Identification of enterococcus species isolated from commercial fish feeds and infected fish specimens

The genus of lactic acid bacteria Enterococci are Gram-positive cocci bacterium that can survive in different environmental conditions such as water, plants, and soil. They are also bacteriological signs of fecal contamination. In aquaculture facilities, Enterococcus species have appeared as one of the crucial opportunistic fish pathogens. Enterococcus-caused fish diseases have been reported in different fish species like yellow tail, turbot, and tilapia. Even though Enterococcus species are used as probiotics and are members of the gastrointestinal flora, they also have pathogenic potential to produce septicemia, wound infections, urinary tract infections, and others. In this study, we isolated bacterial strains from affected rainbow trout and trout feed specimens. Based on the API 20 strep test kit, they were determined as Enterococcus faecium. While fish-isolated samples had 74.4%-99.9% similarity to E. faecium, trout feed isolated samples had 98.4%-99.9% similarity to E. faecium. In order to identify the isolates of the trout feed, PCR was performed using universal 16S rRNA primers. Sequence results indicate that the samples were E. faecium and E. faecalis. The phylogenetic tree was constructed with other Enterococcus species of 16S rRNA, and our samples were located in the E. faecium and E. faecalis species. In conclusion, there may be contamination of Enterococcus with food or other factors. Enterococcus sp. strains are opportunistic microorganisms and cause pathogenicity when the host immunity weakens. Even though all samples with API 20 strep test kit were identified as E. faecium, they had the lower percentage similarity, so they may be E. faecalis and other Enterococcus species. Thus, further studies are needed to understand their probiotic and pathogenicity functions in aquaculture production

Assessment of Household Food Security and Coping Strategies in Wolaita Zone: The Case of Sodo Zuria Woreda

This study attempts to assess household food security and local coping strategies of rural farm households in Sodo Zuria Woreda, Wolaita Zone. Data were collected from 150 sample farm households from six peasant administration (PAs) using systematic random sampling techniques. Primary data were collected by conducting a household survey. In addition, focus group discussions and key informant interviews were used. Secondary data were collected from various sources. The data were analyzed using descriptive statistics such as mean, minimum and maximum, standard deviation, percentage and frequency distribution.   Moreover, T-test and chi-square tests were used to describe characteristic of food secure and insecure households. In general Sodo Zuria Woreda, suffer from chronic food insecurity.  From the total sample households about 72 % are food insecure while the rest 28% are food secure. More than 80% of the respondents face serious food shortage for six to eight months a year. The result revealed   that factors associated with size of farm land, number of livestock and draught oxen, off-farm and non-farm incomes,   dependency ratio, educational level of household head, and uses of agricultural inputs are significantly related to household food security. Copings strategies including reducing size and number of meals, borrowing grains or cash from relatives and friends, engaging in daily labor, sale of livestock and  household  equipment,  begging,  withdrawing  children  from  school  and  seasonal migration were found to be common practices prevailed in the region. Thus, Distribution of moisture  stress  tolerant  crop  varieties  and  improved  technologies  that  increase  the productivity of land and livestock should be given higher priority to enhance sustainable food security in the region.  It is also crucial to promote intensive agriculture   and non-farm activities, as well as strengthening credit institutions   to boost agricultural production and income, and thereby attain   improved   food security. Keywords: Food security, food insecurity, livelihood, coping mechanis

Three dimensional modeling via photographs for documentation of a village bath

24th International CIPA Symposium; Strasbourg; France; 2 September 2013 through 6 September 2013The aim of this study is supporting the conceptual discussions of architectural restoration with three dimensional modeling of monuments based on photogrammetric survey. In this study, a 16th century village bath in Ulamiş, Seferihisar, and Izmir is modeled for documentation. Ulamiş is one of the historical villages within which Turkish population first settled in the region of Seferihisar - Urla. The methodology was tested on an antique monument; a bath with a cubical form. Within the limits of this study, only the exterior of the bath was modeled. The presentation scale for the bath was determined as 1 / 50, considering the necessities of designing structural interventions and architectural ones within the scope of a restoration project. The three dimensional model produced is a realistic document presenting the present situation of the ruin. Traditional plan, elevation and perspective drawings may be produced from the model, in addition to the realistic textured renderings and wireframe representations. The model developed in this study provides opportunity for presenting photorealistic details of historical morphologies in scale. Compared to conventional drawings, the renders based on the 3d models provide an opportunity for conceiving architectural details such as color, material and texture. From these documents, relatively more detailed restitution hypothesis can be developed and intervention decisions can be taken. Finally, the principles derived from the case study can be used for 3d documentation of historical structures with irregular surfaces

3D registration and integrated segmentation framework for heterogeneous unmanned robotic systems

