Symbiotic capability of calopo rhizobia from an agrisoil with different crops in Pernambuco

Biological nitrogen fixation by rhizobium-legume symbiosis represents one of the most important nitrogen sources for plants and depends strongly on the symbiotic efficiency of the rhizobium strain. This study evaluated the symbiotic capacity of rhizobial isolates from calopo (CALOPOGONIUM MUCUNOIDES) taken from an agrisoil under BRACHIARIA DECUMBENS pasture, sabiá (MIMOSA CAESALPINIIFOLIA) plantations and Atlantic Forest areas of the Dry Forest Zone of Pernambuco. A total of 1,575 isolates were obtained from 398 groups. A single random isolate of each group was authenticated, in randomized blocks with two replications. Each plant was inoculated with 1 mL of a bacterial broth, containing an estimated population of 10(8) rhizobial cells mL-1. Forty-five days after inoculation, the plants were harvested, separated into shoots, roots and nodules, oven-dried to constant mass, and weighed. Next, the symbiotic capability was tested with 1.5 kg of an autoclaved sand:vermiculite (1:1) mixture in polyethylene bags. The treatments consisted of 122 authenticated isolates, selected based on the shoot dry matter, five uninoculated controls (treated with 0, 50, 100, 150, or 200 kg ha-1 N) and a control inoculated with SEMIA 6152 (=BR1602), a strain of BRADYRHIZOBIUM JAPONICUM The test was performed as described above. The shoot dry matter of the plants inoculated with the most effective isolates did not differ from that of plants treated with 150 kg ha-1 N. Shoot dry matter was positively correlated with all other variables. The proportion of effective isolates was highest among isolates from SABIÁ forests. There was great variation in nodule dry weight, as well as in N contents and total N.

Comparing the artificial neural network with parcial least squares for prediction of soil organic carbon and pH at different moisture content levels using visible and near-infrared spectroscopy

Visible and near infrared (vis-NIR) spectroscopy is widely used to detect soil properties. The objective of this study is to evaluate the combined effect of moisture content (MC) and the modeling algorithm on prediction of soil organic carbon (SOC) and pH. Partial least squares (PLS) and the Artificial neural network (ANN) for modeling of SOC and pH at different MC levels were compared in terms of efficiency in prediction of regression. A total of 270 soil samples were used. Before spectral measurement, dry soil samples were weighed to determine the amount of water to be added by weight to achieve the specified gravimetric MC levels of 5, 10, 15, 20, and 25 %. A fiber-optic vis-NIR spectrophotometer (350-2500 nm) was used to measure spectra of soil samples in the diffuse reflectance mode. Spectra preprocessing and PLS regression were carried using Unscrambler® software. Statistica® software was used for ANN modeling. The best prediction result for SOC was obtained using the ANN (RMSEP = 0.82 % and RPD = 4.23) for soil samples with 25 % MC. The best prediction results for pH were obtained with PLS for dry soil samples (RMSEP = 0.65 % and RPD = 1.68) and soil samples with 10 % MC (RMSEP = 0.61 % and RPD = 1.71). Whereas the ANN showed better performance for SOC prediction at all MC levels, PLS showed better predictive accuracy of pH at all MC levels except for 25 % MC. Therefore, based on the data set used in the current study, the ANN is recommended for the analyses of SOC at all MC levels, whereas PLS is recommended for the analysis of pH at MC levels below 20 %.

Development and evaluation of neural network models to estimate daily solar radiation at Córdoba, Argentina

The objective of this work was to develop neural network models of backpropagation type to estimate solar radiation based on extraterrestrial radiation data, daily temperature range, precipitation, cloudiness and relative sunshine duration. Data from Córdoba, Argentina, were used for development and validation. The behaviour and adjustment between values observed and estimates obtained by neural networks for different combinations of input were assessed. These estimations showed root mean square error between 3.15 and 3.88 MJ m-2 d-1 . The latter corresponds to the model that calculates radiation using only precipitation and daily temperature range. In all models, results show good adjustment to seasonal solar radiation. These results allow inferring the adequate performance and pertinence of this methodology to estimate complex phenomena, such as solar radiation.

HETEROTIC GROUP FORMATION IN PSIDIUM GUAJAVA L. BY ARTIFICIAL NEURAL NETWORK AND DISCRIMINANT ANALYSIS

ABSTRACT The present study aimed at evaluating the heterotic group formation in guava based on quantitative descriptors and using artificial neural network (ANN). For such, we evaluated eight quantitative descriptors. Large genetic variability was found for the eight quantitative traits in the 138 genotypes of guava. The artificial neural network technique determined that the optimal number of groups was three. The grouping consistency was determined by linear discriminant analysis, which obtained classification percentage of the groups, with a value of 86 %. It was concluded that the artificial neural network method is effective to detect genetic divergence and heterotic group formation.

