Modeling large scale species abundance with latent spatial processes

Modeling species abundance patterns using local environmental features is an important, current problem in ecology. The Cape Floristic Region (CFR) in South Africa is a global hot spot of diversity and endemism, and provides a rich class of species abundance data for such modeling. Here, we propose a multi-stage Bayesian hierarchical model for explaining species abundance over this region. Our model is specified at areal level, where the CFR is divided into roughly $37{,}000$ one minute grid cells; species abundance is observed at some locations within some cells. The abundance values are ordinally categorized. Environmental and soil-type factors, likely to influence the abundance pattern, are included in the model. We formulate the empirical abundance pattern as a degraded version of the potential pattern, with the degradation effect accomplished in two stages. First, we adjust for land use transformation and then we adjust for measurement error, hence misclassification error, to yield the observed abundance classifications. An important point in this analysis is that only $28$ of the grid cells have been sampled and that, for sampled grid cells, the number of sampled locations ranges from one to more than one hundred. Still, we are able to develop potential and transformed abundance surfaces over the entire region. In the hierarchical framework, categorical abundance classifications are induced by continuous latent surfaces. The degradation model above is built on the latent scale. On this scale, an areal level spatial regression model was used for modeling the dependence of species abundance on the environmental factors.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS335 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Non-linear interval-valued intuitionistic fuzzy number (IVIFN) approach for an EPQ model with optimal investment in defective items considering learning effects

In recent years, research has primarily focused on addressing imprecision in linear forms; however, uncertainty often occurs in non-linear forms as well. This study extends the concept of Linear Interval-Valued Intuitionistic Fuzzy Numbers (LIVIFN) to Non-Linear Interval-Valued Intuitionistic Fuzzy Numbers (NLIVIFN), establishing their formulation and parametric structure along with their logical significance. Different geometric representations of NLIVIFN are analysed and classified. Furthermore, an intuitification technique is developed, which holds significant value for improving crispification skills. A realistic example is presented to demonstrate the impact of NLIVIFN on an Economic Production Quantity (EPQ) model, focusing on an imperfect product with learning and reworking of defective items. A procedure is introduced to determine the optimal shipment size and defective percentage by minimizing the average expected total cost. Results indicate that investment in learning leads to a 98% recovery rate of defective items, providing economic benefits to manufacturers. Additionally, a 50% increase in demand stimulates learning, increasing production by 36% and reducing defective item production by 51%. Finally, a comparative analysis underscores the value of this novel work, showcasing its effectiveness in addressing non-linear uncertainties and enhancing production processes, cost efficiency, and decision-making in supply chain management

Non-Conventional Copyright: Do New and Atypical Works Deserve Protection?

Enrico Bonadio and Nicola Lucchi (eds.

Connecting Rashba and Dresselhaus spin-orbit interactions to inversion asymmetry in perovskite oxide heterostructures

Inversion asymmetry, combined with spin orbit interaction, leads to Rashba or Dresselhaus effects, or combinations of them that are promising for technologies based on antiferromagnetic spintronics. Since understanding the exact nature of spin-orbit interaction is crucial for developing a technology based on it, mapping the nature of inversion asymmetry with the type of spin-orbit interaction becomes the key. We simulate a perovskite oxide heterostructure LaAlO$_3|$SrIrO$_3|$SrTiO$_3$ preserving the inversion symmetry within density functional theory to demonstrate the relation between the nature of inversion asymmetry and the corresponding Rashba or Dresselhaus-type interaction. With progressive distortion in the heterostructure, we find how the structure inversion asymmetry sets in with distorted bond lengths and bond angles, leading to Rashba effect in the system. Further, introduction of tilted IrO$_6$ octahedra leads to bulk inversion asymmetry, helping a combined Rashba-Dresselhaus interaction to set in. A comparison of the spin textures obtained from our DFT calculations and theoretical modeling helps us identify the exact nature of the interactions. Besides demonstrating the connection between the nature of asymmetry with Rashba and Dresselhaus interactions, our work may serve as a guide to identifying different types of Rashba-like spin-orbit interactions.Comment: 14 pages, 10 figure