Slow Release Drug Dissolution Profile Prediction in Pharmaceutical Manufacturing: a Multivariate and Machine Learning Approach

Slow release drugs must be manufactured to meet target specifications with respect to dissolution curve profiles. In this paper we consider the problem of identifying the drivers of dissolution curve variability of a drug from historical manufacturing data. Several data sources are considered: raw material parameters, coating data, loss on drying and pellet size statistics. The methodology employed is to develop predictive models using LASSO, a powerful machine learning algorithm for regression with high-dimensional datasets. LASSO provides sparse solutions facilitating the identification of the most important causes of variability in the drug fabrication process. The proposed methodology is illustrated using manufacturing data for a slow release drug.

Multivariate Density Estimation: An SVM Approach

We formulate density estimation as an inverse operator problem. We then use convergence results of empirical distribution functions to true distribution functions to develop an algorithm for multivariate density estimation. The algorithm is based upon a Support Vector Machine (SVM) approach to solving inverse operator problems. The algorithm is implemented and tested on simulated data from different distributions and different dimensionalities, gaussians and laplacians in $R^2$ and $R^{12}$. A comparison in performance is made with Gaussian Mixture Models (GMMs). Our algorithm does as well or better than the GMMs for the simulations tested and has the added advantage of being automated with respect to parameters.

A note on estimating market–based minimum capital risk requirements: a multivariate GARCH approach

Internal risk management models of the kind popularized by J. P. Morgan are now used widely by the world’s most sophisticated financial institutions as a means of measuring risk. Using the returns on three of the most popular futures contracts on the London International Financial Futures Exchange, in this paper we investigate the possibility of using multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models for the calculation of minimum capital risk requirements (MCRRs). We propose a method for the estimation of the value at risk of a portfolio based on a multivariate GARCH model. We find that the consideration of the correlation between the contracts can lead to more accurate, and therefore more appropriate, MCRRs compared with the values obtained from a univariate approach to the problem.

On the variability of the surface environment response to synoptic forcing over complex terrain: a multivariate data analysis approach

Synoptic climatology relates the atmospheric circulation with the surface environment. The aim of this study is to examine the variability of the surface meteorological patterns, which are developing under different synoptic scale categories over a suburban area with complex topography. Multivariate Data Analysis techniques were performed to a data set with surface meteorological elements. Three principal components related to the thermodynamic status of the surface environment and the two components of the wind speed were found. The variability of the surface flows was related with atmospheric circulation categories by applying Correspondence Analysis. Similar surface thermodynamic fields develop under cyclonic categories, which are contrasted with the anti-cyclonic category. A strong, steady wind flow characterized by high shear values develops under the cyclonic Closed Low and the anticyclonic H–L categories, in contrast to the variable weak flow under the anticyclonic Open Anticyclone category.

Elicitation of multivariate prior distributions: A nonparametric Bayesian approach

In the context of Bayesian statistical analysis, elicitation is the process of formulating a prior density f(.) about one or more uncertain quantities to represent a person's knowledge and beliefs. Several different methods of eliciting prior distributions for one unknown parameter have been proposed. However, there are relatively few methods for specifying a multivariate prior distribution and most are just applicable to specific classes of problems and/or based on restrictive conditions, such as independence of variables. Besides, many of these procedures require the elicitation of variances and correlations, and sometimes elicitation of hyperparameters which are difficult for experts to specify in practice. Garthwaite et al. (2005) discuss the different methods proposed in the literature and the difficulties of eliciting multivariate prior distributions. We describe a flexible method of eliciting multivariate prior distributions applicable to a wide class of practical problems. Our approach does not assume a parametric form for the unknown prior density f(.), instead we use nonparametric Bayesian inference, modelling f(.) by a Gaussian process prior distribution. The expert is then asked to specify certain summaries of his/her distribution, such as the mean, mode, marginal quantiles and a small number of joint probabilities. The analyst receives that information, treating it as a data set D with which to update his/her prior beliefs to obtain the posterior distribution for f(.). Theoretical properties of joint and marginal priors are derived and numerical illustrations to demonstrate our approach are given. (C) 2010 Elsevier B.V. All rights reserved.

Uniform approach to linear and nonlinear interrelation patterns in multivariate time series

Currently, a variety of linear and nonlinear measures is in use to investigate spatiotemporal interrelation patterns of multivariate time series. Whereas the former are by definition insensitive to nonlinear effects, the latter detect both nonlinear and linear interrelation. In the present contribution we employ a uniform surrogate-based approach, which is capable of disentangling interrelations that significantly exceed random effects and interrelations that significantly exceed linear correlation. The bivariate version of the proposed framework is explored using a simple model allowing for separate tuning of coupling and nonlinearity of interrelation. To demonstrate applicability of the approach to multivariate real-world time series we investigate resting state functional magnetic resonance imaging (rsfMRI) data of two healthy subjects as well as intracranial electroencephalograms (iEEG) of two epilepsy patients with focal onset seizures. The main findings are that for our rsfMRI data interrelations can be described by linear cross-correlation. Rejection of the null hypothesis of linear iEEG interrelation occurs predominantly for epileptogenic tissue as well as during epileptic seizures.

A unified approach to modeling multivariate binary data using copulas over partitions

Many seemingly disparate approaches for marginal modeling have been developed in recent years. We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the proposed copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts.

Microwave-Assisted Extraction of Phenolic Compounds from Almond Skin Byproducts (Prunus amygdalus): A Multivariate Analysis Approach

A microwave-assisted extraction (MAE) procedure to isolate phenolic compounds from almond skin byproducts was optimized. A three-level, three-factor Box–Behnken design was used to evaluate the effect of almond skin weight, microwave power, and irradiation time on total phenolic content (TPC) and antioxidant activity (DPPH). Almond skin weight was the most important parameter in the studied responses. The best extraction was achieved using 4 g, 60 s, 100 W, and 60 mL of 70% (v/v) ethanol. TPC, antioxidant activity (DPPH, FRAP), and chemical composition (HPLC-DAD-ESI-MS/MS) were determined by using the optimized method from seven different almond cultivars. Successful discrimination was obtained for all cultivars by using multivariate linear discriminant analysis (LDA), suggesting the influence of cultivar type on polyphenol content and antioxidant activity. The results show the potential of almond skin as a natural source of phenolics and the effectiveness of MAE for the reutilization of these byproducts.

Tail conditional expectation for multivariate distributions : a game theory approach

This paper proposes using the Shapley values in allocating the total tail conditional expectation (TCE) to each business line (X j, j = 1, ... , n) when there are n correlated business lines. The joint distributions of X j and S (S = X1 + X2 + ⋯ + X n) are needed in the existing methods, but they are not required in the proposed method.

Examining psychosocial influences on speeding in Australian and Chinese contexts : a social learning approach

