No description available.

No description available.

Colony Collapse Disorder (CCD) has been a major threat to bee colonies around the world which affects vital human food crop pollination. The decline in bee population can have tragic consequences, for humans as well as the bees and the ecosystem. Bee health has been a cause of urgent concern for farmers and scientists around the world for at least a decade but a specific cause for the phenomenon has yet to be conclusively identified. A normal hive inspection can be very disruptive for the bee colony, as the hive needs to be disassembled to visually assess hive health from the inside by collecting larvae and egg data. This work uses Machine Learning and Computer Vision methodologies to develop techniques to monitor hive health without disrupting the bee colony residing in the hive. Bee traffic refers to the number of bees moving in a given area in front of the hive over a given period of time. Bee traffic is related to forager traffic. Forager traffic is the number of bees moving out of the beehive. Forager traffic is a crucial factor in determining and monitoring food availability, food demand, colony age structure, the impact of pesticides, etc. on beehives. This work focuses on estimating bee traffic levels in a given hive and associate this information with data collected through manual beehive inspections.

Darbo tikslas – ištirti bičių kolonijų produkcines ir reprodukcines savybes, laikant įvairaus dydţio aviliuose. Siekiant šio tikslo bus iškelti sekantys uţdaviniai: 1. Įtakoti bičių kolonijų pavasarinio apsiskraidymo laiką; 2. Įvertinti bičių svorį po pavasarinio apsiskraidymo; 3. Nustatyti avilio tipo įtaką bičių šeimų ţiemojimui bei jų fiziologinei būklei; 4. Nustatyti traninių perų pasirodymo laiką bičių kolonijoje; 5. Nustatyti Varroa destructor erkių įtaką tranų lervučių masei; 6. Nustatyti avilio talpumo įtaką motinių lopšelių skaičiui bei bičių produkcijai. Buvo stebimas bičių kolonijų pavasarinis apsiskraidymas, sveriamos bitės po pavasarinio apsiskraidymo, stebimas aptūptų bitėmis korių skaičius 2010–2011 metais. Apţiūrimi pirmieji traniniai perai bičių kolonijoje, sveriant tranų lervas nustatinėjama erkių Varroa destructor įtaka jų masei, įvertinama bičių kolonijų fiziologinė būklė. Taip pat registruojami motininiai lopšeliai bičių šeimose ir medaus kopimo metu atliekama išsukto medaus apskaita. Bičių apsiskraidymas vyko dviem etapais, pirmą kartą apsiskraidė 4 bičių šeimos, o antra likusios 12 šeimų. Ištyrus bičių svorį po pavasarinio apsiskraidymo paaiškėjo, kad iš 60 % tirtų bičių svoris yra 0,11 g, o likusių bičių svoris maţesnis. Nustatant avilio įtaką bičių šeimų ţiemojimui 2010–2011 metais, buvo nustatyta, kad 27 korių aviliuose bičių šeimos yra stipriausios. Apţiūrint bičių kolonijas, iš 16 bičių šeimų, 4 šeimos buvo su pirmaisiais traniniais perais... [toliau žr. visą tekstą] / The aim – to investigate bee colony productive and reproductive characteristics. To achieve this objective will be to raise the following goals: 1. Influence first fly colonies in spring time; 2. To evaluate the weight of the bees in the spring after first fly; 3. Identify type of impact on the hive of bees and their families in winter physiological condition; 4. Set the time of the appearance of peers traninių bee colony; 5. Determine the impact of Varroa destructor mites in drone larvae masses; 6. Set the capacity of the hive mother influence the number of nursery and bee products. Colonies were observed in spring first flight, weighed after spring first flight bees, bees, combs sits observed number 2010–2011 years. Also examined the first drones peers bee colony, weighing the drone larvae of identical mite Varroa destructor influence their weight, assess the physiological state of bee colonies. The nursery also recorded maternal families of honey bees climbing in done unfolded honey accounting. First flight bees gone through two phases, the first flight four colonies, and the second the remaining 12 families. Examination of the weight of the bee first fly spring showed that 60% of tested bees weighing 0.1138 g and the weight of the remaining bees. In determining the impact of the hive bee families influence winter 2010–2011, has been found that 27 hives of bees honeycomb is the strongest of the family. Survey of bee colonies in 16 hives, four families were the first drone... [to full text]

No description available.

No description available.

