A significant number of recent scientific papers have raised awareness of changes in the biological world of bees, problems with their extinction, and, as a consequence, their impact on humans and the environment. This work relies on precision beekeeping in apiculture and raises the scale of measurement and prediction results using the system we developed, which was designed to cover beehive ecosystem. It is equipped with an IoT modular base station that collects a wide range of parameters from sensors on the hive and a bee counter at the hive entrance. Data are sent to the cloud for storage, analysis, and alarm generation. A time-series forecasting model capable of estimating the volume of bee exits and entrances per hour, which simulates dependence between environmental conditions and bee activity, was devised. The applied mathematical models based on recurrent neural networks exhibited high accuracy. A web application for monitoring and prediction displays parameters, measured values, and predictive and analytical alarms in real time. The predictive component utilizes artificial intelligence by applying advanced analytical methods to find correlation between sensor data and the behavioral patterns of bees, and to raise alarms should it detect deviations. The analytical component raises an alarm when it detects measured values that lie outside of the predetermined safety limits. Comparisons of the experimental data with the model showed that our model represents the observed processes well.

We give a survey of graph spectral techniques used in computer sciences. The survey consists of a description of particular topics from the theory of graph spectra independently of the areas of Computer science in which they are used. We have described the applications of some important graph eigenvalues (spectral radius, algebraic connectivity, the least eigenvalue etc.), eigenvectors (principal eigenvector, Fiedler eigenvector and other), spectral reconstruction problems, spectra of random graphs, Hoffman polynomial, integral graphs etc. However, for each described spectral technique we indicate the fields in which it is used (e.g. in modelling and searching Internet, in computer vision, pattern recognition, data mining, multiprocessor systems, statistical databases, and in several other areas). We present some novel mathematical results (related to clustering and the Hoffman polynomial) as well.

Nonprofit, nongovernmental organizations (NGOs) are important development actors that operate in ecosystems of multi and cross-sector stakeholders in order to reach the most vulnerable social groups of people. Having this in mind, our goal was to explore to what extent their social capital influences their knowledge in order to propose a model that could optimize their knowledge management maturity through social resources embedded into their structure. Therefore, we focused on NGOs from the European Union and the Western Balkans operating in the international development and cooperation sector. We first conducted a survey among 215 NGOs and then cross-checked the received data through desk research and in-depth interviews. We utilized collected structured data and devised a binary classification model capable of discriminating high level from the low level of knowledge management maturity in NGOs, and multi-class classification model for estimating the actual category of knowledge management maturity which can simulate the real connection among social capital and existing knowledge management maturity. Our findings provide valuable data to the NGO management, regardless of where the NGO comes from, of the number of employees it has, and the number of projects it runs. The mathematical models based on neural networks used in this paper show with high accuracy what the knowledge management maturity level (for each output) is in case there exists prior information about social capital (inputs).

Many research centers and medical institutions have been accumulating a vast amount of various biological and chemical data over the past decade and this trend continues. Based on Linked Data vision, many semantic applications for distributed access

Vibrations while driving, regardless of their intensity and shape, have the most obvious effect of reducing driving comfort. Seat-to-head frequency response function (STHT) is a complex relationship resulting from the movement of the head due to the...