Analysis of the physical properties of an in-vessel composting matrix

Efficient compost production requires a thorough understanding of the process dynamics in terms of the feedstock materials used and the interactions of the physical properties involved. The main properties affecting the composting process are temperature, moisture content, bulk density, porosity and oxygen availability. In this study, the correlations between a selected set of physical properties of a batch-composting matrix were determined. The key physical changes in the composting materials for a blend of woodchips, chicken manure and mixed green vegetables have been monitored during a 36-day composting period in a 200-L rotary drum. The daily measurements conducted on the solid samples included temperature, pH, volatile solids, bulk density, moisture content, free airspace and substrates particle density while the carbon dioxide release was monitored weekly using jar respiration tests. The results from the compost process monitoring were a maximum temperature rise to 66.3 °C over the first 3 days, a marked decrease in free airspace from 76.3% to 40.0% at the end of the process, a variation in average composting material particle density from 1097 kg/m³ to 2325 kg/m³, and an increase in wet bulk density from 255 kg/m³ to 628 kg/m³. Correlations developed among free airspace, wet bulk density, dry bulk density and wet moisture content were in agreement with previously determined equations from literature. Free airspace varied linearly with both dry and wet bulk densities (R² value of 0.89 and 0.95, respectively) while the free airspace-wet moisture content profile followed a fourth degree polynomial trend with a correlation coefficient of 0.63.     

Mining partial periodic correlations in time series

Recently, periodic pattern mining from time series data has been studied extensively. However, an interesting type of periodic pattern, called partial periodic (PP) correlation in this paper, has not been investigated. An example of PP correlation is that power consumption is high either on Monday or Tuesday but not on both days. In general, a PP correlation is a set of offsets within a particular period such that the data at these offsets are correlated with a certain user-desired strength. In the above example, the period is a week (7 days), and each day of the week is an offset of the period. PP correlations can provide insightful knowledge about the time series and can be used for predicting future values. This paper introduces an algorithm to mine time series for PP correlations based on the principal component analysis (PCA) method. Specifically, given a period, the algorithm maps the time series data to data points in a multidimensional space, where the dimensions correspond to the offsets within the period. A PP correlation is then equivalent to correlation of data when projected to a subset of the dimensions. The algorithm discovers, with one sequential scan of data, all those PP correlations (called minimum PP correlations) that are not unions of some other PP correlations. Experiments using both real and synthetic data sets show that the PCA-based algorithm is highly efficient and effective in finding the minimum PP correlations. Zhen He is a lecturer in the Department of Computer Science at La Trobe University. His main research areas are database systems optimization, time series mining, wireless sensor networks, and XML information retrieval. Prior to joining La Trobe University, he worked as a postdoctoral research associate in the University of Vermont. He holds Bachelors, Honors and Ph.D degrees in Computer Science from the Australian National University. X. Sean Wang received his Ph.D degree in Computer Science from the University of Southern California in 1992. He is currently the Dorothean Chair Professor in Computer Science at the University of Vermont. He has published widely in the general area of databases and information security, and was a recipient of the US National Science Foundation Research Initiation and CAREER awards. His research interests include database systems, information security, data mining, and sensor data processing. Byung Suk Lee is associate professor of Computer Science at the University of Vermont. His main research areas are database systems, data modeling, and information retrieval. He held positions in industry and academia: Gold Star Electric, Bell Communications Research, Datacom Global Communications, University of St. Thomas, and currently University of Vermont. He was also a visiting professor at Dartmouth College and a participating guest at Lawrence Livermore National Laboratory. He served on international conferences as a program committee member, a publicity chair, and a special session organizer, and also on US federal funding proposal review panel. He holds a BS degree from Seoul National University, MS from Korea Advanced Institute of Science and Technology, and Ph.D from Stanford University. Alan C. H. Ling is an assistant professor at Department of Computer Science in University of Vermont. His research interests include combinatorial design theory, coding theory, sequence designs, and applications of design theory.     

Semi-linear Stochastic Difference Equations

We consider in this paper a class of vector valued processes that have the form Y _n + 1 = A _n ( Y _n ) + B _n . B _n is assumed to be stationary ergodic and A _n is assumed to have a divisibility property. This class includes linear stochastic difference equations as well as multi-type branching processes (with a discrete or with a continuous state space). We derive explicit expressions for the probability distribution as well as for the two first moments of state vectors at the stationary regime. We then apply this approach to derive two formalisms to describe the infinite server queue. The first is based on a branching process approach adapted to phase type service time distributions. The second is based on a linear stochastic difference equation and is adapted to independent and generally distributed service times with bounded support. In both cases we allow for generally distributed arrival process (not necessarily i.i.d. nor Markovian).

