A Spectral Moore Bound for Bipartite Semiregular Graphs

Let $b(k,\ell,\theta)$ be the maximum number of vertices of valency $k$ in a $(k,\ell)$-semiregular bipartite graph with second largest eigenvalue $\theta$. We obtain an upper bound for $b(k,\ell,\theta)$ for $0 < \theta < \sqrt{k-1} + \sqrt{\ell-1}$. This bound is tight when there exists a distance-biregular graph with particular parameters, and we develop the necessary properties of distance-biregular graphs to prove this.Comment: 20 page

ECONOMIC EFFICIENCY OF MAIN SOIL TYPES FROM BÂRZAVA PLAIN FOR WHEAT AND CORN CROPS

This paper represents an economic study of the main soil types in Plain Bârzava forwheat and corn. Studying the economic efficiency of soil is important because according to it we can draw conclusions about the effectiveness and profitability of crops.Knowing evaluation notes I could find natural production potential of soils that wheat and corn crops by multiplying the grade of evaluation with 60 kg / point for wheat and 75 kg / point to corn.Economic comparison was obtained by multiplying production by 0.7 lei / kg forwheat crop and 0.8 lei / kg for corn, amounts representing the price / kg practiced in the summer of 2011

Using eCognition Definiens for automated detection of snow avalanches from optical imagery

Detection of avalanches from remotely collected optical imagery has been tested through analysis of image properties such as brightness, contrast, and different measures of texture. There have been few publications on the subject, providing an excellent opportunity for new developments. The work conducted at NGI in 2011 aimed at detecting fresh snow avalanches from very-high resolution (VHR) optical imagery. The research presented in this Technical Note has been supported by the Ministry of Petroleum and Energy (OED) through the Norwegian Water Resources and Energy Directorate (NVE).Norges Forskningsråd (NFR

Using eCognition Definiens for automated detection of snow avalanche deposits from very high resolution optical imagery - New developments

The identification of snow avalanche deposits from high resolution optical satellite imagery had been the focus of the project "avalRS” which NGI, together with the Norwegian Computing Centre and Statens Veivesen, had carried out for the European Space Agency (2008-2011; e.g., Frauenfelder et al., 2011). The algorithms developed have produced variable results, often working well in certain situations and poorly in others. In 2011 using the object oriented image processing software eCognition, NGI developed two prototype algorithms on its own. The two algorithms were developed for (i) QuickBird satellite imagery, and (ii) Leica ADS-40 airborne imagery (cf. Lato and Frauenfelder, 2012).Aspart of the continuation of this research program, the algorithms developed in 2011 were published in the journal *Natural Hazards and Earth System Sciences* (Lato et al., 2012a) as well as presented at International conferences, e.g., at the "International Snow Science Workshop 2012” in Anchorage, Alaska (Lato et al., 2012b). Overall the developments have been accepted well within the community, the preliminary results demonstrate the possibility of numerous research and commercial applications. In parallel with the publication and presentation of the research results in 2012, new satellite images containing snow avalanche deposits were tested with the algorithms in eCognition. An overview of the data, the region it represents, as well as a discussion of the results is included in this document.Norges vassdrags- og energidirektorat (NVE), Region Ves

Response surface method as a tool for heavy clay firing process optimization: Roofing tiles

Heavy clay samples collected in close vicinity of Toplička Mala Plana, Serbia, were surveyed to examine their possible use in heavy clay industry. The representative raw material, which contained the lowest content of clay minerals and the highest content of carbonates, was enriched with two more plastic clays. Chemical and mineralogical composition, as well as particle size distribution, were determined to distinct the samples. The samples in the form of tiles, hollow blocks and cubes were prepared following the usual practice in ceramic laboratories. The effect of process parameters, such as temperature (850-950 °C) and concentration of the added clays (both in the range of 0-10 wt.%), were investigated in terms of compressive strength, water ab­sorption, firing shrinkage, weight loss during firing and volume mass of cubes. The optimal conditions were determined by the response surface method, coupled with the fuzzy synthetic evaluation algorithm, using mem­bership trapezoidal function, and showed that these materials can be used for roofing tiles production

Application of artificial neural networks in performance prediction of cement mortars with various mineral additives

The machine learning technique for prediction and optimization of building material performances became an essential feature in the contemporary civil engineering. The Artificial Neural Network (ANN) prognosis of mortar behavior was conducted in this study. The model appraised the design and characteristics of seventeen either building or high-temperature mortars. Seven different cement types were employed. Seventeen mineral additives of primary and secondary origin were embedded in the mortar mixtures. Cluster Analysis and Principal Component Analysis designated groups of similar mortars assigning them a specific purpose based on monitored characteristics. ANN foresaw the quality of designed mortars. The impact of implemented raw materials on the mortar quality was assessed and evaluated. ANN outputs highlighted the high suitability level of anticipation, i.e., 0.999 during the training period, which is regarded appropriate enough to correctly predict the observed outputs in a wide range of processing parameters. Due to the high predictive accuracy, ANN can replace or be used in combination with standard destructive tests thereby saving the construction industry time, resources, and capital. Good performances of altered cement mortars are positive sign for widening of economical mineral additives application in building materials and making progress towards achieved carbon neutrality by reducing its emission

Comparison of the most popular operating systems in terms of functionalities

The main purpose of research is comparison of the following modern operating systems: Windows 10, Windows 11, MacOS Catalina and Linux Ubuntu 20.04 LTS. An analysis was made in terms of functionalities and time needed to perform basic activities. The systems were selected on the basis on performed popularity analysis, by using StatCounter [1] statistic. To study each operating system it was necessary to create two test stands corresponding to the requirements of the systems. Conducted research were divided on two sections. In the first one, analysis of the possessed functionalities, assessment of the advancement and ease of using them was performed. In the second section, examination was carried out to compare the operating system in terms of the time of performing specific activities

Characterization of rock slopes through slope mass rating using 3D point clouds

Rock mass classification systems are widely used tools for assessing the stability of rock slopes. Their calculation requires the prior quantification of several parameters during conventional fieldwork campaigns, such as the orientation of the discontinuity sets, the main properties of the existing discontinuities and the geo-mechanical characterization of the intact rock mass, which can be time-consuming and an often risky task. Conversely, the use of relatively new remote sensing data for modelling the rock mass surface by means of 3D point clouds is changing the current investigation strategies in different rock slope engineering applications. In this paper, the main practical issues affecting the application of Slope Mass Rating (SMR) for the characterization of rock slopes from 3D point clouds are reviewed, using three case studies from an end-user point of view. To this end, the SMR adjustment factors, which were calculated from different sources of information and processes, using the different softwares, are compared with those calculated using conventional fieldwork data. In the presented analysis, special attention is paid to the differences between the SMR indexes derived from the 3D point cloud and conventional field work approaches, the main factors that determine the quality of the data and some recognized practical issues. Finally, the reliability of Slope Mass Rating for the characterization of rocky slopes is highlighted.This work was partially funded by the University of Alicante (vigrob-157 Project and GRE1404 Project), the Generalitat Valenciana (Projects GV/2011/044 and ACOMP/2014/136), the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER under Projects TEC2011-28201-C02-02 and TIN2014-55413-C2-2-P