Harvesting and wood transport planning with SNAP III program (Scheduling and Network Analysis Program) in a pine plantation in Southeast Brazil

The objective of this study was to verify the potential of SNAP III (Scheduling and Network Analysis Program) as a support tool for harvesting and wood transport planning in Brazil harvesting subsystem definition and establishment of a compatible route were assessed. Initially, machine operational and production costs were determined in seven subsystems for the study area, and quality indexes, construction and maintenance costs of forest roads were obtained and used as SNAP III program input data. The results showed, that three categories of forest road occurrence were observed in the study area: main, secondary and tertiary which, based on quality index, allowed a medium vehicle speed of about 41, 30 and 24 km/hours and a construction cost of about US$ 5,084.30, US$ 2,275.28 and US$ 1,650.00/km, respectively. The SNAP III program used as a support tool for the planning, was found to have a high potential tool in the harvesting and wood transport planning. The program was capable of defining efficiently, the harvesting subsystem on technical and economical basis, the best wood transport route and the forest road to be used in each period of the horizon planning.

Rainfall erosivity for the State of Rio de Janeiro estimated by artificial neural network

The Artificial Neural Networks (ANNs) are mathematical models method capable of estimating non-linear response plans. The advantage of these models is to present different responses of the statistical models. Thus, the objective of this study was to develop and to test ANNs for estimating rainfall erosivity index (EI30) as a function of the geographical location for the state of Rio de Janeiro, Brazil and generating a thematic visualization map. The characteristics of latitude, longitude e altitude using ANNs were acceptable to estimating EI30 and allowing visualization of the space variability of EI30. Thus, ANN is a potential option for the estimate of climatic variables in substitution to the traditional methods of interpolation.

Determination of the influence of the variation of reducing and non-reducing sugars on coffee quality with use of artificial neural network

The present study aimed at evaluating the use of Artificial Neural Network to correlate the values resulting from chemical analyses of samples of coffee with the values of their sensory analyses. The coffee samples used were from the Coffea arabica L., cultivars Acaiá do Cerrado, Topázio, Acaiá 474-19 and Bourbon, collected in the southern region of the state of Minas Gerais. The chemical analyses were carried out for reducing and non-reducing sugars. The quality of the beverage was evaluated by sensory analysis. The Artificial Neural Network method used values from chemical analyses as input variables and values from sensory analysis as output values. The multiple linear regression of sensory analysis values, according to the values from chemical analyses, presented a determination coefficient of 0.3106, while the Artificial Neural Network achieved a level of 80.00% of success in the classification of values from the sensory analysis.

Experimental evaluation of the performance of a wireless sensor network in agricultural environments

The aim of this study was to perform an experimental study to evaluate the proper operation distance between the nodes of a wireless sensor network available on the market for different agricultural crops (maize, physic nut, eucalyptus). The experimental data of the network performance offers to farmers and researchers information that might be useful to the sizing and project of the wireless sensor networks in similar situations to those studied. The evaluation showed that the separation of the nodes depends on the type of culture and it is a critical factor to ensure the feasibility of using WSN. In the configuration used, sending packets every 2 seconds, the battery life was about four days. Therefore, the autonomy may be increased with a longer interval of time between sending packets.

Influence of distance, geometry and number of control stations on quality of local geodetic networks for the purpose of georeferencing of rural properties

The implementation of local geodetic networks for georeferencing of rural properties has become a requirement after publication of the Georeferencing Technical Standard by INCRA. According to this standard, the maximum distance of baselines to GNSS L1 receivers is of 20 km. Besides the length of the baseline, the geometry and the number of geodetic control stations are other factors to be considered in the implementation of geodetic networks. Thus, this research aimed to examine the influence of baseline lengths higher than the regulated limit of 20 km, the geometry and the number of control stations on quality of local geodetic networks for georeferencing, and also to demonstrate the importance of using specific tests to evaluate the solution of ambiguities and on the quality of the adjustment. The results indicated that the increasing number of control stations has improved the quality of the network, the geometry has not influenced on the quality and the baseline length has influenced on the quality; however, lengths higher than 20 km has not interrupted the implementation, with GPS L1 receiver, of the local geodetic network for the purpose of georeferencing. Also, the use of different statistical tests, both for the evaluation of the resolution of ambiguities and for the adjustment, have enabled greater clearness in analyzing the results, which allow that unsuitable observations may be eliminated.

Silting in the dense reservoir network of the Pereira de Miranda catchment

This study aims to analyze the impacts of the reservoir network within Pereira de Miranda - CE catchment (also called Pentecoste) over sediment transport and storage capacity of the system. The survey of the "damming" was carried out using satellite images. We identified 502 erosion units, derived from overlaying maps of the Universal Soil Loss Equation parameters, which allowed the estimation of localized erosion in the basin and identification of areas potentially generating sediment. In order to estimate silting in Pentecoste reservoir, different system structure scenarios were considered. An average erosion rate of 59 t ha-1year-1 was estimated. According to the model, the silting of Pentecoste reservoir may vary from 1.1 to 2.6% per decade, depending on the scenario considered. It is also observed that the reservoirs upstream can retain up to 58% of the sediment that would reach the Pentecoste reservoir. Very small reservoirs with a capacity of up to 100,000 m³, although representing only 1.83% of the system water availability, are able to retain almost 8% of total sediment produced.

