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Modeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method. Based upon short courses delivered by the authors, it provides a complete and current compendium of fundamental to advanced methodologies along with exercises at the end of each chapter to improve understanding, as well as data and a solutions manual which is available on an accompanying website. Complementing Pawlowsky-Glahn's earlier collective text that provides an overview of the state-of-the-art in this field, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.

Is vision necessary for the development of the categorical organization of the Ventral Occipito-Temporal Cortex (VOTC)? We used fMRI to characterize VOTC responses to eight categories presented acoustically in sighted and early blind individuals, and visually in a separate sighted group. We observed that VOTC reliably encodes sound categories in sighted and blind people using a representational structure and connectivity partially similar to the one found in vision. Sound categories were, however, more reliably encoded in the blind than the sighted group, using a representational format closer to the one found in vision. Crucially, VOTC in blind represents the categorical membership of sounds rather than their acoustic features. Our results suggest that sounds trigger categorical responses in the VOTC of congenitally blind and sighted people that partially match the topography and functional profile of the visual response, despite qualitative nuances in the categorical organization of VOTC between modalities and groups. The world is full of rich and dynamic visual information. To avoid information overload, the human brain groups inputs into categories such as faces, houses, or tools. A part of the brain called the ventral occipito-temporal cortex (VOTC) helps categorize visual information. Specific parts of the VOTC prefer different types of visual input; for example, one part may tend to respond more to faces, whilst another may prefer houses. However, it is not clear how the VOTC characterizes information. One idea is that similarities between certain types of visual information may drive how information is organized in the VOTC. For example, looking at faces requires using central vision, while looking at houses requires using peripheral vision. Furthermore, all faces have a roundish shape while houses tend to have a more rectangular shape. Another possibility, however, is that the categorization of different inputs cannot be explained just by vision, and is also be driven by higher-level aspects of each category. For instance, how humans use or interact with something may also influence how an input is categorized. If categories are established depending (at least partially) on these higher-level aspects, rather than purely through visual likeness, it is likely that the VOTC would respond similarly to both sounds and images representing these categories. Now, Mattioni et al. have tested how individuals with and without sight respond to eight different categories of information to find out whether or not categorization is driven purely by visual likeness. Each category was presented to participants using sounds while measuring their brain activity. In addition, a group of participants who could see were also presented with the categories visually. Mattioni et al. then compared what happened in the VOTC of the three groups – sighted people presented with sounds, blind people presented with sounds, and sighted people presented with images – in response to each category. The experiment revealed that the VOTC organizes both auditory and visual information in a similar way. However, there were more similarities between the way blind people categorized auditory information and how sighted people categorized visual information than between how sighted people categorized each type of input. Mattioni et al. also found that the region of the VOTC that responds to inanimate objects massively overlapped across the three groups, whereas the part of the VOTC that responds to living things was more variable. These findings suggest that the way that the VOTC organizes information is, at least partly, independent from vision. The experiments also provide some information about how the brain reorganizes in people who are born blind. Further studies may reveal how differences in the VOTC of people with and without sight affect regions typically associated with auditory categorization, and potentially explain how the brain reorganizes in people who become blind later in life.

• Meta-analyses enable synthesis of results from globally distributed experiments to draw general conclusions about the impacts of global change factors on ecosystem function. Traditional meta-analyses, however, are challenged by the complexity and diversity of experimental results. We illustrate how several key issues can be addressed by a multivariate, hierarchical Bayesian meta-analysis (MHBM) approach applied to information extracted from published studies. • We applied an MHBM to log-response ratios for aboveground biomass (AB, n = 300), belowground biomass (BB, n = 205) and soil CO₂ exchange (SCE, n = 544), representing 100 studies. The MHBM accounted for study duration, climate effects and covariation among the AB, BB and SCE responses to elevated CO₂ (eCO₂) and/or warming. • The MHBM revealed significant among-study covariation in the AB and BB responses to experimental treatments. The MHBM imputed missing duration (4.2%) and climate (6%) data, and revealed that climate context governs how eCO₂ and warming impact ecosystem function. Predictions identified biomes that may be particularly sensitive to eCO₂ or warming, but that are under-represented in global change experiments. • The MHBM approach offers a flexible and powerful tool for synthesising disparate experimental results reported across multiple studies, sites and response variables.

