Croton tiglium (Linn) seed as an ideal eradicant of pests and predators in fish farms

The toxicity of Croton tiglium seed is very effective in the eradication of most of the aquatic fauna except a few species of hard shelled crustaceans such as crabs and prawns which are resistant to even very high concentrations of it. Its toxicity ranged between 0.4 and 2.2 p.p.m. for different species of fishes. Application of homogenized C. tiglium seed at the rate of 10 kg/ha (0.5 m depth) is found effective for the eradication of aquatic pests and predators of fish farms. While its toxicity lasts for 5-8 days in still water ponds, it is only for 1-3 days in tidal ponds with frequent replenishment of water. This method is thus most useful for the initial preparation of the ponds for pisciculture.

Water budgeting studies on the hatchery and nursery rearing practices for the common carp, Cyprinus carpio (Linnaeus, 1758)

Aquaculture systems are an integral element of rural development and therefore should be environment friendly as well as socially and economically designed. From the economic standpoint, one of the major constraints for the development of sustainable aquaculture includes externalities generated by competition in access to a limited resource. This study was conducted as an investigation into the water requirement for the hatchery and nursery production phases of common carp, Cyprinus carpio (Linnaeus, 1758) at the Maharashtra State Fish Seed Farm at Khopoli in Raigad Dist. of Maharashtra during the winter months from November to February. The water budgeting study involves the quantification of water used in every stage of production in hatchery and nursery systems and aimed at becoming a foundation for the minimization of water during production without affecting the yield; thereby conserving water and upholding the theme of sustainable aquaculture. The total water used in a single operation cycle was estimated to be 11,25,040 L [sic]. Out of the total water consumed, 4.74% water was used in the pre-operational management steps, 4.48% was consumed during breeding, 62.72% was consumed in the hatching phase, 21.50% was used for hatchery rearing and 6.56% was consumed during conditioning. In the nursery ponds, the water gain was primarily the regulated inflow coming through the irrigation channel. The total quantum of water used in the nursery rearing was 31,60,800 L [sic]. The initial filling and regulated inflow formed 42.60% and 57.40% respectively of water gain, while evaporation, seepage and discharge contributed 20.71%, 36.46% and 42.82% respectively to the water loss. The total water expended for the entire operation was 1,21,61,120 L [sic]. Water expense occurred to produce a single spawn in the hatchery system was calculated and found to be 0.56 L while the water expended to produce one fry was calculated as 4.86 L. The study fulfills the hydrological equation described by Winter (1981) and Boyd (1985). It also validates the water budget simulation model that can be used for forecasting water requirements for aquaculture ponds (Nath and Bolte, 1998).

Forecasting high-frequency futures returns using online langevin dynamics

Forecasting the returns of assets at high frequency is the key challenge for high-frequency algorithmic trading strategies. In this paper, we propose a jump-diffusion model for asset price movements that models price and its trend and allows a momentum strategy to be developed. Conditional on jump times, we derive closed-form transition densities for this model. We show how this allows us to extract a trend from high-frequency finance data by using a Rao-Blackwellized variable rate particle filter to filter incoming price data. Our results show that even in the presence of transaction costs our algorithm can achieve a Sharpe ratio above 1 when applied across a portfolio of 75 futures contracts at high frequency. © 2011 IEEE.

Forecasting Daily Chlorophyll a Concentration during the Spring Phytoplankton Bloom Period in Xiangxi Bay of the Three-Gorges Reservoir by Means of a Recurrent Artificial Neural Network

A recurrent artificial neural network was used for 0-and 7-days-ahead forecasting of daily spring phytoplankton bloom dynamics in Xiangxi Bay of Three-Gorges Reservoir with meteorological, hydrological, and limnological parameters as input variables. Daily data from the depth of 0.5 m was used to train the model, and data from the depth of 2.0 m was used to validate the calibrated model. The trained model achieved reasonable accuracy in predicting the daily dynamics of chlorophyll a both in 0-and 7-days-ahead forecasting. In 0-day-ahead forecasting, the R-2 values of observed and predicted data were 0.85 for training and 0.89 for validating. In 7-days-ahead forecasting, the R-2 values of training and validating were 0.68 and 0.66, respectively. Sensitivity analysis indicated that most ecological relationships between chlorophyll a and input environmental variables in 0-and 7-days-ahead models were reasonable. In the 0-day model, Secchi depth, water temperature, and dissolved silicate were the most important factors influencing the daily dynamics of chlorophyll a. And in 7-days-ahead predicting model, chlorophyll a was sensitive to most environmental variables except water level, DO, and NH3N.

