Regeneration ecology and woody species diversity in the remnant natural forest at Wondo Genet, Ethiopia – Potential for landscape restoration

Regeneration ecology, diversity of native woody species and its potential for landscape restoration was studied in the remnant natural forest at the College of Forestry and Natural Resources at Wondo Genet, Ethiopia. The type of forest is Afromontane rainforest , with many valuable tree species like Aningeria adolfi-friederici, and it is an important provider of ecological, social and economical services for the population that lives in this area. The study contains two parts, natural regeneration studies (at the natural forest) and interviews with farmers in the nearby village of the remnant patch. The objective of the first part was to investigate the floristic composition, densitiy and regeneration profiles of native woody species in the forest, paying special attention to woody species that are considered the most relevant (socio-economic). The second part provided information on woody species preferred by the farmers and on multiple uses of the adjacent natural forest, it also provided information and analysed perceptions on forest degradation. Systematic plot sampling was used in the forest inventory. Twenty square plots of 20 x 20 m were assessed, with 38 identified woody species (the total number of species was 45), representing 26 families. Of these species 61% were trees, 13% shrubs, 11% lianas and 16% species that could have both life forms. An analysis of natural regeneration of five important tree species in the natural forest showed that Aningeria adolfi-friederici had the best regeneration results. An analysis of population structure (as determined by height classes) of two commercially important woody species in the forest, Aningeria adolfi-friederici and Podocarpus falcatus, showed a marked difference: Aningeria had a typical “reversed J” frequency distribution, while Podocarpus showed very low values in all height classes. Multi dimensional scaling (MDS) was used to map the sample plots according to their similarity in species composition, using the Sørensen quantitative index, coupled with indicator species analysis .Three groups were identified with respective indicator species: Group 1 – Adhatoda schimperiana, Group 2 – Olea hochstetteri , Group 3 – Acacia senegal and Aningeria adolfi-friederici. Thirty questionnaire interviews were conducted with farmers in the village of Gotu Onoma that use the nearby remant forest patch. Their tree preferences were exotic species such as Eucalyptus globulus for construction and fuelwood and Grevillea robusta for shade and fertility. Considering forest land degradation farmers were aware of the problem and suggested that the governmental institutions address the problem by planting more Eucalyptus globulus. The natural forest seemed to have moderate levels of disturbance and it was still floristically diverse. However, the low rate of natural regeneration of Podocarpus falcatus suggested that this species is threatened and must be a priority in conservation actions. Plantations and agroforestry seem to be possible solutions for rehabilitation of the surrounding degraded lands, thereby decreasing the existent pressure in the remnant natural forest.

Design of a FIR filter for image restoration using principal component neural networks

The neural network finds its application in many image denoising applications because of its inherent characteristics such as nonlinear mapping and self-adaptiveness. The design of filters largely depends on the a-priori knowledge about the type of noise. Due to this, standard filters are application and image specific. Widely used filtering algorithms reduce noisy artifacts by smoothing. However, this operation normally results in smoothing of the edges as well. On the other hand, sharpening filters enhance the high frequency details making the image non-smooth. An integrated general approach to design a finite impulse response filter based on principal component neural network (PCNN) is proposed in this study for image filtering, optimized in the sense of visual inspection and error metric. This algorithm exploits the inter-pixel correlation by iteratively updating the filter coefficients using PCNN. This algorithm performs optimal smoothing of the noisy image by preserving high and low frequency features. Evaluation results show that the proposed filter is robust under various noise distributions. Further, the number of unknown parameters is very few and most of these parameters are adaptively obtained from the processed image.

A new FIR filter for image restoration

Image filtering techniques have potential applications in biomedical image processing such as image restoration and image enhancement. The potential of traditional filters largely depends on the apriori knowledge about the type of noise corrupting the image. This makes the standard filters to be application specific. For example, the well-known median filter and its variants can remove the salt-and-pepper (or impulse) noise at low noise levels. Each of these methods has its own advantages and disadvantages. In this paper, we have introduced a new finite impulse response (FIR) filter for image restoration where, the filter undergoes a learning procedure. The filter coefficients are adaptively updated based on correlated Hebbian learning. This algorithm exploits the inter pixel correlation in the form of Hebbian learning and hence performs optimal smoothening of the noisy images. The application of the proposed filter on images corrupted with Gaussian noise, results in restorations which are better in quality compared to those restored by average and Wiener filters. The restored image is found to be visually appealing and artifact-free

Computation of restoration of ligand response in the random kinetics of a prostate cancer cell signaling pathway

