The Earliest Optical Observations of GRB 030329

We present the earliest optical imaging observations of GRB 030329 related to SN 2003dh. The burst was detected by the HETE-2 satellite at 2003 March 29, 11:37:14.67 UT. Our wide-field monitoring started 97 minutes before the trigger and the burst position was continuously observed. We found no precursor or contemporaneous flare brighter than $V=5.1$ ($V=5.5$) in 32 s (64 s) timescale between 10:00 and 13:00 UT. Follow-up time series photometries started at 12:51:39 UT (75 s after position notice through the GCN) and continued for more than 5 hours. The afterglow was $Rc= 12.35\pm0.07$ at $t=74$ min after burst. Its fading between 1.2 and 6.3 hours is well characterized by a single power-law of the form $f{\rm(mJy)} = (1.99\pm0.02{\rm (statistic)}\pm0.14{\rm (systematic)}) \times (t/1 {\rm day})^{-0.890\pm 0.006 {\rm (statistic)}\pm 0.010 {\rm (systematic)}}$ in $Rc$-band. No significant flux variation was detected and upper limits are derived as $(\Delta f/f)_{\rm RMS} = 3-5$% in minutes to hours timescales and $(\Delta f/f)_{\rm RMS} = 35-5$% in seconds to minutes timescales. Such a featureless lightcurve is explained by the smooth distribution of circumburst medium. Another explanation is that the optical band was above the synchrotron cooling frequency where emergent flux is insensitive to the ambient density contrasts. Extrapolation of the afterglow lightcurve to the burst epoch excludes the presence of an additional flare component at $t<10$ minutes as seen in GRB 990123 and GRB 021211.Comment: ApJL, in pres

Household packaging waste management

Household packaging waste (HPW) has an important environmental impact and economic relevance. Thus there are networks of collection points (named “ecopontos” in Portugal) where HPW may be deposited for collection by waste management companies. In order to optimize HPW logistics, accurate estimates of the waste generation rates are needed to calculate the number of collections required for each ecoponto in a given period of time. The most important factors to estimate HPW generation rates are linked to the characteristics of the population and the social and economic activities around each ecoponto location. We developed multiple linear regression models and artificial neural networks models to forecast the number of collections per year required for each location. For operational short term planning purposes, these forecasts need to be adjusted for seasonality in order to determine the required number of collections for the relevant planning period. In this paper we describe the methodology used to obtain these forecasts.This research has been partially supported by COMPETE: POCI-01-0145-FEDER007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio