Possibilistic networks for uncertainty knowledge processing in student diagnosis

In this paper, a possibilistic network implementation for uncertain knowledge modeling of the diagnostic process is proposed as a means to achieve student diagnosis in intelligent tutoring system. This approach is proposed in the object oriented programming domain for diagnosis of students learning errors and misconception. In this expertise domain dependencies between data exist that are encoded in the structure of network. Also, it is available qualitative information about these data which are represented and interpreted with qualitative approach of possibility theory. The aim of student diagnosis system is to ensure an adapted support for the student and to sustain the student in personalized learning process and errors explanation

Response of seeds and pollen of Onobrychis viciifolia and Onobrychis oxyodonta var. armena to NaCl stress

Sainfoin (Onobrychis viciifolia Scop.) is an important forage legume crop with 52 species adapted to dry and poor soils in Turkey, but little is known about the effects of salinity on germination and seedling growth in arid and semiarid regions suffering from salinity problem. The seeds and pollen of two species of sainfoin O. viciifolia and O. oxyodonta var. armena (Syn: O. armena) were exposed to 0, 5, 10, 20 and 30 dS m-1 of NaCl under in vivo and in vitro conditions and evaluated for germination under salt stress by comparing germination percentage, mean germination time, root and shoot length, fresh and dry seedling weight and dry matter. Increased salinity levels generally resulted in decrease in all traits except time to germination, dry seedling weight and dry matter, which increased at high salinity levels. O. viciifolia seeds germinated and grew more rapidly compared to O. armena seeds under NaCl stress. No decrease in germination and seedling growth up to 10 dS m-1 was recorded. On the other hand, there was a clear difference for germination and seedling growth between in vivo and in vitro conditions. Lower values were obtained from in vitro experiments; suggesting that mineral salts, sucrose and agar may have resulted in higher osmotic potential inhibiting germination and seedling growth of species compared in vivo conditions. Decrease in pollen germination with increasing salinities was very sharp, indicating that pollen germination had higher sensitive to salinity. But, pollen grains of O. armena germinated rapidly compared to O. viciifolia. The results emphasize that in vivo experiments could be used for screening of NaCl tolerance in sainfoin cultivars without expensive chemicals and sophisticated equipments, but pollen germination is more appropriate for its wild relatives

THE "PHOTOSYNTHESIS–GROWTH–STRESS MEMORY" RELATIONSHIP IN PLANTS UNDER CONDITIONS OF MOISTURE FLUCTUATION AND RECURRENT DROUGHT: MANAGEMENT OPTIONS

The effect of co-activation of the stress-memory formation potential under repeated drought of Glycine max (Merr.) L. plants was recorded using cytokinin (CK), thiourea (TH) and, especially, complex preparation Polyel. Glycine max plants (Merr.) L. of Moldovitsa, Nadejda and Magia varieties, exposed to two cycles of “drought–rehydration” at the “first trifoliate leaf” and “flowering – pods formation” phases served as test subjects. The tolerance-inducing effect manifests itself by maintaining the content of assimilatory pigments, photosynthesis and growth processes at a significantly higher level. After the restoration of the optimal moisture background, plants pre-treated with CK, TH and the preparation Polyel, which endured moderate stress in the initial stages of ontogenesis, had restored functional processes. The information obtained in this work certainly opens the management perspective of the ability to form stress memory, adaptation and tolerance of plants to the unfavourable fluctuation of humidity and recurrent drought. The management possibilities of plant adaptation and tolerance are discussed

Spatial abundance and clustering of Culicoides (Diptera: Ceratopogonidae) on a local scale

BACKGROUND: Biting midges, Culicoides, of the Obsoletus group and the Pulicaris group have been involved in recent outbreaks of bluetongue virus and the former was also involved in the Schmallenberg virus outbreak in northern Europe. METHODS: For the first time, here we investigate the local abundance pattern of these two species groups in the field by intensive sampling with a grid of light traps on 16 catch nights. Neighboring trap catches can be spatially dependent on each other, hence we developed a conditional autoregressive (CAR) model framework to test a number of spatial and non-spatial covariates expected to affect Culicoides abundance. RESULTS: The distance to sheep penned in the corner of the study field significantly increased the abundance level up to 200 meters away from the sheep. Spatial clustering was found to be significant but could not be explained by any known factors, and cluster locations shifted between catch nights. No significant temporal autocorrelation was detected. CAR models for both species groups identified a significant positive impact of humidity and significant negative impacts of precipitation and wind turbulence. Temperature was also found to be significant with a peak at just below 16 degrees Celcius. Surprisingly, there was a significant positive impact of wind speed. The CAR model for the Pulicaris group also identified a significant attraction to the smaller groups of sheep placed in the field. Furthermore, a large number of spatial covariates which were incorrectly found to be significant in ordinary regression models were not significant in the CAR models. The 95% C.I. on the prediction estimates ranged from 20.4% to 304.8%, underlining the difficulties of predicting the abundance of Culicoides. CONCLUSIONS: We found that significant spatial clusters of Culicoides moved around in a dynamic pattern varying between catch nights. This conforms with the modeling but was not explained by any of the tested covariates. The mean abundance within these clusters was up to 11 times higher for the Obsoletus group and 4 times higher for the Pulicaris group compared to the rest of the field

Computational modeling with spiking neural networks

This chapter reviews recent developments in the area of spiking neural networks (SNN) and summarizes the main contributions to this research field. We give background information about the functioning of biological neurons, discuss the most important mathematical neural models along with neural encoding techniques, learning algorithms, and applications of spiking neurons. As a specific application, the functioning of the evolving spiking neural network (eSNN) classification method is presented in detail and the principles of numerous eSNN based applications are highlighted and discussed

A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

Reefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250. m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250. m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints. © 2011 Elsevier B.V.Michael J. Watts, Yuxiao Li, Bayden D. Russell, Camille Mellin, Sean D. Connell, Damien A. Fordha