Cost-effective suppression and eradication of invasive predators

Introduced predators can have pronounced effects on naïve prey species; thus, predator control is often essential for conservation of threatened native species. Complete eradication of the predator, although desirable, may be elusive in budget-limited situations, whereas predator suppression is more feasible and may still achieve conservation goals. We used a stochastic predator-prey model based on a Lotka-Volterra system to investigate the cost-effectiveness of predator control to achieve prey conservation. We compared five control strategies: immediate eradication, removal of a constant number of predators (fixed-number control), removal of a constant proportion of predators (fixed-rate control), removal of predators that exceed a predetermined threshold (upper-trigger harvest), and removal of predators whenever their population falls below a lower predetermined threshold (lower-trigger harvest). We looked at the performance of these strategies when managers could always remove the full number of predators targeted by each strategy, subject to budget availability. Under this assumption immediate eradication reduced the threat to the prey population the most. We then examined the effect of reduced management success in meeting removal targets, assuming removal is more difficult at low predator densities. In this case there was a pronounced reduction in performance of the immediate eradication, fixed-number, and lower-trigger strategies. Although immediate eradication still yielded the highest expected minimum prey population size, upper-trigger harvest yielded the lowest probability of prey extinction and the greatest return on investment (as measured by improvement in expected minimum population size per amount spent). Upper-trigger harvest was relatively successful because it operated when predator density was highest, which is when predator removal targets can be more easily met and the effect of predators on the prey is most damaging. This suggests that controlling predators only when they are most abundant is the "best" strategy when financial resources are limited and eradication is unlikely. © 2008 Society for Conservation Biology.

Tradeoff of average service cost and average delay for the state dependent M/M/1 queue

The optimal tradeoff between average service cost rate and average delay, is addressed for a M/M/1 queueing model with queue-length dependent service rates, chosen from a finite set. We provide an asymptotic characterization of the minimum average delay, when the average service cost rate is a small positive quantity V more than the minimum average service cost rate required for stability. We show that depending on the value of the arrival rate, the assumed service cost rate function, and the possible values of the service rates, the minimum average delay either a) increases only to a finite value, b) increases without bound as log(1/V), or c) increases without bound as 1/V, when V down arrow 0. We apply the analysis to a flow-level resource allocation model for a wireless downlink. We also investigate the asymptotic tradeoff for a sequence of policies which are obtained from an approximate fluid model for the M/M/1 queue.

Channel estimation relying on the minimum bit-error-ratio criterion for BPSK and QPSK signals

The authors consider the channel estimation problem in the context of a linear equaliser designed for a frequency selective channel, which relies on the minimum bit-error-ratio (MBER) optimisation framework. Previous literature has shown that the MBER-based signal detection may outperform its minimum-mean-square-error (MMSE) counterpart in the bit-error-ratio performance sense. In this study, they develop a framework for channel estimation by first discretising the parameter space and then posing it as a detection problem. Explicitly, the MBER cost function (CF) is derived and its performance studied, when transmitting binary phase shift keying (BPSK) and quadrature phase shift keying (QPSK) signals. It is demonstrated that the MBER based CF aided scheme is capable of outperforming existing MMSE, least square-based solutions.

Production of quality dried small indigenous fish species products using low cost solar tunnel drier

A low cost solar drier was constructed using locally available materials. The size of the drier was 20x3.6x3 having drying capacity of 80 kg of SIS (w/w). Optimization of moisture content was observed for mola, dhela, chapila, chanda and puti at temperature ranges between 40-45°C and 50-55°C in solar tunnel drier. There was little or no change in moisture content at temperature below 40°C during the first 3 hours. Then the moisture content declined gradually with the increase of drying period. On the other hand, at temperature between 50-55°C, moisture content started to decline after 2 hours of drying. The moisture content of the sample reached at about 16% after 26 hours of sun drying at 40-45°C and 20 hours at 50-55°C. The optimum temperature for producing high quality dried products was 45-50°C in solar tunnel drier. The temperature and relative humidity outside and inside the dryers (with fish) at various locations were recorded from 8.00am to 4.00pm. The normal atmospheric ambient temperature was recorded in the range of 25-37°C from at 8:00am to 4:00pm. During the same period the atmospheric relative humidity recorded was in the range of 30-58%. On the other hand, the maximum temperature inside the dryers was recorded in the range of 28-65°C. The lowest temperature recorded was 28°C in the morning and at 13.00pm the highest temperature 65°C was recorded. The maximum relative humidity 58% found in the afternoon and minimum of 28% at noon. There was inverse relationship between temperature intensity of sunshine and humidity which decreased as sunshine increased. In total, it took around 26 hours of drying to reduce the moisture level to about 16%.

