This study aimed to investigate the fecal microbiota of buffaloes of different breeds, ages, and genders reared in Türkiye using metagenomic analysis. For this purpose, a total of 96 stool samples were collected from four farms in Istanbul and Kirklareli, including 48 from buffalo calves and 48 from adult buffaloes. Among these 24 buffalo calves and 24 adult buffaloes belonged to the Italian Mediterranean breed while the remaining 24 of each group were of the Anatolian breed. Additionally, for both the Anatolian and Italian Mediterranean breeds, an equal number of males and females (12 each) were selected. After being transported to the laboratory under appropriate conditions, DNA was extracted from the samples. The samples were poolled into groups of four, resulting in a total of 24 pooled samples. From the extractions, 24 pools of 4 were created according to age and gender. Sequencing was performed using the Oxford Nanopore MinION, followed by bioinformatics analyses. A total of 180 MB of sequencing data was obtained. Based on analysis, 489 operational taxonomic units (OTUs) were identified. Among the groups, the highest number of OTUs (275) was detected in Anatolian buffalo calves younger than 3 months (Group 1). In contrast, the lowest number of OTUs (167) was observed in pregnant females older than two years from the Italian Mediterranean breed. At the phylum level Firmicutes (68.3%), Bacteriodetes (29.4%) and Proteobacteria (1.8%) were found to be the dominant phyla across all groups. The Firmicutes/Bacteriodetes ratio was highest (2.99%) in Anatolian buffalo calves older than three months, while the lowest ratio (1.57%) was recorded in Italian Mediterranean buffalo calves of the same age group. Notably, the Proteobacteria rate reached 9.27% in female Anatolian buffaloes older than two years. At the family level, Ruminoccaceae (47.8%) was the dominant family across all groups. At the genus level, Ruminnococcus (39.6%) was the dominant genus in all groups except one, where Lactobacillus (20.4%) was the most abundant genus in buffalo calves younger than three months that were fed with milk and feed. As a result, microbial colonization data were obtained for both buffaloes breeds in Turkiye. While this study contributes to the limited literature on this subject, further research is needed to support and expend these findings.
The identification of a person by their facial images should be possible in various diverse routes, for example, by capturing an image of the face in the visible range utilizing an economical camera or by utilizing the infrared patterns of facial heat emission. Facial Recognition in visible light ordinarily display key features from the central segment of the facial image utilizing a wide assortment of cameras in visible light framework extricate central from the captured images that don't change after some time while staying away from shallow central, for example, facial appearance or hair. Tremendous demand of face recognition framework being developed present day there are lots of challenges in a biometric framework for personal identification, for access to control of secured areas and different applications like identity validation for social welfare, crime detection, ATM access, computer security, and so on lighting conditions , posture and facial appearance are the real difficulties experiences in face recognition framework. Performance criteria, for example, recognition accuracy, system assumptions, training time, and execution time are the feasibility of a neural system approach. This work brief a literature summary of neural system based face recognition system.
Nowadays, fracture mechanics modelling for strengthening of structural members is a challenging issue for structural engineers. The developed fracture mechanics modelling is applicable for identifying crack propagation in concrete structural members such as beams, walls and beam-column joints. In the present study, a numerical model is developed to simulate crack propagation in beam- column joint strengthened with CFRP. To validate the proposed model, two beam-column joints are made and tested. The proposed model and the experimental results are compared to the model predictions based on conventional fracture models carried out using commercial finite element software (ABAQUS). The results of the CFRP-strengthened beam-column joints by using proposed model show good agreement with the experimental results (8 to 12%), whereas the results from numerical analysis using ABAQUS software are considerably greater than experimental results (21 to 28 %). It was also observed that crack propagation is controlled by the CFRP sheets in the beam. The average decrease is approximately 37% of the crack length of the control beam column joint. The results revealed that cracks formed in the joint area in the control specimen, while extensive cracks appeared in the beam in the specimen strengthened by CFRP.
