Reductions in insecticide use from adoption of Bt cotton in South Africa: impacts on economic performance and toxic load to the environment

The study reported presents the findings relating to commercial growing of genetically-modified Bt cotton in South Africa by a large sample of smallholder farmers over three seasons (1998/99, 1999/2000, 2000/01) following adoption. The analysis presents constructs and compares groupwise differences for key variables in Bt v. non-Bt technology and uses regressions to further analyse the production and profit impacts of Bt adoption. Analysis of the distribution of benefits between farmers due to the technology is also presented. In parallel with these socio-economic measures, the toxic loads being presented to the environment following the introduction of Bt cotton are monitored in terms of insecticide active ingredient (ai) and the Biocide Index. The latter adjusts ai to allow for differing persistence and toxicity of insecticides. Results show substantial and significant financial benefits to smallholder cotton growers of adopting Bt cotton over three seasons in terms of increased yields, lower insecticide spray costs and higher gross margins. This includes one particularly wet, poor growing season. In addition, those with the smaller holdings appeared to benefit proportionately more from the technology (in terms of higher gross margins) than those with larger holdings. Analysis using the Gini-coefficient suggests that the Bt technology has helped to reduce inequality amongst smallholder cotton growers in Makhathini compared to what may have been the position if they had grown conventional cotton. However, while Bt growers applied lower amounts of insecticide and had lower Biocide Indices (per ha) than growers of non-Bt cotton, some of this advantage was due to a reduction in non-bollworm insecticide. Indeed, the Biocide Index for all farmers in the population actually increased with the introduction of Bt cotton. The results indicate the complexity of such studies on the socio-economic and environmental impacts of GM varieties in the developing world.

A combined engineering and statistical model of UK domestic appliance electrical load profiles

The development of a combined engineering and statistical Artificial Neural Network model of UK domestic appliance load profiles is presented. The model uses diary-style appliance use data and a survey questionnaire collected from 51 suburban households and 46 rural households during the summer of 2010 and2011 respectively. It also incorporates measured energy data and is sensitive to socioeconomic, physical dwelling and temperature variables. A prototype model is constructed in MATLAB using a two layer feed forward network with back propagation training which has a 12:10:24 architecture. Model outputs include appliance load profiles which can be applied to the fields of energy planning (microrenewables and smart grids), building simulation tools and energy policy.

Long term individual load forecast under different electrical vehicles uptake scenarios

More and more households are purchasing electric vehicles (EVs), and this will continue as we move towards a low carbon future. There are various projections as to the rate of EV uptake, but all predict an increase over the next ten years. Charging these EVs will produce one of the biggest loads on the low voltage network. To manage the network, we must not only take into account the number of EVs taken up, but where on the network they are charging, and at what time. To simulate the impact on the network from high, medium and low EV uptake (as outlined by the UK government), we present an agent-based model. We initialise the model to assign an EV to a household based on either random distribution or social influences - that is, a neighbour of an EV owner is more likely to also purchase an EV. Additionally, we examine the effect of peak behaviour on the network when charging is at day-time, night-time, or a mix of both. The model is implemented on a neighbourhood in south-east England using smart meter data (half hourly electricity readings) and real life charging patterns from an EV trial. Our results indicate that social influence can increase the peak demand on a local level (street or feeder), meaning that medium EV uptake can create higher peak demand than currently expected.

Effect of 830 nm low-level laser therapy applied before high-intensity exercises on skeletal muscle recovery in athletes

Our aim was to investigate the immediate effects of bilateral, 830 nm, low-level laser therapy (LLLT) on high-intensity exercise and biochemical markers of skeletal muscle recovery, in a randomised, double-blind, placebo-controlled, crossover trial set in a sports physiotherapy clinic. Twenty male athletes (nine professional volleyball players and eleven adolescent soccer players) participated. Active LLLT (830 nm wavelength, 100 mW, spot size 0.0028 cm(2), 3-4 J per point) or an identical placebo LLLT was delivered to five points in the rectus femoris muscle (bilaterally). The main outcome measures were the work performed in the Wingate test: 30 s of maximum cycling with a load of 7.5% of body weight, and the measurement of blood lactate (BL) and creatine kinase (CK) levels before and after exercise. There was no significant difference in the work performed during the Wingate test (P > 0.05) between subjects given active LLLT and those given placebo LLLT. For volleyball athletes, the change in CK levels from before to after the exercise test was significantly lower (P = 0.0133) for those given active LLLT (2.52 U l(-1) +/- 7.04 U l(-1)) than for those given placebo LLLT (28.49 U l(-1) +/- 22.62 U l(-1)). For the soccer athletes, the change in blood lactate levels from before exercise to 15 min after exercise was significantly lower (P < 0.01) in the group subjected to active LLLT (8.55 mmol l(-1) +/- 2.14 mmol l(-1)) than in the group subjected to placebo LLLT (10.52 mmol l(-1) +/- 1.82 mmol l(-1)). LLLT irradiation before the Wingate test seemed to inhibit an expected post-exercise increase in CK level and to accelerate post-exercise lactate removal without affecting test performance. These findings suggest that LLLT may be of benefit in accelerating post-exercise recovery.

