Jose Camacho – författare
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3 produkter
3 produkter
766 kr
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Information Structure in Indigenous Languages of the Americas
Syntactic Approaches
Inbunden, Engelska, 2010
2 426 kr
Skickas inom 5-8 vardagar
The study of the interaction between syntax and information structure has attracted a great deal of attention since the publication of foundational works on this subject such as Enric Vallduví's (1992) The Informational Component and Knud Lambrecht's (1994) Information Structure and Sentence Form. The book inserts itself in this contemporary interest by providing a collection of articles on different aspects of the syntax-pragmatics interface in the indigenous languages of The Americas. The first chapter provides a brief introduction of the some of the basic descriptive issues addressed in them, and of some of the theoretical tools that have been developed to analyze them. The reader finds articles that focus mostly on empirical issues, while others are mostly oriented to theoretical issues. Diverse theoretical approaches are addressed, including Minimalism, Optimality-theoretic syntax, and Meaning-Text Theory. The volume includes articles on the following topics: the grammatical means to encode pragmatic notions in Tariana (A. Aikhenvald); the relation between clause structure and information structure in Lushootseed (D. Beck); the split distribution of null subjects in Shipibo (J. Camacho and J. Elías-Ulloa); the syntactic structure of left-peripheral discourse-related functions in Kuikuro (B. Franchetto and M. Santos), an agglutinative and head final language; word order and focus patterns in Yaqui (L. Guerrero and V. Belloro); SVO and topicalization in Yucatec Maya (R. Gutiérrez-Bravo and J. Monforte); the structure of the left-periphery in Karaja (Maia) and the interaction between the wh-words and polarity sensitivity in Southern Quechua (L. Sánchez).
Data Science for Batch Processes
Statistical Learning, Monitoring and Understanding
Inbunden, Engelska, 2026
1 490 kr
Kommande
Overview of methods for bilinear modeling of batch data, including theory, methodologies and examples for experienced professionals in the biotech, pharmaceutical and petrochemical industries.Process Analytical Technologies (PAT) have become increasingly important with the establishment of the quality-by-design paradigm in industrial processes, particularly where batch operation is standard. PAT plays an instrumental role in advancing process understanding and operational efficiency, while strengthening safety and reliability to ensure consistent on-spec product quality and minimize environmental impact. Empirical methods based on latent variables, often referred to as chemometric methods, are a main component of PAT. When used alongside Batch Multivariate Statistical Process Control (BMSPC), these methods enable the timely detection and diagnosis of process upsets. Furthermore, process understanding can be improved by applying Latent Variable Models (LVMs), such as Principal Component Analysis (PCA) and Partial Least Squares (PLS), particularly relevant in batch processes, where the inherent complexity of the model results in a high degree of uncertainty in the operation.Data Science for Batch Processes: Statistical Learning, Monitoring and Understanding provides a comprehensive and rigorous examination of the bilinear modeling and monitoring of batch processes, comprising data alignment, pre-processing, three-way-to-two-way data transformation, data analysis and design of monitoring systems, including practical challenges and considerations when analyzing multi-dimensional batch data. Case studies and hands-on MATLAB examples using the MVBatch toolbox bridge theory and practice, illustrating how these methods can be applied.Data Science for Batch Processes: Statistical Learning, Monitoring and Understanding is an essential guide for professionals and academics who seek both foundational knowledge and advanced techniques in batch processes and data analysis.