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Biological samples of lipids and metabolites degrade after extensive years in −80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of −80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS–DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of −80 °C storage. With receiver operator characteristic curves, sparse PLS–DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at −80 °C when producing predictive metrics from polar metabolites, more so than lipids. [ABSTRACT FROM AUTHOR]

Contaminated air is unhealthy for people to breathe and live in. To maintain the sustainability of clean air, air pollution must be analyzed and controlled, especially after unhealthy events. To do so, the characteristics of unhealthy events, namely intensity, duration, and severity are studied using multivariate modeling. In this study, the vine copula approach is selected to study the characteristics data. Vine copula is chosen here because it is more potent than the standard multivariate distributions, and multivariate copulas, especially in modeling the tails related to extreme events. Here, all nine different vine copulas are analyzed and compared based on model fitting and the comparison of models. In model fitting, the best model obtained is Rv123-Joint-MLE, a model with a root nodes sequence of 123, and optimized using the joint maximum likelihood. The components for the best model are the Tawn type 1 and Rotated Tawn type 1 180 degrees representing the pair copulas of (intensity, duration), and (intensity, severity), respectively, with the Survival Gumbel for the conditional pair copula of (duration, severity; intensity). Based on the best model, the tri-variate dependence structure of the intensity, duration, and severity relationship is positively correlated, skewed, and follows an asymmetric distribution. This indicates that the characteristic's, including intensity, duration, and severity, tend to increase together. Using comparison tests, the best model is significantly different from others, whereas only two models are quite similar. This shows that the best model is well-fitted, compared to most models. Overall, this paper highlights the capability of vine copula in modeling the asymmetric dependence structure of air pollution characteristics, where the obtained model has a better potential to become a tool to assess the risks of extreme events in future work. [ABSTRACT FROM AUTHOR]

Biological samples of lipids and metabolites degrade after extensive years in −80 °C storage. We aimed to determine if associated multivariate models are also impacted. Prior TOFI_Asia metabolomics studies from our laboratory established multivariate models of metabolic risks associated with ethnic diversity. Therefore, to compare multivariate modelling degradation after years of −80 °C storage, we selected a subset of aged (≥5-years) plasma samples from the TOFI_Asia study to re-analyze via untargeted LC-MS metabolomics. Samples from European Caucasian (n = 28) and Asian Chinese (n = 28) participants were evaluated for ethnic discrimination by partial least squares discriminative analysis (PLS–DA) of lipids and polar metabolites. Both showed a strong discernment between participants ethnicity by features, before (Initial) and after (Aged) 5-years of −80 °C storage. With receiver operator characteristic curves, sparse PLS–DA derived confusion matrix and prediction error rates, a considerable reduction in model integrity was apparent with the Aged polar metabolite model relative to Initial modelling. Ethnicity modelling with lipids maintained predictive integrity in Aged plasma samples, while equivalent polar metabolite models reduced in integrity. Our results indicate that researchers re-evaluating samples for multivariate modelling should consider time at −80 °C when producing predictive metrics from polar metabolites, more so than lipids.

Robust and fast in vivo treatment verification is expected to increase the clinical efficacy of proton therapy. The combined detection of prompt gamma rays and neutrons has recently been proposed for this purpose and shown to increase the monitoring accuracy. However, the potential of this technique is not fully exploited yet since the proton range reconstruction relies only on a simple landmark of the particle production distributions. Here, we apply machine learning based feature selection and multivariate modelling to improve the range reconstruction accuracy of the system in an exemplary lung cancer case in silico. We show that the mean reconstruction error of this technique is reduced by 30%–50% to a root mean squared error per spot of 0.4, 1.0, and 1.9 mm for pencil beam scanning spot intensities of 108, 107, and 106 initial protons, respectively. The best model performance is reached when combining distribution features of both gamma rays and neutrons. This confirms the advantage of hybrid gamma/neutron imaging over a single-particle approach in the presented setup and increases the potential of this system to be applied clinically for proton therapy treatment verification.

Issues related to the influence of reservoir properties uncertainty on oil field development modelling are considered. To increase the reliability of geological-hydrodynamic mathematical model in the course of multivariate matching, the influence of reservoir properties uncertainty on the design technological parameters of development was estimated, and their mutual influence was determined. The optimal conditions for the development of the deposit were determined, and multivariate forecasts were made. The described approach of history matching and calculation of the forecast of technological development indicators allows to obtain a more reliable and a less subjective history match as well as to increase the reliability of long-term and short-term forecasts.

