How many genetic variants remain to be discovered?

10.1371/journal.pone.0007969PLoS ONE412

Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application

Water Footprint Analysis in Krueng Aceh Watershed, Aceh Province, Indonesia

Water is one the most important natural resources to maintain human life and all other living things in the earth. Around 65% water were consumed for drinking purpose, while others were used for daily needs. The increasing amount of work on water use and scarcity in relation to consumption and trade has led to the emergence of the field of Water Footprint (WF). Climate change, rural development, world population growth and industrialization have placed considerable stress on the local availability of water resources. Thus, it is necessary to perform study in order to analyze water demands and supply for sustainable water availability. Recently, water footprint analysis has been widely draw attention to the scientists and engineers. The water footprint analysis is closely related with virtual water from which it is defined as total water volume used for consumption and trade. The main aim of this present study is to analyze and assess the total water requirement based on community water footprint in Krueng Aceh watershed area. The virtual water used in this study are dominant consumption food commodities. The result shows that water footprint per capita in Krueng Aceh watershed area was 674.52 m3/year. Water footprint for rural and urban population were 608.27 m3/year and 740.77 m3/year respectively. The WF of food consumption in urban area of Krueng Aceh watershed is 690.74 m3 / capita / year and 584.22 m3/capita/year or average 625.69 m3/capita/year, while for non-food, the WF per capita is 24.05 m3/year in rural or 32.46% of the total water footprint. Non-food consumption per capita in Krueng Aceh and in urban areas is 50.03 m3/year or 67.53%. The total water demand based on the water footprint is 378,906,655.05 m3 in 2015 which is consumed by most of residents in the Krueng Aceh watershed area. Furthermore, total WF in rural and urban area are 193,489,128.95 m3 and 185,417,526.10 m3 respectively

Human genetics and genomics a decade after the release of the draft sequence of the human genome

10.1186/1479-7364-5-6-577Human Genomics56577-62

Relationship Between Concentration and Discharge on Storm Events: Case Study at Cakardipa Catchment, Cisukabirus Subwatershed, Upper Ciliwung Watershed, Bogor, West Java

River nutrient loadings rates are frequently determined from discharge and hydrochemistry relationships using regression techniques. Unfortunately such methods as a conventional technique are inadequate for dealing with the problem such as differences in shape and direction of loop forming in individual and seasonal storms. Besides the relationships are nonlinear and time-dependent, they also varies from site to site. There is a currently method to study hysteresis between discharge and concentration of hydrochemistry. The relationship between discharge and solute concentration was investigated at Cakardipa catchment, Upper Ciliwung watershed, between the years of 2009-2010. The characteristics of the hysteresis loops were used to evaluate the temporal variation of the relative contribution to stream flow of source waters at Cakardipa Catchment including groundwater (CG), soil water (CSO), and rain water (CR). Chemical water analysis was carried out on 497 water samples on storm event. The chemical analysis of storm event of Februari 14, 2010 was carried out for the concentrations of K+, Ca2+, Mg2+, Na+, SiO2, SO42-NO3-, Cl-, and HCO3-. Results of the experiment showed that concentrations displayed circular hysteresis loops during the events, highlighting the complex relation among solutes and discharge during storm hydrographs. The solutes of K, Na, and Ca produced  concave curvature, anti-clockwise hysteresis loops, and positive  trend, so that classified as A2 loops with components ranking were CR> CG> CSO. .The solutes of Mg, SO4, NO3 assumed to come from groundwater produced convex curvature, clockwise hysteresis loops, and positive trend, indicating a concentration component ranking of CG > CR > CSO (C2 model). While Si and Cl produced clockwise hysteresis loops, indicating a concentration component ranking of CG> CSO> CR  which was C1 model.Keywords: Discharge, hydrochemistry, hysteresis, storm event[How to Cite: Heryani N, H Pawitan, MYJ Purwanto and K Subagyono. 2012. Relationship between Concentration and Discharge on Storm Events: Case Study at Cakardipa Catchment, Cisukabirus Subwatershed, Upper Ciliwung Watershed, Bogor, West Java. J Trop Soils 17 (1): 85-95. doi: 10.5400/jts.2012.17.1.85] [Permalink/DOI: www.dx.doi.org/10.5400/jts.2012.17.1.85

Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data

Resource demand estimation is essential for the application of analyical models, such as queueing networks, to real-world systems. In this paper, we investigate maximum likelihood (ML) estimators for service demands in closed queueing networks with load-independent and load-dependent service times. Stemming from a characterization of necessary conditions for ML estimation, we propose new estimators that infer demands from queue-length measurements, which are inexpensive metrics to collect in real systems. One advantage of focusing on queue-length data compared to response times or utilizations is that confidence intervals can be rigorously derived from the equilibrium distribution of the queueing network model. Our estimators and their confidence intervals are validated against simulation and real system measurements for a multi-tier application