Speeding remains a significant contributing factor to road trauma internationally, despite increasingly sophisticated speed management strategies being adopted around the world. Increases in travel speed are associated with increases in crash risk and crash severity. As speed choice is a voluntary behaviour, driver perceptions are important to our understanding of speeding and, importantly, to designing effective behavioural countermeasures. The four studies conducted in this program of research represent a comprehensive approach to examining psychosocial influences on driving speeds in two countries that are at very different levels of road safety development: Australia and China. Akers’ social learning theory (SLT) was selected as the theoretical framework underpinning this research and guided the development of key research hypotheses. This theory was chosen because of its ability to encompass psychological, sociological, and criminological perspectives in understanding behaviour, each of which has relevance to speeding. A mixed-method design was used to explore the personal, social, and legal influences on speeding among car drivers in Queensland (Australia) and Beijing (China). Study 1 was a qualitative exploration, via focus group interviews, of speeding among 67 car drivers recruited from south east Queensland. Participants were assigned to groups based on their age and gender, and additionally, according to whether they self-identified as speeding excessively or rarely. This study aimed to elicit information about how drivers conceptualise speeding as well as the social and legal influences on driving speeds. The findings revealed a wide variety of reasons and circumstances that appear to be used as personal justifications for exceeding speed limits. Driver perceptions of speeding as personally and socially acceptable, as well as safe and necessary were common. Perceptions of an absence of danger associated with faster driving speeds were evident, particularly with respect to driving alone. An important distinction between the speed-based groups related to the attention given to the driving task. Rare speeders expressed strong beliefs about the need to be mindful of safety (self and others) while excessive speeders referred to the driving task as automatic, an absent-minded endeavour, and to speeding as a necessity in order to remain alert and reduce boredom. For many drivers in this study, compliance with speed limits was expressed as discretionary rather than mandatory. Social factors, such as peer and parental influence were widely discussed in Study 1 and perceptions of widespread community acceptance of speeding were noted. In some instances, the perception that ‘everybody speeds’ appeared to act as one rationale for the need to raise speed limits. Self-presentation, or wanting to project a positive image of self was noted, particularly with respect to concealing speeding infringements from others to protect one’s image as a trustworthy and safe driver. The influence of legal factors was also evident. Legal sanctions do not appear to influence all drivers to the same extent. For instance, fear of apprehension appeared to play a role in reducing speeding for many, although previous experiences of detection and legal sanctions seemed to have had limited influence on reducing speeding among some drivers. Disregard for sanctions (e.g., driving while suspended), fraudulent demerit point use, and other strategies to avoid detection and punishment were widely and openly discussed. In Study 2, 833 drivers were recruited from roadside service stations in metropolitan and regional locations in Queensland. A quantitative research strategy assessed the relative contribution of personal, social, and legal factors to recent and future self-reported speeding (i.e., frequency of speeding and intentions to speed in the future). Multivariate analyses examining a range of factors drawn from SLT revealed that factors including self-identity (i.e., identifying as someone who speeds), favourable definitions (attitudes) towards speeding, personal experiences of avoiding detection and punishment for speeding, and perceptions of family and friends as accepting of speeding were all significantly associated with greater self-reported speeding. Study 3 was an exploratory, qualitative investigation of psychosocial factors associated with speeding among 35 Chinese drivers who were recruited from the membership of a motoring organisation and a university in Beijing. Six focus groups were conducted to explore similar issues to those examined in Study 1. The findings of Study 3 revealed many similarities with respect to the themes that arose in Australia. For example, there were similarities regarding personal justifications for speeding, such as the perception that posted limits are unreasonably low, the belief that individual drivers are able to determine safe travel speeds according to personal comfort with driving fast, and the belief that drivers possess adequate skills to control a vehicle at high speed. Strategies to avoid detection and punishment were also noted, though they appeared more widespread in China and also appeared, in some cases, to involve the use of a third party, a topic that was not reported by Australian drivers. Additionally, higher perceived enforcement tolerance thresholds were discussed by Chinese participants. Overall, the findings indicated perceptions of a high degree of community acceptance of speeding and a perceived lack of risk associated with speeds that were well above posted speed limits. Study 4 extended the exploratory research phase in China with a quantitative investigation involving 299 car drivers recruited from car washes in Beijing. Results revealed a relatively inexperienced sample with less than 5 years driving experience, on average. One third of participants perceived that the certainty of penalties when apprehended was low and a similar proportion of Chinese participants reported having previously avoided legal penalties when apprehended for speeding. Approximately half of the sample reported that legal penalties for speeding were ‘minimally to not at all’ severe. Multivariate analyses revealed that past experiences of avoiding detection and punishment for speeding, as well as favourable attitudes towards speeding, and perceptions of strong community acceptance of speeding were most strongly associated with greater self-reported speeding in the Chinese sample. Overall, the results of this research make several important theoretical contributions to the road safety literature. Akers’ social learning theory was found to be robust across cultural contexts with respect to speeding; similar amounts of variance were explained in self-reported speeding in the quantitative studies conducted in Australia and China. Historically, SLT was devised as a theory of deviance and posits that deviance and conformity are learned in the same way, with the balance of influence stemming from the ways in which behaviour is rewarded and punished (Akers, 1998). This perspective suggests that those who speed and those who do not are influenced by the same mechanisms. The inclusion of drivers from both ends of the ‘speeding spectrum’ in Study 1 provided an opportunity to examine the wider utility of SLT across the full range of the behaviour. One may question the use of a theory of deviance to investigate speeding, a behaviour that could, arguably, be described as socially acceptable and prevalent. However, SLT seemed particularly relevant to investigating speeding because of its inclusion of association, imitation, and reinforcement variables which reflect the breadth of factors already found to be potentially influential on driving speeds. In addition, driving is a learned behaviour requiring observation, guidance, and practice. Thus, the reinforcement and imitation concepts are particularly relevant to this behaviour. Finally, current speed management practices are largely enforcement-based and rely on the principles of behavioural reinforcement captured within the reinforcement component of SLT. Thus, the application