Edge-sealing, which involves treating the edges of wood products, improves water resistance. This study investigated the feasibility of edge-sealed cross-laminated timber (CLT) panels to reduce capillary water uptake, thereby resisting mould formation. The water and vapour permeabilities of ten characteristically different single-layer sealant coating systems were systematically determined. Multi-modal assessment leveraged by computed tomography (CT) scanning methodology was used to enhance detection of material characteristics beyond the standard coating permeability assessment. Moisture content was observed to change during the specimens’ absorption and desorption depending on the sealant system applied. The results revealed different characteristics of coatings during the water absorption and desorption stages. Findings from this study were used to develop recommendations regarding the water resistance of coating systems, curing time, susceptibility to mould formation, and industrial applicability. Results suggest that edge-sealed CLT could minimise the risk of mould formation, which can occur at worksites with minimal weather protection. The method developed in this study provides a basis to evaluate new coating systems and determine which use case is the best for a particular coating type. This study also incorporates insights from industry to identify future research orientations, which may pave the way for new designs and assessment techniques.

Directional sensitivity, the more efficient response of cerebral autoregulation to increases, compared to decreases, in mean arterial pressure (MAP), has been demonstrated with repeated squat-stand maneuvers (SSM). In 43 healthy subjects (26 male, 23.1 ± 4.2 years old), five min. recordings of cerebral blood velocity (bilateral Doppler ultrasound), MAP (Finometer), end-tidal CO2 (capnograph), and heart rate (ECG) were obtained during sitting (SIT), standing (STA) and SSM. A new analytical procedure, based on autoregressive-moving average models, allowed distinct estimates of the autoregulation index (ARI) by separating the MAP signal into its positive (MAP+D) and negative (MAP−D) derivatives. ARI+D was higher than ARI−D (p < 0.0001), SIT: 5.61 ± 1.58 vs 4.31 ± 2.16; STA: 5.70 ± 1.24 vs 4.63 ± 1.92; SSM: 4.70 ± 1.11 vs 3.31 ± 1.53, but the difference ARI+D–ARI−D was not influenced by the condition. A bootstrap procedure determined the critical number of subjects needed to identify a significant difference between ARI+D and ARI−D, corresponding to 24, 37 and 38 subjects, respectively, for SSM, STA and SIT. Further investigations are needed on the influences of sex, aging and other phenotypical characteristics on the phenomenon of directional sensitivity of dynamic autoregulation. [ABSTRACT FROM AUTHOR]

We present an improved method for in-vivo proton range verification by prompt gamma-ray timing based on multivariate statistical modelling. To this end, prompt gamma-ray timing distributions acquired during pencil beam irradiation of an acrylic glass phantom with air cavities of different thicknesses were analysed. Relevant distribution features were chosen using forward variable selection and the Least Absolute Shrinkage and Selection Operator (LASSO) from a feature assortment based on recommendations of the Image Biomarker Standardisation Initiative. Candidate models were defined by multivariate linear regression and evaluated based on their coefficient of determination R2 and root mean square error RMSE. The newly developed models showed a clearly improved predictive power (R2 > 0.7) compared to the previously used models (R2 < 0.5) and allowed for the identification of introduced air cavities in a scanned treatment field. These results demonstrate that elaborate statistical models can enhance prompt gamma-ray based treatment verification and increase its potential for routine clinical application.

Road traffic casualties represent a hidden global epidemic, demanding evidence‐based interventions. This paper demonstrates a network lattice approach for identifying road segments of particular concern, based on a case study of a major city (Leeds, UK), in which 5862 crashes of different severities were recorded over an 8‐year period (2011–2018). We consider a family of Bayesian hierarchical models that include spatially structured and unstructured random effects to capture the dependencies between the severity levels. Results highlight roads that are more prone to collisions, relative to estimated traffic volumes, in the north‐west and south of city centre. We analyse the modifiable areal unit problem (MAUP), proposing a novel procedure to investigate the presence of MAUP on a network lattice. We conclude that our methods enable a reliable estimation of road safety levels to help identify 'hotspots' on the road network and to inform effective local interventions. [ABSTRACT FROM AUTHOR]

Vibration analysis, a vital tool in the scheduling of equipment for maintenance is used to assess the useful life of equipment for allocation of resources to mitigate downtime. Compared to previous approaches of univariate prediction, this study presents a more practical model by employing vibration analysis data as a multivariate problem in predicting the remaining useful life (RUL) of an equipment. Applying the model, Multiple Linear Regression (MLR) and Linear Programming (LP) were explored to determine the deterioration rate and the RUL of the equipment. The results showed that the MLR had a high predictive accuracy on the data sets. Furthermore, a p-value of 1.546e-06 and Multiple R-squared value of 0.8215 were obtained showing that the MLR appears to be a good prediction model. From the solution of the LP formulation, the RUL of the equipment was 181 days. These results closely matched the historical data of the equipment which implied this model could be used for planning of maintenance activity for this equipment and any similar equipment. [ABSTRACT FROM AUTHOR]