Research practices that can prevent an inflation of false-positive rates

Recent studies have indicated that research practices in psychology may be susceptible to factors that increase false-positive rates, raising concerns about the possible prevalence of false-positive findings. The present article discusses several practices that may run counter to the inflation of false-positive rates. Taking these practices into account would lead to a more balanced view on the false-positive issue. Specifically, we argue that an inflation of false-positive rates would diminish, sometimes to a substantial degree, when researchers (a) have explicit a priori theoretical hypotheses, (b) include multiple replication studies in a single paper, and (c) collect additional data based on observed results. We report findings from simulation studies and statistical evidence that support these arguments. Being aware of these preventive factors allows researchers not to overestimate the pervasiveness of false-positives in psychology and to gauge the susceptibility of a paper to possible false-positives in practical and fair ways

Heartbeat of the Southern Oscillation explains ENSO climatic resonances

The El Ni~no-Southern Oscillation (ENSO) nonlinear oscillator phenomenon has a far reaching influence on the climate and human activities. The up to 10 year quasi-period cycle of the El Ni~no and subsequent La Ni~na is known to be dominated in the tropics by nonlinear physical interaction of wind with the equatorial waveguide in the Pacific. Long-term cyclic phenomena do not feature in the current theory of the ENSO process. We update the theory by assessing low (>10 years) and high (<10 years) frequency coupling using evidence across tropical, extratropical, and Pacific basin scales. We analyze observations and model simulations with a highly accurate method called Dominant Frequency State Analysis (DFSA) to provide evidence of stable ENSO features. The observational data sets of the Southern Oscillation Index (SOI), North Pacific Index Anomaly, and ENSO Sea Surface Temperature Anomaly, as well as a theoretical model all confirm the existence of long-term and short-term climatic cycles of the ENSO process with resonance frequencies of {2.5, 3.8, 5, 12–14, 61–75, 180} years. This fundamental result shows long-term and short-term signal coupling with mode locking across the dominant ENSO dynamics. These dominant oscillation frequency dynamics, defined as ENSO frequency states, contain a stable attractor with three frequencies in resonance allowing us to coin the term Heartbeat of the Southern Oscillation due to its characteristic shape. We predict future ENSO states based on a stable hysteresis scenario of short-term and long-term ENSO oscillations over the next century

Identifying Areas Affected By Fires In Sumatra Based On TIME Series Of Remotely Sensed Fire Hotspots And Spatial Modeling

Wildfires threaten the environment not only at local scales, but also at wider scales. Rapid monitoring system to detect active wildfires has been provided by satellite remote sensing technology, particularly through the advancement on thermal infrared sensors. However, satellite-based fire hotspots data, even at relatively high temporal resolution of less than one-day revisit period, such as time series of fire hotspots collected from TERRA and AQUA MODIS, do not tell exactly if they are fire ignitions or fire escapes, since other factors like wind, slope, and fuel biomass significantly drive the fire spread. Meanwhile, a number of biophysical fire simulation models have been developed, as tools to understand the roles of biophysical factors on the spread of wildfires. Those models explicitly incorporate effects of slope, wind direction, wind speed, and vegetative fuel on the spreading rate of surface fire from the ignition points across a fuel bed, based on either field or laboratory experiments. Nevertheless, none of those models have been implemented using real time fire data at relatively large extent areas. This study is aimed at incorporating spatially explicit time series data of weather (i.e. wind direction and wind speed), remotely sensed fuel biomass and remotely sensed fire hotspots, as well as incorporating more persistent biophysical factors (i.e. terrain), into an agent-based fire spread model, in order to identify fire ignitions within time series of remotely sensed fire hotspots

Two dimensional smoothing via an optimised Whittaker smoother

Background In many applications where moderate to large datasets are used, plotting relationships between pairs of variables can be problematic. A large number of observations will produce a scatter-plot which is difficult to investigate due to a high concentration of points on a simple graph. In this article we review the Whittaker smoother for enhancing scatter-plots and smoothing data in two dimensions. To optimise the behaviour of the smoother an algorithm is introduced, which is easy to programme and computationally efficient. Results The methods are illustrated using a simple dataset and simulations in two dimensions. Additionally, a noisy mammography is analysed. When smoothing scatterplots the Whittaker smoother is a valuable tool that produces enhanced images that are not distorted by the large number of points. The methods is also useful for sharpening patterns or removing noise in distorted images. Conclusion The Whittaker smoother can be a valuable tool in producing better visualisations of big data or filter distorted images. The suggested optimisation method is easy to programme and can be applied with low computational cost