Soil spectroscopy has experienced a tremendous increase in soil property characterisation, and can be used not only in the laboratory but also from the space (imaging spectroscopy). Partial least squares (PLS) regression is one of the most common approaches for the calibration of soil properties using soil spectra. Besides functioning as a calibration method, PLS can also be used as a dimension...

Partial Least Squares (PLS) dimension reduction is known to give good prediction accuracy in the context of classification with high-dimensional microarray data. In this paper, the classification procedure consisting of PLS dimension reduction and linear discriminant analysis on the new components is compared with some of the best state-of-the-art classification methods. Moreover, a boosting al...

BACKGROUND Primary lateral sclerosis (PLS) is a progressive upper motor neuron neurodegenerative condition. The diagnosis is made using clinical history, objective neurological assessment, and exclusion of other neurodegenerative disorders. OBJECTIVE To evaluate the role of fluorodeoxyglucose F18 positron emission tomography and 3-dimensional stereotactic surface projection in the diagnosis o...

Understanding the processes that determine the distribution of populations is a fundamental goal in ecology. In this study, I determined the relative contribution of space and the biotic and abiotic environment to the distribution of the palm-leaf skeletonizer Homaledra sabalella (PLS; Lepidoptera: Coleophoridae) among patchily distributed dwarf palmettos (Sabal minor; Arecaceae). Based on surv...

Partial least squares (PLS) has proven to be a important multivariate analytic tool for positron emission tomographic and, more recently, event-related potential (ERP) data. The application to ERP incorporates the ability to analyze space and time together, a feature that has obvious appeal for event-related functional magnetic resonance imaging (fMRI) data. This paper presents the extension of...

Segmentation is a critical issue for PLS path modeling. Group specific model estimates can significantly differ from other groups or the overall model estimates (Ringle, Wende, & Will, 2007). Sequential segmentation strategies on the manifest data level, such as K-means or tree clustering, usually fail to identify groups of data with distinctive inner path model estimates (Ringle, 2006). The au...

In many domains (marketing, social sciences, etc.) the variables of interest are often not directly observable. Complex causal relationships between such latent variables may be modeled by means of Structural Equation Models (SEM). Two different methodologies exist for the estimation of such models: SEM-ML (Maximum Likelihood Approach to Structural Equation Models), also known as LISREL (Linear...

Primary lateral sclerosis (PLS) has been regarded as a rare, extreme form of amyotrophic lateral sclerosis (ALS). Like ALS, it is a clinical diagnosis without established biomarkers. We sought to explore loss of cerebral myelin in relation to clinical features, including cognitive impairment, in cases of both ALS and PLS. A novel MRI sequence (mcDESPOT) sensitive to water pools within myelin an...

A multivariate dominant factor based non-linearized PLS model is proposed. The intensities of different lines were taken to construct a multivariate dominant factor model, which describes the dominant concentration information of the measured species. In constructing such a multivariate model, non-linear transformation of multi characteristic line intensities according to the physical mechanism...

در این پژوهش سعی می‌­شود مدلی برای شبیه‌­سازی چرخه عمر صنعت برق با استفاده از شبیه‌سازی عامل­بنیان ارائه شود، مدل، 5 عامل استخراج شد و شبیه­‌سازی به کمک نرم‌افزار Anylogic صورت پذیرفت. بهینه‌­سازی مدل چهار سناریو نظر خبرگان شد. سناریوی نخست، جذابیت نیروگاه گازی سیکل ترکیبی کاهش یافت بر دو دیگر افزوده نتیجه افزایش تولید آبی است که توجه کمبود منابع کشور به‌صرفه نیست. دوم ورود یک فناوری جدید میزان...