Minimum maneuver time calculation using convex optimization

The problem of calculating the minimum lap or maneuver time of a nonlinear vehicle, which is linearized at each time step, is formulated as a convex optimization problem. The formulation provides an alternative to previously used quasi-steady-state analysis or nonlinear optimization. Key steps are: the use of model predictive control; expressing the minimum time problem as one of maximizing distance traveled along the track centerline; and linearizing the track and vehicle trajectories by expressing them as small displacements from a fixed reference. A consequence of linearizing the vehicle dynamics is that nonoptimal steering control action can be generated, but attention to the constraints and the cost function minimizes the effect. Optimal control actions and vehicle responses for a 90 deg bend are presented and compared to the nonconvex nonlinear programming solution. Copyright © 2013 by ASME.

Cost-effectiveness of alternative powertrains for reduced energy use and CO2 emissions in passenger vehicles

This work analysed the cost-effectiveness of avoiding carbon dioxide (CO2) emissions using advanced internal combustion engines, hybrids, plug-in hybrids, fuel cell vehicles and electric vehicles across the nine UK passenger vehicles segments. Across all vehicle types and powertrain groups, minimum installed motive power was dependent most on the time to accelerate from zero to 96.6km/h (60mph). Hybridising the powertrain reduced the difference in energy use between vehicles with slow (t z - 60 > 8 s) and fast acceleration (t z - 60 < 8 s) times. The cost premium associated with advanced powertrains was dependent most on the powertrain chosen, rather than the performance required. Improving non-powertrain components reduced vehicle road load and allowed total motive capacity to decrease by 17%, energy use by 11%, manufacturing cost premiums by 13% and CO2 emissions abatement costs by 15%. All vehicles with advanced internal combustion engines, most hybrid and plug-in hybrid powertrains reduced net CO2 emissions and had lower lifetime operating costs than the respective segment reference vehicle. Most powertrains using fuel cells and all electric vehicles had positive CO2 emissions abatement costs. However, only vehicles using advanced internal combustion engines and parallel hybrid vehicles may be attractive to consumers by the fuel savings offsetting increases in vehicle cost within two years. This work demonstrates that fuel savings are possible relative to today's fleet, but indicates that the most cost-effective way of reducing fuel consumption and CO2 emissions is by advanced combustion technologies and hybridisation with a parallel topology. © 2014 Elsevier Ltd.

Quantifying robustness and dissipation cost of yeast cell cycle network: The funneled energy landscape perspectives

We study the origin of robustness of yeast cell cycle cellular network through uncovering its underlying energy landscape. This is realized from the information of the steady-state probabilities by solving a discrete set of kinetic master equations for the network. We discovered that the potential landscape of yeast cell cycle network is funneled toward the global minimum, G1 state. The ratio of the energy gap between G1 and average versus roughness of the landscape termed as robustness ratio ( RR) becomes a quantitative measure of the robustness and stability for the network. The funneled landscape is quite robust against random perturbations from the inherent wiring or connections of the network. There exists a global phase transition between the more sensitive response or less self-degradation phase leading to underlying funneled global landscape with large RR, and insensitive response or more self-degradation phase leading to shallower underlying landscape of the network with small RR. Furthermore, we show that the more robust landscape also leads to less dissipation cost of the network. Least dissipation and robust landscape might be a realization of Darwinian principle of natural selection at cellular network level. It may provide an optimal criterion for network wiring connections and design.

A generic tool for cost estimating in aircraft design

A methodology to estimate the cost implications of design decisions by integrating cost as a design parameter at an early design stage is presented. The model is developed on a hierarchical basis, the manufacturing cost of aircraft fuselage panels being analysed in this paper. The manufacturing cost modelling is original and relies on a genetic-causal method where the drivers of each element of cost are identified relative to the process capability. The cost model is then extended to life cycle costing by computing the Direct Operating Cost as a function of acquisition cost and fuel burn, and coupled with a semi-empirical numerical analysis using Engineering Sciences Data Unit reference data to model the structural integrity of the fuselage shell with regard to material failure and various modes of buckling. The main finding of the paper is that the traditional minimum weight condition is a dated and sub-optimal approach to airframe structural design.

Modelling lay-up automation and production rate interaction on the cost of large stiffened panel components

This paper presents an integrated design and costing method for large stiffened panels for the purpose of investigating the influence and interaction of lay-up technology and production rate on manufacturing cost. A series of wing cover panels (≈586kg, 19·9m2) have been sized with realistic requirements considering manual and automated lay-up routes. The integrated method has enabled the quantification of component unit cost sensitivity to changes in annual production rate and employed equipment maximum deposition rate. Moreover the results demonstrate the interconnected relationship between lay-up process and panel design, and unit cost. The optimum unit cost solution when using automated lay-up is a combination of the minimum deposition rate and minimum number of lay-up machines to meet the required production rate. However, the location of the optimum unit cost, at the boundaries between the number of lay-up machines required, can make unit cost very sensitive to small changes in component design, production rate, and equipment maximum deposition rate. - See more at: http://aerosociety.com/News/Publications/Aero-Journal/Online/1941/Modelling-layup-automation-and-production-rate-interaction-on-the-cost-of-large-stiffened-panel-components#sthash.0fLuu9iG.dpuf

A mixed cost-function adaptive algorithm for ADSL time-domain equalization

We propose a mixed cost-function adaptive initialization algorithm for the time domain equalizer in a discrete multitone (DMT)-based asymmetric digital subscriber line. Using our approach, a higher convergence rate than that of the commonly used least-mean square algorithm is obtained, whilst attaining bit rates close to the optimum maximum shortening SNR and the upper bound SNR. Furthermore, our proposed method outperforms the minimum mean-squared error design for a range of time domain equalizer (TEQ) filter lengths. The improved performance outweighs the small increase in computational complexity required. A block variant of our proposed algorithm is also presented to overcome the increased latency imposed on the feedback path of the adaptive system.