Aims and Objectives: This study aims to examine the association between person, environment, health and illness factors, pain care and the patient experience in the emergency department, guided by symptom management theory. Background: Current outcome measures of pain care in the emergency department focus on process measures such as the time taken to deliver analgesic medication. Patient‐reported outcomes of pain care are rare in emergency department literature and predominantly focus on patient satisfaction. Measuring overall patient experience is common, with extensive surveys undertaken in the United Kingdom, United States of America and Australia; however, these are not used as an outcome of pain care. Design: Prospective cohort study. Methods: One hundred and ninety patients arriving at a large, inner‐city adults‐only emergency department in moderate to severe pain were recruited to answer a modified version of the emergency department patient experience of care survey. Results: Fifteen factors were identified as influencing the patient experience of care when presenting in pain. These influences of patient experience included the emergency department environment, time to first analgesic medication and the provision of analgesic medication. Conclusions: In addition to pain care factors, there is a significant association between the emergency department environment—especially workload, throughput and patient placement—and the experience of patients who present in pain to the emergency department. Relevance to Clinical Practice: This study demonstrated an association between time to first analgesic medication and the patient experience of care. Providing timely care, including pain care, in emergency departments is difficult, but necessary to improve the patient experience of care. [ABSTRACT FROM AUTHOR]

Imaging genetic studies aim to test how genetic information influences brain structure and function by combining neuroimaging-based brain features and genetic data from the same individual.Most studies focus on individual correlation and association tests between genetic variants and a single measurement of the brain. Despite the great success of univariate approaches, given the capacity of neuroimaging methods to provide a multiplicity of cerebral phenotypes, the development and application of multivariate methods become crucial.In this article, we review novel methods and strategies focused on the analysis of multiple phenotypes and genetic data. We also discuss relevant aspects of multi-trait modelling in the context of neuroimaging data.

Massive Open Online Courses, universally labelled as MOOCs, become more and more relevant in the era of digitalization of higher education. The availability of free education resources without access restrictions for a plenty of potential users has changed the learning market in a way unthinkable only few decades ago. This form of web-based education allows to track all the actions of the students, thus providing an information base to understand how students' behaviour can influence their performance. The paper proposes a structural equation model in the framework of the component-based approach to measure which are the main factors affecting students' performance (Partial Least Squares Path Modelling). The novelty of the approach is the simultaneous analysis of more than one factor that exerts an impact on the performance. The analysis is carried out on the log data of a course available on the edX MOOCs platform named FedericaX. [ABSTRACT FROM AUTHOR]

Aim and Objective: To examine the factors associated with time to first analgesic medication in the emergency department. Background: Pain is the most common symptom presenting to the emergency department, and the time taken to deliver analgesic medication is a common outcome measure. Factors associated with time to first analgesic medication are likely to be multifaceted, but currently poorly described. Design: Retrospective cohort study. Methods: Cox proportional hazards regression modelling was undertaken to evaluate the associations between person, environment, health and illness variables within Symptom Management Theory and time to first analgesic medication in a sample of adult patients presenting with moderate‐to‐severe pain to an emergency department over twelve months. This study was completed in line with the STROBE statement. Results: 383 patients were included in the study, 290 (75.92%) of these patients received an analgesic medication in a median time of 45 minutes (interquartile range, 70 minutes). A model containing nine explanatory variables associated with time to first analgesic medication was identified. These nine variables (employment status, discharge location, triage score, Charlson score, arrival pain score, socio‐economic status, first location, daily total treatment time and patient time to be seen) represent all of the domains of the Symptom Management Theory. Conclusions: Person, environment, health and illness factors are associated with the time taken to deliver analgesic medication to those in pain in the emergency department. This study demonstrates the complexity of factors associated with pain care and the applicability of Symptom Management Theory to pain care in the emergency department. Relevance to Clinical Practice: Identifying a model of factors that are associated with the time in which the most common symptom presenting to the emergency department is treated allows for targeted interventions to groups likely to receive poor care and a framework for its evaluation. [ABSTRACT FROM AUTHOR]

