Isometric Log-ratio Transformation Research Articles

Using isometric log-ratio in compositional data analysis for developing a groundwater pollution index

This study introduces a novel groundwater pollution index (GPI) formulated through compositional data analysis (CoDa) and robust principal component analysis (RPCA) to enhance groundwater quality assessment. Using groundwater quality monitoring data from sites impacted by the 2010–2011 foot-and-mouth disease outbreak in South Korea, CoDa uncovers critical hydrochemical differences between leachate-influenced and background groundwater. The GPI was developed by selecting key subcompositional parts (NH4+-N, Cl-, and NO3--N) using RPCA, performing the isometric log-ratio (ILR) transformation, and normalizing the results to environmental standards, thereby providing a more precise and accurate assessment of pollution. Validated against government criteria, the GPI has shown its potential as an alternative assessment tool, with its reliability confirmed by receiver operating characteristic curve analysis. This study highlights the essential role of CoDa, especially the ILR -transformation, in overcoming the limitations of traditional statistical methods that often neglect the relative nature of hydrochemical data. Our results emphasize the utility of the GPI in significantly advancing groundwater quality monitoring and management by addressing a methodological gap in the quantitative assessment of groundwater pollution.

A novel point process model for neuronal spike trains

Point process provides a mathematical framework for characterizing neuronal spiking activities. Classical point process methods often focus on the conditional intensity function, which describes the likelihood at any time point given its spiking history. However, these models do not describe the central tendency or importance of the spike train observations. Based on the recent development on the notion of center-outward rank for point process, we propose a new modeling framework on spike train data. The new likelihood of a spike train is a product of the marginal probability on the number of spikes and the probability of spike timings conditioned on the same number. In particular, the conditioned distribution is calculated by adopting the well-known Isometric Log-Ratio transformation. We systematically compare the new likelihood with the state-of-the-art point process likelihoods in terms of ranking, outlier detection, and classification using simulations and real spike train data. This new framework can effectively identify templates as well as outliers in spike train data. It also provides a reasonable model, and the parameters can be efficiently estimated with conventional maximum likelihood methods. It is found that the proposed likelihood provides an appropriate ranking on the spike train observations, effectively detects outliers, and accurately conducts classification tasks in the given data.

CoDA-Based Geo-Electrochemical Prospecting Prediction of Uranium Orebodies in Changjiang Area, Guangdong Province, China

In order to achieve a breakthrough in the exploration of uranium orebodies in the Changjiang area of Guangdong province in China, the geo-electrochemical exploration method is used for mineral resource prediction. The logarithmically and isometric log-ratio (ilr) transformations are applied to the geo-electrochemical data in this study area to extract geochemical anomalies. The relationship between element associations and mineralization is revealed through descriptive statistical analysis and further biplot analysis. Then, the energy spectrum density–area fractal model (S-A model) is used to identify geochemical backgrounds and anomalies. The results show that: (i) the logarithmically and ilr-transformed data are more uniform, and they more or less obey the rules of normal distribution; (ii) the biplot shows that the ilr-transformed data eliminates the closure effect, and the robust principal component analysis (RPCA) has a better indicative significance for element associations—PC1 reveals the mineralization element association dominated by U and the multiple periods of hydrothermal activity; (iii) the S-A method could extract the local anomalies from different geochemical backgrounds, which indicates mineralization is more reliable. Finally, four favorable prospecting targets are delineated based on the geological and geochemical indicators.

Incorporating principal component analysis into Hotelling [formula omitted] control chart for compositional data monitoring

In the manufacturing industry, compositional data (CoDa) is a vital quality characteristic to be monitored. The proposed study has introduced a Hotelling T2 control chart using principal component analysis to monitor CoDa explicitly. The proposed method overcomes the limitations of previous approaches by utilizing isometric log-ratio transformation to map the CoDa into unrestricted real space. A Monte Carlo simulation procedure is employed to evaluate the performance of the proposed multivariate control chart. The simulation involves varying combinations of the number of variables (p) and subgroup sizes to obtain metrics such as the average run length when the process is out-of-control (OOC) and the upper control limit. The article presents a comprehensive analysis of the proposed control chart’s statistical performance. These visual aids facilitate a deeper understanding of the method’s effectiveness in detecting OOC conditions in CoDa. Furthermore, two illustrative examples using synchronous machines and components of electronic scrap are provided to illustrate the practical implementation and effectiveness of the Hotelling T2-based principal component control chart for p=5-part and p=12-part CoDa in real-world scenarios.

