The Effect of Supplementary Feeding with Different Pollens in Autumn on Colony Development under Natural Environment and In Vitro Lifespan of Honey Bees

Simple Summary In the present study, the effect of feeding with pollen sources with different protein content on colony performance, wintering ability and in-vitro longevity of colonies that weakened after feeding with pine honey in autumn or that needed to enter the winter period were investigated. The experiment was carried out in 48 colonies divided into six groups as follows: control, syrup, mixed pollen, Cistus creticus pollen (Pink rock-rose), Papaver somniferum pollen (Opium poppy), and commercial bee cake group. The effect of nutritional differences on survival was found to be statistically significant in vitro and this supports the colony results in the natural environment. As a result, P. somniferum pollen is a good preference to be used in feeding colonies in beekeeping, due to its rich nutritional content. Honey bees need pollen and nectar sources to survive in nature. Particularly, having young bees in colonies is vital before wintering, and proper feeding is necessary to achieve this. In the present study, the effect of feeding with pollen sources of different protein content on colony performance, wintering ability and in-vitro longevity of colonies that weakened after feeding with pine honey in autumn, or that needed to enter the winter period, was investigated. The experiment was carried out in 48 colonies divided into six groups as follows: control, syrup, mixed pollen, Cistus creticus pollen (Pink rock-rose), Papaver somniferum pollen (Opium poppy), and commercial bee cake groups. In particular, the P. somniferum pollen group was different (p < 0.01) from the other experiment groups with the number of bee frames (3.44), the area with brood (1184.14 cm(2)) and the wintering ability of 92.19%. The effect of nutritional differences on survival was found to be statistically significant in vitro and this supports the colony results in the natural environment (p < 0.001). The P. somniferum group has the longest longevity with 23 days. Pollen preferences of honey bees were P. somniferum, C. creticus, and mixed pollen, respectively.Pollen Preferences of Honey bees [TAGEM/HAYSUD/B/20/A4/P5/1890]; Turkish Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies (TAGEM)This article was produced from the project The Pollen Preferences of Honey bees and the Effects of Pollen Use inWinter on Colony Dynamic (TAGEM/HAYSUD/B/20/A4/P5/1890) supported by The Turkish Ministry of Agriculture and Forestry, General Directorate of Agricultural Research and Policies (TAGEM)

An unsupervised audio segmentation method using Bayesian information criterion

Audio segmentation is a well-known problem which can be considered from various angles. In the context of this paper, audio segmentation problem is to extract small "homogeneous" pieces of audio in which the content does not change in terms of the present audio events. The proposed method is compared with the well-known segmentation method; Bayesian Information Criterion (BIC) based Divide-and-Conquer, in terms of average segment duration and computational complexity

SPEECH DETECTION ON BROADCAST AUDIO

Speech boundary detection contributes to performance of speech based applications such as speech recognition and speaker recognition. Speech boundary detector implemented in this study works on broadcast audio as a pre-processor module of a keyword spotter. Speech boundary detection is handled in 3 steps. At first step, audio data is segmented into homogeneous regions in an unsupervised manner. After an ACTIVITY/NON-ACTIVITY decision is made for each region, ACTIVITY regions are classified as Speech/Non-speech via Gaussian Mixture Model (GMM) based classification. GMM's are trained using a novel feature, Spectral Flow Direction (SFD), and an improved multi-band harmonicity feature in addition to widely used Mel Frequency Cepstral Coefficients (MFCC's)

Content Based Video Copy Detection with Coarse Features

Content Based Copy Detection is an alternative approach to invisible watermarking for tracking duplicate data. Primary stages are creating a database using the features belonging to the original data and searching query data in terms of its features in this database. Features must be robust against targeted attacks and discriminative enough to distinguish different content. In this work, we propose reducing the precision of feature values to attain robustness and increasing the number and dimension of features to attain discriminativity. To this end, we create a feature database using different features, which correspond to different information sources, together. We detect the original sources of the query videos in this database. which is composed of coarse features, by feature comparison. Effectiveness of the proposed method against various attacks is observed through experiments

Content Based Copy Detection with Coarse Audio-Visual Fingerprints

Content based copy detection (CBCD) emerges as a viable choice against active detection methodology of watermarking. The very first reason is that the media already under circulation cannot be marked and secondly, CBCD inherently can endure various severe attacks, which watermarking cannot. Although in general, media content is handled independently as visual and audio in this work both information sources are utilized in a unified framework, in which coarse representation of fundamental features are employed. From the copy detection perspective, number of attacks on audio content is limited with respect to visual case. Therefore audio, if present, is an indispensable part of a robust video copy detection system. In this study, the validity of this statement is presented through various experiments on a large data set

Multimodal concept detection in broadcast media: KavTan

Concept detection stands as an important problem for efficient indexing and retrieval in large video archives. In this work, the KavTan System, which performs high-level semantic classification in one of the largest TV archives of Turkey, is presented. In this system, concept detection is performed using generalized visual and audio concept detection modules that are supported by video text detection, audio keyword spotting and specialized audio-visual semantic detection components. The performance of the presented framework was assessed objectively over a wide range of semantic concepts (5 high-level, 14 visual, 9 audio, 2 supplementary) by using a significant amount of precisely labeled ground truth data. KavTan System achieves successful high-level concept detection performance in unconstrained TV broadcast by efficiently utilizing multimodal information that is systematically extracted from both spatial and temporal extent of multimedia data