Shewhart's control charts and process capability ratio applied to a sewage treatment station

The current study used statistical methods of quality control to evaluate the performance of a sewage treatment station. The concerned station is located in Cascavel city, Paraná State. The evaluated parameters were hydrogenionic potential, settleable solids, total suspended solids, chemical oxygen demand and biochemical oxygen demand in five days. Statistical analysis was performed through Shewhart control charts and process capability ratio. According to Shewhart charts, only the BOD(5.20) variable was under statistical control. Through capability ratios, we observed that except for pH the sewage treatment station is not capable to produce effluents under characteristics that fulfill specifications or standard launching required by environmental legislation.

Comparative analysis of different meat traceability systems using multiple criteria and a social network approach

The adoption of a proper traceability system is being incorporated into meat production practices as a method of gaining consumer confidence. The various partners operating in the chain of meat production can be considered a social network, and they have the common goal of generating a communication process that can ensure each characteristic of the product, including safety. This study aimed to select the most appropriate meat traceability system “from farm to fork” that could be applied to Brazilian beef and pork production for international trade. The research was done in three steps. The first used the analytical hierarchy process (AHP) for selecting the best on-farm livestock traceability. In the second step, the actors in the meat production chain were identified to build a framework and defined each role in the network. In the third step, the selection of the traceability system was done. Results indicated that with an electronic traceability system, it is possible to acquire better connections between the links in the chain and to provide the means for managing uncertainties by creating structures that facilitate information flow more efficiently.

Predictive Capability of HPV and Pap Tests in Screening for Cervical Cancer over a Three-Year Follow-up

Purpose To compare the predictive capability of HPV and Pap smear tests for screening pre-cancerous lesions of the cervix over a three-year follow-up, in a population of users of the Brazilian National Health System (SUS). Methods This is a retrospective cohort study of 2,032 women with satisfactory results for Pap smear and HPV tests using second-generation hybrid capture,made in a previous study. We followed them for 36 months with data obtained from medical records, the Cervix Cancer Information System (SISCOLO), and the Mortality Information System (SIM). The outcome was a histological diagnosis of cervical intraepithelial neoplasia grade 2 or more advanced lesions (CIN2ş). We constructed progression curves of the baseline test results for the period, using the Kaplan-Meier method, and estimated sensitivity, specificity, positive and negative predictive value, and positive and negative likelihood ratios for each test. Results A total of 1,440 women had at least one test during follow-up. Progression curves of the baseline test results indicated differences in capability to detect CIN2ş (p < 0.001) with significantly greater capability when both tests were abnormal, followed by only a positive HPV test. The HPV test was more sensitive than the Pap smear (88.7% and 73.6%, respectively; p < 0.05) and had a better negative likelihood ratio (0.13 and 0.30, respectively). Specificity and positive likelihood ratio of the tests were similar. Conclusions These findings corroborate the importance of HPV test as a primary cervical cancer screening.

Detection of Horizontal Two-Phase Flow Patterns Through a Neural Network Model

One of the main problems related to the transport and manipulation of multiphase fluids concerns the existence of characteristic flow patterns and its strong influence on important operation parameters. A good example of this occurs in gas-liquid chemical reactors in which maximum efficiencies can be achieved by maintaining a finely dispersed bubbly flow to maximize the total interfacial area. Thus, the ability to automatically detect flow patterns is of crucial importance, especially for the adequate operation of multiphase systems. This work describes the application of a neural model to process the signals delivered by a direct imaging probe to produce a diagnostic of the corresponding flow pattern. The neural model is constituted of six independent neural modules, each of which trained to detect one of the main horizontal flow patterns, and a last winner-take-all layer responsible for resolving when two or more patterns are simultaneously detected. Experimental signals representing different bubbly, intermittent, annular and stratified flow patterns were used to validate the neural model.

Network connectance and autonomy analyses of the photosynthetic apparatus in tropical tree species from different successional groups under contrasting irradiance conditions

Biological systems are complex dynamical systems whose relationships with environment have strong implications on their regulation and survival. From the interactions between plant and environment can emerge a quite complex network of plant responses rarely observed through classical analytical approaches. The objective of this current study was to test the hypothesis that photosynthetic responses of different tree species to increasing irradiance are related to changes in network connectances of gas exchange and photochemical apparatus, and alterations in plant autonomy in relation to the environment. The heat dissipative capacity through daily changes in leaf temperature was also evaluated. It indicated that the early successional species (Citharexylum myrianthum Cham. and Rhamnidium elaeocarpum Reiss.) were more efficient as dissipative structures than the late successional one (Cariniana legalis (Mart.) Kuntze), suggesting that the parameter deltaT (T ºCair - T ºCleaf) could be a simple tool in order to help the classification of successional classes of tropical trees. Our results indicated a pattern of network responses and autonomy changes under high irradiance. Considering the maintenance of daily CO2 assimilation, the tolerant species (C. myrianthum and R. elaeocarpum) to high irradiance trended to maintain stable the level of gas exchange network connectance and to increase the autonomy in relation to the environment. On the other hand, the late successional species (C. legalis) trended to lose autonomy, decreasing the network connectance of gas exchange. All species showed lower autonomy and higher network connectance of the photochemical apparatus under high irradiance.