Geopolymer concrete (GPC) has drawn widespread attention as a universally accepted ideal green material to improve environmental conditions in recent years. The present study systematically quantifies and compares the environmental impact of fly ash GPC and ordinary Portland cement (OPC) concrete under different strength grades by conducting life cycle assessment (LCA). The alkali activator solution to fly ash ratio (S/F), sodium hydroxide concentration (CNaOH), and sodium silicate to sodium hydroxide ratio (SS/SH) were further used as three key parameters to consider their sensitivity to strength and CO2 emissions. The correlation and influence rules were analyzed by Multivariate Analysis of Variance (MANOVA) and Gray Relational Analysis (GRA). The results indicated that the CO2 emission of GPC can be reduced by 62.73%, and the correlation between CO2 emission and compressive strength is not significant for GPC. The degree of influence of the three factors on the compressive strength is CNaOH (66.5%) > SS/SH (20.7%) > S/F (9%) and on CO2 emissions is S/F (87.2%) > SS/SH (10.3%) > CNaOH (2.4%). Fly ash GPC effectively controls the environmental deterioration without compromising its compressive strength; in fact, it even in favor.

A reference guide for applications of SEM using Mplus Structural Equation Modeling: Applications Using Mplus is intended as both a teaching resource and a reference guide. Written in non-mathematical terms, this book focuses on the conceptual and practical aspects of Structural Equation Modeling (SEM). Basic concepts and examples of various SEM models are demonstrated along with recently developed advanced methods, such as mixture modeling and model-based power analysis and sample size estimate for SEM. The statistical modeling program, Mplus, is also featured and provides researchers with a flexible tool to analyze their data with an easy-to-use interface and graphical displays of data and analysis results. Key features: Presents a useful reference guide for applications of SEM whilst systematically demonstrating various advanced SEM models, such as multi-group and mixture models using Mplus. Discusses and demonstrates various SEM models using both cross-sectional and longitudinal data with both continuous and categorical outcomes. Provides step-by-step instructions of model specification and estimation, as well as detail interpretation of Mplus results. Explores different methods for sample size estimate and statistical power analysis for SEM. By following the examples provided in this book, readers will be able to build their own SEM models using Mplus. Teachers, graduate students, and researchers in social sciences and health studies will also benefit from this book.

Groundwater contamination studies are important to understand the risks to public health. In this study, groundwater quality, major ion chemistry, sources of contaminants, and related health risks were evaluated for North-West Delhi, India, a region with a rapidly growing urban population. Groundwater samples collected from the study area were analysed for physicochemical parameters — pH, electrical conductivity, total dissolved solids, total hardness, total alkalinity, carbonate, bicarbonate, chloride, nitrate, sulphate, fluoride, phosphate, calcium, magnesium, sodium and potassium. Investigation of hydrochemical facies revealed that bicarbonate was the dominant anion while magnesium was the dominant cation. Multivariate analysis using principal component analysis and Pearson correlation matrix indicated that major ion chemistry in the aquifer under study is primarily due to mineral dissolution, rock-water interactions and anthropogenic factors. Water quality index values showed that only 20% of the samples were acceptable for drinking. Due to high salinity, 54% of the samples were unfit for irrigation purposes. Nitrate and fluoride concentrations ranged from 0.24 to 380.19 mg/l and 0.05 to 7.90 mg/l, respectively due to fertilizer use, wastewater infiltration and geogenic processes. The health risks from high levels of nitrate and fluoride were calculated for males, females, and children. It was found that health risk from nitrate is more than fluoride in the study region. However, the spatial extent of risk from fluoride is more indicating that more people suffer from fluoride pollution in the study area. The total hazard index for children was found to be more than adults. Continuous monitoring of groundwater and application of remedial measures are recommended to improve the water quality and public health in the region.

Groundwater is essential for the sustainable development of the Guanzhong Basin, China, and its quality is mainly controlled by hydrogeochemical processes and anthropogenic pollution. This study used statistical and multivariate statistical analysis approaches to recognize the hydrogeochemical processes and affecting factors of groundwater in the central part of the Guanzhong Basin. Correlations among 14 hydrochemical parameters were statistically examined. Principal component analysis (PCA), factor analysis (FA), and hierarchical cluster analysis (HCA) techniques were applied to analyze the physicochemical variables to understand the affecting factors of groundwater quality in the study area. The correlation analysis results indicate that cation exchange is the dominant process affecting the concentration of Na+ and Ca2+ in the groundwater. Both the PCA and FA indicate that minerals dissolution/precipitation and human activities are the key factors that affect groundwater quality. All parameters except CO32− and pH increase from C1 to C4 obtained through the Q mode HCA. C4 has a hydrochemical type of SO4–Na·K, indicating that the sample of this cluster is primarily influenced by anthropogenic processes.

