An application of the analytic hierarchy process and fuzzy logic inference in a decision support system for forage selection

Forage selection plays a prominent role in the process of returning cultivated lands back into grasslands. The conventional method of selecting forage species can only provide attempts for problem-solving without considering the relationships among the decision factors globally. Therefore, this study is dedicated to developing a decision support system to help farmers correctly select suitable forage species for the target sites. After collecting data through a field study, we developed this decision support system. It consists of three steps: (1) the analytic hierarchy process (AHP), (2) weights determination, and (3) decision making. In the first step, six factors influencing forage growth were selected by reviewing the related references and by interviewing experts. Then a fuzzy matrix was devised to determine the weight of each factor in the second step. Finally, a gradual alternative decision support system was created to help farmers choose suitable forage species for their lands in the third step. The results showed that the AHP and fuzzy logic are useful for forage selection decision making, and the proposed system can provide accurate results in a certain area (Gansu Province) of China.

A Robust Amorphous Hierarchy from Persistent Nodes

For a very large network deployed in space with only nearby nodes able to talk to each other, we want to do tasks like robust routing and data storage. One way to organize the network is via a hierarchy, but hierarchies often have a few critical nodes whose death can disrupt organization over long distances. I address this with a system of distributed aggregates called Persistent Nodes, such that spatially local failures disrupt the hierarchy in an area proportional to the diameter of the failure. I describe and analyze this system, which has been implemented in simulation.

Learning Classes Correlated to a Hierarchy

Trees are a common way of organizing large amounts of information by placing items with similar characteristics near one another in the tree. We introduce a classification problem where a given tree structure gives us information on the best way to label nearby elements. We suggest there are many practical problems that fall under this domain. We propose a way to map the classification problem onto a standard Bayesian inference problem. We also give a fast, specialized inference algorithm that incrementally updates relevant probabilities. We apply this algorithm to web-classification problems and show that our algorithm empirically works well.

Synoptic conditions governing upwelling along the Polish Baltic coast

The study analyses atmospheric feedback to the occurrence of upwelling along the Polish Baltic coast. Upwelling events were identified on the basis of daily mean sea surface temperature (SST) maps from the period 1982–2010 derived from the NOAA OI SST V2 High Resolution Dataset. Synoptic conditions favourable to the occurrence of upwelling were determined on the basis of the NCEP/NCAR reanalysis data. On average, there are approximately 23 days with upwelling each year along the Polish Baltic coast, which account for approximately 13%

Determining Acceptance Possibility for a Quantum Computation is Hard for the Polynomial Hierarchy

It is shown that determining whether a quantum computation has a non-zero probability of accepting is at least as hard as the polynomial time hierarchy. This hardness result also applies to determining in general whether a given quantum basis state appears with nonzero amplitude in a superposition, or whether a given quantum bit has positive expectation value at the end of a quantum computation. This result is achieved by showing that the complexity class NQP of Adleman, Demarrais, and Huang, a quantum analog of NP, is equal to the counting class coC=P.

A Memory Model for Concept Hierarchy Representation and Commonsense Reasoning

Most associative memory models perform one level mapping between predefined sets of input and output patterns1 and are unable to represent hierarchical knowledge. Complex AI systems allow hierarchical representation of concepts, but generally do not have learning capabilities. In this paper, a memory model is proposed which forms concept hierarchy by learning sample relations between concepts. All concepts are represented in a concept layer. Relations between a concept and its defining lower level concepts, are chunked as cognitive codes represented in a coding layer. By updating memory contents in the concept layer through code firing in the coding layer, the system is able to perform an important class of commonsense reasoning, namely recognition and inheritance.

The role of stimulus salience and attentional capture across the neural hierarchy in a stop-signal task.

Inhibitory motor control is a core function of cognitive control. Evidence from diverse experimental approaches has linked this function to a mostly right-lateralized network of cortical and subcortical areas, wherein a signal from the frontal cortex to the basal ganglia is believed to trigger motor-response cancellation. Recently, however, it has been recognized that in the context of typical motor-control paradigms those processes related to actual response inhibition and those related to the attentional processing of the relevant stimuli are highly interrelated and thus difficult to distinguish. Here, we used fMRI and a modified Stop-signal task to specifically examine the role of perceptual and attentional processes triggered by the different stimuli in such tasks, thus seeking to further distinguish other cognitive processes that may precede or otherwise accompany the implementation of response inhibition. In order to establish which brain areas respond to sensory stimulation differences by rare Stop-stimuli, as well as to the associated attentional capture that these may trigger irrespective of their task-relevance, we compared brain activity evoked by Stop-trials to that evoked by Go-trials in task blocks where Stop-stimuli were to be ignored. In addition, region-of-interest analyses comparing the responses to these task-irrelevant Stop-trials, with those to typical relevant Stop-trials, identified separable activity profiles as a function of the task-relevance of the Stop-signal. While occipital areas were mostly blind to the task-relevance of Stop-stimuli, activity in temporo-parietal areas dissociated between task-irrelevant and task-relevant ones. Activity profiles in frontal areas, in turn, were activated mainly by task-relevant Stop-trials, presumably reflecting a combination of triggered top-down attentional influences and inhibitory motor-control processes.

Factors governing the deep ventilation of the Red Sea

A variety of data based on hydrographic measurements, satellite observations, reanalysis databases, and meteorological observations are used to explore the interannual variability and factors governing the deep water formation in the northern Red Sea. Historical and recent hydrographic data consistently indicate that the ventilation of the near-bottom layer in the Red Sea is a robust feature of the thermohaline circulation. Dense water capable to reach the bottom layers of the Red Sea can be regularly produced mostly inside the Gulfs of Aqaba and Suez. Occasionally, during colder than usual winters, deep water formation may also take place over coastal areas in the northernmost end of the open Red Sea just outside the Gulfs of Aqaba and Suez. However, the origin as well as the amount of deep waters exhibit considerable interannual variability depending not only on atmospheric forcing but also on the water circulation over the northern Red Sea. Analysis of several recent winters shows that the strength of the cyclonic gyre prevailing in the northernmost part of the basin can effectively influence the sea surface temperature (SST) and intensify or moderate the winter surface cooling. Upwelling associated with periods of persistent gyre circulation lowers the SST over the northernmost part of the Red Sea and can produce colder than normal winter SST even without extreme heat loss by the sea surface. In addition, the occasional persistence of the cyclonic gyre feeds the surface layers of the northern Red Sea with nutrients, considerably increasing the phytoplankton biomass.

The hierarchy of directional interactions in visual motion processing

It is well known that context influences our perception of visual motion direction. For example, spatial and temporal context manipulations can be used to induce two well-known motion illusions: direction repulsion and the direction after-effect (DAE). Both result in inaccurate perception of direction when a moving pattern is either superimposed on (direction repulsion), or presented following adaptation to (DAE), another pattern moving in a different direction. Remarkable similarities in tuning characteristics suggest that common processes underlie the two illusions. What is not clear, however, is whether the processes driving the two illusions are expressions of the same or different neural substrates. Here we report two experiments demonstrating that direction repulsion and the DAE are, in fact, expressions of different neural substrates. Our strategy was to use each of the illusions to create a distorted perceptual representation upon which the mechanisms generating the other illusion could potentially operate. We found that the processes mediating direction repulsion did indeed access the distorted perceptual representation induced by the DAE. Conversely, the DAE was unaffected by direction repulsion. Thus parallels in perceptual phenomenology do not necessarily imply common neural substrates. Our results also demonstrate that the neural processes driving the DAE occur at an earlier stage of motion processing than those underlying direction repulsion.