Land-cover classification in a moist tropical region of Brazil with Landsat Thematic Mapper imagery

This research aims to improve land-cover classification accuracy in a moist tropical region in Brazil by examining the use of different remote-sensing-derived variables and classification algorithms. Different scenarios based on Landsat Thematic Mapper (TM) spectral data and derived vegetation indices and textural images and different classification algorithms, maximum likelihood classification (MLC), artificial neural network (ANN), classification tree analysis (CTA) and object-based classification (OBC), were explored. The results indicate that a combination of vegetation indices as extra bands into Landsat TM multi-spectral bands did not improve the overall classification performance, but the combination of textural images was valuable for improving vegetation classification accuracy. In particular, the combination of both vegetation indices and textural images into TM multi-spectral bands improved the overall classification accuracy (OCA) by 5.6% and the overall kappa coefficient (OKC) by 6.25%. Comparison of the different classification algorithms indicated that CTA and ANN have poor classification performance in this research, but OBC improved primary forest and pasture classification accuracies. This research indicates that use of textural images or use of OBC are especially valuable for improving the vegetation classes such as upland and liana forest classes that have complex stand structures and large patch sizes.     

A hybrid proposal for cross-sectoral analysis of knowledge management

At present time, although many theoretical formulations have been successfully proposed, there is a lack of ICT-based tools to support practical deployment of knowledge management (KM) in real settings. To bridge this gap, a hybrid artificial intelligence system is proposed in present study, aimed at gaining deeper knowledge about KM practices in four different economic sectors. By means of soft computing, companies are diagnosed according to their status regarding KM and subsequent explanations about crucial KM practices and perspectives are generated. Interesting conclusions are then derived from these explanations, allowing KM managers to optimise their decisions and obtain better results. Experimental results of real-life data from Spanish companies associated with different economic sectors validate the proposed combination of techniques.     

Kinetics of mechanically induced anatase-to-rutile phase transformations under inelastic impact conditions

This work investigates the kinetics of the anatase-to-rutile phase transformation of TiO₂ powders during ball milling. Two elementary stages are observed, namely the transformation of anatase to the TiO₂ II phase and of this latter to rutile. Such consecutive reactions were studied under inelastic impact regimes. Based on a suitable modeling of transformation kinetics, the fraction of powder processed on average at each collision was estimated. The mass of powder involved in phase transformation and microstructural refinement processes at each collision was then worked out by a systematic variation of the powder charge inside the reactor.     

Linking ecological information and radiative transfer models to estimate fuel moisture content in the Mediterranean region of Spain: Solving the ill-posed inverse problem

Live fuel moisture content (FMC) is a key factor required to evaluate fire risk and its operative and accurate estimation is essential for allocating pre-fire resources as a part of fire prevention. This paper presents an operative and accurate procedure to estimate FMC though MODIS (moderate resolution imaging spectrometer) data and simulation models. The new aspects of the method are its consideration of several ecological criteria to parameterize the models and consistently avoid simulating unrealistic spectra which might produce indetermination (ill-posed) problems when inverting the model. The methodology was operatively applicable to 12 shrubland plots located in different provinces of the Mediterranean region of Spain and tested with field data collected in those areas. The results showed that the proposed method efficiently tracks changes of FMC with average errors around 15%. However the model under-estimates FMC values higher than 135.68% since those situations were not included in the simulation scheme and the inversion precision is also dependent on an accurate estimation of LAI. These limitations will be overcome in future work mainly by including spectral signatures of vegetation with FMC values higher than 135.68% in the simulations, and by exploring new methods for LAI retrieval. Further efforts will also be devoted to extend this approach to other ecosystems.     

Synthesis and properties of aliphatic spirodilactam diphenol containing polyesters

A series of aromatic polyesters containing 1,6-diazaspiro[4,4]-nonane-2,7-dione were synthesized under phase-transfer conditions. The copolymers were obtained in essentially quantitative yield, theses were soluble in common organic solvents, and would readily form clear, colorless films from solution. The optimum conditions of polymerization, was obtained via polycondensation at room temperature and reaction time of 4 h in chloroform. All polymers were characterized by FTIR, GPC, viscosity, water contact angle, water absorption, TGA, DSC and TMA. The prepared polyesters showed excellent thermal stability, as measured by TGA (10 wt% loss), are only moderate due to the alicyclic component and range from 365 to 401 °C in air; however, glass transition temperatures are quite high (245-309 °C). The inherent viscosities of these solutions ranged from 0.77 to 1.40 dl g⁻¹, depending on the polyester structure. The structures of the polyesters were confirmed by FTIR spectroscopy.     

