European Union Accession and Migrant Smuggling in Serbia

On February 6th, 2018, the European Commission adopted an EU Western Balkans Strategy for accession by 2025. Serbia was named the front runner for membership based on the accession conditions that are required to be completed. The strategy is controversial because the region is struggling with correct implementation to sustain the Union’s values of democracy, rule of law and human rights. With the recent migration crisis, routes through Greece, Former Yugoslavia Republic of Macedonia and Serbia are being frequently used to reach EU member states. The goal for migrants is not to settle in the Western Balkans but use the pathway to gain access to the European Union. From 2009 to 2017, this has opened up a highly profitable market for organized crime networks that already had problems in the region. When analyzing chapters 23 & 24 of the EU’s accession criteria, the country of Serbia as a front runner raises concerns with the problem of migrant smuggling. The smuggling networks are being used by migrants for their expert knowledge of the region and ways around migration policies. There are reports by the United Nations High Commissioner for Refugees and several non-governmental organizations on human rights abuses during the smuggling process. The networks have created a fully functioning illegal business that brings in millions of dollars each year; this is not acceptable for the European Union. If Serbia is accepted into the EU, this will dangerously set a new standard for membership and possibly develop future problems for the Union

The effect of copper sulfate and zinc oxide in a drench on the gain and health of newly arrived calves

One hundred and fifty-four, newly arrived, bull calves averaging 295 lb were either drenched with a copper-zinc (Cu-Zn) solution or water at arrival. The Cu-Zn drench did not affect gains during a 56-day trial. Additionally, no differences occurred in morbidity or the number of antibiotic treatments required per animal

En studie av karakteristikker som gjør produktomtaler hjelpsomme

Online product reviews are an important source of information that facilitates the consumer in the purchase decision process. This study investigates the correlation between three review characteristics and the perceived helpfulness of online reviews. These variables are founded in the theoretical background of information economics. Drawing on the theoretical foundation of information economics these variables are then tested by the product types provided from this theory, namely search goods and experience goods. An analysis of 120 reviews from three different website across four products indicated that the most significant correlation existed between helpfulness and review length. Review timeliness proved to have an inconsequential effect on helpfulness, while the effect of star rating was dependent on product type. Correlations are then discussed in greater detail, after which a theoretical and practical implications are mentioned. Lastly limitations and future research directions are evaluated and suggested.Online product reviews are an important source of information that facilitates the consumer in the purchase decision process. This study investigates the correlation between three review characteristics and the perceived helpfulness of online reviews. These variables are founded in the theoretical background of information economics. Drawing on the theoretical foundation of information economics these variables are then tested by the product types provided from this theory, namely search goods and experience goods. An analysis of 120 reviews from three different website across four products indicated that the most significant correlation existed between helpfulness and review length. Review timeliness proved to have an inconsequential effect on helpfulness, while the effect of star rating was dependent on product type. Correlations are then discussed in greater detail, after which a theoretical and practical implications are mentioned. Lastly limitations and future research directions are evaluated and suggested

The effect of protected lysine-methionine on gain and health of newly arrived calves

Long-hauled calves averaging 293 lb were allotted to groups fed with or without protected lysine-methionine (Smartamine ML®). Protected lysine-methionin e did not improve ADG in the first 28 days but did improve ADG from 29 to 56 days. It also reduced morbidity (16.1 vs 34.2%) from day 29 to 56. Based on this research, the response of long-hauled calves to protected lysine-methionine in the diet appears to occur after they have recovered from the stress of shipment

Effect of cooked molasses tubs on performance and health of newly received stocker calves

Eight paired comparisons conducted at three field sites with 1059 newly-received lightweight stocker calves were used to determine the effect of free-choice cooked molasses tubs designed for receiving cattle on 28-day receiving period performance, percentage of cattle treated for respiratory disease, and death loss. At all sites, cattle received similar management with the exception that cooked molasses tubs were added to half of the pens immediately following initial processing. Weight gains were similar (P=0.36) for cattle with or without access to tubs (43 and 38 lb, respectively). The addition of tubs also did not affect the number of cattle treated (P=0.48) or percent death loss (P=0.61); however, there was a numerical decrease in death loss for cattle with access to tubs (2.7 vs 1.8%). Tub consumption (0.245 lb/day) based on beginning and ending weights of the tubs, was below the desired level of 0.5 lb/day. Low tub consumption may have compromised any potential for improved performance or overall health response for cattle offered free access to cooked molasses tubs

