Optimized Task Group Aggregation-Based Overflow Handling on Fog Computing Environment Using Neural Computing

It is a non-deterministic challenge on a fog computing network to schedule resources or jobs in a manner that increases device efficacy and throughput, diminishes reply period, and maintains the system well-adjusted. Using Machine Learning as a component of neural computing, we developed an improved Task Group Aggregation (TGA) overflow handling system for fog computing environments. As a result of TGA usage in conjunction with an Artificial Neural Network (ANN), we may assess the model’s QoS characteristics to detect an overloaded server and then move the model’s data to virtual machines (VMs). Overloaded and underloaded virtual machines will be balanced according to parameters, such as CPU, memory, and bandwidth to control fog computing overflow concerns with the help of ANN and the machine learning concept. Additionally, the Artificial Bee Colony (ABC) algorithm, which is a neural computing system, is employed as an optimization technique to separate the services and users depending on their individual qualities. The response time and success rate were both enhanced using the newly proposed optimized ANN-based TGA algorithm. Compared to the present work’s minimal reaction time, the total improvement in average success rate is about 3.6189 percent, and Resource Scheduling Efficiency has improved by 3.9832 percent. In terms of virtual machine efficiency for resource scheduling, average success rate, average task completion success rate, and virtual machine response time are improved. The proposed TGA-based overflow handling on a fog computing domain enhances response time compared to the current approaches. Fog computing, for example, demonstrates how artificial intelligence-based systems can be made more efficient.

The Avoiding Manoeuvre Against Aggregated Group of Obstacles Moving Around the Airplane

Successful avoidance of a mid air collision with moving obstacles depends on solutions of some most essential problems, e.g.: quick detection of an obstacle, verification whether detected obstacle is a critical one and making right decision on evasive manoeuvre. This decision – making process requires an appropriate identification of a threat’s nature, including whether detected obstacles should be treated as one aggregated group. Aggregation of obstacles moving in short distance one to the other is a typical case. The paper addresses also the case of inclusion the obstacle to the group objects moving in longer distances one to the other. The algorithm used for deciding whether a moving obstacle should be added to (aggregated with) a given group has been presented. A method for computing its characteristic parameters has been presented too. Selected scenarios of avoiding the aggregated group of moving obstacles have been simulated and results obtained illustrates problems considered.

Wisdom of crowds benefits perceptual decision making across difficulty levels

Decades of research on collective decision making has claimed that aggregated judgment of multiple individuals is more accurate than expert individual judgement. A longstanding problem in this regard has been to determine how decisions of individuals can be combined to form intelligent group decisions. Our study consisted of a random target detection task in natural scenes, where human subjects (18 subjects, 7 female) detected the presence or absence of a random target as indicated by the cue word displayed prior to stimulus display. Concurrently the neural activities (EEG signals) were recorded. A separate behavioural experiment was performed by different subjects (20 subjects, 11 female) on the same set of images to categorize the tasks according to their difficulty levels. We demonstrate that the weighted average of individual decision confidence/neural decision variables produces significantly better performance than the frequently used majority pooling algorithm. Further, the classification error rates from individual judgement were found to increase with increasing task difficulty. This error could be significantly reduced upon combining the individual decisions using group aggregation rules. Using statistical tests, we show that combining all available participants is unnecessary to achieve minimum classification error rate. We also try to explore if group aggregation benefits depend on the correlation between the individual judgements of the group and our results seem to suggest that reduced inter-subject correlation can improve collective decision making for a fixed difficulty level.

Hypothesis: The highly folded brain surface might be structured and located so as to facilitate inter-brain synchronization

We integrate recent findings from neuro-anatomy, electroencephalography, quantum biology and social/neurodevelopment to propose that the brain surface might be specialised for communication with other brains. Ground breaking, but still small-scale, research has demonstrated that human brains can act in synchrony and detect the brain activity of other human brains. Group aggregation, in all species, maximises community support and safety but does not depend on verbal or visual interaction. The morphology of the brain’s outermost layers, across a wide range of species, exhibits a highly folded fractal structure that is likely to maximise exchange at the surface: in humans, a reduced brain surface area is associated with disorders of social communication. The brain sits in a vulnerable exposed location where it is prone to damage, rather than being housed in a central location such as within the ribcage. These observations have led us to the hypothesis that the brain surface might be specialised for interacting with other brains at its surface, allowing synchronous non-verbal interaction. To our knowledge, this has not previously been proposed or investigated.

Cold trapped - Correcting locomotion dependent observation biases in thermal preference of Drosophila

Sensing environmental temperatures is essential for the survival of ectothermic organisms. In Drosophila, two methodologies are used to study temperature preferences (TP) and the genes involved in thermosensation: two-choice assays and temperature gradients. Whereas two-choice assays reveal a relative TP, temperature gradients can identify the absolute Tp. One drawback of gradients is that small ectothermic animals are susceptible to cold-trapping: a physiological inability to move at the cold area of the gradient. Often cold-trapping cannot be avoided, biasing the resulting TP to lower temperatures. Two mathematical models were previously developed to correct for cold-trapping. These models, however, focus on group behaviour which can lead to overestimation of cold-trapping due to group aggregation. Here we present a mathematical model that estimates the behaviour of individual Drosophilain temperature gradients. The model takes the spatial dimension and temperature difference of the gradient into account, as well as the rearing temperature of the flies. Furthermore, it allows quantifying cold-trapping, reveals true TP, and differentiates between temperature preference and tolerance. Online simulation is hosted at http://igloo.uni-goettingen.de. The code can be accessed at https://github.com/zerotonin/igloo.

Injectable shear-thinning hydrogels with enhanced strength and temperature stability based on polyhedral oligomeric silsesquioxane end-group aggregation

A strategy based on the physical association of POSS end-groups was designed to reinforce shear-thinning hydrogels, and their shear-thinning and recovery properties remained unchanged.