The Value of Considering Autocorrelation Between Inflows in the Stochastic Planning of Water Resource Systems

Reliability programming formulations offer a family ofexplicit stochastic models for planning the operationof complex water resources systems. These models usecumulative probability distributions of the sum ofinflows to characterize their variability in theplanning period. Applicability of these models for avariety of problems has been limited, mainly due tothe assumption of independence between inflows indifferent time periods that leads to the derivation ofconservative operating policies. This paper presentsthree new approaches to overcome this limitation. Theperformance of the proposed approaches is demonstratedthrough comparison of the operating policies derivedfrom these approaches and the Independent Approach. Operational planning of the Manitoba Hydro energygeneration system, a predominantly hydro-based utilitycompany in Manitoba (Canada), is used as the case study.     

Two-dimensional correlation analysis of nuclear magnetic resonance metabonomics data

Two-dimensional (2D) correlation analysis has been used in this study to identify changes in complex nuclear magnetic resonance (NMR) metabonomics spectra of rat urine samples obtained during a study in which vasculitis (vascular injury), an important safety element in preclinical trials, was induced. Two types of correlation analysis were performed, along the variables and along the samples, and both 2D covariance and correlation coefficient maps were calculated. The binned and 'raw' NMR spectra were analyzed (0.04 and 0.001 ppm resolution, respectively). Good correlation was found among the major peaks of the binned spectra, and two groups of samples were identified using sample-sample 2D correlation maps. Much more complex correlation features were obtained from the 'raw' spectra, in which the specific, butterfly-like patterns were obtained in the covariance map but with only a few significant correlation coefficients in the corresponding 2D correlation maps. In terms of classification, the same group of the last nine spectra that indicated the end of the process and clustered in the 2D sample-sample covariance map of the binned data was also found in the 2D sample-sample covariance map of the raw NMR spectra but, again, not in the 2D correlation coefficient map. A discussion is given on the details of the application of the correlation analysis with regard to spectral data resolution, alignment, the effect of actual intensities of the NMR signal, and reference to various results from 2D correlation analysis of vibrational spectra.     

Analyzing Raman maps of pharmaceutical products by sample-sample two-dimensional correlation

Sample-sample (SS) two-dimensional (2D) correlation spectroscopy is applied in this study as a spectral selection tool to produce chemical images of real-world pharmaceutical samples consisting of two, three, and four components. The most unique spectra in a Raman mapping spectral matrix are found after analysis of the covariance matrix. (This is obtained by multiplying the original mapping data matrix by itself.) These spectra are identified by analyzing the slices of the covariance matrix at the positions where covariance values are at maxima. Chemical images are subsequently produced in a univariate fashion by visually selecting the wavenumbers in the extracted spectra that are least overlapped. The performance of SS 2D correlation is compared with principal component analysis in terms of highlighting the most prominent spectral differences across the whole data set (which typically comprises several thousand spectra) and determining the total number of species present. In addition, the selection of the unique spectra by SS 2D correlation is compared with the selection obtained by the orthogonal projection approach (OPA). Both comparisons are found to be satisfactory and demonstrate that a quite simple SS 2D correlation routine can be used for producing reliable images of unknown samples. The main benefit of using SS 2D correlation is that it is based on a few data processing commands that can be executed separately and produce results that are closely related to the chemical features of the system.     

Collaborative Filtering Using a Regression-Based Approach

The task of collaborative filtering is to predict the preferences of an active user for unseen items given preferences of other users. These preferences are typically expressed as numerical ratings. In this paper, we propose a novel regression-based approach that first learns a number of experts describing relationships in ratings between pairs of items. Based on ratings provided by an active user for some of the items, the experts are combined by using statistical methods to predict the users preferences for the remaining items. The approach was designed to efficiently address the problem of data sparsity and prediction latency that characterise collaborative filtering. Extensive experiments on Eachmovie and Jester benchmark collaborative filtering data show that the proposed regression-based approach achieves improved accuracy and is orders of magnitude faster than the popular neighbour-based alternative. The difference in accuracy was more evident when the number of ratings provided by an active user was small, as is common for real-life recommendation systems. Additional benefits were observed in predicting items with large rating variability. To provide a more detailed characterisation of the proposed algorithm, additional experiments were performed on synthetic data with second-order statistics similar to that of the Eachmovie data. Strong experimental evidence was obtained that the proposed approach can be applied to data over a large range of sparsity scenarios and is superior to non-personalised predictors even when ratings data are very sparse.     