In the field of mineral resources extraction, one main challenge is to meet production targets in terms of geometallurgical properties. These properties influence the processing of the ore and are often represented in resource modeling by coregionalized variables with a complex relationship between them. Valuable data are available about geometalurgical properties and their interaction with the beneficiation process given sensor technologies during production monitoring. The aim of this research is to update resource models as new observations become available. A popular method for updating is the ensemble Kalman filter. This method relies on Gaussian assumptions and uses a set of realizations of the simulated models to derive sample covariances that can propagate the uncertainty between real observations and simulated ones. Hence, the relationship among variables has a compositional nature, such that updating these models while keeping the compositional constraints is a practical requirement in order to improve the accuracy of the updated models. This paper presents an updating framework for compositional data based on ensemble Kalman filter which allows us to work with compositions that are transformed into a multivariate Gaussian space by log-ratio transformation and flow anamorphosis. This flow anamorphosis, transforms the distribution of the variables to joint normality while reasonably keeping the dependencies between components. Furthermore, the positiveness of those variables, after updating the simulated models, is satisfied. The method is implemented in a bauxite deposit, demonstrating the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

Life history strategies are fundamental to the ecology and evolution of organisms and are important for understanding extinction risk and responses to global change. Using global datasets and a multiple response modelling framework we show that trait‐climate interactions are associated with life history strategies for a diverse range of plant species at the global scale. Our modelling framework informs our understanding of trade‐offs and positive correlations between elements of life history after accounting for environmental context and evolutionary and trait‐based constraints. Interactions between plant traits and climatic context were needed to explain variation in age at maturity, distribution of mortality across the lifespan and generation times of species. Mean age at maturity and the distribution of mortality across plants' lifespan were under evolutionary constraints. These findings provide empirical support for the theoretical expectation that climatic context is key to understanding trait to life history relationships globally. [ABSTRACT FROM AUTHOR]

Despite growing attention to the ecological and evolutionary importance of consistent individual differences in behaviour (animal personality), long-term field studies quantifying factors associated with behavioural repeatability remain rare. Here, we studied animal personalities over an 8-year period, representing 6 study years, in a wild population of the long-lived sleepy lizard, Tiliqua rugosa. Using Bayesian generalized linear mixed models for 170 unique individuals and a total of 379 lizard-years, we (1) considered the effects of a suite of predictors – particularly lizard sex, mass and tick counts – on lizard aggression and boldness (2) assessed repeatability (i.e. consistent differences among individuals), of these behaviours over different timescales and between lizard sexes and (3) evaluated the correlation, or behavioural syndrome, between aggression and boldness. We found that males were marginally more aggressive and bolder than females, mass had no significant effect and tick loads exhibited a positive relationship with aggression and boldness. For repeatability, we found that even with the long timescales considered in this study, aggression and boldness were both repeatable – across the entire data set (∼0.4 and ∼0.3, respectively) using all lizards, as well as among years (∼0.4 and ∼0.4, respectively) using lizards observed in multiple years (93 for aggression, 73 for boldness). Repeatability did not differ substantially between the sexes. We found no syndrome between aggression and boldness – despite a weak positive correlation in multivariate mixed models, the 95% credible interval for this correlation included zero. Our results are notable because they demonstrate that wild animals may exhibit consistent personality differences in ecologically relevant behaviours over extended periods even in the face of substantial temporal variation in ecological and social factors, a fact that has likely ecological and evolutionary consequences. • Wild, long-lived (20+ years) lizards exhibited personality in the field. • Repeatability of aggression and boldness did not decline over 8 years. • Males and females did not differ markedly in their long-term consistency patterns. • Sex and parasites (ticks) were associated with changes in aggression and boldness. [ABSTRACT FROM AUTHOR]

Local adaptation features critically in shaping species responses to changing environments, complicating efforts to revegetate degraded areas. Rapid climate change poses an additional challenge that could reduce fitness of even locally sourced seeds in restoration. Predictive restoration strategies that apply seeds with favourable adaptations to future climate may promote long‐term resilience. Landscape genomics is increasingly used to assess spatial patterns in local adaption and may represent a cost‐efficient approach for identifying future‐adapted genotypes. To demonstrate such an approach, we genotyped 760 plants from 64 Mojave Desert populations of the desert annual Plantago ovata. Genome scans on 5,960 SNPs identified 184 potentially adaptive loci related to climate and satellite vegetation metrics. Causal modelling indicated that variation in potentially adaptive loci was not confounded by isolation by distance or isolation by habitat resistance. A generalized dissimilarity model (GDM) attributed spatial turnover in potentially adaptive loci to temperature, precipitation and NDVI amplitude, a measure of vegetation green‐up potential. By integrating a species distribution model (SDM), we find evidence that summer maximum temperature may both constrain the range of P. ovata and drive adaptive divergence in populations exposed to higher temperatures. Within the species' current range, warm‐adapted genotypes are predicted to experience a fivefold expansion in climate niche by midcentury and could harbour key adaptations to cope with future climate. We recommend eight seed transfer zones and project each zone into its relative position in future climate. Prioritizing seed collection efforts on genotypes with expanding future habitat represents a promising strategy for restoration practitioners to address rapidly changing climates. [ABSTRACT FROM AUTHOR]

