Lower-Critical Spin-Glass Dimension from 23 Sequenced Hierarchical Models

The lower-critical dimension for the existence of the Ising spin-glass phase is calculated, numerically exactly, as $d_L = 2.520$ for a family of hierarchical lattices, from an essentially exact (correlation coefficent $R^2 = 0.999999$) near-linear fit to 23 different diminishing fractional dimensions. To obtain this result, the phase transition temperature between the disordered and spin-glass phases, the corresponding critical exponent $y_T$, and the runaway exponent $y_R$ of the spin-glass phase are calculated for consecutive hierarchical lattices as dimension is lowered.Comment: 5 pages, 2 figures, 1 tabl

CYTools: A Software Package for Analyzing Calabi-Yau Manifolds

We provide a user's guide to version 1.0 of the software package CYTools, which we designed to compute the topological data of Calabi-Yau hypersurfaces in toric varieties. CYTools has strong capabilities in analyzing and triangulating polytopes, and can easily handle even the largest polytopes in the Kreuzer-Skarke list. We explain the main functions and the options that can be used to optimize them, including example computations that illustrate efficient handling of large numbers of polytopes. The software, installation instructions, and a Jupyter notebook tutorial can be found at https://cy.tools.Comment: v1: 42 pages, 2 figures. Software package available at https://cy.tool

Fabrication, Characterization and Modeling of Aligned Polyacrylonitrile-Based Electrospun Carbon Nanofibers

Electrospinning is widely used to produce carbon nanofiber from polyacrylonitrile (PAN). The alignment of fibers may vary depending on electrospinning condition. In this study, an electrospinning setup is developed to fabricate aligned and uniform yarns from PAN, employing an adjustable rotating disc. Effects of relative humidity (RH) on fiber diameter and mechanical properties of electrospun, stabilized and carbonized nanofibers are investigated. Average fiber diameter increases from 365 nm to 602 nm by increasing RH 22% to 60%. Additionally, mechanical properties are reduced by increasing RH. Nanofibers are generated at low RH show poor mechanical properties. 22% RH yields the best mechanical properties. Collector geometry and rotating speed influence electrospun nanofiber alignment. The nanofiber diameter distribution, porosity, orientation, and mechanical properties are investigated. A unique approach is adopted to test the nanofiber films in tension using a dynamic mechanical analyzer (DMA). Furthermore, 2-dimensional FEM analysis is performed to investigate electric field distribution around the collector. It is observed that the speed of the rotating disc can help improve the alignment of nanofibers in the film. It is also absolved that the electric field is more intense and uniform on the collector surface for wire and mesh type collectors compared to foil collectors. Nanofibers electrospun with wire type collector show the highest alignment due to the intense uniform electric field and tensile properties of carbonized nanofiber films. Electrode geometry is another electrospinning element that influences the fiber alignment. Three different tip electrode systems are investigated; single blind needle, flash needle, where the needle is located in a copper cylinder and completely flush with the edge of the cylinder, and protruded needle, where the needle passes through a copper cylinder and protrudes 0.5 mm past the edge of the cylinder. Similarly, 2D FEM is studied to obtain electric field distribution of the needle region. The alignment and diameter of nanofibers vary by changes in the needle system when all other electrospinning parameters are kept constant. The flash and protruded type of electrode yields more uniform and better fiber alignment. Furthermore, Taylor cone and straight jet formation dependence on flow rate and applied voltage are investigated using a high speed camera. an average fiber diameter of 422 nm is obtained for needle type while 389 nm is obtained for the protruded needle and fiber alignment was also improved with varying electrode types. Stabilization conditions influence mechanical properties of carbon nanofibers. The effects of hot drawing of electrospun PAN nanofiber yarns and pre-stress during stabilization on the mechanical properties of stabilized yarns is investigated. The as-spun PAN nanofibers are mechanically stretched to stretch ratios (λ) of 1, 2 and 3 at 135 oC and subsequently stabilized at 260°C in air for 180 min under different mechanical pre-stress conditions, up to 5 MPa. Fiber diameter distribution is investigated via SEM, and tensile properties are measured via dynamic DMA. It has been found that stretching significantly improves the tensile strength of electrospun and stabilized fibers, while decreasing average fiber diameter. Pre-stress during stabilization has an important role on mechanical properties. Unstretched fibers show weaker mechanical properties comparing to stretched fibers. A tensile strength of about 401 MPa is obtained for λ=2 produced at 1 MPa pre-stress while stabilizing, compared to about 191 MPa for λ=0. Determining mechanical properties of a single filament carbon nanofibers is an extremely complicated and requires expensive equipment. A statistical model is developed to determine single filament tensile strength from bundle test. A Weibull statistical model is modified to analysis to estimate tensile strength of single filament electrospun carbon nanofiber from bundle test. The tensile strength is obtained 2.52 GPa where the standard deviation of fiber angle distribution is 2.7o. Tensile strength is calculated 1.66 GPa for standard deviation 15.8o while 2.7o is 2.52 GPa. A relation between Weibull parameters and alignment is obtained from experimental results. Tensile strength and failure strain of 0o standard deviation is calculated from estimated Weibull parameters

