Land-use change and windstorms legacies drove the recolonization dynamics of laurel forests in Tenerife, Canary islands

Laurel forests are quite relevant for biodiversity conservation and are among the island ecosystems most severely damaged by human activities.In the past,Canary laurel forests have been greatly altered by logging,livestock and agriculture.The remains of laurel forests are currently protected in the Canary Islands(Spain).However,we miss basic information needed for their restoration and adaptive management,such as tree longevity,growth potential and responsiveness to natural and anthropogenic disturbances.Using dendrochronological methods,we studied how forest dynamic is related to land-use change and windstorms in two well-preserved laurel forests on Tenerife Island.Wood cores were collected from over 80 trees per stand at three stands per forest.We used ring-width series to estimate tree ages and calculate annual basal area increments(BAI),cumulative diameter increases,and changes indicative of released and suppressed growth.Twelve tree species were found in all stands,with Laurus novocanariensis,Ilex canariensis and Morella faya being the most common species.Although some individuals were over 100 years old,61.8%-88.9% of the trees per stand established between 1940 and 1970,coinciding with a post-war period of land abandonment,rural exodus and the onset of a tourism economy.Some trees have shown growth rates larger than 1 cm diameter per year and most species have had increasing BAI trends over the past decades.Strong growth releases occurred after windstorms at both sites,but the effects of windstorms were site-dependent,with the 1958 storm affecting mainly the eastern tip of the island(Anaga massif)and the 1991 storm the western tip(Teno massif).Given the great ability of laurel forest trees to establish after land use cessation and to increase growth after local disturbances such as windstorms,passive restoration may be sufficient to regenerate this habitat in currently degraded areas.

The Tight Coupling and Non-Linear Relationship between the Macroscopic Electrical and Optical Concomitants of Electrochemical CNS Waves Reflect the Non-Linear Dynamics of Neural Glial Propagation

In isolated chick retina, the visualization of electrochemical self-organized patterns is possible due to the presence of macroscopic intrinsic optical signals (IOSs). Isolated circular waves, standing patterns, and self-sustained sequences of spirals are all easily obtained using an IOS approach. In this paper we present the tight coupling and non-linear relationship between optical and electrical wave concomitants, and potassium-induced whole tissue excitability changes. Elementary statistical methods and time series analyses were applied to two sets of data: 1) solitary circular retinal spreading depression waves, and 2) tissue response to exogenous potassium fast pulses. The results were interpreted from the point of view of non-linear thermodynamical concepts and volume phase transitions in polyanionic gels according to the Tasaki action potential model. From these and previous results, it is clear that the glial network and extracellular matrix contribute to the propagation and emergence of these patterns.

Simultaneous optimization of even flow and land and timber value in forest planning:a continuous approach

Background:Forest management planning involves deciding which silvicultural treatment should be applied to each stand and at what time to best meet the objectives established for the forest.For this,many mathematical formulations have been proposed,both within the linear and non-linear programming frameworks,in the latter case generally considering integer variables in a combinatorial manner.We present a novel approach for planning the management of forests comprising single-species,even-aged stands,using a continuous,multi-objective formulation(considering economic and even flow)which can be solved with gradient-type methods.Results:The continuous formulation has proved robust in forest with different structures and different number of stands.The results obtained show a clear advantage of the gradient-type methods over heuristics to solve the problems,both in terms of computational time(efficiency)and in the solution obtained(effectiveness).Their improvement increases drastically with the dimension of the problem(number of stands).Conclusions:It is advisable to rigorously analyze the mathematical properties of the objective functions involved in forest management planning models.The continuous bi-objective model proposed in this paper works with smooth enough functions and can be efficiently solved by using gradient-type techniques.The advantages of the new methodology are summarized as:it does not require to set management prescriptions in advance,it avoids the division of the planning horizon into periods,and it provides better solutions than the traditional combinatorial formulations.Additionally,the graphical display of trade-off information allows an a posteriori articulation of preferences in an intuitive way,therefore being a very interesting tool for the decision-making process in forest planning.

Studies on Capacity Expansion of Fuel Plants for Nuclear Research Reactors

The demand for nuclear fuel for research reactors is rising worldwide. Thus, the production facilities of this kind of fuel need reliable guidance on how to augment their production in order to meet the increasing demand efficiently and safely. We proposed a specific procedure for increasing production capacity. That procedure was tested with data from a real plant, which produces plate-type fuel elements loaded with LEU U3Si2-Al fuel. The test was made by means of discrete event simulation, and the results indicated the proposed procedure is efficient in raising production capacity.

