Time series analysis of coal mine accident experience

This study investigates several forecasting techniques that can be useful to mine safety managers for studying mine accident rate behavior. Three time series models were studied for extrapolation of accident rates. These models are applied to historical accident incidence data from a coal mine. Further, a method is presented for evaluating the three models for the selection of an appropriate model. For this particular mine application, it is concluded that the more complex Box-Jenkins ARMA model as well as first order autoregressive model do not give better results than the simple exponential smoothing model. However, when the random variations or autocorrelations in the accident experience rates between periods are different, the models may predict differently. As such, specific models must be developed for each mine on the basis of statistical analysis of the mine accident experience data over time. Moreover, the importance of incorporating human judgement to interpret the results of statistical forecasting cannot be overemphasized. Integration of policy or operating changes, which may impact mine safety performance, with statistical forecasting techniques is essential to arrive at a realistic prediction of future performance.     

FLOOD STAGE FORECASTING WITH SUPPORT VECTOR MACHINES1

ABSTRACT: Machine learning techniques are finding more and more applications in the field of forecasting. A novel regression technique, called Support Vector Machine (SVM), based on the statistical learning theory is explored in this study. SVM is based on the principle of Structural Risk Minimization as opposed to the principle of Empirical Risk Minimization espoused by conventional regression techniques. The flood data at Dhaka, Bangladesh, are used in this study to demonstrate the forecasting capabilities of SVM. The result is compared with that of Artificial Neural Network (ANN) based model for one‐lead day to seven‐lead day forecasting. The improvements in maximum predicted water level errors by SVM over ANN for four‐lead day to seven‐lead day are 9.6 cm, 22.6 cm, 4.9 cm and 15.7 cm, respectively. The result shows that the prediction accuracy of SVM is at least as good as and in some cases (particularly at higher lead days) actually better than that of ANN, yet it offers advantages over many of the limitations of ANN, for example in arriving at ANN's optimal network architecture and choosing useful training set. Thus, SVM appears to be a very promising prediction tool.     

Constraints to improved energy efficiency in agricultural pumpsets: the case of India

Supply to electrical pumpsets for irrigation purposes accounts for about 25% of the total electricity sold in India. The number of electrical pumpsets is expected to increase from 8.5 million in 1989–90 to 10.5 million in 1994–95. Low load factors (10–12%) and low densities (1–4 pumpsets/km²) mean large investments for the electric utility in order to supply electricity for irrigation. It is reported that there exists a potential to save at least 10% of the electricity supplied to this sector by means of simple retrofit measures. However, conditions in the field pose constraints to such retrofits. Some of those constraints are highlighted in this paper. It is also suggested that immediate attempts be made to ensure that all new connections are energy efficient. This paper suggests that the additional cost incurred to ensure new energy efficient connections and to maintain retrofit energy efficient pumpsets must be looked on as a necessary cost for energy efficiency.     

Water Supply Planning Considering Uncertainties in Future Water Demand and Climate: A Case Study in an Illinois Watershed

Ensuring an adequate, reliable, clean, and affordable water supply for citizens and industries requires informed, long-range water supply planning, which is critically important for water security. A balance between water supply and demand must be considered for a long-term plan. However, water demand projections are often highly uncertain. Climate change could impact the hydrologic processes, and consequently, threaten water supply. Thus, understanding the uncertainties in future water demand and climate is critical for developing a sound water supply plan. In Illinois, regional water supply planning attempts to explore the impacts of future water demand and climate on water supply using scenario analyses and hydrologic modeling. This study is aimed at developing a water supply planning framework that considers both future water demand and climate change impacts. This framework is based on the Soil and Water Assessment Tool to simulate the watershed hydrology and conduct scenario analyses that consider the uncertainties in both future water demand and climate as well as their impacts on water supply. The framework was applied to water supply planning efforts in the Kankakee River watershed. The Kankakee River watershed model was calibrated and validated to observed streamflow records at four long-term United States Geological Survey streamflow gages. Because of the many model parameters involved, the calibration process was automated and was followed by a manual refinement, resulting in good model performance. Long-range water demand projections were prepared by the Illinois State Water Survey. Six future water demand scenarios were established based on a suite of assumptions. Climate scenarios were obtained from the Coupled Model Intercomparison Projection Phase 5 datasets. Three representative concentration pathways (RCPs), RCP2.6, RCP4.5, and RCP8.5, are used in the study. The scenario simulation results demonstrated that climate change appears to have a greater impact on water availability in the study area than water demand. The framework developed in this study can also be used to explore the impacts of uncertainties of water demand and climate on water supply and can be extended to other regions and watersheds.     

Real-world vehicle emissions: A summary of the 15th coordinating research council on-road vehicle emissions workshop

The Coordinating Research Council held its 15th workshop in April 2005, with nearly 90 presentations describing the most recent mobile source-related emissions research. In this paper, the authors summarize the presentations from researchers who are engaged in improving our understanding of the contribution of mobile sources to air quality. Participants in the workshop discussed emission models and emission inventories, results from gas- and particle-phase emissions studies from spark-ignition and diesel-powered vehicles (with an emphasis in this workshop on particle emissions), effects of fuels on emissions, evaluation of in-use emissions control programs, and efforts to improve our capabilities in performing on-board emissions measurements, as well as topics for future research.     

Persistent locomotor rhythmicity in the prawns Penaeus indicus and P. monodon

The locomotory activity of adult Penaeus indicus Milne Edwards and P. monodon (Fabricius) collected from the Vellar estuary, South India, in 1983 was monitored continuously for a period of 40 d in the laboratory. Both species exhibited persistent circatidal and circadian rhythmicity, the former displayed peak activity during predicated times of high water in their original habitat. The latter exhibited peak activity during periods corresponding to in situ night-time, with almost no activity during the day, except at the time of high tide. The tidal and daily rhythms were evident for only 3 to 4 d and 5 to 6 d, respectively, under laboratory conditions. The amplitude of the rhythms displayed variations in each semilunar period, reflecting the synchrony and dissynchrony of the tidal and daily rhythms. Activity was correlated with phases of the moon, peaking at times of new and full moon, and showing a scattered pattern with smaller peaks during the first and third quarters.     

Dyes removal from water using polymeric nanocomposites: a review

Environmental Chemistry Letters - Most dyes are pollutants that should be removed from waters due to their mutagenic and carcinogenic properties, requiring advanced adsorption methods. Here, we...     

Determination of fluopicolide in grapes and soil using an easy sample preparation method and gas chromatography-mass spectrometry

A simple analytical method was developed and validated to determine fluopicolide residues in grapes and soil using the “Quick, Easy, Cheap, Effective, Rugged and Safe” approach followed by gas chromatography-mass spectrometry in the selected ion monitoring mode. Fluopicolide residues were extracted from grape and soil samples with acetonitrile, and the extracts were cleaned up by dispersive solid-phase extraction with primary secondary amine sorbent. The limit of quantification was 0.02 mg kg^?1 with grape and soil samples spiked at three levels, and recoveries were from 95% to 101% and 94% to 101% with relative standard deviations from 0.2% to 4.4%. The method is less expensive and safer than existing analytical methods for grapes and soil.