The development and application of multivariate statistical techniques in process monitoring has gained substantial interest over the past two decades in academia and industry alike. Initially developed for monitoring and fault diagnosis in complex systems, such techniques have been refined and applied in various engineering areas, for example mechanical and manufacturing, chemical, electrical and electronic, and power engineering. The recipe for the tremendous interest in multivariate statistical techniques lies in its simplicity and adaptability for developing monitoring applications. In contrast, competitive model, signal or knowledge based techniques showed their potential only whenever cost-benefit economics have justified the required effort in developing applications.Statistical Monitoring of Complex Multivariate Processes presents recent advances in statistics based process monitoring, explaining how these processes can now be used in areas such as mechanical and manufacturing engineering for example, in addition to the traditional chemical industry.This book:Contains a detailed theoretical background of the component technology.Brings together a large body of work to address the field's drawbacks, and develops methods for their improvement.Details cross-disciplinary utilization, exemplified by examples in chemical, mechanical and manufacturing engineering.Presents real life industrial applications, outlining deficiencies in the methodology and how to address them.Includes numerous examples, tutorial questions and homework assignments in the form of individual and team-based projects, to enhance the learning experience.Features a supplementary website including Matlab algorithms and data sets.This book provides a timely reference text to the rapidly evolving area of multivariate statistical analysis for academics, advanced level students, and practitioners alike.

Quality checks, assessments, and the assurance of food products, raw materials, and food ingredients is critically important to ensure the safeguard of foods of high quality for safety and public health. Nevertheless, quality checks, assessments, and the assurance of food products along distribution and supply chains is impacted by various challenges. For instance, the development of portable, sensitive, low-cost, and robust instrumentation that is capable of real-time, accurate, and sensitive analysis, quality checks, assessments, and the assurance of food products in the field and/or in the production line in a food manufacturing industry is a major technological and analytical challenge. Other significant challenges include analytical method development, method validation strategies, and the non-availability of reference materials and/or standards for emerging food contaminants. The simplicity, portability, non-invasive, non-destructive properties, and low-cost of NIR spectrometers, make them appealing and desirable instruments of choice for rapid quality checks, assessments and assurances of food products, raw materials, and ingredients. This review article surveys literature and examines current challenges and breakthroughs in quality checks and the assessment of a variety of food products, raw materials, and ingredients. Specifically, recent technological innovations and notable advances in quartz crystal microbalances (QCM), electroanalytical techniques, and near infrared (NIR) spectroscopic instrument development in the quality assessment of selected food products, and the analysis of food raw materials and ingredients for foodborne pathogen detection between January 2019 and July 2020 are highlighted. In addition, chemometric approaches and multivariate analyses of spectral data for NIR instrumental calibration and sample analyses for quality assessments and assurances of selected food products and electrochemical methods for foodborne pathogen detection are discussed. Moreover, this review provides insight into the future trajectory of innovative technological developments in QCM, electroanalytical techniques, NIR spectroscopy, and multivariate analyses relating to general applications for the quality assessment of food products.

Changes in key odorants of shaken black tea (SBT) during its manufacture were determined using headspace solid-phase microextraction (HS-SPME) combined with comprehensive two-dimensional gas chromatography–time-of-flight mass spectrometry (GC × GC–TOFMS) and multivariate data analysis. A total of 241 volatiles was identified, comprising 49 aldehydes, 40 esters, 29 alcohols, 34 ketones, 30 aromatics, 24 alkenes, 17 alkanes, 13 furans, and 5 other compounds. A total of 27 volatiles had average relative odor activity values (rOAVs) greater than 1, among which (E)-β-ionone, (E,Z)-2,6-nonadienal, and 1-octen-3-one exhibited the highest values. According to the criteria of variable importance in projection (VIP) > 1, p < 0.05, and |log2FC| > 1, 61 discriminatory volatile compounds were screened out, of which 26 substances were shared in the shaking stage (FL vs. S1, S1 vs. S2, S2 vs. S3). The results of the orthogonal partial least squares discriminate analysis (OPLS-DA) differentiated the influence of shaking, fermentation, and drying processes on the formation of volatile compounds in SBT. In particular, (Z)-3-hexenol, (Z)-hexanoic acid, 3-hexenyl ester, (E)-β-farnesene, and indole mainly formed in the shaking stage, which promoted the formation of the floral and fruity flavor of black tea. This study enriches the basic theory of black tea flavor quality and provide the theoretical basis for the further development of aroma quality control.