Analyzing out-of-control signals of T2 control chart for compositional data using artificial neural networks

Various multivariate control charts (CCs) are applied to monitor compositional data (CoDa) processes post an isometric log-ratio (ilr) transformation aimed at assessing in-control or out-of-control (OOC) conditions. While optimal multivariate CCs effectively detect shifts in the overall mean vector, challenges arise when shifts occur in specific variables rather than the overall mean vector. This complexity in signal interpretation using traditional multivariate CCs prompts the need for improved approaches. To address this issue, this study introduces the application of a multilayer perceptron neural network (MLPNN) with back-propagation (BP) learning to interpret OOC signals in Hotelling’s T2 CC for CoDa. The proposed model aids practitioners in identifying atypical variables responsible for OOC situations instead of focusing solely on mean shifts. This capability to detect atypical variables enhances process control strategies, leading to more efficient industrial operations. The model’s performance is assessed through two cases: one involving p=3-part CoDa and another with p=5-part CoDa. Shifts are introduced by altering variable means using various combinations. For comparison, the study also presents results obtained from multivariate data analysis using MLPNN with BP. The results demonstrate that the MLPNN consistently provides more accurate outcomes in the case of CoDa than multivariate data. Applying the ilr transformation improves the MLPNN’s efficacy in accurately interpreting OOC signals within the CoDa domain. An application is reported to interpret the OOC signal during the working hours of machine operators in an industry.

Spatial Overlay Analysis of Geochemical Singularity Index α-Value of Porphyry Cu Deposit in Gangdese Metallogenic Belt, Tibet, Western China

The statistical modeling with ILR-RPCA-back CLR has two problems when dealing with the closure effect of geochemical data. Firstly, after performing isometric logratio (ilr) transformation, robust principal component analysis (RPCA) is employed for processing. The double-plot diagram illustrates that the element sequence transformation occurs in the first and second principal components, while the unique principal component remains unattainable. Secondly, by transforming both the score and load into the centered logratio (CLR) space using the U matrix, it is possible to obtain a score result that corresponds to the original order of elements according to the CLR = ILR·U formula. However, for obtaining a load result that corresponds to the original order of elements, an alternative formula “CLR = UT·ILR” must be used instead. In order to determine the optimal element assemblage for porphyry copper deposits, this study conducted statistical analysis on mineral assemblages from discovered deposits in the Gangdese metallogenic belt and identified Cu, Mo, Au, Ag, W, and Bi as key elements associated with porphyry copper deposits. Subsequently, by analyzing the singularities of the composite elements, the spatial overlay of the combined element is carried out, and concentration-area (C-A) fractal filtering is applied to identify the anomaly and background areas. To facilitate comparison, we conducted an analysis of various mineral and ore deposit types, revealing the following findings: (1) Combination elements exhibit superior recognition capability than single elements in porphyry copper deposits; (2) Skarn-type copper deposits unrelated to porphyry show a high degree of dissimilarity compared to those related to porphyry; (3) this method offers advantages over the single element method in evaluating porphyry gold deposits by reducing anomaly levels and initial investment during the evaluation stage for porphyry copper anomalies; (4) However, this method has limited ability in distinguishing between porphyry copper and molybdenum deposits.