Levels of polybrominated diphenyl ethers in house dust in Central Poland

Polybrominated diphenyl ethers (PBDEs) are used as flame retardants in numerous products, from which they are emitted to the environment, including house dust. House dust is a source of human exposure to these compounds by ingestion. The aim of this article was to determine the levels of selected PBDEs in the house dust and indicate their potential sources of origin. PBDE congeners: BDE‐47, BDE‐99, BDE‐153 and BDE‐209, were analyzed in 129 samples. The geometric mean levels (and 95% CIs) of the aforementioned congeners amounted to 3.8 (3.1–4.7) ng/g, 4.5 (3.5–5.6) ng/g, 2.2 (2.1–2.4) ng/g and 345 (269–442) ng/g respectively. BDE‐209 was the dominant congener in the majority of tested samples. We found a statistically significant correlation between the concentrations of BDE‐47 and the computer operating time per day (r_s – 0.18) and the living area (r_s – 0.20). Statistically significant higher levels of BDE‐99 were found in homes where the floor was not replaced during the last 2 years.     

On the verification of the effect of water depth on the performance of basin type solar stills

Many experimental and numerical studies have been done on different configurations of solar stills to optimize the design by examining the effect of climatic, operational and design parameters on its performance. The majority of the investigators presented their results in scatter diagrams rather than correlations. One of the most important of the operational parameters that has received a considerable attention in the literature is the brine depth. A good number of the investigations on the effect of brine depth are cited in this study. For each of these studies, a correlation was developed from the data reported by each study. A concluding correlation from all brine depth data was developed. The correlation showed a decreasing trend in the productivity with the increase in the brine depth. An experimental study was subsequently conducted to verify this trend by an experimental investigation on a solar still that was constructed and tested with five different brine depths, namely 1, 4, 6, 8 and 10 cm. The present study validated the decreasing trend in productivity with the increase of brine depth and showed that the still productivity could be influenced by the brine depth by up to 48%.     

相对论核─核碰撞中粒子的横向动量模关联

本文对１．２ＡＧｅＶＡｒ＋ＢａＩ２和２．１ＡＧｅＶＮｅ＋ＮａＦ碰撞的Ｂｅｖａｌａｃ流光室４π实验事件末态粒子的横向运动特征进行了研究。发现末态粒子的横向运动不仅存在方位角关联，而且还存在横向动量模的关联。研究表明；在Ａｒ十ＢａＩ２碰撞实验中，粒子的横向动量模关联比Ｎｅ＋ＮａＦ碰撞实验强。并且，两种碰撞实验中的粒子方位角关联比其各自的横向动量模关联强。     

Line creep in paper peeling

The dynamics of a “peeling front” or an elastic line is studied under creep (constant load) conditions. Our experiments show in most cases an exponential dependence of the creep velocity on the inverse force (mass) applied. In particular, the dynamical correlations of the avalanche activity are discussed here. We compare various avalanche statistics to those of a line with non-local elasticity, and study various measures of the experimental avalanche-avalanche and temporal correlations such as the autocorrelation function of the released energy and aftershock activity. From all these we conclude, that internal avalanche dynamics seems to follow “line depinning”-like behavior, in rough agreement with the depinning model. Meanwhile, the correlations reveal subtle complications not implied by depinning theory. Moreover, we also show how these results can be understood from a geophysical point of view.     

Correlation between the compositional and pasting properties of various potato starches

Multiple linear regression equations were used to develop the correlation between the compositional and rapid visco-analysis (RVA) pasting properties of various potato starches. The amylose of potato starches had a negative correlation with the peak viscosity (PV) and breakdown (BD) and a positive correlation with the setback viscosity (SV) and peak viscosity temperature (PVT). By contrast, phosphorus had a positive correlation with PV, BD, and SV and a negative correlation with PVT. In addition, the median granule size had a positive correlation with PV and BD. By contrast, a negative correlation of the median granule size was observed with SV and PVT. The correlation coefficients of amylose–phosphorus, amylose–granule size, and phosphorus–granule size interactions indicated that amylose had more influence than had phosphorus or had the median granule size on PV and BD. Furthermore, amylose had a greater influence than had the granule size on SV and PVT. Similarly, amylose had more influence than had phosphorus or had the median granule size on PVT. However, the correlation developed in this study was useful for predicting the influence of a specific component and the compositional interaction on the RVA pasting properties.