EP50

Gerald L. Stokka and Thomas R. Falkner, Preventive herd health program, Kansas State University, November 1998

En Introduksjon til Kunstig Syn i Autonom Kjøring

Autonom kjøring er en av de fremtredende teknologiene i dagens samfunn. Et bredt spekter av applikasjoner bruker derfor denne teknologien for fordelene den gir. For eksempel vil en autonom kjørende robot frigjøre arbeidskraft og øke produktiviteten i bransjer som krever rask transport. For å oppnå disse fordelene krever det imidlertid utvikling av pålitelig og nøyaktig programvare og algoritmer som skal implementeres i disse autonome kjøresytemene. Ettersom dette feltet har vokst gjennom årene, har forskjellige selskaper implementert denne teknologien med stor suksess. Dermed gjør det økte fokuset på autonom kjøre teknologi dette til et aktuelt tema å forske på. Siden utvikling av et autonomt kjøresystem er et krevende tema, fokuserer dette prosjektet kun på hvordan kunstig syn kan brukes i autonome kjøresystemer. Først og fremst utvikles en kunstig syns basert programvare for autonom kjøring. Programvaren er først implementert på et lite forhåndslaget kjøretøy i bok størrelse. Dette systemet brukes deretter til å teste programvarens funksjonalitet. Autonome kjørefunksjoner som fungerer tilfredsstillende på det lille test kjøretøyet blir også testet på et større kjøretøy for å se om programvaren fungerer for andre systemer. Videre er den en utviklede programvaren begrenset til enkelte autonome kjørehandlinger. Dette inkluderer handlinger som å stoppe når en hindring eller et stoppskilt er oppdaget, kjøring på en enkel vei og parkering. Selv om dette bare er noen få autonome kjøre funksjoner, er de grunnleggende operasjoner som kan gjøre det autonome kjøresystemet allerede anvendelig for forskjellige brukstilfeller. Ulike kunstig syn metode for gjenstands deteksjon har blitt implementert for å oppdage ulike typer gjenstander som hindringer og skilt for å bestemme kjøretøyets miljø. Programvaren inkluderer også bruk av en linje deteksjonsmetode for å oppdage vei- og parkerings linjer som brukes til å sentrere og parkere kjøretøyet. Dessuten skapes et fuglebilde av den fysiske verden fra kamera bilder som skal brukes som et miljøkart for å planlegge den mest optimale rute i forskjellige scenarier. Til slutt blir disse implementeringene kombinert for å bygge kjørelogikken til kjøretøyet, noe som gjør det i stand til å utføre kjørehandlingene nevnt i forrige avsnitt. Ved bruk av den utviklede programvaren for kjøreoppgave, deteksjon av hindringer, viste resultatet at selv om de faktiske hindringene ble oppdaget, var det scenarier der blokkader ble oppdaget selv om det ikke var noen. På den annen side var den utviklede funksjonen med å stoppe når et stoppskilt blir oppdaget svært nøyaktig og pålitelig ettersom den utførte som forventet. Når det gjelder de resterende to implementerte handlingene, sentrering og parkering av kjøretøyet, slet systemet med å oppnå et lovende resultat. Til tross for det viste de fysiske valideringstestene uten bruk av kjøretøymodell positive resultater, men med mindre avvik fra ønsket resultat. Samlet sett har programvaren potensial for å bli anvendelig i mer krevende scenarier, men det er behov for videre utvikling for å fikse noen problemområder først.Autonomous driving is one of the rising technology in today's society. Thus, a wide range of applications uses this technology for the benefits it yields. For instance, an autonomous driving robot will free up the labor force and increase productivity in industries that require rapid transportation. However, to gain these benefits, it requires the development of reliable and accurate software and algorithms to be implemented in these autonomous driving systems. As this field has been growing over the years, different companies have implemented this technology with great success. Thus, the increased focus on autonomous driving technology makes this a relevant topic to perform research on. As developing an autonomous driving system is a demanding topic, this project focuses solely on how computer vision can be used in autonomous driving systems. First and foremost, a computer-vision based autonomous driving software is developed. The software is first implemented on a small premade book-size vehicle. This system is then used to test the software's functionality. Autonomous driving functions that perform satisfactorily on the small test vehicle are also tested on a larger vehicle to see if the software works for other systems. Furthermore, the developed software is limited to some autonomous driving actions. This includes actions such as stopping when a hindrance or a stop sign is detected, driving on a simple road, and parking. Although these are only a few autonomous driving actions, they are fundamental operations that can make the autonomous driving system already applicable to different use cases. Different computer vision methods for object detection have been implemented for detecting different types of objects such as hindrances and signs to determine the vehicle's environment. The software also includes the usage of a line detection method for detecting road and parking lines that are used for centering and parking the vehicle. Moreover, a bird-view of the physical world is created from the camera output to be used as an environment map to plan the most optimal path in different scenarios. Finally, these implementations are combined to build the driving logic of the vehicle, making it able to perform the driving actions mentioned in the previous paragraph. When utilizing the developed software for the driving task, hindrance detection, the result showed that although the actual hindrances were detected, there were scenarios where blockades were detected even though there were none. On the other hand, the developed function of stopping when a stop sign is detected was highly accurate and reliable as it performed as expected. With regard to the remaining two implemented actions, centering and parking the vehicle, the system struggled to achieve a promising result. Despite that, the physical validation tests without the use of a vehicle model showed positive outcomes although with minor deviation from the desired result. Overall, the software showed potential to be developed even further to be applicable in more demanding scenarios, however, the current issues must be addressed first