Aitchison’s Compositional Data Analysis 40 Years on: A Reappraisal

The development of John Aitchison’s approach to compositional data analysis is followed since his paper read to the Royal Statistical Society in 1982. Aitchison’s logratio approach, which was proposed to solve the problematic aspects of working with data with a fixed-sum constraint, is summarized and reappraised. It is maintained that the properties on which this approach was originally built, the main one being subcompositional coherence, are not required to be satisfied exactly—quasi-coherence is sufficient, that is near enough to being coherent for all practical purposes. This opens up the field to using simpler data transformations, such as power transformations, that permit zero values in the data. The additional property of exact isometry, which was subsequently introduced and not in Aitchison’s original conception, imposed the use of isometric logratio transformations, but these are complicated and problematic to interpret, involving ratios of geometric means. If this property is regarded as important in certain analytical contexts, for example, unsupervised learning, it can be relaxed by showing that regular pairwise logratios, as well as the alternative quasi-coherent transformations, can also be quasi-isometric, meaning they are close enough to exact isometry for all practical purposes. It is concluded that the isometric and related logratio transformations such as pivot logratios are not a prerequisite for good practice, although many authors insist on their obligatory use. This conclusion is fully supported here by case studies in geochemistry and in genomics, where the good performance is demonstrated of pairwise logratios, as originally proposed by Aitchison, or Box–Cox power transforms of the original compositions where no zero replacements are necessary.

Statistical depth for point process via the isometric log-ratio transformation

Statistical depth, a useful tool to measure the center-outward rank of multivariate and functional data, is still under-explored in temporal point processes. Recent studies on point process depth proposed a weighted product of two terms - one indicates the depth of the cardinality of the process, and the other characterizes the conditional depth of the temporal events given the cardinality. The second term is of great challenge because of the apparent nonlinear structure of event times, and so far only parametric representations such as Gaussian and Dirichlet densities have been adopted in the definitions. However, these parametric forms ignore the underlying distribution of the process events and are difficult to apply to complicated patterns. To deal with these problems, a novel distribution-based approach is proposed to the conditional depth via the well-known Isometric Log-Ratio (ILR) transformation on the inter-event times. Motivated by a uniform distribution on simplex, the new method, called the ILR depth, is formally defined for general point process via a Time Rescaling. The mathematical properties of the ILR depth are thoroughly examined and the new method is well illustrated by using Poisson and non-Poisson processes to demonstrate its superiority over previous methods. Finally, the ILR depth is applied in a real dataset and the result clearly shows its effectiveness in ranking.

Analyzing abnormal pattern of hotelling [formula omitted] control chart for compositional data using artificial neural networks

Compositional data (CoDa) has been monitored in statistical process monitoring, where multivariate control charts (CCs) such as Hotelling TC2, MEWMA-CoDa, and MCUSUM-CoDa are commonly used to determine if a process is in-control. However, these charts can encounter problems when there is an out-of-control (OOC) process due to various factors such as shifts in variables, outliers, or trends. To address this issue, a pattern recognition (PR) tool using multilayer feed-forward neural networks (MLFFNN) is proposed to accurately recognize CoDa patterns. In the simulation study, six different models in simplex sample space are used to induce trends and shifts in CoDa, and sufficient samples are generated to evaluate the proposed PR model’s performance. The isometric log-ratio (ilr) transformation is applied to CoDa to convert the data from simplex sample space to real space. The Hotelling TC2 statistic is obtained from the generated values after applying the ilr transformation. TC2 statistic is then standardized for MLFFNN, and a back-propagation learning algorithm is used to accurately fit the PR model. Results show the proposed model accurately identifies the CCs pattern, even during OOC processes. A time budget CoDa is analyzed to demonstrate the proposed model’s effectiveness in recognizing patterns.

Analysis of the 24-h activity cycle: An illustration examining the association with cognitive function in the Adult Changes in Thought study.

The 24-h activity cycle (24HAC) is a new paradigm for studying activity behaviors in relation to health outcomes. This approach inherently captures the interrelatedness of the daily time spent in physical activity (PA), sedentary behavior (SB), and sleep. We describe three popular approaches for modeling outcome associations with the 24HAC exposure. We apply these approaches to assess an association with a cognitive outcome in a cohort of older adults, discuss statistical challenges, and provide guidance on interpretation and selecting an appropriate approach. We compare the use of the isotemporal substitution model (ISM), compositional data analysis (CoDA), and latent profile analysis (LPA) to analyze 24HAC. We illustrate each method by exploring cross-sectional associations with cognition in 1,034 older adults (Mean age = 77; Age range = 65-100; 55.8% female; 90% White) who were part of the Adult Changes in Thought (ACT) Activity Monitoring (ACT-AM) sub-study. PA and SB were assessed with thigh-worn activPAL accelerometers for 7-days. For each method, we fit a multivariable regression model to examine the cross-sectional association between the 24HAC and Cognitive Abilities Screening Instrument item response theory (CASI-IRT) score, adjusting for baseline characteristics. We highlight differences in assumptions and the scientific questions addressable by each approach. ISM is easiest to apply and interpret; however, the typical ISM assumes a linear association. CoDA uses an isometric log-ratio transformation to directly model the compositional exposure but can be more challenging to apply and interpret. LPA can serve as an exploratory analysis tool to classify individuals into groups with similar time-use patterns. Inference on associations of latent profiles with health outcomes need to account for the uncertainty of the LPA classifications, which is often ignored. Analyses using the three methods did not suggest that less time spent on SB and more in PA was associated with better cognitive function. The three standard analytical approaches for 24HAC each have advantages and limitations, and selection of the most appropriate method should be guided by the scientific questions of interest and applicability of each model's assumptions. Further research is needed into the health implications of the distinct 24HAC patterns identified in this cohort.