Prehistoric landscape reconstructions are still considered an unsolved methodological issue in archaeological research, and this includes the perception and transformation of an individual landscape in relation to situational and local ecosystem performances. Which parts of the landscape offered the potential for land-use and which areas were rather unsuitable due to a variety of environmental preconditions? The modern perception of the archaeological record that is distributed in the modern landscape does not necessarily represent a realistic dispersal of past human activity, but rather reflects the current state of archaeological research and modern land-use strategies. This contribution provides a critical assessment of spatial analyses of large and unstructured archaeological datasets and the non-reconstructibility of past, individually perceived palaeolandscapes.

The aim of the present assay is to provide a simple algorithm as well as a didactic theoretical framework that may serve as an introduction to understanding modern habitat suitability (HS) modelling techniques in Ecology and Biogeography. The proposal is built on classical descriptive statistics and classical ecological theories. Shelford's theory of a bell-shaped curve of tolerance is used to assign suitability values to individual sites, according to their deviations from the optimal requirements of a hypothetical biological species. Liebig's law of the minimum is applied to assess the overall suitability given a multivariate set of environmental factors. To illustrate the algorithms, hypothetical examples are given with small sets of values simulating data extracted from maps with environmental information. The reader/lecturer is invited to reproduce these small-scale examples using common spreadsheet software or to apply them at a large scale using raster datasets in any advanced geographic information system. As didactical outcomes, this algorithm allows introducing students to the general form and applications of bell-shaped exponential equations (with mu and sigma parameters), understanding the convenience of the law of the minimum when analysing multivariate datasets and the philosophical understanding of certainty/uncertainty when working with the multidimensional niche theory. [ABSTRACT FROM AUTHOR]

Loanword use has dominated the literature on language contact and its salient nature continues to draw interest from linguists and non-linguists. Traditionally, loanwords were investigated by means of raw frequencies, which are at best uninformative and at worst misleading. Following a new wave of studies which look at loans from a quantitatively more informed standpoint, modelling "success" by taking into account frequency of the counterparts available in the language adopting the loanwords, we propose a similar model of loan-use and demonstrate its benefits in a case study of loanwords from Māori into (New Zealand) English. Our model contributes to previous work in this area by combining both the success measure mentioned above with a rich range of linguistic characteristics of the loanwords (such as loan length and word class), as well as a similarly detailed group of sociolinguistic characteristics of the speakers using them (gender, age and ethnicity of both, speakers and addresses). Our model is unique in bringing together of all these factors at the same time. The findings presented here illustrate the benefit of a quantitatively balanced approach to modelling loanword use. Furthermore, they illustrate the complex interaction between linguistic and sociolinguistic factors in such language contact scenarios. [ABSTRACT FROM AUTHOR]

Quantifying the organic carbon content of soil over large areas is essential for characterising the soil and the effects of its management. However, analytical methods can be laborious and costly. Reflectance spectroscopy is a well-established and widespread method for estimating the chemical-element content of soils. The aim of this study was to estimate the soil organic carbon (SOC) content using hyperspectral remote sensing. The data were from soils from two localities in the semi-arid region of Brazil. The spectral reflectance factors of the collected soil samples were recorded at wavelengths ranging from 350–2500 nm. Pre-processing techniques were employed, including normalisation, Savitzky–Golay smoothing and first-order derivative analysis. The data (n = 65) were examined both jointly and by soil class, and subdivided into calibration and validation to independently assess the performance of the linear methods. Two multivariate models were calibrated using the SOC content estimated in the laboratory by principal component regression (PCR) and partial least squares regression (PLSR). The study showed significant success in predicting the SOC with transformed and untransformed data, yielding acceptable-to-excellent predictions (with the performance-to-deviation ratio ranging from 1.40–3.38). In general, the spectral reflectance factors of the soils decreased with the increasing levels of SOC. PLSR was considered more robust than PCR, whose wavelengths from 354 to 380 nm, 1685, 1718, 1757, 1840, 1876, 1880, 2018, 2037, 2042, and 2057 nm showed outstanding absorption characteristics between the predicted models. The results found here are of significant practical value for estimating SOC in Neosols and Cambisols in the semi-arid region of Brazil using VIS-NIR-SWIR spectroscopy.

