In many types of researches and studies including those performed by the sciences of agriculture and plant sciences, large quantities of data are frequently obtained that must be analyzed using different data mining techniques. Sometimes data mining involves the application of different methods of statistical data analysis. Exploratory Factor Analysis (EFA) is frequently used as a technique for data reduction and structure detection in data mining. In our survey, we study the EFA applied to data mining, focusing on the problem of establishing of the optimal number of factors to be retained. The number of factors to retain is the most important decision to take after the factor extraction in EFA. Many researchers discussed the criteria for choosing the optimal number of factors. Mistakes in factor extraction may consist in extracting too few or too many factors. An inappropriate number of factors may lead to erroneous conclusions. A comprehensive review of the state-of-the-art related to this subject was made. The main focus was on the most frequently applied factor selection methods, namely Kaiser Criterion, Cattell's Scree test, and Monte Carlo Parallel Analysis. We have highligthed the importance of the analysis in some research, based on the research specificity, of the total cumulative variance explained by the selected optimal number of extracted factors. It is necessary that the extracted factors explain at least a minimum threshold of cumulative variance. ExtrOptFact algorithm presents the steps that must be performed in EFA for the selection of the optimal number of factors. For validation purposes, a case study was presented, performed on data obtained in an experimental study that we made on Brassica napus plant. Applying the ExtrOptFact algorithm for Principal Component Analysis can be decided on the selection of three components that were called Qualitative, Generative, and Vegetative, which explained 92% of the total cumulative variance.

The paper investigates the cognitive complexity associated with the design of group decision processes (GDP) in relation with some basic contextual factors such us task complexity, users' creativity and problem space complexity. The analysis is done by conducting a socio-simulation experiment for an envisioned software tool that acts as collaborative environment for the GDP design. The simulation results provide some insights on how to engineer context-adaptable functionalities aiming at minimizing the cognitive complexity associated with the GDP design. Although the research is carried out for a specific case, namely the GDSS technology, the results may be easily replicated for any sort of collaborative working environment where the cognitive complexity associated with its effective use is playing an important role.

Up until now, studies of Kauffman network stability have focused on the conditions resulting from the structure of the network. Negative feedbacks have been modeled as ice (nodes that do not change their state) in an ordered phase but this blocks the possibility of breaking out of the range of correct operation. This first, very simplified approximation leads to some incorrect conclusions, e.g., that life is on the edge of chaos. We develop a second approximation, which discovers half-chaos and shows its properties. In previous works, half-chaos has been confirmed in autonomous networks, but only using node function disturbance, which does not change the network structure. Now we examine half-chaos during network growth by adding and removing nodes as a disturbance in autonomous and open networks. In such evolutions controlled by a ‘small change’ of functioning after disturbance, the half-chaos is kept but spontaneous modularity emerges and blurs the picture. Half-chaos is a state to be expected in most of the real systems studied, therefore the determinants of the variability that maintains the half-chaos are particularly important in the application of complex network knowledge.

Abstract:The traveling salesman problem (TSP) has many applications in economy, transport logic [1] etc. It also has a wide range of applicability in the mobile robot path planning optimization [2]. The paper presents research result of solving the path planning subproblem of the navigation of an intelligent autonomous mobile robotic agent. Collecting objects by a mobile robotic agent is the final problem that is intended to be solved. For the robotic mobile agent's path planning is used an unsupervised neural network that can find a closely optimal path between two points in the agent's working area. We have considered a modification of the criteria function of the winner neuron selection. Simulation results are discussed at the end of the paper. The next future development is the hardware implementation of the selforganizing map with real time functioning.

At the end of any orthodontic treatment, retention is a necessary phase. Unfortunately, the current retention devices and the lack of proper oral hygiene on the part of patients lead to the accumulation of dental plaque, periodontal inflammation, and gingival retraction. Our retrospective study included 116 adult patients wearing various types of orthodontic retainers. To quantitatively determine the accumulation of dental plaque, we used the Quigley–Hein plaque index modified by Turesky and the Navy plaque index modified by Rustogi. Another studied parameter was related to the gingival recession associated with retention devices. We had investigated the correctness of patients’ dental hygiene, their preferences for auxiliary means of oral hygiene, the consistency with which they wear the mobile retainers, and respect the orthodontist’s instructions; we also investigated the inconveniences and the accidents that may occur during the retention period. Statistical analysis showed that plaque accumulation is significantly lower in the case of mobile retainer than fixed retainer wearers; the exception was the Hawley plate, where the interdental plaque was more than in all the other studied retainers. Periodontal recessions were more frequent in the case of fixed retainer wearing. Flossing was the most commonly used auxiliary mean for oral hygiene. The compliance of women in wearing vacuum-formed retainers was better than that of men. Patients with a class III history had more plaque accumulation, and class II/1 had the most problems related to detachment/damage of fixed retainers. Mobile retainers proved better results for oral hygiene, but fixed retainers cannot be waved.

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In this paper, we propose a novel metric called MetrIntPair (Metric for Pairwise Intelligence Comparison of Agent‐Based Systems) for comparison of two cooperative multiagent systems problem‐solving intelligence. MetrIntPair is able to make an accurate comparison by taking into consideration the variability in intelligence in problem‐solving. The metric could treat the outlier intelligence indicators, intelligence measures that are statistically different from those others. For evaluation of the proposed metric, we realized a case study for two cooperative multiagent systems applied for solving a class of NP‐hard problems. The results of the case study proved that the small difference in the measured intelligence of the multiagent systems is the consequence of the variability. There is no statistical difference between the intelligence quotients/level of the multiagent systems. Both multiagent systems should be classified in the same intelligence class.

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