Association of physical activity with loss of knee joint space width over two years: a compositional data analysis in the Osteoarthritis Initiative

There is continued debate as to how engaging in physical activity (PA), including moderate-to-vigorous PA (MVPA), light PA (LPA), and sedentary time (SED), affects one's risk for knee osteoarthritis (OA). Traditional regression methods do not account for the codependence of these categories of PA, whereby when one category increases, the others must decrease. Thus, we used compositional data analysis (CoDA) to examine time spent in each category of PA, or PA composition, and its association with loss of knee joint space width (JSW), a common indicator of knee OA progression. We performed a secondary analysis of data from a subset of participants in the Osteoarthritis Initiative. These participants had minute-by-minute activity data collected over 7 days at baseline; we then categorized each minute as MVPA, LPA, or SED. Our exposure, PA composition, represented min/day spent in each category. Our outcome, medial JSW loss, was the difference in medial tibiofemoral JSW from baseline to 2years later. We employed CoDA, using an isometric log-ratio transformation, to examine the association of PA composition with medial JSW loss over 2years, adjusting for potential confounders. We included 969 participants (age: 64.5 years, 56% female, body mass index [BMI]: 28.8kg/m2). Mean PA composition was: MVPA 9.1min/day, LPA 278min/day, SED 690min/day. Per adjusted regression models, higher MVPA was not associated with greater medial JSW loss (β=-0.0005, P=0.97), nor was LPA (β=0.06, P=0.27) or SED (β=-0.06, P=0.21). Using CoDA, PA composition was not associated with medial JSW loss over 2years.

Investigating zero-state and steady-state performance of MEWMA-CoDa control chart using variable sampling interval

ABSTRACT Traditional process monitoring control charts (CCs) focused on sampling methods using fixed sampling intervals ( FSI s). The variable sampling intervals ( VSI s) scheme is receiving increasing attention, in which the sampling interval ( SI ) length varies according to the process monitoring statistics. A shorter SI is considered when the process quality indicates the possibility of an out-of-control (OOC) situation; otherwise, a longer SI is preferred. The VSI multivariate exponentially moving average for compositional data ( VSI - MEWMA CoDa ) CC based on a coordinate representation using isometric log-ratio ( ilr ) transformation is proposed in this study. A methodology is proposed to obtain the optimal parameters by considering the zero-state ( ZS ) average time to signal ( ZATS ) and the steady-state (SS) average time to signal ( SATS ). The statistical performance of the proposed CC is evaluated based on a continuous-time Markov chain ( CTMC ) method for both cases, the ZS and the SS using a fixed value of in-control (IC) ATS 0 . Simulation results demonstrate that the VSI - MEWMA CoDa CC has significantly decreased the OOC average time to signal ( ATS ) than the FSI MEWMA CoDa CC. Moreover, it is found that the number of variables (d) has a negative impact on the ATS of the VSI - MEWMA CoDa CC, and the subgroup size (n) has a mildly positive impact on the ATS of the VSI - MEWMA CoDa CC. At the same time, the SATS of the VSI - MEWMA CoDa CC is less than the ZATS of the VSI - MEWMA CoDa CC for all the values of n and d. The proposed VSI - MEWMA CoDa CC under steady-State performs effectively compared to its competitors, such as the FSI - MEWMA CoDa CC, the VSI - T 2 CoDa CC and the FSI - T 2 CoDa CC. An example of an industrial problem from a plant in Europe is also given to study the statistical significance of the VSI - MEWMA CoDa CC.