En Introduksjon til Kunstig Syn i Autonom Kjøring

Autonomous driving is one of the rising technology in today’s society. Thus, a wide range of applications uses this technology for the benefits it yields. For instance, an autonomous driving robot will free up the labor force and increase productivity in industries that require rapid transportation. However, to gain these benefits, it requires the development of reliable and accurate software and algorithms to be implemented in these autonomous driving systems. As this field has been growing over the years, different companies have implemented this technology with great success. Thus, the increased focus on autonomous driving technology makes this a relevant topic to perform research on. As developing an autonomous driving system is a demanding topic, this project focuses solely on how computer vision can be used in autonomous driving systems. First and foremost, a computer-vision based autonomous driving software is developed. The software is first imple- mented on a small premade book-size vehicle. This system is then used to test the software’s functionality. Autonomous driving functions that perform satisfactorily on the small test vehicle are also tested on a larger vehicle to see if the software works for other systems. Furthermore, the developed software is limited to some autonomous driving actions. This includes actions such as stopping when a hindrance or a stop sign is detected, driving on a simple road, and parking. Although these are only a few autonomous driving actions, they are fundamental operations that can make the autonomous driving system already applicable to different use cases. Different computer vision methods for object detection have been implemented for detecting different types of objects such as hindrances and signs to determine the vehicle’s environment. The software also includes the usage of a line detection method for detecting road and parking lines that are used for centering and parking the vehicle. Moreover, a bird-view of the physical world is created from the camera output to be used as an environment map to plan the most optimal path in different scenarios. Finally, these implementations are combined to build the driving logic of the vehicle, making it able to perform the driving actions mentioned in the previous paragraph. When utilizing the developed software for the driving task, hindrance detection, the result showed that although the actual hindrances were detected, there were scenarios where block- ades were detected even though there were none. On the other hand, the developed function of stopping when a stop sign is detected was highly accurate and reliable as it performed as expected. With regard to the remaining two implemented actions, centering and parking the vehicle, the system struggled to achieve a promising result. Despite that, the physical validation tests without the use of a vehicle model showed positive outcomes although with minor deviation from the desired result. Overall, the software showed potential to be developed even further to be applicable in more demanding scenarios, however, the current issues must be addressed first