The Relationship between Perceived Overqualification and Counterproductive Work Behaviors: Moderating Role of Perceived Distributive Justice

Employee behaviors can be classified into two basic groups as positive and negative organizational behaviors. One of the negative organizational behaviors is counterproductive work behaviours. It is aimed to reveal the effects of perceived overqualification on counterproductive work behaviours and moderating role of distributive justice through an empirical study. In this respect, the data obtained from 398 employees in hospitality enterprises was analyzed by means of structural equation modelling (SEM). It is found that there is a positive relationship between perceived overqualification and counterproductive work behaviours, and perceived distributive justice moderates the relationship between perceived overqualification and counterproductive work behaviours towards colleagues. Some theoretical and managerial implications are offered about the variables. Distributive justice is effective in reducing counterproductive work behaviours which emerged from perceived overqualification. Managers need to control the factors that lead to perceived overqualification and implement strategies that can activate catalyst variables, lessening or eliminating its negative consequences. In addition, limitations of the study and suggestions for future studies are provided

Neural Network Field Theories: Non-Gaussianity, Actions, and Locality

Both the path integral measure in field theory and ensembles of neural networks describe distributions over functions. When the central limit theorem can be applied in the infinite-width (infinite-$N$) limit, the ensemble of networks corresponds to a free field theory. Although an expansion in $1/N$ corresponds to interactions in the field theory, others, such as in a small breaking of the statistical independence of network parameters, can also lead to interacting theories. These other expansions can be advantageous over the $1/N$-expansion, for example by improved behavior with respect to the universal approximation theorem. Given the connected correlators of a field theory, one can systematically reconstruct the action order-by-order in the expansion parameter, using a new Feynman diagram prescription whose vertices are the connected correlators. This method is motivated by the Edgeworth expansion and allows one to derive actions for neural network field theories. Conversely, the correspondence allows one to engineer architectures realizing a given field theory by representing action deformations as deformations of neural network parameter densities. As an example, $\phi^4$ theory is realized as an infinite-$N$ neural network field theory.Comment: 49 pages, plus references and appendice

Quality and yield response of soybean (Glycine max L. Merrill) to drought stress in sub–humid environment

The aim of the study was to determine the response of soybean [Glycine max (L.) Merr.] to drought at various stages of development in a sub-humid environment of Turkey. Drought-stress treatments was applied to plants in 2005 and 2006 by withholding irrigation at six critical stages: completely vegetative (fifth trifoliate) (T2), flowering (T3), podding (T4), seed fill (T5), full bloom + podding (T6), and podding + seed fill (T7). Growth and production was compared in each treatment to full irrigated (T1) and nonirrigated (T8) controls. Each drought treatment reduced shoot biomass and seed yield compared to wellwatered plants, but only  non-irrigated plants or plants droughted at vegetative or flowering stages produced fewer seed pods and seeds. Seed protein and oil content was highest among treatments when plants were droughted during the seed filling stage. Yield increased exponentially with crop water use and ranged from 2.1 - 2.5 tons haSup>-1 in non-irrigated plants to 3.5 - 4.0 tons ha-1 in the well-watered controls. However, plants droughted during the vegetative stage of development produced the highest yield per unit of irrigation water applied (that is,  irrigation water use efficiency). This research results will be useful for maximizing soybean production and/or seed quality when irrigation water is limited.Key words: Glycine max, flowering, irrigation, seed development, water use efficiency