Local polynomial method for ensemble forecast of time series

We present a nonparametric approach based on local polynomial regression for ensemble forecast of time series. The state space is first reconstructed by embedding the univariate time series of the response variable in a space of dimension (<i>D</i>) with a delay time (&tau;). To obtain a forecast from a given time point <i>t</i>, three steps are involved: (i) the current state of the system is mapped on to the state space, known as the feature vector, (ii) a small number (<i>K</i>=&alpha;*<i>n</i>, &alpha;=fraction (0,1] of the data, <i>n</i>=data length) of neighbors (and their future evolution) to the feature vector are identified in the state space, and (iii) a polynomial of order <i>p</i> is fitted to the identified neighbors, which is then used for prediction. A suite of parameter combinations (<i>D</i>, &tau;, &alpha;, <i>p</i>) is selected based on an objective criterion, called the Generalized Cross Validation (GCV). All of the selected parameter combinations are then used to issue a T-step iterated forecast starting from the current time <i>t</i>, thus generating an ensemble forecast which can be used to obtain the forecast probability density function (PDF). The ensemble approach improves upon the traditional method of providing a single mean forecast by providing the forecast uncertainty. Further, for short noisy data it can provide better forecasts. We demonstrate the utility of this approach on two synthetic (Henon and Lorenz attractors) and two real data sets (Great Salt Lake bi-weekly volume and NINO3 index). This framework can also be used to forecast a vector of response variables based on a vector of predictors

PCR-based sex determination of cetaceans and dugong from the Indian seas

A sex-determination technique based on PCR amplifi- cation of genomic DNA extracted from the skin tissue has been standardized in cetaceans and dugong sam-pled from the Indian seas. A Y-chromosome-specific region (SRY or Sex-determining Y-chromosome gene) of 210–224 bp size in the genome has been amplified (only in males) using specific PCR primers. A fragment of the ZFX/ZFY (zinc finger protein genes located both on the X and Y chromosomes respectively) re-gion in the size range 442–445 bp is also amplified (in both sexes) using another pair of primers simultaneously as positive controls for confirmation of sex. Molecular sexing was standardized in spinner dolphin (Stenella longirostris), bridled dolphin (Stenella attenuata), bottlenose dolphin (Tursiops aduncus), Indo-Pacific humpbacked dolphin (Sousa chinensis), Risso’s dolphin (Grampus griseus), finless porpoise (Neopho-caena phocaenoides), sperm whale (Physeter macro-cephalus), blue whale (Balaenoptera musculus), Bryde’s whale (Balaenoptera edeni) and dugong (Dugong du-gon), which are all vulnerable/endangered species pro- tected under the Indian Wildlife Act

On-the-fly reconfigurable logic

©2005 COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only. Copyright 2004 Society of Photo-Optical Instrumentation Engineers. This paper was published in Smart Structures, Devices, and Systems II, edited by Said F. Al-Sarawi, Proceedings of SPIE Vol. 5649 and is made available as an electronic reprint with permission of SPIE. One print or electronic copy may be made for personal use only. Systematic or multiple reproduction, distribution to multiple locations via electronic or other means, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.Reconfigurable Circuit (RC) platforms can be configured to implement complex combinatorial and sequential logic. In this paper we investigate various RC technologies and discuss possible methods to optimise their power, speed and area. To address the drawbacks of existing RC technologies we propose a generic architecture we call "OFRL" (On-the-Fly Reconfigurable Logic). Our objective is to provide a low power, high speed platform for reconfigurable circuit and dynamically reconfigurable logic applications that use fewer transistors than existing technologies.Kamal Rajagopalan, Braden Phillips, and Derek Abbot

A linear mixed effects model for seasonal forecasts of Arctic sea ice retreat

With sea ice cover declining in recent years, access to open Arctic waters has become a growing interest to numerous stakeholders. Access requires time for planning and preparation, which creates the need for accurate seasonal forecasts of summer sea ice characteristics. One important attribute is the timing of sea ice retreat, of which current statistical and dynamic sea ice models struggle to make accurate seasonal forecasts. We develop a linear mixed effects model to provide forecast of sea ice retreat over five major regions of the Arctic – Beaufort, Chukchi, East Siberian, Laptev, and Kara Seas. In this, the fixed effect – i.e. the mean influence of the atmosphere on sea ice retreat – is modeled using predictors that directly influence the dynamics or thermodynamics of sea ice, and random effects are grouped regionally to capture the local-scale effects on sea ice. The model exhibits very good skill in forecast of sea ice retreat at lead times of up to half a year over these regions

“Environmental Hypertensionology” The Effects of Environmental Factors on Blood Pressure in Clinical Practice and Research

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/88111/1/j.1751-7176.2011.00543.x.pd