The paper proposes a novel framework for registering and segmenting 3D point clouds of large-scale natural terrain and complex environments coming from a multisensor heterogeneous robotics system, consisting of unmanned aerial and ground vehicles. This framework involves data acquisition and pre-processing, 3D heterogeneous registration and integrated multi-sensor based segmentation modules. The first module provides robust and accurate homogeneous registrations of 3D environmental models based on sensors' measurements acquired from the ground (UGV) and aerial (UAV) robots. For 3D UGV registration, we proposed a novel local minima escape ICP (LME-ICP) method, which is based on the well known iterative closest point (ICP) algorithm extending it by the introduction of our local minima estimation and local minima escape mechanisms. It did not require any prior known pose estimation information acquired from sensing systems like odometry, global positioning system (GPS), or inertial measurement units (IMU). The 3D UAV registration has been performed using the Structure from Motion (SfM) approach. In order to improve and speed up the process of outliers removal for large-scale outdoor environments, we introduced the Fast Cluster Statistical Outlier Removal (FCSOR) method. This method was used to filter out the noise and to downsample the input data, which will spare computational and memory resources for further processing steps. Then, we co-registered a point cloud acquired from a laser ranger (UGV) and a point cloud generated from images (UAV) generated by the SfM method. The 3D heterogeneous module consists of a semi-automated 3D scan registration system, developed with the aim to overcome the shortcomings of the existing fully automated 3D registration approaches. This semi-automated registration system is based on the novel Scale Invariant Registration Method (SIRM). The SIRM provides the initial scaling between two heterogenous point clouds and provides an adaptive mechanism for tuning the mean scale, based on the difference between two consecutive estimated point clouds' alignment error values. Once aligned, the resulting homogeneous ground-aerial point cloud is further processed by a segmentation module. For this purpose, we have proposed a system for integrated multi-sensor based segmentation of 3D point clouds. This system followed a two steps sequence: ground-object segmentation and color-based region-growing segmentation. The experimental validation of the proposed 3D heterogeneous registration and integrated segmentation framework was performed on large-scale datasets representing unstructured outdoor environments, demonstrating the potential and benefits of the proposed semi-automated 3D registration system in real-world environments

Semi-Automated 3D Registration for Heterogeneous Unmanned Robots Based on Scale Invariant Method

This paper addresses the problem of 3D registration of outdoor environments combining heterogeneous datasets acquired from unmanned aerial (UAV) and ground (UGV) vehicles. In order to solve this problem, we introduced a novel Scale Invariant Registration Method (SIRM) for semi-automated registration of 3D point clouds. The method is capable of coping with an arbitrary scale difference between the point clouds, without any information about their initial position and orientation. Furthermore, the SIRM does not require having a good initial overlap between two heterogeneous datasets. Our method strikes an elegant balance between the existing fully automated 3D registration systems (which often fail in the case of heterogeneous datasets and harsh outdoor environments) and fully manual registration approaches (which are labour-intensive). The experimental validation of the proposed 3D heterogeneous registration system was performed on large-scale datasets representing unstructured and harsh outdoor environments, demonstrating the potential and benefits of the proposed 3D registration system in real-world environments

Fast Iterative 3D Mapping for Large-Scale Outdoor Environments with Local Minima Escape Mechanism

This paper introduces a novel iterative 3D mapping framework for large scale natural terrain and complex environments. The framework is based on an Iterative-Closest-Point (ICP) algorithm and an iterative error minimization mechanism, allowing robust 3D map registration. This was accomplished by performing pairwise scan registrations without any prior known pose estimation information and taking into account the measurement uncertainties due to the 6D coordinates (translation and rotation) deviations in the acquired scans. Since the ICP algorithm does not guarantee to escape from local minima during the mapping, new algorithms for the local minima estimation and local minima escape process were proposed. The proposed framework is validated using large scale field test data sets. The experimental results were compared with those of standard, generalized and non-linear ICP registration methods and the performance evaluation is presented, showing improved performance of the proposed 3D mapping framework

Fast Statistical Outlier Removal Based Method for Large 3D Point Clouds of Outdoor Environments

This paper proposes a very effective method for data handling and preparation of the input 3D scans acquired from laser scanner mounted on the Unmanned Ground Vehicle (UGV). The main objectives are to improve and speed up the process of outliers removal for large-scale outdoor environments. This process is necessary in order to filter out the noise and to downsample the input data which will spare computational and memory resources for further processing steps, such as 3D mapping of rough terrain and unstructured environments. It includes the Voxel-subsampling and Fast Cluster Statistical Outlier Removal (FCSOR) subprocesses. The introduced FCSOR represents an extension on the Statistical Outliers Removal (SOR) method which is effective for both homogeneous and heterogeneous point clouds. This method is evaluated on real data obtained in outdoor environment

A transition process from information systems acceptance to infusion behaviour in online brand communities : a socialization process perspective