Assessment of resource potential from mine tailings using geostatistical modeling for compositions: A methodology and application to Katherine Mine site, Arizona, USA

The mining industry, in most cases, targets a specific valuable commodity that is present in small quantities within large volumes of extracted material. After milling and processing, most of the extracted material and the effluents are stored as waste (tailings) in impoundments, such as dams or waste dumps, or are backfilled into underground mines. In time, tailing materials may become an issue of environmental and health concern due to the hazardous elements, ions, and oxides contained within the waste material. In addition, handling and storage of such waste in dams may pose the risk of dam failure with catastrophic consequences to nature and nearby communities. On the other hand, tailings may offer potential as secondary sources of critical elements (CEs), including rare earth elements (REEs), which may have been overlooked during primary production and processing. Therefore, treating mine tailings as a resource has economic and environmental benefits by reducing the waste from new and historical mine sites through remining. One of the critical steps for taking advantage of these benefits is to spatially quantify the resources and the pollutants, which require the application of adequate data analysis and modeling methods, often to compositional geochemical data. Utilizing adequate methods is especially important for correctly quantifying resource potential, as the quantities will often be at low concentrations.This work presents quantification of resource potential (Au, Ag, Cu, Zn, Pb) and elements of environmental concern (Hg and As) from the tailings of a historic mine site, Katherine Mine, AZ, USA. Data reported by the U.S. Bureau of Mines (USBM) after extensive field campaigns in the 1990s, including sampling from tailing impoundment and surrounding areas for geochemical characterization and geophysical surveys, were used. First, compositional data (CoDa) analysis was employed to explore associations of sampling locations, geochemical parts, and the clustering of samples. Next, sequential Gaussian simulation (SGSIM) was applied to samples that showed a genetic link to tailing material after isometric log-ratio transformation (ilr) and mix/max autocorrelation factor (MAF) transformation for spatial modeling and uncertainty evaluation. Geostatistical results revealed spatial variability of concentrations within the tailing area. Uncertainty evaluation based on realizations indicated that Cu (14.27–20.01 t), Zn (44.23–76.23 t), and Pb (22.56–38.28 t) are the most abundant elements within a 5 %–95 % interval, followed by Ag and Au (~5.3 and 0.18 t, at 50th percentile), respectively. Of the elements of health concern, As was found to be ~4.8 t (50th percentile) in the tailing area. The work also showed that ~0.51 t As, 0.005 t Hg, 0.020 t of Au, and 0.62 t of Ag were carried to Lake Mohave by an ephemeral stream called Katherine Wash, which transects the tailings.

On designing efficient multivariate exponentially weighted moving average control chart for compositional data using variable sample size

ABSTRACT Conventionally, the standard process monitoring control charts (s) focused on fixed sample size (). An optimal statistical scheme is proposed in this study using a variable sample size to enhance the performance of a multivariate exponentially weighted moving average () control chart () for compositional data () (i.e. - ) based on a coordinate representation using isometric log-ratio transformation (). A methodology is proposed to obtain the optimal parameters by considering the zero-state () average run length () and the steady-state () average run length () conditions of the process. The statistical performance of the proposed is evaluated based on a continuous-time Markov chain () method for both cases (i.e. the and the ) using a fixed value of in-control (IC) average run length . For benchmarking reasons, the out-of-control () performance of the - is compared against the traditional - with in terms of ; the proposed shows better performance than the - . The and of the - are always less than that of the - at some certain level of shifts. The proposed - performs, on average, () and (for ) more effectively than - . Moreover, it is found that the number of variables (d) has a negative impact on the run length characteristics of the - . When the value of d increases, the and of the - also increase. The of the - is less than the of the - for all the combinations of sample size (n) and d, i.e. under , the proposed performs on average (for d = 3) and (for d = 5) better than the situation. An example of an industrial problem of grid production for a European plant is also given to study the statistical significance and implementation of the - over the existing - .

