ordering

Tracer and chemical surface diffusion coefficients for oxygen chemisorbed on W(110) have been investigated by means of Monte Carlo modeling, taking full advantage of the numerical power of a supermassive parallel supercomputer. The simulations were performed for a lattice gas with up to fourth nearest neighbor interactions. These were chosen to reproduce the experimentally determined O W(110) phase diagram for θ ≤ 0.5 which shows a dominating p(2 × 1) phase around half coverage. It was found that the p(2 × 1) ordering strongly influences surface diffusion at low temperatures. The tracer diffusion coefficient shows a strong minimum slightly below half coverage and a very small maximum above half coverage. This behavior is attributed to the change of the dominating lattice defects of the p(2 × 1) phase from vacancies in filled rows below θ = 0.5 to interstitials in empty rows above θ = 0.5. For the p(2 × 1) ordered lattice gas phase the chemical diffusion coefficient exhibits a strong maximum which becomes more pronounced as the temperature is lowered. This is attributed to the behavior of mean square fluctuations 〈(δN) 2〉 〈N〉 , i.e. the inverse of the thermodynamic factor, which dominates the chemical diffusion coefficient under these circumstances. The p(2 × 1) ordering causes the surface diffusion to become highly anisotropic. Attempts to simulate the increase in activation energy with coverage found experimentally showed that this was not possible without invoking adsorbate induced changes in the saddle point energies for diffusion.

Presolve Analysis of Linear Programs Prior to Applying an Interior Point Method

Several issues concerning an analysis of large and sparse linear programming problems prior to solving them with an interior point based optimizer are addressed in this paper. Three types of presolve procedures are distinguished. Routines from the first class repeatedly analyze an LP problem formulation: eliminate empty or singleton rows and columns, look for primal and dual forcing or dominated constraints, tighten bounds for variables and shadow prices or just the opposite, relax them to find implied free variables. The second type of analysis aims at reducing a fill-in of the Cholesky factor of the normal equations matrix used to compute orthogonal projections and includes a heuristic for increasing the sparsity of the LP constraint matrix and a technique of splitting dense columns in it. Finally, routines from the third class detect, and remove, different linear dependecies of rows and columns in a constraint matrix. Computational results on problems from the Netlib collection, including some recently added infeasible ones, are given.

On Ensuring Multilayer Wirability by Stretching Layouts

Every knock‐knee layout is four‐layer wirable. However, there are knock‐knee layouts that cannot be wired in less than four layers. While it is easy to determine whether a knock‐knee layout is one‐layer wirable or two‐layer wirable, the problem of determining three‐layer wirability of knock‐knee layouts is NP‐complete. A knock‐knee layout may be stretched vertically (horizontally) by introducing empty rows (columns) so that it can be wired in fewer than four layers. In this paper we discuss two different types of stretching schemes. It is known that under these two stretching schemes, any knock‐knee layout is three‐layer wirable by stretching it up to (4/3) of the knock‐knee layout area (upper bound). We show that there are knock‐knee layouts that when stretched and wired in three layers under scheme I (II) require at least 1.2 (1.07563) of the original layout area. Our lower bound for the area increase factor can be used to guide the search for effective stretching‐based dynamic programming three‐layer wiring algorithms similar to the one presented in [8].

Coupled-chain approximation for driven lattice-gas models.

A coupled-chain approximation is presented for lattice-gas models driven by an external electric field. The method is used to study a square lattice gas exhibiting anisotropic ground states consisting of alternately occupied and empty rows or columns. This approximation suggests a small decrease in T c when increasing the electric field and the continuous phase transition becomes first order

Stretching and three-layer wiring planar layouts

It is well known that every planar layout is four-layer wirable. If we decrease the number of layers, there are planar layouts that cannot be wired in three layers and the problem of determining whether a planar layout is three-layer wirable is NP-complete. A planar layout may be stretched vertically (horizontally) by introducing empty rows (columns). Clearly, stretching a planar layout increases its area; however, if it is stretched in appropriate locations it can be wired in fewer than four layers. It is well known that every planar layout W can be stretched and two-layer wired in an area not larger than twice the area of W. The bound on the wiring area is best possible, i.e. there are planar layouts that need to be stretched by this factor of two. Every planar layout W can be stretched and three-layer wired in an area not larger than ( 4 3 ) times the area of W. The bound on the wiring area is best possible for wirings obtained by partitioning diagonal diagrams, i.e. there are planar layouts that need to be stretched by the factor 4 3 when the wiring is obtained by partitioning diagonal diagrams. In this paper we investigate the problem of stretching and then wiring in three layers a planar layout. We show that for a class of planar layouts, the additional area is no more than 1 4 of the planar layout area. We develop an algorithm to construct an optimal area layout for the case when each of the sublayouts must be wirable by one of a given set of algorithms.

Structure of the Ag/Si(111) surface by scanning tunneling microscopy.

We present the first real-space images, obtained by scanning tunneling microscopy, which show the ordered atomic structures of the Ag/Si(111) surface for several submonolayer coverages. The (\ensuremath{\surd}3 \ifmmode\times\else\texttimes\fi{} \ensuremath{\surd}3 )R30\ifmmode^\circ\else\textdegree\fi{} surface is shown to be of the honeycomb structure, and the room-temperature coverage is determined to be theta=(2/3). The (1\ifmmode\times\else\texttimes\fi{}3) overlayer consists predominantly of rows of Ag atoms separated by two empty rows. At low coverage the surface shows long-range 7\ifmmode\times\else\texttimes\fi{}7 order, but patches occur within the 7\ifmmode\times\else\texttimes\fi{}7 cells whree Si adatoms are absent.

Ordering of hydrogen overlayers on the (110) surfaces of nickel and palladium.

The adsorption of hydrogen on the (110) surfaces of Ni and Pd substrates is studied within an embedded-cluster model. The preferred adsorption sites for the H atoms are found to be buckled-bridge positions. Calculation of the pair interactions shows that the most stable configuration on the surface of both substrates is the zigzag chain with H atoms located on either side of the (110) rows. Alternation between filled and empty (110) rows and between zigzag chains of different phase is expected for Ni, while for Pd the most favorable configuration is the occupation of all (110) rows by parallel chains. These results which compare well with the available experimental data are discussed in terms of the electronic properties of these systems.

Coadsorption de l'oxygène et du monoxyde de carbone sur des surfaces de rhénium

Temperature programmed desorption ( 2.65 K sec ) has been used to study carbon monoxide and mixed layers of carbon monoxide and oxygen on rhenium ribbons, strongly oriented parallel to the (0001) plane. Four binding states, populated in decreasing energy have been detected. Interpretation of the results on β states agrees qualitatively with King's model postulating dissociation of carbon monoxide molecules and a repulsive interaction energy between carbon and oxygen atoms. However, in the coadsorbed layers studies, it is shown that all the oxygen atoms do not play a part in the recombination process, during desorption, and that when oxygen is adsorbed after carbon monoxide, a displacement reaction occurs, due to apparent transfer from β states towards molecular α states. Optimization of the results on pure carbon monoxide layers leads to an interactional energy ω, equal to 3 kcal mole , and is only possible if is assumed that β states are formed on alternatively filled and empty rows.