Social media such as Facebook, Youtube, Twitter, and online communities plays an important role for knowledge production and diffusion as well as discussions among people. Among social media, online brand communities (OBCs) have recently received attention from both academics and practitioners due to the practical benefits of OBCs for consumers and companies. For consumers, knowledge sharing and its collective activities help them to make purchase decisions and to protect themselves against firms’ monopoly and oligopoly or collusion and anticompetitive actions. For companies, new ideas and feedback on brand products created by OBC members are useful input to develop new products and enhance existing product lines. Therefore, active content generation by community members is one of the critical success factors of OBCs. However, many scholars argue that only a few members who are more devoted to a community are tending to engage in OBC activities and many community members tend to remain in the periphery (sometimes called ‘lurkers') of the community by using OBCs merely for gathering information without any contributions. Therefore, it is important to make members in the periphery of the community transit to the core to increase members’ intentions and ‘devoted members’ to produce more valuable benefits for both consumers and firms. In spite of its importance, the literature is lacking in efforts to explain how and when community members in the periphery transit to the core of the community in a long-term perspective. This study aims to reveal how and why OBC members transit from the periphery to the core of the community and how to increase their intention to use OBC from a long-term perspective. OBC use behaviour is classified into, largely, two categories according to the purposes of an OBC: behaviour with a brand product consumption purpose; and behaviour with a social relationship building purpose. This study classifies OBC members as three clusters by social identity theory: tourists, minglers, and devoted members (devotees and insiders). The devoted members have valuable consumption knowledge of brand and strong social bonds in the OBC and the OBC members become a devoted member by accumulated brand knowledge and experiences through long-term OBC use. Therefore, from a socialisation aspect, this study adopts organisational socialisation theory as the theoretical lens to explain how and why the members evolve from novice members as tourist to devoted members in OBC contexts. Socialisation theories argue that there are usually three sequential stages for a member to gain full membership in a community: pre-entry, accommodation, and affiliation. In addition, this study adopts IS implementation theory to understand OBC user behaviours from an IS use behaviour perspective: acceptance in the pre-entry stage and routinisation in the accommodation stage and infusion in the affiliation stage. By reviewing socialisation theory and IS implementation theory, this study finds four significant motivations, those of information quality, trust, sense of belonging, and brand loyalty for intention of OBC use from the acceptance (pre-entry) to infusion (affiliation) stages. To integrate the socialisation perspective with the IS use perspective, this study adopts a technology acceptance model (TAM) as a theoretical framework to link to motivators in different OBC use behaviour from the acceptance to infusion stages. As a result, this study proposes a conceptual framework to explain the OBC members’ transition process from acceptance (pre-entry) to infusion (affiliation). The aim of this study is to predict and explain the transition of motivators for OBC use from pre-entry to affiliation and how to improve members’ intention of OBC use from a long-term perspective ultimately to foster ‘devoted members’. This study adopts an online survey targeting 518 participants who belong to 17 OBCs in South Korea and the conceptual framework is validated. The results show that all factors (i.e. information quality, trust, sense of belonging, brand loyalty) are significant determinants to increase intention to use OBCs and the factors have a causal relationship with each other to form a transition process from the acceptance (pre-entry) to infusion (affiliation) stages. This study also reveals that brand loyalty has a significant role to explain the transition process and directly influence user intention to use OBCs. The sense of belonging also directly affects members’ intention to use OBCs but has less impact than brand loyalty. In addition, the results indicate that TAM is an appropriate model to predict user behaviours in a long-term perspective to explain the change of OBC use behaviour from the acceptance to infusion stage and confirms that perceived usefulness and perceived ease of use have significant impact on the intention to use OBCs as in other IS studies. Understanding the transition process within OBCs has theoretical and practical implications. Theoretically, it will extend our understanding of how IS end users transit from acceptance behaviour to continued use and extended use of information systems in virtual community contexts. For managers, this study will provide them with insight on how to retain potential consumers in OBCs and facilitate their activities to gain consumer feedback on existing and new products.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Vision-Depth Landmarks and Inertial Fusion for Navigation in Degraded Visual Environments

This paper proposes a method for tight fusion of visual, depth and inertial data in order to extend robotic capabilities for navigation in GPS-denied, poorly illuminated, and texture-less environments. Visual and depth information are fused at the feature detection and descriptor extraction levels to augment one sensing modality with the other. These multimodal features are then further integrated with inertial sensor cues using an extended Kalman filter to estimate the robot pose, sensor bias terms, and landmark positions simultaneously as part of the filter state. As demonstrated through a set of hand-held and Micro Aerial Vehicle experiments, the proposed algorithm is shown to perform reliably in challenging visually-degraded environments using RGB-D information from a lightweight and low-cost sensor and data from an IMU.Comment: 11 pages, 6 figures, Published in International Symposium on Visual Computing (ISVC) 201