Optically stimulated luminescence (OSL) dating of fine-grained (4–11 μm) fluvial sediments is rarely attempted but is crucial for constraining the evolution of mud-dominated floodplains. This study investigated the applicability of OSL dating to fine-grained deposits in the Mekong River, Cambodia based on a very young (<600 yr) point-bar to riverbank succession and modern flood deposits. In succession, fine-grained quartz OSL provided the youngest ages, whereas age estimates by multi-grain sand-sized quartz OSL, and feldspar and polymineral infrared-stimulated luminescence are >200 years older than the fine-grained quartz OSL age estimates. Ages of fine-grained quartz OSL are concordant with the minimum ages obtained from the single-grain quartz OSL. These results are supported by the generally small residual doses (<0.1 Gy) measured in modern fine-grained floodplain deposits. This indicates that fine-grained sediments in the Mekong River (Cambodia) are sufficiently bleached at deposition and can yield reliable quartz OSL ages for establishing the chronology of the floodplain. The sufficient bleaching of fine-grained quartz partly results from the long transport distance and may also occur in other large river systems.
In surveying problems we almost always use unbiased estimators; however, even unbiased estimator might yield biased assessments, which is due to data. In statistics one distinguishes several types of such biases, for example, sampling, systemic or response biases. Considering surveying observation sets, bias from data might result from systematic or gross errors of measurements. If nonrandom errors in an observation set are known, then bias can easily be determined for linear estimates (e.g., least squares estimates). In the case of non-linear estimators, it is not so simple. In this paper we are focused on a vertical displacement analysis and we consider traditional least squares estimate, two Msplitestimates and two basic robust estimates, namely M-estimate, R-estimate. The main aim of the paper is to assess estimate biases empirically by applying Monte Carlo method. The smallest biases are obtained for M- and R-estimates, especially for a high magnitude of a gross error. On the other hand, there are several cases when Msplitestimates are the best. Such results are acquired when the magnitude of a gross error is moderate or small. The outcomes confirm that bias of Msplitestimates might vary for different point displacements.
Energy crises is the one of the major problem that was faced by Pakistan in order to overcome on that crises Pakistan need to be developed and improvement in energy sector, Throughout in the country the demand of water and power increasing day by day therefore hydropower project are the need of the hour in Pakistan. Before initiation of any project EIA play important role in evaluating the nature of the project on different factors. Government of Pakistan planned one of the mega hydropower project diamer basha dam was planned in Gilgit Baltistan. It was intended to conduct the research work on describing significant factors so as to evaluate the influence of the project on them and develop guidelines for environmental assessment for these factors. To find out these significant factors the methodology was adapted to conducting field investigation. Besides to assess the relevant impact questionnaires were developed. Finally, in order to reduce the negative impact of the project on the predefine factor mitigation measure was suggested. It is anticipated that this study work support in developing structure work to be executed as mitigation measures and boost the advantages of the project.