Aim: To determine the association between time to first analgesic medication and emergency department length of stay (ED LOS). Design: Retrospective cohort study. Method: We conducted this study in a large, inner‐city emergency department and included adult patients who presented with pain as a symptom and received analgesic medication(s). Study participants were identified from a random selection of 2,000 adult patients who presented between August–October 2018. The relationship between ED LOS and time to first analgesic medication was described using bivariate and multivariate linear regression. Results: Of the 2,000 randomly selected patients presenting between August and October 2018, 727 (36.35%) had pain as a symptom on arrival, 423 (21.15%) had analgesic medication administered. The median time to first analgesic medication was in 62 (interquartile range: 36–105) minutes and median ED LOS was 218 (interquartile range: 160–317.5) minutes. After adjusting for the effects of sex, urgency of the presentation, emergency department location first seen by clinician, departure destination and workload metrics (average daily time to be seen and daily access block). Time to first analgesic medication was independently associated with ED LOS, contributing to 6.96% of the variance in the multivariate model. Conclusion: Providing analgesic medication faster to patients presenting in pain, in addition to previously demonstrated positive patient outcomes, may decrease their ED LOS. Impact: Reducing ED LOS through faster pain care, benefits the patient through faster pain relief and can benefit the department by reducing the total amount of care delivered in the emergency department. Reducing total care delivery frees up resources to improve the care to all emergency department patients. [ABSTRACT FROM AUTHOR]

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Modelling of multivariate complex deposits with the presence of several correlated attributes is a very challenging issue in the mining industry which can be addressed using existing multivariate analysis method. In this study, some of these multivariate methods, such as Step-wise Conditional Transformation (SCT), Minimum/maximum Autocorrelation Factors (MAF) and Projection Pursuit Multivariate Transform (PPMT), were applied to a data set of Mehdiabad deposit. The data set is containing core samples to be analysed for Pb, Zn, Cu, and Ag. At the first stage, the variables were transformed by mentioned methods and a set of validations were performed to the transformation results. Next, the transformed variables were simulated using sequential Gaussian simulation and the results were analysed as well. Based on the validation reviews, it was concluded that the PPMT could present more reliable outcomes. Furthermore, for every transformation, the grade-tonnage curves for each transformed variable were calculated based on the E-type values of the simulations and the discrepancies between them were also investigated. The results of this study can be also used in mine planning and risk measurement during mining. [ABSTRACT FROM AUTHOR]

Energy production is reorganised to mitigate the pressure on the global environment. This reorganisation leads to an increase in the production of wood ash (WA). Multivariate modelling was used to identify the link between production parameters and the physiochemicalcharacteristics of different WAs and to determine which production parameters result in the WAs most suitable for utilisation in cement-based materials. Based on the multivariate model partial least square, WA originating from circulating fluidised bed combustion of wood chips made from whole trees is the optimal type of WA when utilised as a supplementary cementing material with pozzolanic activity. WA originating from the combustion of wood chips made from whole trees is the optimal type of WA when utilised as a supplementary cementing material with hydraulic activity. Furthermore, the combustion method and type of ash were seen to have the largest influence on the physiochemical characteristics of WAs compared to the other production parameters included in this study. [ABSTRACT FROM AUTHOR]

Abstract After the ban of battery cages in 1988, a welfare control programme for laying hens was developed in Sweden. Its goal was to monitor and ensure that animal welfare was not negatively affected by the new housing systems. The present observational study provides an overview of the current welfare status of commercial layer flocks in Sweden and explores the complexity of welfare aspects by investigating and interpreting the inter-relationships between housing system, production type (i.e. organic or conventional), facilities, management and animal welfare indicators. For this purpose, a machine learning procedure referred to as structure discovery was applied to data collected through the welfare programme during 2010–2014 in 397 flocks housed in 193 different farms. Seventeen variables were fitted to an Additive Bayesian Network model. The optimal model was identified by an exhaustive search of the data iterated across incremental parent limits, accounting for prior knowledge about causality, potential over-dispersion and clustering. The resulting Directed Acyclic Graph shows the inter-relationships among the variables. The animal-based welfare indicators included in this study – flock mortality, feather condition and mite infestation – were indirectly associated with each other. Of these, severe mite infestations were rare (4% of inspected flocks) and mortality was below the acceptable threshold (< 0.6%). Feather condition scored unsatisfactory in 21% of the inspected flocks; however, it seemed to be only associated to the age of the flock, ruling out any direct connection with managerial and housing variables. The environment-based welfare indicators – lighting and air quality – were an issue in 5 and 8% of the flocks, respectively, and showed a complex inter-relationship with several managerial and housing variables leaving room for several options for intervention. Additive Bayesian Network modelling outlined graphically the underlying process that generated the observed data. In contrast to ordinary regression, it aimed at accounting for conditional independency among variables, facilitating causal interpretation. [ABSTRACT FROM AUTHOR]

New Findings: What is the central question of this study?Does oestrous cycle synchronization influence myoelectrical activity of porcine myometrium?What is the main finding and its importance?Exogenous hormones used to synchronize oestrus in pigs altered myoelectrical activity, which was effectively modelled. Higher‐order multivariate statistic modelling provided evidence of similar activity in both types of oestrus, but a larger order of EMG signals during induced oestrus. Higher‐order statistical analysis of the probabilistic model suggests the beginning of the early follicular phase and the mid‐luteal phase to be most important in evaluation of the natural patterns of myoelectrical activity. Higher‐order multivariate cumulants are more informative than classical statistics in characterization of myoelectrical activity changes in porcine myometrium. In pig production units, control of the oestrous cycle and synchronization of ovulation have become routine herd management procedures. During the oestrous cycle, in both induced and spontaneous conditions, the ovaries and the uterus undergo hormone‐dominated physiological changes, which are consistent with the hypothesis that there is a functional role of uterine contractions in promoting fertilization. We have used electromyography to determine whether the use of exogenous hormones, such as equine chorionic gonadotrophin and human chorionic gonadotrophin, which have the potential to control the timing of ovulation in female pigs, changes the multivariate relationships between parameters of electrical bursts and modulates the patterns of myoelectrical activity. We used the mathematical approach of higher‐order multivariate cumulants in complex modelling of the myometrial electrical activity. The experiment was conducted on 12 mature Polish Landrace sows, and uterine activity was recorded during both spontaneous and induced oestrous cycles. The burst parameters were determined using six features in the time domain and, after Fast Fourier transformation, in the frequency domain. Evaluation of myoelectrical activity patterns was conducted based on classical univariate statistical methods and multivariate probabilistic modelling. The classical statistical approach indicated weaker myoelectrical activity after hormonal stimulation, whereas the higher‐order multivariate statistical model showed evidence of similar status of activity and a larger order of signals during induced oestrus. Routine oestrous cycle synchronization affects the multivariate probabilistic model of myometrial electrical activity. [ABSTRACT FROM AUTHOR]

The variety is one of the most important factors to generate difference of chemical compositions, which unavoidably influences the quality of natural medicine. Thus, simple and rapid authentication of different variants has great academic and practical significance. In this study, the goal was achieved with the help of near infrared spectroscopy (NIR) and chemometrics by using Gastrodia elata Blume as an example. A total of 540 samples including two classes of variants and their forms were investigated as a whole. The mean spectra of samples of each class and their 2-D synchronous correlation spectra were simultaneously applied to discover the difference of chemical characteristics. After hybrid pre-processing of the first and second derivative combined with Savitzky-Golay and Norris filtering, partial least squares discrimination analysis (PLS-DA) on the basis of latent variable projection was used to assess the feasibility for classification. The results show higher prediction accuracy in both internal test set and external prediction set. In order to further improve the robustness for modeling, three methods for wavelength selection were comprehensively compared to optimize PLS-DA models, including variable importance in the projection (VIP), random frog (RF), and Monte Carlo uninformative variable elimination (MC-UVE). The prediction accuracy of combination of the 2nd derivative, Norris, MC-UVE and PLS-DA achieved to 99.11% and 98.89% corresponding to the internal test set and external prediction set, respectively. The strategies proposed in this work perform effectiveness for rapid and accurate authentication of variants of plants with high chemical complexity. • The combination of NIR and chemometric methods was proposed for simple and rapid recognition of three variants G. elata Bl. • The strategy for optimal model was attained, including second-order derivative, Norris, MC-UVE, and PLS-DA for modelling. • The optimal modle showed good prediction accuracy of 99.11% and 98.89% to test and prediction sets, respectively. [ABSTRACT FROM AUTHOR]

The quality requirements of wood biofuels are regulated by a series of harmonized international standards. These standards define the technical parameter limits that influence the quality of solid biomass as a fuel. In 2014 the European reference standard for solid biofuel was replaced by the International ISO standard. In the case of wood chips, the main difference between the European and International standards is the definition of particle size distribution classes. In this context, this study analyses the quality of wood chips and its variation over the years according to the "former" (EN 14691-4) and "in force" (ISO 17225-4) standards. A Soft Independent Modelling of Class Analogy (SIMCA) model was built to predict the best quality of wood chips and to clarify the relationship between quality and standard parameters, time and changes in the standard regulations. The results show that, compared to the EN standards, classification with the ISO standards increases the samples belonging to the best quality classes and decreases the not classified samples. Furthermore, all the SIMCA models have a high sensitivity (>90%), reflect the differences introduced to the quality standards and are therefore suitable for monitoring the quality of wood chips and their changes. [ABSTRACT FROM AUTHOR]