Effect of abutment's height and framework alloy on the load distribution of mandibular cantilevered implant-supported prosthesis

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

Influences of thermal and electric load fluctuations in the cogeneration attractiveness

The use of mean values of thermal and electric demand can be justifiable for synthesising the configuration and for estimating the economic results because it simplifies the analysis in a preliminary feasibility study of a cogeneration plant. For determining the cogeneration scheme that best fits the energetic needs of a process several cycles and combinations must be considered, and those technically feasible will be analysed according to economic models. Although interesting for a first approach, this procedure do not consider that the peaks and valleys present in the load patterns will impose additional constraints relatively to the equipment capacities. In this paper, the effects of thermal and electric load fluctuation to the cogeneration plant design were considered. An approach for modelling these load variability is proposed for comparing two competing thermal and electric parity competing schemes. A gas turbine associated to a heat recovery steam generator was then proposed and analysed for thermal- and electric-following operational strategies. Thermal-following option revealed to be more attractive for the technical and economic limits defined for this analysis. (c) 2006 Elsevier Ltd. All rights reserved.

Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.

Sensitivity analysis by neural networks applied to power systems transient stability

This work presents a procedure for transient stability analysis and preventive control of electric power systems, which is formulated by a multilayer feedforward neural network. The neural network training is realized by using the back-propagation algorithm with fuzzy controller and adaptation of the inclination and translation parameters of the nonlinear function. These procedures provide a faster convergence and more precise results, if compared to the traditional back-propagation algorithm. The adaptation of the training rate is effectuated by using the information of the global error and global error variation. After finishing the training, the neural network is capable of estimating the security margin and the sensitivity analysis. Considering this information, it is possible to develop a method for the realization of the security correction (preventive control) for levels considered appropriate to the system, based on generation reallocation and load shedding. An application for a multimachine power system is presented to illustrate the proposed methodology. (c) 2006 Elsevier B.V. All rights reserved.

Electric load forecasting using a fuzzy ART&ARTMAP neural network

This work presents a neural network based on the ART architecture ( adaptive resonance theory), named fuzzy ART& ARTMAP neural network, applied to the electric load-forecasting problem. The neural networks based on the ARTarchitecture have two fundamental characteristics that are extremely important for the network performance ( stability and plasticity), which allow the implementation of continuous training. The fuzzy ART& ARTMAP neural network aims to reduce the imprecision of the forecasting results by a mechanism that separate the analog and binary data, processing them separately. Therefore, this represents a reduction on the processing time and improved quality of the results, when compared to the Back-Propagation neural network, and better to the classical forecasting techniques (ARIMA of Box and Jenkins methods). Finished the training, the fuzzy ART& ARTMAP neural network is capable to forecast electrical loads 24 h in advance. To validate the methodology, data from a Brazilian electric company is used. (C) 2004 Elsevier B.V. All rights reserved.

Multinodal Load Forecasting in Power Electric Systems using a Neural Network with Radial Basis Function

In this paper we present the results of the use of a methodology for multinodal load forecasting through an artificial neural network-type Multilayer Perceptron, making use of radial basis functions as activation function and the Backpropagation algorithm, as an algorithm to train the network. This methodology allows you to make the prediction at various points in power system, considering different types of consumers (residential, commercial, industrial) of the electric grid, is applied to the problem short-term electric load forecasting (24 hours ahead). We use a database (Centralised Dataset - CDS) provided by the Electricity Commission de New Zealand to this work.

An effortless hybrid method to solve economic load dispatch problem in power systems

This paper proposes a new approach and coding scheme for solving economic dispatch problems (ED) in power systems through an effortless hybrid method (EHM). This novel coding scheme can effectively prevent futile searching and also prevents obtaining infeasible solutions through the application of stochastic search methods, consequently dramatically improves search efficiency and solution quality. The dominant constraint of an economic dispatch problem is power balance. The operational constraints, such as generation limitations, ramp rate limits, prohibited operating zones (POZ), network loss are considered for practical operation. Firstly, in the EHM procedure, the output of generator is obtained with a lambda iteration method and without considering POZ and later in a genetic based algorithm this constraint is satisfied. To demonstrate its efficiency, feasibility and fastness, the EHM algorithm was applied to solve constrained ED problems of power systems with 6 and 15 units. The simulation results obtained from the EHM were compared to those achieved from previous literature in terms of solution quality and computational efficiency. Results reveal that the superiority of this method in both aspects of financial and CPU time. (C) 2011 Elsevier Ltd. All rights reserved.