Machine learning models for predicting soil particle size fractions from routine soil analyses in Quebec

AbstractSoil texture and particle size distribution are important soil properties for understanding and interpreting the multiple processes and interactions in soil agrosystems. However, soil particle size analysis is rarely included in routine soil laboratory analyses due to cost but can be derived from other soil physico‐chemical properties and from more powerful regressions using machine learning techniques. The performance of multiple linear regression and four machine learning algorithms (MLAs)—K‐nearest neighbor (KNN), Random Forest, extra‐gradient boosting (XGBoost), and multilayer neural network (NeuralNetwork)—was compared to predict particle size fractions of clay, sand, and silt using routine analyses of soil pH, cation exchange capacity, Mehlich‐3 extracted elements, and density of dried sieved soil from 8,364 soil samples distributed across Quebec. Particle size fractions were predicted as compositional data using isometric log ratio transformation. The XGBoost model performed best, with RMSE values of 7, 10, and 12% and R2 values of .77, .57, and .73 for the prediction of clay, silt, and sand fractions, respectively. Feature importance classification varied from one model to another, but the best predictors remained the same regardless of the model used. Sieved soil density measured in the laboratory and Mehlich‐3 Mg, K, Fe, and Zn ranked as better predictors of particle size fractions. Predicting soil particle sizes using routine soil physico‐chemical properties and MLAs appears an option for incomplete legacy datasets and a promising economic alternative to current methods carried out in commercial laboratories.

Proportional allocation with soil depth improved mapping soil organic carbon stocks

Soil organic carbon (SOC) is vital to the assessment of land quality, management of farmland and ecological environment, and carbon cycle. A more accurate spatial prediction of multilayer soil organic carbon density (SOCD) can contribute to a better interpretation of the changes in multilayer SOC stocks and carbon dynamics. However, previous mapping techniques still have limitations, such as ignoring the relationship of profile depths, not further taking advantage of vertical distribution and surface categorical information. In addition, it is unclear whether it is better to model each depth interval of SOC separately or to model the total layer and then allocate it. Here, we propose two new methods based on the proportional allocation of soil depth for multilayer mapping: vertical log-ratio method (VLR) of SOCD by applying the percentage of SOCD data and isometric log-ratio (ILR) transformation, and vertical distribution method (VD) of SOCD by considering different land-use types. We compared five methods, including the two new methods, the exponential and equal-area spline functions, and independent modeling without depth information. We combined these five methods with the generalized linear model (GLM) and random forest (RF) to produce predictions of the Sanjiang Plain, northeastern China. The results demonstrated that SOCD did not always decrease with increasing soil depth, and classification of SOCD vertical distribution features needs to be considered by combining with soil depths. For accuracy assessment, the exponential mode with both GLM and RF over-calculated the predicted values and performed poorly, indicating that the blind use of depth information increased the prediction error. The spline function prediction was scarcely better than that of independent modeling. The proportional allocation methods performed better than other separate modeling methods for accuracy and interpretability with GLM or RF, especially for the middle and surface layers. The GLM generated more aggregated predictions than the RF, losing the distribution pattern of the original data. Therefore, we recommend RF combined with proportional allocation methods for spatial SOCD prediction in large-scale study areas. We calculated the SOC stocks in the Sanjiang Plain using our new methods, which were more reasonable compared with those of previous studies and had the advantages of in-depth information, environmental variable selection, and model optimization. Our findings provide not only other perspectives for SOCD mapping, with more fully integrated depth information and more accurate assessment of multilayer SOC stocks, but also provide guidance for the evaluation of land quality, farmland, and ecological environmental management.

Boosting the Scalability of Farm-Level Models: Efficient Surrogate Modeling of Compositional Simulation Output

Surrogate modeling can overcome computational and data-privacy constraints of micro-scale economic models and support their incorporation into large-scale simulations and interactive simulation experiments. We compare four data-driven methods to reproduce the aggregated crop area response simulated by farm-level modeling in response to price variation. We use the isometric log-ratio transformation to accommodate the compositional nature of the output and sequential sampling with stability analysis for efficient model selection. Extreme gradient boosting outperforms multivariate adaptive regressions splines, random forest regression, and classical multinomial-logistic regression and achieves high goodness-of-fit from moderately sized samples. Explicitly including ratio terms between price input variables considerably improved prediction, even for highly automatic machine learning methods that should in principle be able to detect such input variable interaction automatically. The presented methodology provides a solid basis for the use of surrogate modeling to support the incorporation of micro-scale models into large-scale integrated simulations and interactive simulation experiments with stakeholders.