The small hive beetle (Aethina tumida) is an invasive pest from sub-Saharan Africa that has posed increasing threats to European honey bee (Apis mellifera) colonies in the United States over the past two decades. While control has been attempted, consistently effective management strategies still not been developed. This study sought to explore novel experimental methods to better understand and use A. tumida biology to target this pest. One aspect of A. tumida biology that has emerged as potential basis for improved control is olfactory manipulation, which could be used to disrupt beetles as they seek out A. mellifera colonies. Through olfactometry and electroantennography, key volatiles in A. tumida attraction and repulsion were tested and sensitivity of A. tumida to several attractants and repellents was quantified on behavioral and physiological levels. An additional source of attractive volatiles is the A. tumida fungal symbiont Kodamaea ohmeri, which ferments larval waste and is present throughout the A. tumida lifecycle, both externally and in the GI tract. This study explored the development of feeding and soil bioassays to test the effects of several insecticides on A. tumida larvae. Feeding and injection bioassays were also used to deliver a fungicide with the goal of repressing K. ohmeri, which was expected to detrimentally impact A. tumida health. The results of this work enhance our current knowledge or A. tumida biology and provide a useful basis for development of safe and selective management A. tumida management options for the future. / Doctor of Philosophy / The small hive beetle is an invasive European honey bee pest that poses a significant threat to apiaries in the United States. These beetles feed on hive products and brood, pollute the hive with fermenting waste, and, in severe infestations, cause colonies to abandon their hives. This project investigated previously unexplored control options that take small hive beetle biology into account. Small hive beetles have an exceptional sense of smell compared to other beetles, and this ability helps them to locate honey bee hives. Therefore, behavioral responses to attractants and repellants were tested through olfactometry, in which beetles were given a choice to travel toward or away from specific odors or odor blends. Responses to these odors on a physiological level were also quantified through electrical recordings of beetle antennae. Small hive beetles are also known to have a yeast-like symbiont, which is present throughout the small hive beetle lifecycle, both externally and internally. Feeding bioassasy for small hive beetle larvae, along with soil bioassays for the delivery of insecticides were also developed and used to test several compounds against small hive beetle larvae. Feeding bioassays were also used to deliver a fungicide to larval and adult beetles with the goal of repressing internal fungal activity The results of these studies help expand the knowledge of small hive beetle olfaction and provide a background for the development of novel control options to effectively manage this destructive pest.

Computer forensics has become an important technology in providing evidence in investigations of computer misuse, attacks against computer systems and more traditional crimes like money laundering and fraud where digital devices are involved. Investigators frequently perform preliminary analysis at the crime scene on suspects‟ devices to determine the existence of any inappropriate materials such as child pornography on them and conduct further analysis after the seizure of computers to glean leads or valuable evidence. Hence, it is crucial to design a tool which is portable and can perform efficient instant analysis. Many tools have been developed for this purpose, such as Computer Online Forensic Evidence Extractor (COFEE), but unfortunately, they become ineffective in cases where forensic data has been removed. In this thesis, we design a portable forensic tool which can be used to compliment COFEE for preliminary screening to analyze unallocated disk space by adopting a space efficient data structure of fingerprint hash tables for storing the massive forensic data from law enforcement databases in a flash drive and utilizing hash tree indexing for fast searching. We also apply group testing to identify the fragmentation point of the file and locate the starting cluster of each fragment based on statistics on the gap between the fragments. Furthermore, in order to retrieve evidence and clues from unallocated space by recovering deleted files, a file structure based carving algorithm for Windows registry hive files is presented based on their internal structure and unique patterns of storage. / UOIT

Data is everywhere. The current Technological advancements in Digital, Social media and the ease at which the availability of different application services to interact with variety of systems are causing to generate tremendous volumes of data. Due to such varied services, Data format is now not restricted to only structure type like text but can generate unstructured content like social media data, videos and images etc. The generated Data is of no use unless been stored and analyzed to derive some Value. Traditional Database systems comes with limitations on the type of data format schema, access rates and storage sizes etc. Hadoop is an Apache open source distributed framework that support storing huge datasets of different formatted data reliably on its file system named Hadoop File System (HDFS) and to process the data stored on HDFS using MapReduce programming model. This thesis study is about building a Data Architecture using Hadoop and its related open source distributed frameworks to support a Data flow pipeline on a low commodity hardware. The Data flow components are, sourcing data, storage management on HDFS and data access layer. This study also discuss about a use case to utilize the architecture components. Sqoop, a framework to ingest the structured data from database onto Hadoop and Flume is used to ingest the semi-structured Twitter streaming json data on to HDFS for analysis. The data sourced using Sqoop and Flume have been analyzed using Hive for SQL like analytics and at a higher level of data access layer, Hadoop has been compared with an in memory computing system using Spark. Significant differences in query execution performances have been analyzed when working with Hadoop and Spark frameworks. This integration helps for ingesting huge Volumes of streaming json Variety data to derive better Value based analytics using Hive and Spark.

The aim of the work is an analysis influence of years and altitude above sea levels on the honey production of sample localitys in different altitude above sea levels. For observation of the years 2002{--}2006 was choice five localitys in different altitude above sea levels (472 {--} 650 m). The interaction sea level and year is a determine factor wich influence honey production of one bee colony from 89.77%. On increase sea level about 100 m is speeding of the honey production about 5 kg of one bee colony. Than the direct influence one factor (altitude above sea level, year) is strikinger on the honey production their interaction.