Biomechanics Studies in Dentistry: Bioengineering Applied in Oral Implantology

The application of engineering knowledge in dentistry has helped the understanding of biomechanics aspects related to osseointegrated implants. Several techniques have been used to evaluate the biomechanical load oil implants comprising the use of photoelastic stress analysis, finite element stress analysis, and strain-gauge analysis. Therefore, the purpose of this Study was to describe engineering methods used in dentistry to evaluate the biomechanical behavior of osseointegrated implants. Photoelasticity provides good qualitative information oil the overall location and concentration of stresses but produces limited quantitative information. The method serves as ail important tool for determining the critical stress points in a material and is often used for determining stress concentration factors in irregular geometries. The application of strain-gauge method oil dental implants is based oil the use of electrical resistance strain gauges and its associated equipment and provides both in vitro and vivo measurements strains under static and dynamic loads. However, strain-gauge method provides only the data regarding strain at the gauge. Finite element analysis can Simulate stress using a computer-created model to calculate stress, strain, and displacement. Such analysis has the advantage of allowing several conditions to be changed easily and allows measurement of stress distribution around implants at optional points that are difficult to examine clinically All the 3 methodologies call be useful to evaluate biomechanical implant behavior close to the clinical condition but the researcher should have enough knowledge in model fabrication (experimental delineation) and results analysis.

Effect of compression load and temperature on thermomechanical tests for gutta-percha and Resilon (R)

Tanomaru-Filho M, Silveira GF, Reis JMSN, Bonetti-Filho I, Guerreiro-Tanomaru JM. Effect of compression load and temperature on thermomechanical tests for gutta-percha and Resilon (R). International Endodontic Journal, 44, 1019-1023, 2011.Aim To analyse a method used to evaluate the thermomechanical properties of gutta-percha and Resilon at different temperatures and compression loads.Methodology Two hundred and seventy specimens measuring 10 mm in diameter and 1.5 mm in height were made from the following materials: conventional gutta-percha (GCO). thermoplastic gutta-percha (GTP) and Resilon (R) cones (RE). After 24 h, the specimens were placed in water at 50 degrees C. 60 degrees C or 70 degrees C for 60 s. After that, specimens were placed between two glass slabs, and loads weighing 1.0, 3.0 or 5.0 kg were applied. Images of the specimens were digitized before and after the test and analysed using imaging software to determine their initial and final areas. The thermomechanical property of each material was determined by the difference between the initial and final areas of the specimens. Data were subjected to ANOVA and SNK tests at 5% significance. To verify a possible correlation between the results of the materials, linear regression coefficients (r) were calculated.Results Data showed higher flow area values for RE under all compression loads at 70 degrees C and under the 5.0 kg load at 60 degrees C (P < 0.05). Regarding gutta-percha, GTP showed higher flow under loads weighing 3.0 and 5.0 kg. at 60 and 70 degrees C (P < 0.05). GCO presented higher flow at 70 degrees C with a load of 5.0 kg. Regression analyses showed a poor linear correlation amongst the results of the materials under the different experimental conditions.Conclusion Gutta-percha and Resilon (R) cones require different compression loads and temperatures for evaluation of their thermomechanical properties. For all materials, the greatest flow occurred at 70 degrees C under a load of 5.0 kg: therefore. these parameters may be adopted when evaluating endodontic tilling materials.

Bond strength and morphology of resin materials applied to the occlusal surface of primary molars

International Journal of Paediatric Dentistry 2012; 22: 435441 Background. Hydrophilic adhesives may be used as pit and fissure sealants (sealants), but there is concern about the ability of self-etching adhesives to bond sealants to enamel. Aim. To study the bond strength (BS) and morphology of adhesive systems used as sealants. Design. OptiBond FL, OptiBond All-in-One, combined OptiBond All-in-One + OptiBond FL adhesive, and Fluroshield were applied to the occlusal surfaces of 16 primary molars (n = 4). Teeth were stored in distilled water (24 h at 37 degrees C) and sectioned through the interface to obtain sticks (0.8 mm2) tested under a tensile load (0.5 mm/min). Failure modes were observed. Data were analysed by ANOVA and Tukeys tests (a = 5%). The morphology of 12 primary molars was examined in terms of the etching pattern and resin reproduction. Results. Differences in the BS were found (P = 0.001), with OptiBond FL showing the highest (36.84 +/- 5.7 MPa), Fluroshield (24.26 +/- 2.13 MPa) and OptiBond All-in-One (17.12 +/- 4.97 MPa) similar, and OptiBond All-in-One + OptiBond FL adhesive the lowest (9.8 +/- 2.94 MPA). OptiBond FL showed the best results in terms of morphology. Conclusion. Under the conditions of this study, OptiBond FL was the best material to be used for sealing.

Variable structure controller and observer applied to induction machine

This work will propose the control of an induction machine in field coordinates with imposed stator current based on theory of variable structure control and sliding mode. We describe the model of an induction machine in field coordinates with imposed stator current and we show the design of variable structure control and sliding mode to get a desirable dynamic performance of that plant. To estimate the inaccessible states we will use a state observer (estimator) based on field coordinates induction machine. We will present the results of simulations in any operation condition (start, speed reversal and load) and with parameters variation of the machine compared to a PI control scheme.