Chemical Ecology of Cave-Dwelling Millipedes: Defensive Secretions of the Typhloiulini (Diplopoda,Julida, Julidae)

Cave animals live under highly constant ecological conditions and in permanent darkness, and many evolutionary adaptations of cave-dwellers have been triggered by their specific environment. A similar “cave effect” leading to pronounced chemical interactions under such conditions may be assumed, but the chemoecology of troglobionts is mostly unknown. We investigated the defensive chemistry of a largely cave-dwelling julid group, the controversial tribe “Typhloiulini”, and we included some cave-dwelling and some endogean representatives. While chemical defense in juliform diplopods is known to be highly uniform, and mainly based on methyl- and methoxy-substituted benzoquinones, the defensive secretions of typhloiulines contained ethyl-benzoquinones and related compounds. Interestingly, ethyl-benzoquinones were found in some, but not all cave-dwelling typhloiulines, and some non-cave dwellers also contained these compounds. On the other hand, ethyl-benzoquinones were not detected in troglobiont nor in endogean typhloiuline outgroups. In order to explain the taxonomic pattern of ethyl-benzoquinone occurrence, and to unravel whether a cave-effect triggered ethyl-benzoquinone evolution, we classed the “Typhloiulini” investigated here within a phylogenetic framework of julid taxa, and traced the evolutionary history of ethyl-benzoquinones in typhloiulines in relation to cave-dwelling. The results indicated a cave-independent evolution of ethyl-substituted benzoquinones, indicating the absence of a “cave effect” on the secretions of troglobiont Typhloiulini. Ethyl-benzoquinones probably evolved early in an epi- or endogean ancestor of a clade including several, but not all Typhloiulus (basically comprising a taxonomic entity known as “Typhloiulus sensu stricto”) and Serboiulus. Ethyl-benzoquinones are proposed as novel and valuable chemical characters for julid systematics.     

A Combinatorial Procedure to Determine the Full Range of Potential Operating Scenarios for a Dam System

Traditional dam safety assessment tends to place the focus on estimating the probabilities of failure for the system based on a few subjectively-chosen operating scenarios. The techniques used to assess these systems rely on linear chains of events and are incapable of considering component interactions, feedbacks and non-linear behaviour. This paper uses a systems approach to develop a new technique that puts possibilities first and is capable of generating an exhaustive list of potential component operating state scenarios for the system. The scenarios can then be simulated using a Monte-Carlo approach to determine a wide range of system behaviour (outcomes) for each scenario, taking into account component interactions and feedbacks. A component operating states database is presented where the system can be broken down into various levels of detail and the operating states and causal factors for each component can be defined. The Cartesian product of each operating state set is used to derive the full range of potential operating scenarios, with each scenario consisting of a single operating state for every component in the system. The list of scenarios can be used as an input to a simulation model which can then be run many times for each scenario, using Monte-Carlo inputs which vary the timing and severity of events as well as the inflows. The approach for automated scenario generation is demonstrated on a simple and complex representation of a hydropower system and the scenarios are generated and counted. Results show that increasing system complexity results in exponentially increasing numbers of potential operating scenarios, with the simple system having a total of 1.1?×?10⁶ operating states in comparison to the complex system which has 1.83?×?10²⁷. The approach presented in this paper (a) reduces the subjectivity associated with traditional dam safety assessments through automated scenario generation, and (b) improves the ability to understand component interaction and feedbacks by describing a Monte Carlo simulation approach which can be used for scenario simulation. By understanding how the system responds to the full range of potential operating conditions, dam owners and asset managers can make informed decisions relating to the improvement of operating strategies and implementation of system upgrades.

     

Integrated Reservoir Management System for Adaptation to Climate Change: The Nakdong River Basin in Korea

This study begins with the premise that current reservoir management systems do not take into account the potential effects of climate change on optimal performance. This study suggests an approach in which multi-purpose reservoirs can adapt to climate change using optimal rule curves developed by an integrated water resources management system. The system has three modules: the Weather Generator model, the Hydrological Model, and the Differential Evolution Optimization Model. Two general circulation models (GCMs) are selected as examples of both dry and wet conditions to generate future climate scenarios. This study is using the Nakdong River basin in Korea as a case study, where water supply is provided from the reservoir system. Three different climate change conditions (historic, wet and dry) are investigated through the compilation of six 60 years long scenarios. The optimal rule curves for three multi-purpose reservoirs in the basin are developed for each scenario. The results indicate that although the rule curve for large-size reservoir is less sensitive to climate change, medium or small-size reservoirs are very sensitive to those changes. We further conclude that the large reservoir should be used to release more water, while small or medium-size reservoirs should store inflow to mitigate severe drought damages in the basin.     

Multi-variable selection of the main mine shaft location

Selecting the location of the main mine shaft is a demanding multi-criteria task. The solution does not depend only on identification of the potential location for the construction and the criteria conditions set for the selection, but also on the applied procedure in the analysis. Since there is no general scientific agreement on the selection of the most suitable quantitative method for the support to decision making for this type of analyses, the problem is becoming even more complex. In terms of the Velenje Mine shaft location, the authors describe the approach that can be an exit from this situation. The idea of an approach is not the selection of the most suitable decision making support method but a multi-model procedure where several multi-criteria methods are being used simultaneously. In case when the techniques do not yield the same order of alternatives, it is advised to generate the final order using the method of ponderation.