of SLT to a behaviour such as speeding offers promise in advancing our understanding of the factors that influence speeding, as well as extending our knowledge of the application of SLT. Moreover, SLT could act as a valuable theoretical framework with which to examine other illegal driving behaviours that may not necessarily be seen as deviant by the community (e.g., mobile phone use while driving). This research also made unique contributions to advancing our understanding of the key components and the overall structure of Akers’ social learning theory. The broader SLT literature is lacking in terms of a thorough structural understanding of the component parts of the theory. For instance, debate exists regarding the relevance of, and necessity for including broader social influences in the model as captured by differential association. In the current research, two alternative SLT models were specified and tested in order to better understand the nature and extent of the influence of differential association on behaviour. Importantly, the results indicated that differential association was able to make a unique contribution to explaining self-reported speeding, thereby negating the call to exclude it from the model. The results also demonstrated that imitation was a discrete theoretical concept that should also be retained in the model. The results suggest a need to further explore and specify mechanisms of social influence in the SLT model. In addition, a novel approach was used to operationalise SLT variables by including concepts drawn from contemporary social psychological and deterrence-based research to enhance and extend the way that SLT variables have traditionally been examined. Differential reinforcement was conceptualised according to behavioural reinforcement principles (i.e., positive and negative reinforcement and punishment) and incorporated concepts of affective beliefs, anticipated regret, and deterrence-related concepts. Although implicit in descriptions of SLT, little research has, to date, made use of the broad range of reinforcement principles to understand the factors that encourage or inhibit behaviour. This approach has particular significance to road user behaviours in general because of the deterrence-based nature of many road safety countermeasures. The concept of self-identity was also included in the model and was found to be consistent with the definitions component of SLT. A final theoretical contribution was the specification and testing of a full measurement model prior to model testing using structural equation modelling. This process is recommended in order to reduce measurement error by providing an examination of the psychometric properties of the data prior to full model testing. Despite calls for such work for a number of decades, the current work appears to be the only example of a full measurement model of SLT. There were also a number of important practical implications that emerged from this program of research. Firstly, perceptions regarding speed enforcement tolerance thresholds were highlighted as a salient influence on driving speeds in both countries. The issue of enforcement tolerance levels generated considerable discussion among drivers in both countries, with Australian drivers reporting lower perceived tolerance levels than Chinese drivers. It was clear that many drivers used the concept of an enforcement tolerance in determining their driving speed, primarily with the desire to drive faster than the posted speed limit, yet remaining within a speed range that would preclude apprehension by police. The quantitative results from Studies 2 and 4 added support to these qualitative findings. Together, the findings supported previous research and suggested that a travel speed may not be seen as illegal until that speed reaches a level over the prescribed enforcement tolerance threshold. In other words, the enforcement tolerance appears to act as a ‘de facto’ speed limit, replacing the posted limit in the minds of some drivers. The findings from the two studies conducted in China (Studies 2 and 4) further highlighted the link between perceived enforcement tolerances and a ‘de facto’ speed limit. Drivers openly discussed driving at speeds that were well above posted speed limits and some participants noted their preference for driving at speeds close to ‘50% above’ the posted limit. This preference appeared to be shaped by the perception that the same penalty would be imposed if apprehended, irrespective of what speed they travelling (at least up to 50% above the limit). Further research is required to determine whether the perceptions of Chinese drivers are mainly influenced by the Law of the People’s Republic of China or by operational practices. Together, the findings from both studies in China indicate that there may be scope to refine enforcement tolerance levels, as has happened in other jurisdictions internationally over time, in order to reduce speeding. Any attempts to do so would likely be assisted by the provision of information about the legitimacy and purpose of speed limits as well as risk factors associated with speeding because these issues were raised by Chinese participants in the qualitative research phase. Another important practical implication of this research for speed management in China is the way in which penalties are determined. Chinese drivers described perceptions of unfairness and a lack of transparency in the enforcement system because they were unsure of the penalty that they would receive if apprehended. Steps to enhance the perceived certainty and consistency of the system to promote a more equitable approach to detection and punishment would appear to be welcomed by the general driving public and would be more consistent with the intended theoretical (deterrence) basis that underpins the current speed enforcement approach. The use of mandatory, fixed penalties may assist in this regard. In many countries, speeding attracts penalties that are dependent on the severity of the offence. In China, there may be safety benefits gained from the introduction of a similar graduated scale of speeding penalties and fixed penalties might also help to address the issue of uncertainty about penalties and related perceptions of unfairness. Such advancements would be in keeping with the principles of best practice for speed management as identified by the World Health Organisation. Another practical implication relating to legal penalties, and applicable to both cultural contexts, relates to the issues of detection and punishment avoidance. These two concepts appeared to strongly influence speeding in the current samples. In Australia, detection avoidance strategies reported by participants generally involved activities that are not illegal (e.g., site learning and remaining watchful for police vehicles). The results from China were similar, although a greater range of strategies were reported. The most common strategy reported in both countries for avoiding detection when speeding was site learning, or familiarisation with speed camera locations. However, a range of illegal practices were also described by Chinese drivers (e.g., tampering with or removing vehicle registration plates so as to render the vehicle unidentifiable on camera and use of in-vehicle radar detectors). With regard to avoiding punishment when apprehended, a range of strategies were reported by drivers from both countries, although a greater range of strategies were reported by Chinese drivers. As the results of the current research indicated that detection avoidance was strongly associated with greater self-reported speeding in both samples, efforts to reduce avoidance opportunities are strongly recommended. The practice of randomly scheduling speed camera locations, as is current practice in Queensland, offers one way to minimise site learning. The findings of this research indicated that this practice should continue. However, they also indicated that additional strategies are needed to reduce opportunities to evade detection. The use of point-to-point speed detection (also known as sectio