The dynamic behavior of engineering materials is an important parameter which is greatly affects the performance of these materials. To investigate the dynamic performance of materials, loading -rate effect should be taken in to account. It was found that increasing of loading-rate, the trend of yield stress or dynamic strength of materials is increased, also it was observed that the stress strain versus the common logarithmic of loading-rate may be subdivided to three effective zones. Saturated zone ,weak sensitivity zone and strong sensitivity zone. From results summarized for composite materials, metals, and polymers in this study, it can be found that without taken into account the type of materials, such three zones behavior are exists. In the high strain rates area , the dynamic behavior of materials is characterized by increasing load rate sensitivity, by increasing effects of inertia forces and the adiabatic character of the mechanical deformation
La reconstrucción tridimensional urbana requiere la combinación de datos de diferentes sensores, tales como cámaras, sistemas inerciales, GPS y sensores láser. En este artículo presentamos un sistema completo para la generación de mapas globales (de visión profunda) texturizados. Tambien generamos las superficies que describen los objetos. En detalle describimos el proceso para construir mapas fotorrealistas incluyendo la calibración de los sensores y de la plataforma de adquisición, los pre-procesamientos de las nubes de puntos y de las imágenes, la extracción de la textura, la fusion de datos, el mallado y la eliminación de huecos en las superficies debido a problemas en el mallado, además del análisis de resultados. El algoritmo desarrollado realiza su trabajo en dos etapas: En la primera, se calibran los sensores y se pre-procesan los datos obtenidos por los sensores, por ejemplo se rectifican las imágenes obtenidas por la cámara esférica. En la segunda, se post-procesan los datos de los sensores, por ejemplo extraer la textura de las imágenes y correlacionarla con los datos del LiDAR. Utilizando los métodos que aquí se presentan para la reconstrucción geométrica y fotorrealista para el mapeo de texturas, hemos reconstruido modelos urbanos 3D de gran calidad. Los resultados cumplieron con los objetivos: generar modelos globales texturizados conservando la geometría de las escenas escaneadas, utilizar la información de incertidumbre y sensibilidad para generar mapas globales con alta precisión.
Social media networks like Twitter, Facebook, WhatsApp etc. are most commonly used medium for sharing news, opinions and to stay in touch with peers. Messages on twitter are limited to 140 characters. This led users to create their own novel syntax in tweets to express more in lesser words. Free writing style, use of URLs, markup syntax, inappropriate punctuations, ungrammatical structures, abbreviations etc. makes it harder to mine useful information from them. For each tweet, we can get explicit time stamp, the name of the user, the social network the user belongs to, or even the GPS coordinates if the tweet is created with a GPS-enabled mobile device. With these features, Twitter is, in nature, a good resource for detecting and analyzing the real time events happening around the world. By using the speed and coverage of Twitter, we can detect events, a sequence of important keywords being talked, in a timely manner which can be used in different applications like natural clamity relief support, earthquake relief support, product launches, suspicious activity detection etc. The keyword detection process from Twitter can be seen as a two step process: detection of keyword in the raw text form (words as posted by the users) and keyword normalization process (reforming the users’ unstructured words in the complete meaningful English language words). In this paper a keyword detection technique based upon the graph, spanning tree and Page Rank algorithm is proposed. A text normalization technique based upon Levenshtein distance, demetaphone algorithm and dictionary mapping has also been proposed to work upon the unstructured keywords as produced by the proposed keyword detector. The proposed normalization technique is validated using the standard lexnorm 1.2 dataset. The proposed system is used to detect the keywords from Twiter text being posted at real time. The detected and normalized keywords were further validated from the search engine results at later time for detection of events.
Multiple linear regressions (MLR) play an important role in evaluating the linear relationship between Greenhouse Gases (GHG) and economic variables. This study uses the data of two developing countries (India and Pakistan) for the purpose of investigating and predicting the linear relationship between anthropogenic N2O and economic indicators. MLR analysis tools have been used to investigate the association of anthropogenic N2O with economic indicators of cereal production (metric tons), exports of goods and services (current US$), imports of goods and services (current US$), urban population, and fossil fuel energy consumption. For India model the P-value for f-test and R2 value of model were found to be 1.52658 × 10-35 and 0.9853 respectively whereas for Pakistan model the p-value for f-test and R2 value of model were found to be 8.63078 × 10-19 and 0.9207 respectively which indicated the good fitting of both models. The fossil fuel and cereal production were found to be the most important indicators of N2O emissions for India and Pakistan model respectively. External validation and Mean Absolute Percentage Error (MAPE) proved that results of regression models were much closer to the actual values for both countries.
Now a days the quantity of digital images has grown astronomically, a consequence of the intense use of digital cameras, multimedia services and due to the storefront that the Internet turned into. Besides, in many areas, the use of image analysis has increased. To tackle this rapid growth it is required to develop image retrieval systems which operate on a large scale. The primary aim is to build a robust system that creates, manages and query image databases in an accurate manner. For better indexing and search outcome a content based image retrieval (CBIR) filter images based on their semantic contents (eg. objects, categories, relationships and meaning),. In past numerous content based image retrieval system based on various algorithm like fuzzy based and discrete cosine has been proposed but unreal network based restoration have some advantages over traditional content based algorithm . There has been an incredible measure of research work done in the Content-Based Image Retrieval field, the greater part of which covers issues, for example, Indexing Structures, Feature Representation and User Relevance Feedback. There has not been much examination of the example size and its consequences for the exactness of a retrieval framework.