Memory Cost Analysis for OpenFlow Multiple Table Lookup

Multiple Table Lookup architectures in Software Defined Networking (SDN) open the door for exciting new network applications. The development of the OpenFlow protocol supported the SDN paradigm. However, the first version of the OpenFlow protocol specified a single table lookup model with the associated constraints in flow entry numbers and search capabilities. With the introduction of multiple table lookup in OpenFlow v1.1, flexible and efficient search to support SDN application innovation became possible. However, implementation of multiple table lookup in hardware to meet high performance requirements is non-trivial. One possible approach involves the use of multi-dimensional lookup algorithms. A high lookup performance can be achieved by using embedded memory for flow entry storage. A detailed study of OpenFlow flow filters for multi-dimensional lookup is presented in this paper. Based on a proposed multiple table lookup architecture, the memory consumption and update performance using parallel single field searches are evaluated. The results demonstrate an efficient multi-table lookup implementation with minimum memory usage.

A hybrid mixed cost-function TEQ initialization algorithm for ADSL modems

In this paper, we present a hybrid mixed cost-function adaptive initialization algorithm for the time domain equalizer in a discrete multitone (DMT)-based asymmetric digital subscriber loop. Using our approach, a higher convergence rate than that of the commonly used least-mean square algorithm is obtained, whilst attaining bit rates close to the optimum maximum shortening SNR and the upper bound SNR. Moreover, our proposed method outperforms the minimum mean-squared error design for a range of TEQ filter lengths.

The implications of mandatory low-cost fuel provision

This work studies fuel retail firms’ strategic behavior in a two-dimensional product differentiation framework. Following the mandatory provision of “low-cost” fuel we consider that capacity constraints force firms to eliminate of one the previously offered qualities. Firms play a two-stage game choosing fuel qualities from three possibilities (low-cost, medium quality and high quality fuel) and then prices having exogenous opposite locations. In the highest level of consumers’ heterogeneity, a subgame perfect Nash equilibrium exists in which firms both choose minimum quality differentiation. Consumers’ are worse off if no differentiation occurs in medium and high qualities. The effect over prices from the mandatory “low-cost” fuel law is ambiguous.

Model-based appraisal of minimum unit pricing for alcohol in Northern Ireland

In 2009, the Sheffield Alcohol Research Group (SARG) at Sheffield University developed the Sheffield Alcohol Policy Model version 2.0 (SAPM) to appraise the potential impact of alcohol policies, including different levels of MUP, for the population of England. In 2013, SARG were commissioned by the DHSSPS and the Department for Social Development to adapt the Sheffield Model to NI in order to appraise the potential impact of a range of alcohol pricing policies. The present report represents the results of this work. Estimates from the Northern Ireland (NI) adaptation of the Sheffield Alcohol Policy Model - version 3 - (SAPM3) suggest: 1. Minimum Unit Pricing (MUP) policies would be effective in reducing alcohol consumption, alcohol related harms (including alcohol-related deaths, hospitalisations, crimes and workplace absences) and the costs associated with those harms. 2. A ban on below-cost selling (implemented as a ban on selling alcohol for below the cost of duty plus the VAT payable on that duty) would have a negligible impact on alcohol consumption or related harms. 3. A ban on price-based promotions in the off-trade, either alone or in tandem with an MUP policy would be effective in reducing alcohol consumption, related harms and associated costs. 4. MUP and promotion ban policies would only have a small impact on moderate drinkers at all levels of income. Somewhat larger impacts would be experienced by increasing risk drinkers, with the most substantial effects being experienced by high risk drinkers. 5. MUP and promotion ban policies would have larger impacts on those in poverty, particularly high risk drinkers, than those not in poverty. However, those in poverty also experience larger relative gains in health and are estimated to marginally reduce their spending due to their reduced drinking under the majority of policies åÊ

Reinforcement Learning Solution for Unit Commitment Problem Considering Minimum Start Up and Shut Down Times

Unit Commitment Problem (UCP) in power system refers to the problem of determining the on/ off status of generating units that minimize the operating cost during a given time horizon. Since various system and generation constraints are to be satisfied while finding the optimum schedule, UCP turns to be a constrained optimization problem in power system scheduling. Numerical solutions developed are limited for small systems and heuristic methodologies find difficulty in handling stochastic cost functions associated with practical systems. This paper models Unit Commitment as a multi stage decision making task and an efficient Reinforcement Learning solution is formulated considering minimum up time /down time constraints. The correctness and efficiency of the developed solutions are verified for standard test systems