Application of forecasting structural cracks technique of 3D move in Chengdao buried hill

3DMove software, based on the three-dimension structural model of geologic interpretation, can forecast reservoir cracks from the point of view of formation of the structural geology, and analyze the characteristics of the cracks. 3DMove software dominates in forecasting cracks. We forecast the developments and directions of the cracks in Chengbei buried hill with the application of forecasting technique in 3DMove software, and obtain the chart about strain distributing on top in buried hill and the chart about relative density and orientation and the chart about the analysis of crack unsealing. In Chengbei 30 buried hill zone, north-west and north-east and approximately east-west cracks in Cenozoic are very rich and the main directions in every fault block are different. Forecasting results that are also verified by those of drilling approximately accord with the data from well logging, the case of which shows that the technique has the better ability in forecasting cracks, and takes more effects on exploration and exploitation of crack reservoir beds in ancient buried hill reservoirs.

Collaboration on procurement of e-content between the National Health Service and higher education in the UK

Urquhart, C. J., Cox, A. M.& Spink, S. (2007). Collaboration on procurement of e-content between the National Health Service and higher education in the UK. Interlending & Document Supply, 35(3), 164-170. Sponsorship: JISC, LKDN

Uncertainty in wind energy forecasting

Wind energy is the energy source that contributes most to the renewable energy mix of European countries. While there are good wind resources throughout Europe, the intermittency of the wind represents a major problem for the deployment of wind energy into the electricity networks. To ensure grid security a Transmission System Operator needs today for each kilowatt of wind energy either an equal amount of spinning reserve or a forecasting system that can predict the amount of energy that will be produced from wind over a period of 1 to 48 hours. In the range from 5m/s to 15m/s a wind turbine’s production increases with a power of three. For this reason, a Transmission System Operator requires an accuracy for wind speed forecasts of 1m/s in this wind speed range. Forecasting wind energy with a numerical weather prediction model in this context builds the background of this work. The author’s goal was to present a pragmatic solution to this specific problem in the ”real world”. This work therefore has to be seen in a technical context and hence does not provide nor intends to provide a general overview of the benefits and drawbacks of wind energy as a renewable energy source. In the first part of this work the accuracy requirements of the energy sector for wind speed predictions from numerical weather prediction models are described and analysed. A unique set of numerical experiments has been carried out in collaboration with the Danish Meteorological Institute to investigate the forecast quality of an operational numerical weather prediction model for this purpose. The results of this investigation revealed that the accuracy requirements for wind speed and wind power forecasts from today’s numerical weather prediction models can only be met at certain times. This means that the uncertainty of the forecast quality becomes a parameter that is as important as the wind speed and wind power itself. To quantify the uncertainty of a forecast valid for tomorrow requires an ensemble of forecasts. In the second part of this work such an ensemble of forecasts was designed and verified for its ability to quantify the forecast error. This was accomplished by correlating the measured error and the forecasted uncertainty on area integrated wind speed and wind power in Denmark and Ireland. A correlation of 93% was achieved in these areas. This method cannot solve the accuracy requirements of the energy sector. By knowing the uncertainty of the forecasts, the focus can however be put on the accuracy requirements at times when it is possible to accurately predict the weather. Thus, this result presents a major step forward in making wind energy a compatible energy source in the future.

Correcting the errors: Volatility forecast evaluation using high-frequency data and realized volatilities

We develop general model-free adjustment procedures for the calculation of unbiased volatility loss functions based on practically feasible realized volatility benchmarks. The procedures, which exploit recent nonparametric asymptotic distributional results, are both easy-to-implement and highly accurate in empirically realistic situations. We also illustrate that properly accounting for the measurement errors in the volatility forecast evaluations reported in the existing literature can result in markedly higher estimates for the true degree of return volatility predictability.

Oil and gas projects in the Western Amazon: threats to wilderness, biodiversity, and indigenous peoples.