A new approach for finding an appropriate combination of texture parameters for classification

This paper suggests an approach for finding an appropriate combination of various parameters for extracting texture features (e.g. choice of spectral band for extracting texture feature, size of the moving window, quantization level of the image, and choice of texture feature etc.) to be used in the classification process. Gray level co-occurrence matrix (GLCM) method has been used for extracting texture from remotely sensed satellite image. Results of the classification of an Indian urban environment using spatial property (texture), derived from spectral and multi-resolution wavelet decomposed images have also been reported. A multivariate data analysis technique called ‘conjoint analysis’ has been used in the study to analyze the relative importance of these parameters. Results indicate that the choice of texture feature and window size have higher relative importance in the classification process than quantization level or the choice of image band for extracting texture feature. In case of texture features derived using wavelet decomposed image, the parameter ‘decomposition level’ has almost equal relative importance as the size of moving window and the decomposition of images up to level one is sufficient and there is no need to go for further decomposition. It was also observed that the classification incorporating texture features improves the overall classification accuracy in a statistically significant manner in comparison to pure spectral classification.

Multivariate Markov Process Models for the transmission of methicillin-resistant Staphylococcus Aureus in a hospital ward

Methicillin-resistant Staphylococcus Aureus (MRSA) is a pathogen that continues to be of major concern in hospitals. We develop models and computational schemes based on observed weekly incidence data to estimate MRSA transmission parameters. We extend the deterministic model of McBryde, Pettitt, and McElwain (2007, Journal of Theoretical Biology 245, 470–481) involving an underlying population of MRSA colonized patients and health-care workers that describes, among other processes, transmission between uncolonized patients and colonized health-care workers and vice versa. We develop new bivariate and trivariate Markov models to include incidence so that estimated transmission rates can be based directly on new colonizations rather than indirectly on prevalence. Imperfect sensitivity of pathogen detection is modeled using a hidden Markov process. The advantages of our approach include (i) a discrete valued assumption for the number of colonized health-care workers, (ii) two transmission parameters can be incorporated into the likelihood, (iii) the likelihood depends on the number of new cases to improve precision of inference, (iv) individual patient records are not required, and (v) the possibility of imperfect detection of colonization is incorporated. We compare our approach with that used by McBryde et al. (2007) based on an approximation that eliminates the health-care workers from the model, uses Markov chain Monte Carlo and individual patient data. We apply these models to MRSA colonization data collected in a small intensive care unit at the Princess Alexandra Hospital, Brisbane, Australia.