Acid modified bamboo‐type carbon nanotubes and cup‐stacked‐type carbon nanofibres as adsorbent materials: cadmium removal from aqueous solution

BACKGROUND: Nitrogen‐doped multi‐walled carbon nanotubes (CN_x) and cup‐stacked‐type carbon nanofibres (CST) were modified with nitric acid, at 85 °C, for 1–3 h to enhance their cation adsorption capacity, and were used for cadmium removal from aqueous solutions. These carbon nanostructures were characterized by SEM, EDX, FTIR, BET surface area, TGA and acid/base titrations. In addition, cadmium adsorption experiments were carried out at different pH values and constant temperature. RESULTS: EDX and FTIR revealed a 0.4 times increase in oxygen content after acid treatment, which was reflected in an increment of up to 1.93 mmol g^?1 of acid sites and in a shift of about two units of the zero point charge to lower pH values. The BET surface area, in general, decreased as the oxidation time increased. Finally, batch sorption experiments indicated that CN_x are more effective than CST to sequester Cd²⁺ at pH 7; the sorption capacities of these materials increased as pH rose. CONCLUSIONS: The results obtained indicate that it is feasible to modify both physical and chemical properties of the carbon nanostructures CN_x and CST by HNO₃ in such a way that they can be used to adsorb cations present in aqueous solutions. Copyright © 2008 Society of Chemical Industry     

A New Distance Measure for Model-Based Sequence Clustering

We review the existing alternatives for defining model-based distances for clustering sequences and propose a new one based on the Kullback-Leibler divergence. This distance is shown to be especially useful in combination with spectral clustering. For improved performance in real-world scenarios, a model selection scheme is also proposed.     

Dynamic vegetation model as a tool for ecological impact assessments of dam operation

This study presented the results of an application of a floodplain dynamic model to the Nakdong River, South Korea. At the Nakdong River, high flows are reduced by dams and the river bed is degraded. Both changes contribute toward the same result: the floodplain is hydraulically disconnected from the main channel and the morphology of the river has been modified. Such changes brought also to a deep modification in the riparian vegetation distribution, abundance and composition. The focus of the study is on the relationship between the hydrology alterations induced by dams and the successional changes in riparian vegetation. More in detail, the study attempts to adapt an existing dynamic floodplain vegetation model to the Nakdong ecosystem characteristics in order to single out what were the effects of the dam operations that led to a change in the riparian landscape. The dynamic model is targeted on Monsoon floodplain vegetation, it is developed upon a custom developed geoprocessing framework and supported by a standalone user interface. It simulates dynamics of floodplain vegetation communities based on different physical parameters. The general concept of the model is that a vegetation community will either undergo toward a maturation stage or will be destroyed (recycling or retrogression) if the magnitude of key physical parameters is greater than the threshold value for a specific community. The model has been calibrated using hydraulic data spanning the time period 1952–2007. The calibration results have been also used to investigate the impacts on the riparian vegetation given by dams operations. The findings of the research highlight that consecutive years of reduced maximum discharge allowed consistent vegetation colonization of riverine areas that were bare before the dam construction.     

Assigning discounts in a marketing campaign by using reinforcement learning and neural networks

In this work, RL is used to find an optimal policy for a marketing campaign. Data show a complex characterization of state and action spaces. Two approaches are proposed to circumvent this problem. The first approach is based on the self-organizing map (SOM), which is used to aggregate states. The second approach uses a multilayer perceptron (MLP) to carry out a regression of the action-value function. The results indicate that both approaches can improve a targeted marketing campaign. Moreover, the SOM approach allows an intuitive interpretation of the results, and the MLP approach yields robust results with generalization capabilities.