This research is devoted to a systematic review of multivariate models in the context of their application to short-term traffic flow forecasting. A set of discussed models includes autoregressive integrated moving average models (ARIMA and VARMA), error correction models (VECM and EC-VARMA), space-time ARMA (STARMA), and multivariate autoregressive space state models (MARSS). All these models are based on different assumptions about a structure of interrelationships in traffic data (in time, in space or between different traffic characteristics). We discussed base assumptions of these models (such as stationary of traffic flows and spatial independence of data) and their importance in the domain of transport flows. The discussion is supplemented with an empirical application of the models to forecasting of traffic speeds for a small road segment. Empirical conclusions and projected research directions are also presented. [ABSTRACT FROM AUTHOR]

The paper investigates the dependences between levels of severity of road traffic accidents, accounting at the same time for spatial and temporal correlations. The study analyses road traffic accidents data at ward level in England over the period 2005-2013. We include in our model multivariate spatially structured and unstructured effects to capture the dependences between severities, within a Bayesian hierarchical formulation. We also include a temporal component to capture the time effects and we carry out an extensive model comparison. The results show important associations in both spatially structured and unstructured effects between severities, and a downward temporal trend is observed for low and high levels of severity. Maps of posterior accident rates indicate elevated risk within big cities for accidents of low severity and in suburban areas in the north and on the southern coast of England for accidents of high severity. The posterior probability of extreme rates is used to suggest the presence of hot spots in a public health perspective. [ABSTRACT FROM AUTHOR]

Diffuse reflectance Fourier transform infrared spectroscopy (DRIFTS) is a fast, reliable and cost effective analytical method, requiring minimal or no sample preparation. It is commonly used in the course of qualitative and quantitative analysis of pharmaceutical ingredients and food. We demonstrate that simpler and cheaper specular reflectance (SR) accessory working in a DRIFTS like mode (SR-DL) can be an alternative for DIRFTS attachment. An application of a modified SR accessory for quantitative analysis of solids samples is presented. As a case study the concentration of cinnarizine in commercial tablets has been determined from DRIFTS and SR-DL infrared (IR) and near-infrared (NIR) spectra recorded using DTGS (deuterated triglicine sulphate) detector in the IR and NIR regions and InGaAs (indium-gallium arsenide) detector in the NIR range. Based on these spectra Partial Least Squares (PLS) models were constructed and relative standard errors of prediction (RSEP) were calculated for the calibration, validation and analysed data sets. They amounted to 2.4–2.5%, 2.1–2.7% and 2.0–2.6% for the DRIFTS attachment while 2.1–2.2%, 2.0–2.3% and 1.9–2.6%, respectively, for the modified SR accessory. Obtained error values indicate that modified SR accessory can be effectively used for quantification of solid pharmaceutical samples in the mid- and near-infrared regions. [ABSTRACT FROM AUTHOR]

Optical density (OD) based measurements cannot be a reliable proxy for residual biomass concentration in polyhydroxyalkanoate (PHA) accumulating microbial culture, as cell size and morphology change during growth. In this study, four independent analytical methods i.e., OD measurement at 540 nm and 600 nm, tetrazolium reduction assay, and intracellular protein estimation were adopted to model residual biomass growth in Cupriavidus necator. The inter-day variation of calibration slope for residual biomass was significant (p < 0.001), and the regression coefficient (R2) of composite samples across the methods varied between 0.74 and 0.96. A reduced quadratic polynomial model (R2, 0.996; adjusted R2, 0.995, cross-validation R2 0.987) was chosen to predict residual biomass using multi-analytical measurements. Partial least square regression and variable selection suggested the inclusion of OD 540 nm and protein measurement into two retained latent variables, with R2 of 0.986, and adjusted R2 of 0.972. ANN model offered good predictability for residual biomass, showing close agreement of experimental and modelled datasets for training, validation, and test subsets with R2 of 0.998, 0.994, and 0.943, respectively. Analytical sensitivity of quadratic and PLS regression models were 0.058 and 0.164 respectively. A comparison of the proposed methods suggests that all three can be used as an alternative to dry-cell-weight-based residual biomass estimation, avoiding lengthy drying time and PHA extraction and measurement steps. • Optical density is an unreliable predictor for residual biomass. • A multi-analytical method with intracellular protein, tetrazolium assay is proposed. • Reduced quadratic, PLS model and ANN reliably predict residual biomass. • Optical density at 540 nm and protein estimation are retained in PLS regression. [ABSTRACT FROM AUTHOR]