BACKGROUND: The western Amazon is the most biologically rich part of the Amazon basin and is home to a great diversity of indigenous ethnic groups, including some of the world's last uncontacted peoples living in voluntary isolation. Unlike the eastern Brazilian Amazon, it is still a largely intact ecosystem. Underlying this landscape are large reserves of oil and gas, many yet untapped. The growing global demand is leading to unprecedented exploration and development in the region. METHODOLOGY/PRINCIPAL FINDINGS: We synthesized information from government sources to quantify the status of oil development in the western Amazon. National governments delimit specific geographic areas or "blocks" that are zoned for hydrocarbon activities, which they may lease to state and multinational energy companies for exploration and production. About 180 oil and gas blocks now cover approximately 688,000 km(2) of the western Amazon. These blocks overlap the most species-rich part of the Amazon. We also found that many of the blocks overlap indigenous territories, both titled lands and areas utilized by peoples in voluntary isolation. In Ecuador and Peru, oil and gas blocks now cover more than two-thirds of the Amazon. In Bolivia and western Brazil, major exploration activities are set to increase rapidly. CONCLUSIONS/SIGNIFICANCE: Without improved policies, the increasing scope and magnitude of planned extraction means that environmental and social impacts are likely to intensify. We review the most pressing oil- and gas-related conservation policy issues confronting the region. These include the need for regional Strategic Environmental Impact Assessments and the adoption of roadless extraction techniques. We also consider the conflicts where the blocks overlap indigenous peoples' territories.

Future requirements for and supply of ophthalmologists for an aging population in Singapore.

BACKGROUND: Singapore's population, as that of many other countries, is aging; this is likely to lead to an increase in eye diseases and the demand for eye care. Since ophthalmologist training is long and expensive, early planning is essential. This paper forecasts workforce and training requirements for Singapore up to the year 2040 under several plausible future scenarios. METHODS: The Singapore Eye Care Workforce Model was created as a continuous time compartment model with explicit workforce stocks using system dynamics. The model has three modules: prevalence of eye disease, demand, and workforce requirements. The model is used to simulate the prevalence of eye diseases, patient visits, and workforce requirements for the public sector under different scenarios in order to determine training requirements. RESULTS: Four scenarios were constructed. Under the baseline business-as-usual scenario, the required number of ophthalmologists is projected to increase by 117% from 2015 to 2040. Under the current policy scenario (assuming an increase of service uptake due to increased awareness, availability, and accessibility of eye care services), the increase will be 175%, while under the new model of care scenario (considering the additional effect of providing some services by non-ophthalmologists) the increase will only be 150%. The moderated workload scenario (assuming in addition a reduction of the clinical workload) projects an increase in the required number of ophthalmologists of 192% by 2040. Considering the uncertainties in the projected demand for eye care services, under the business-as-usual scenario, a residency intake of 8-22 residents per year is required, 17-21 under the current policy scenario, 14-18 under the new model of care scenario, and, under the moderated workload scenario, an intake of 18-23 residents per year is required. CONCLUSIONS: The results show that under all scenarios considered, Singapore's aging and growing population will result in an almost doubling of the number of Singaporeans with eye conditions, a significant increase in public sector eye care demand and, consequently, a greater requirement for ophthalmologists.

Towards an integrated forecasting system for pelagic fisheries

First results of a coupled modeling and forecasting system for the pelagic fisheries are being presented. The system consists currently of three mathematically fundamentally different model subsystems: POLCOMS-ERSEM providing the physical-biogeochemical environment implemented in the domain of the North-West European shelf and the SPAM model which describes sandeel stocks in the North Sea. The third component, the SLAM model, connects POLCOMS-ERSEM and SPAM by computing the physical-biological interaction. Our major experience by the coupling model subsystems is that well-defined and generic model interfaces are very important for a successful and extendable coupled model framework. The integrated approach, simulating ecosystem dynamics from physics to fish, allows for analysis of the pathways in the ecosystem to investigate the propagation of changes in the ocean climate and lower trophic levels to quantify the impacts on the higher trophic level, in this case the sandeel population, demonstrated here on the base of hindcast data. The coupled forecasting system is tested for some typical scientific questions appearing in spatial fish stock management and marine spatial planning, including determination of local and basin scale maximum sustainable yield, stock connectivity and source/sink structure. Our presented simulations indicate that sandeels stocks are currently exploited close to the maximum sustainable yield, but large uncertainty is associated with determining stock maximum sustainable yield due to stock eigen dynamics and climatic variability. Our statistical ensemble simulations indicates that the predictive horizon set by climate interannual variability is 2–6 yr, after which only an asymptotic probability distribution of stock properties, like biomass, are predictable.

