This study was undertaken to determine the some chemical parameters (moisture, fructose/glucose ratio, hydroxymethylfurfural value, volatile compounds) of honey samples obtained from Kars. 100 honey samples from eight districts of Kars were evaluated. The chemical characterizations were as follows: average moisture (15,5±1,11%), HMF (0,77 ppm ±0.9 ppm), F/G ratio (1,02±0,16). As a result of the gas chromatography–mass spectrometry (GC–MS) analyses, some volatile compounds in honey samples such as alcohols (1,15±1,84%), aldehydes (4,66±6,33%), aliphatic acids and their esters (1,86±4,5%), hydrocarbons (2,32±3,1%), flavonoids (4,34±1,63%), carboxylic acids and their esters (0,24±0,96%), ketones (6,05±6,35%), sugars (15,79±10,74%),vitamins (0,59±2,66%) were determined and quantitative variations of chemical components in different samples were reported.

Wireless Sensor Networks (WSNs) is composed of a huge number of sensor nodes that are densely deployed in a target environment. One of the major fundamental challenges in WSNs is the Topology Control (TC). TC algorithms try to reduction the average of nodes transition radius without reduction of the network connectivity. This paper has proposed a metaheuristic algorithm for TC based on the Vortex Search (VS) algorithm for WSNs termed VSTC. VSTC algorithm dynamically adjusts transition radius of nodes, so the proper transition radius can be determined using VS algorithm, so the position of each node is to calculate and represented in a binary format to enhance the coverage area and reduce the number of active nodes. Furthermore, the proposed algorithm is under a less average number of neighbors and the energy consumption compared with the present algorithms. In addition, the proposed algorithm is simulated with the A1 topology algorithm, A3 topology algorithm, and PSOTCS algorithm, the experimental results have been provided the highest in terms of the high residual energy by 34% and the number of active nodes was decreased by 18%.

In this investigation, an effort is made to enhance the performance of Engineered Cementitious Concrete (ECC) by using hybridation process. Five different mixes are used in this study, hybrid ECC mixes are prepared by the combination is made with poly vinyl alcohol fiber of 1.0% volume fraction and polypropylene fiber with 1.0% volume fraction and followed by another mix of poly vinyl alcohol fiber of 1.0% volume fraction with glass fiber of 1.0% volume fraction. Another three ECC mixes are developed with 2.0% volume fraction of poly vinyl alcohol, polypropylene fiber and steel fiber. This hybridation process has a notable achievement in compressive strength, direct tensile strength and flexural strength

In this paper, we propose hardware architectures for a modular reduction operation used in lattice-based cryptography. We modify the modular reduction algorithms for a set of generalized Mersenne primes. With the proposed architectures, we improve the modular reduction complexity. The proposed design speeds up the modular reduction almost $20\\%$ on the FPGA. To the best of our knowledge, the proposed design has the lowest arithmetic complexity.

Starches have a wide range of uses and their consumption has increased over the years, resulting in a growth in the agro-industries that produce them. Cassava is a very important plant for agri-business and one of the main products obtained from its roots is starch. Although cassava can be harvested throughout the year, its quality varies greatly through the seasons; this is because it is influenced by soil and climatic factors, as well as the genetic characteristics of the species. These influences result in seasonal oscillations in root classification based on the starch content available at the time of product delivery. Faced with this problem, the objective of this study was the collection and evaluation of documentary data for 3 years of product quality samples. This was done in order to observe the situation and propose tools that can minimize problems resulting from the quality of raw material received by starch producers throughout the year. It was observed that in the winter period there was an increase in root starch content, despite the differences between the months not being statistically significantly, they are financially representative of this agro-industry sector. At the end of the study, a proposal for a methodology for calculating payment per gram of starch is presented in order to minimize the problem.

Background: The oleoresins are compounds that are derived from spices and Essential Oils. These materials have anti-inflammatory and antinociceptive properties. Gum Turpentine is an oleoresin obtained from the Pistacia atlantica genus. It has a long history of traditional medicine in Iran. Hydrogels are three-dimensional, cross-linked networks and water-soluble. They used as carriers for controlled drug release systems for delivery their drug in control inflation. The aim of this study was to evaluate the antinociceptive effect of gum turpentine hydrogel using the hot plate test\nMaterials and methods: PH sensitive hydrogel produced by chitosan deviation and open ring Poly-vinylpyrrolidon (OR-PVP). Turpentine gum & olive oil were purchased at a specific grade. After obtaining hydrogel physicochemical characteristics of Gum turpentine, Antinociceptive effect on mice was investigated by Hot plate test. Statistical analysis was performed by ANOVA and T-test. Turpentine gum compounds were identified by Gas chromatography & GC MS.\nResults: Result showed the best Antinociceptive effect of the formulation is OR-PVP with chitosan equal to 0.4. Formulation with 30% Turpentine gum had a good Antinociceptive effect. Chromatographic studies showed that the main compound among terpenoides was α-pinene followed by β-pinene, 3-carene, limonen, myrtenol, with different amounts in oleoresin.\nConclusions: This study showed that the ability of the turpentine gum hydrogel is a good product for release oleoresins, because reduce inflammation and pain.

Wind energy is one of the renewable energy sources which minimize the cost of electricity production. In this paper, a hybrid approach of twin support vector regression and particle swarm optimization (PSO) called PSO-TSVR is presented to forecast wind speed. In order to evaluate the performance of the proposed approach, this paper uses three wind speed data of OpenEI as a case study. The results distinctly show that for this specific datasets, the proposed PSO-TSVR approach has achieved much better forecast performance than the basic TSVR and the genetic algorithm (GA).