A Bayesian Logistic Regression for Probabilistic Forecasts of the Minimum September Arctic Sea Ice Cover

This study introduces a Bayesian logistic regression framework that is capable of providing skillful probabilistic forecasts of Arctic sea ice cover, along with quantifying the attendant uncertainties. The presence or absence of ice (absence defined as ice concentration below 15%) is modeled using a categorical regression model, with atmospheric, oceanic, and sea ice covariates at 1‐ to 7‐month lead times. The model parameters are estimated in a Bayesian framework, thus enabling the posterior predictive probabilities of the minimum sea ice cover and parametric uncertainty quantification. The model is fitted and validated to September minimum sea ice cover data from 1980 through 2018. Results show overall skillful forecasts of the minimum sea ice cover at all lead times, with higher skills at shorter lead times, along with a direct measure of forecast uncertainty to aide in assessing the reliability

Arctic sea ice melt onset favored by an atmospheric pressure pattern reminiscent of the North American-Eurasian Arctic pattern

The timing of melt onset in the Arctic plays a key role in the evolution of sea ice throughout Spring, Summer and Autumn. A major catalyst of early melt onset is increased downwelling longwave radiation, associated with increased levels of moisture in the atmosphere. Determining the atmospheric moisture pathways that are tied to increased downwelling longwave radiation and melt onset is therefore of keen interest. We employed Self Organizing Maps (SOM) on the daily sea level pressure for the period 1979–2018 over the Arctic during the melt season (April–July) and identified distinct circulation patterns. Melt onset dates were mapped on to these SOM patterns. The dominant moisture transport to much of the Arctic is enabled by a broad low pressure region stretching over Siberia and a high pressure over northern North America and Greenland. This configuration, which is reminiscent of the North American-Eurasian Arctic dipole pattern, funnels moisture from lower latitudes and through the Bering and Chukchi Seas. Other leading patterns are variations of this which transport moisture from North America and the Atlantic to the Central Arctic and Canadian Arctic Archipelago. Our analysis further indicates that most of the early and late melt onset timings in the Arctic are strongly related to the strong and weak emergence of these preferred circulation patterns, respectively

HITS: Hurricane Intensity and Track Simulator with North Atlantic Ocean Applications for Risk Assessment

A nonparametric stochastic model is developed and tested for the simulation of tropical cyclone tracks. Tropical cyclone tracks demonstrate continuity and memory over many time and space steps. Clusters of tracks can be coherent, and the separation between clusters may be marked by geographical locations where groups of tracks diverge as a result of the physics of the underlying process. Consequently, their evolution may be non-Markovian. Markovian simulation models, as are often used, may produce tracks that potentially diverge or lose memory quicker than happens in nature. This is addressed here through a model that simulates tracks by randomly sampling track segments of varying length, selected from historical tracks. For performance evaluation, a spatial grid is imposed on the domain of interest. For each grid box, long-term tropical cyclone risk is assessed through the annual probability distributions of the number of storm hours, landfalls, winds, and other statistics. Total storm length is determined at birth by local distribution, and movement to other tropical cyclone segments by distance to neighbor tracks, comparative vector, and age of track. The model is also applied to the conditional simulation of hurricane tracks from specific positions for hurricanes that were not included in the model fitting so as to see whether the probabilistic coverage intervals properly cover the subsequent track. Consequently, tests of both the long-term probability distributions of hurricane landfall and of event simulations from the model are provided

Cancer in the Sindhi population of greater Bombay

The Sindhis are a Hindu subgroup identified by their place of origin and their written spoken language. These are the people who were originally inhabitants of the Province of Sind, which formed a part of the large Bombay Presidency in Undivided India before 1947. The Sindhi Hindus migrated en masse to India after partition. An attempt has been made here to examine the differences found in the site-specific cancer risks among the Sindhi community, the other Hindu groups (such as the Marathi and Gujrati populations) and the Parsi community of Greater Bombay. As the Indian Census Board does not provide age distribution details for the Sindhis, analysis of the data was undertaken employing frequency ratios. Age-standardized cancer ratios (ASCAR) were also utilized for certain calculations. The common sites of cancer appear to vary greatly between the total Bombay population and the Sindhi group. In Sindhi men, for example, cancers of the lung, large bowel, prostate, kidneys and leukemias are most commonly seen, whereas laryngeal and oesophageal cancers predominate in the general population of Bombay. In Sindhi women the breast, uterus, ovary, and skin are the preferred sites, whereas cancers of the cervix and leukemias are predominant in the general population of Bombay. It is interesting to note that there is a degree of similarity in the incidence of cancer at certain anatomical sites, such as the prostate, large intestine, and leukemias in males, and breast, cervix, ovary and uterus in females, between the Sindhi and Parsi communities of Greater Bombay