A Compositional Analysis of Physical Activity, Sedentary Time, and Sleep and Associated Health Outcomes in Children and Adults with Cystic Fibrosis.

This study sought to investigate the association of light physical activity (LPA), moderate-to-vigorous physical activity (MVPA), sedentary time (SED), and sleep with lung function in children and adults with CF. In total, 86 children (41 females; 13.6 ± 2.8 years; FEV1%predicted: 86 ± 1%) and 43 adults (21 females; 24.6 ± 4.7 years; FEV1%predicted: 63 ± 21%) with CF participated in this study. Wrist-worn accelerometery was used to assess PA, SED and sleep. Compositional linear regression models were conducted following normalisation via isometric log-ratio transformations. Sequential binary partitioning was applied to investigate the impact of reallocating 10 to 30 min between each behaviour on FEV1%predicted. A decline in FEV1%predicted was predicted with the reallocation of 30 min from MVPA to SED or LPA or sleep to any other behaviour in children (−3.04–−0.005%) and adults (−3.58–−0.005%). Conversely, improvements in FEV1%predicted were predicted when 30 min was reallocated to MVPA from LPA or SED in children (0.12–1.59%) and adults (0.77–2.10%), or when 30 min was reallocated to sleep from any other behaviour in both children (0.23–2.56%) and adults (1.08–3.58%). This study supports the importance of MVPA and sleep for maintaining and promoting lung function in people with CF.

Corporate internet reporting in Egypt: a pre- and peri-uprising analysis

PurposeThe purpose of this study is to provide a detailed descriptive account and analysis of corporate internet reporting (CIR) practices among non-financial companies listed on the Egyptian Exchange (EGX) at two points in time – December 2010 (pre) and December 2013 (peri) political and social unrest in Egypt.Design/methodology/approachThe study developed a disclosure index to determine the extent of CIR practices among all non-financial companies listed on the EGX in December 2010 and December 2013. The study uses ordinary least squares (OLS) regressions and isometric log-ratio transformations for compositional independent variables to empirically examine the factors affecting CIR in Egypt using a modern institutional theory lens.FindingsThe findings of this investigation suggest that listed companies in Egypt have started embracing the power of the internet as a disclosure channel, but the extent of these practices increased significantly over the investigated period, with great variations evident among the sampled companies in this regard. Such variations were chiefly dependent on the changing institutional actors over the two time frames. Additionally, the findings show that the time factor is particularly important for a given institutional field to induce a sufficient diffusion of corporate practices, especially in periods with drastic institutional change.Practical implicationsThe evidence presented reflects the voluntary nature of CIR practices and the absence of a reinforced regulatory framework for organizing and monitoring such practices, with companies having discretion in terms of the amount and type of information disclosed via their websites. The results should, therefore, provide useful guidelines for regulators and standard-setters in identifying best practices, which, in turn, should allow CIR practices to become more consistent, making them easier to monitor and govern.Originality/valueTo the best of the authors’ knowledge, this is the first study that examines CIR practices at two points in time using a comprehensive disclosure index and a modern institutional theory lens.

Rapid prediction of essential oils major components by Vis/NIRS models using compositional methods

Essential oils (EOs) have often a major compound, which content is important for the industry. Thus, rapid techniques suitable to analyze the EOs composition are of great interest. Near infrared spectroscopy (NIRS) has proven suitable solutions for many similar targets. NIRS depends on multivariate correlation methods, which were designed for the real space, with values comprised between - ∞ and + ∞. However, essential oils compounds are compositional data (CoDa), restricted to a simplex space. These are co-dependent data, therefore their multivariate correlation with the spectral variables needs specific methods. This study evaluates predictive models of the contents of cinnamaldehyde, eugenol methyl-ether and thymol, respectively in EOs of cinnamon, clove and thyme, built by using CoDa method. This last was the isometric log-ratio transformation (ILR). Besides, the calibrations set without CoDa methods, were assessed. The results showed clearly the best prediction in external validation exercises was by using ILR. This predictive exercise pointed out the compositional method provided better model robustness than the calibration without this.