Identification of Artificial Neural Network Models for Three-Dimensional Simulation of a Vibration-Acoustic Dynamic System

Industrial noise can be successfully mitigated with the combined use of passive and Active Noise Control (ANC) strategies. In a noisy area, a practical solution for noise attenuation may include both the use of baffles and ANC. When the operator is required to stay in movement in a delimited spatial area, conventional ANC is usually not able to adequately cancel the noise over the whole area. New control strategies need to be devised to achieve acceptable spatial coverage. A three-dimensional actuator model is proposed in this paper. Active Noise Control (ANC) usually requires a feedback noise measurement for the proper response of the loop controller. In some situations, especially where the real-time tridimensional positioning of a feedback transducer is unfeasible, the availability of a 3D precise noise level estimator is indispensable. In our previous works [1,2], using a vibrating signal of the primary source of noise as an input reference for spatial noise level prediction proved to be a very good choice. Another interesting aspect observed in those previous works was the need for a variable-structure linear model, which is equivalent to a sort of a nonlinear model, with unknown analytical equivalence until now. To overcome this in this paper we propose a model structure based on an Artificial Neural Network (ANN) as a nonlinear black-box model to capture the dynamic nonlinear behaveior of the investigated process. This can be used in a future closed loop noise cancelling strategy. We devise an ANN architecture and a corresponding training methodology to cope with the problem, and a MISO (Multi-Input Single-Output) model structure is used in the identification of the system dynamics. A metric is established to compare the obtained results with other works elsewhere. The results show that the obtained model is consistent and it adequately describes the main dynamics of the studied phenomenon, showing that the MISO approach using an ANN is appropriate for the simulation of the investigated process. A clear conclusion is reached highlighting the promising results obtained using this kind of modeling for ANC.

A Cost-Benefit Evaluation of the Air Quality and Health Impacts in Sao Paulo,Brazil

The objective was to assess the impact on health due to the exposure to air pollution derived from the renewal of the urban bus fleet in S?o Paulo. The study analyzed the substitution of the bus fleet through the variation of the concentration of atmospheric pollutants such as PM10 in the municipality of S?o Paulo and its associated health’s benefits values compared to the investments performed in the bus fleet renewal. PM10 average annual reduction due to the bus improvement system resulted on 22.3%. A cost-benefit evaluation considered the renewal investments’ costs compared to the obtained valued health benefits and it resulted in 4.31. Although the result may suggest a not viable investment, it must be observed that air pollution reduction favors health impacts and that this relation could be improved if additional investments on sustainable transportation increase.

Improvements in the score matrix calculation method using parallel score estimating algorithm

The increasing amount of sequences stored in genomic databases has become unfeasible to the sequential analysis. Then, the parallel computing brought its power to the Bioinformatics through parallel algorithms to align and analyze the sequences, providing improvements mainly in the running time of these algorithms. In many situations, the parallel strategy contributes to reducing the computational complexity of the big problems. This work shows some results obtained by an implementation of a parallel score estimating technique for the score matrix calculation stage, which is the first stage of a progressive multiple sequence alignment. The performance and quality of the parallel score estimating are compared with the results of a dynamic programming approach also implemented in parallel. This comparison shows a significant reduction of running time. Moreover, the quality of the final alignment, using the new strategy, is analyzed and compared with the quality of the approach with dynamic programming.

关于混凝土的可持续性

经过了几次重大的技术改进之后,用硅酸盐水泥制成的混凝土可能是世界上使用最多的人工材料.1997年全球水泥产量达到了15.7亿吨(Humphreys and Mahasenan,2002).这些水泥加上水、碎石和其他物质相当于10500亿吨用于建造房屋、办公楼、污水管道、大坝、混凝土道路等等的建筑材料.……

Stepped machine learning for the development of mineral models: Concepts and applications in the pre-salt reservoir carbonate rocks

Understanding rock mineralogy is essential for formation evaluation,improving the calculation of porosity and hydrocarbon saturation.The primary method to obtain the mineralogy from a well is by applying a model to the geochemical tool’s chemical elements.However,creating a mineralogical model presents challenges such as the minerals’chemical composition and the decision to include a mineral in the model.The traditional application of machine learning can make mineral models less realistic since conventional training is developed based on a set of minerals with different occurrences,lowering some minerals’representativeness.The present research proposes the stepped machine learning(SML),a stepped way to use machine learning to create a mineralogical model from chemical and mineralogical data.A database was assembled with the elemental concentration obtained with XRF analyses and the mineral concentrations obtained with XRD analyses.The chemical elements were Al,Ca,Fe,K,Mg,Mn,Na,Si,and Ti.The minerals were calcite,dolomite,quartz,clays,K-feldspar,plagioclase,and pyroxene.Four algorithms were tested:MLP,GAN,Random Forest,and XGBoost,with XGBoost showing the best results.SML was applied,where a mineral model results are used to train a subsequent model.SML allowed for a significant improvement in some models,notably to clays with an increase in R 2 from 0.597 to 0.853,quartz an increase from 0.673 to 0.869,and calcite,from 0.758 to 0.862.A decrease in the mean squared error of these minerals’models was also observed.The model was applied to the geochemical logs from three wells drilled in the Brazilian pre-salt,and the results were compared with XRD analyzes.The SML model was able to honor the mineral concentrations for different rocks.It is demonstrated that the integration between machine learning tools and geological knowledge in SML was crucial for creating a representative mineralogical model.