Modeling marine ecosystem responses to global climate change: Where are we now and where should we be going?

Modeling of global climate change is moving from global circulation model (GCM)-type projections with coupled biogeochemical models to projections of ecological responses, including food web and upper trophic levels. Marine and coastal ecosystems are highly susceptible to the impacts of global climate change and also produce significant ecosystem services. The effects of global climate change on coastal and marine ecosystems involve a much wider array of effects than the usual temperature, sea level rise, and precipitation. This paper is an overview for a collection of 12 papers that examined various aspects of global climate change on marine ecosystems and comprise this special issue. We summarized the major features of the models and analyses in the papers to determine general patterns. A wide range of ecosystems were simulated using a diverse set of modeling approaches. Models were either 3-dimensional or used a few spatial boxes, and responses to global climate change were mostly expressed as changes from a baseline condition. Three issues were identified from the across-model comparison: (a) lack of standardization of climate change scenarios, (b) the prevalence of site-specific and even unique models for upper trophic levels, and (c) emphasis on hypothesis evaluation versus forecasting. We discuss why these issues are important as global climate change assessment continues to progress up the food chain, and, when possible, offer some initial steps for going forward.

Evaluating machine-learning techniques for recruitment forecasting of seven North East Atlantic fish species

The effect of different factors (spawning biomass, environmental conditions) on recruitment is a subject of great importance in the management of fisheries, recovery plans and scenario exploration. In this study, recently proposed supervised classification techniques, tested by the machine-learning community, are applied to forecast the recruitment of seven fish species of North East Atlantic (anchovy, sardine, mackerel, horse mackerel, hake, blue whiting and albacore), using spawning, environmental and climatic data. In addition, the use of the probabilistic flexible naive Bayes classifier (FNBC) is proposed as modelling approach in order to reduce uncertainty for fisheries management purposes. Those improvements aim is to improve probability estimations of each possible outcome (low, medium and high recruitment) based in kernel density estimation, which is crucial for informed management decision making with high uncertainty. Finally, a comparison between goodness-of-fit and generalization power is provided, in order to assess the reliability of the final forecasting models. It is found that in most cases the proposed methodology provides useful information for management whereas the case of horse mackerel is an example of the limitations of the approach. The proposed improvements allow for a better probabilistic estimation of the different scenarios, i.e. to reduce the uncertainty in the provided forecasts.

Element partitioning and potential mobility within surface dusts on buildings in a polluted urban environment, Budapest.

A voluminous literature exists on the analysis of water-soluble ions extracted from gypsum crusts and patinas formed on building surfaces. However, less data is available on the intermediate dust layer and the important role its complex matrix and constituents play in crust/patina formation. To address this issue, surface dust samples were collected from two buildings in the city of Budapest. Substrate properties, different pollution levels and environmental variations were considered by collecting samples from a city centre granite building exposed to intense traffic conditions and from an oolitic limestone church situated in a pedestrian area outside and high above the main pollution zone. Selective extraction examines both water-soluble ions (Ca2+, Mg2+, Na+, K+, Cl-, NO3- SO42-) and selected elements (Fe, Mn, Zn, Cu, Cr, Pb, Ni) from the water-soluble, exchangeable/carbonate, amorphous Mn, amorphous Fe/Mn, crystalline Fe/Mn, organic and residual phases, their mobility and potential to catalyse heterogeneous surface reactions. Salt weathering processes are highlighted by high concentrations of water-soluble Ca2+, Na+, Cl- and SO42-- at both sites. Manganese, Zn and Cu and to a lesser extent Pb and Ni, are very mobile in the city centre dust, where 30%, 54%, 38%, 11% and 11% of their totals are bound by the water-soluble phase, respectively. Church dust shows a sharp contrast for Mn, Zn, Cu and Pb with only 3%, 1%, 12% and 3% of their totals being bound by the water-soluble phase respectively. This may be due to (a) different environmental conditions at the church e.g. lower humidity (b) continuous replenishment of salts under intensive city centre traffic conditions (c) enrichment in oxidisable organic carbon by a factor of 4.5 and a tenfold increase in acidity in the city centre dust.