Mutation and/or dysfunction of signaling proteins in the mitogen activated protein kinase (MAPK) signal transduction pathway are frequently observed in various kinds of human cancer. Consistent with this fact, in the present study, we experimentally observe that the epidermal growth factor (EGF) induced activation profile of MAP kinase signaling is not straightforward dose-dependent in the PC3 prostate cancer cells. To find out what parameters and reactions in the pathway are involved in this departure from the normal dose-dependency, a model-based pathway analysis is performed. The pathway is mathematically modeled with 28 rate equations yielding those many ordinary differential equations (ODE) with kinetic rate constants that have been reported to take random values in the existing literature. This has led to us treating the ODE model of the pathways kinetics as a random differential equations (RDE) system in which the parameters are random variables. We show that our RDE model captures the uncertainty in the kinetic rate constants as seen in the behavior of the experimental data and more importantly, upon simulation, exhibits the abnormal EGF dose-dependency of the activation profile of MAP kinase signaling in PC3 prostate cancer cells. The most likely set of values of the kinetic rate constants obtained from fitting the RDE model into the experimental data is then used in a direct transcription based dynamic optimization method for computing the changes needed in these kinetic rate constant values for the restoration of the normal EGF dose response. The last computation identifies the parameters, i.e., the kinetic rate constants in the RDE model, that are the most sensitive to the change in the EGF dose response behavior in the PC3 prostate cancer cells. The reactions in which these most sensitive parameters participate emerge as candidate drug targets on the signaling pathway. (C) 2011 Elsevier Ireland Ltd. All rights reserved.

A New Iterative FIR Filter for Image and Video Restoration

Image and video filtering is a key image-processing task in computer vision especially in noisy environment. In most of the cases the noise source is unknown and hence possess a major difficulty in the filtering operation. In this paper we present an error-correction based learning approach for iterative filtering. A new FIR filter is designed in which the filter coefficients are updated based on Widrow-Hoff rule. Unlike the standard filter the proposed filter has the ability to remove noise without the a priori knowledge of the noise. Experimental result shows that the proposed filter efficiently removes the noise and preserves the edges in the image. We demonstrate the capability of the proposed algorithm by testing it on standard images infected by Gaussian noise and on a real time video containing inherent noise. Experimental result shows that the proposed filter is better than some of the existing standard filters

Aquatic Ecosystems: Conservation, Restoration and Management

Aquatic Ecosystems perform numerous valuable environmental functions. They recycle nutrients, purify water, recharge ground water, augment and maintain stream flow, and provide habitat for a wide variety of flora and fauna and recreation for people. A rapid population increase accompanied by unplanned developmental works has led to the pollution of surface waters due to residential, agricultural, commercial and industrial wastes/effluents and decline in the number of water bodies. Increased demands for drainage of wetlands have been accommodated by channelisation, resulting in further loss of stream habitat, which has led to aquatic organisms becoming extinct or imperiled in increasing numbers and to the impairment of many beneficial uses of water, including drinking, swimming and fishing. Various anthropogenic activities have altered the physical, chemical and biological processes within aquatic ecosystems. An integrated and accelerated effort toward environmental restoration and preservation is needed to stop further degradation of these fragile ecosystems. Failure to restore these ecosystems will result in sharply increased environmental costs later, in the extinction of species or ecosystem types, and in permanent ecological damage.

High-resolution restoration of 3D structures from widefield images with extreme low signal-to-noise-ratio

Four-dimensional fluorescence microscopy-which records 3D image information as a function of time-provides an unbiased way of tracking dynamic behavior of subcellular components in living samples and capturing key events in complex macromolecular processes. Unfortunately, the combination of phototoxicity and photobleaching can severely limit the density or duration of sampling, thereby limiting the biological information that can be obtained. Although widefield microscopy provides a very light-efficient way of imaging, obtaining high-quality reconstructions requires deconvolution to remove optical aberrations. Unfortunately, most deconvolution methods perform very poorly at low signal-to-noise ratios, thereby requiring moderate photon doses to obtain acceptable resolution. We present a unique deconvolution method that combines an entropy-based regularization function with kernels that can exploit general spatial characteristics of the fluorescence image to push the required dose to extreme low levels, resulting in an enabling technology for high-resolution in vivo biological imaging.

Restoration Mechanisms During the Friction Stir Processing of Aluminum Alloys

In the current study, correlation of microstructure evolution with bulk crystallographic texture formation during friction stir processing (FSP) of commercial aluminum alloys has been attempted. Electron back-scattered diffraction and X-ray diffraction techniques were employed for characterizing the nugget zone of optimum friction stir processed samples. Volume fraction of measured texture components revealed that the texture formation in aluminum alloys is similar irrespective of the alloy composition. Recrystallization behavior during FSP was more of a composition dependent phenomenon.

A Bayesian Approach to the Restoration of Degraded Audio Signals

In this paper we derive the a posteriori probability for the location of bursts of noise additively superimposed on a Gaussian AR process. The theory is developed to give a sequentially based restoration algorithm suitable for real-time applications. The algorithm is particularly appropriate for digital audio restoration, where clicks and scratches may be modelled as additive bursts of noise. Experiments are carried out on both real audio data and synthetic AR processes and Significant improvements are demonstrated over existing restoration techniques. © 1995 IEEE