Search

Copyright of Boletin de Geologia is the property of Universidad Industrial de Santander and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Revista Interamericana de Investigación, Educación y Pedagogía - RIIEP is the property of Universidad Santo Tomas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Counting cells in a Neubauer chamber on microbiological culture plates is a laborious task that depends on technical experience. As a result, efforts have been made to advance computer vision-based approaches, increasing efficiency and reliability through quantitative analysis of microorganisms and calculation of their characteristics, biomass concentration, and biological activity. However, variability that still persists in these processes poses a challenge that is yet to be overcome. In this work, we propose a solution adopting a YOLOv5 network model for automatic cell recognition and counting in a case study for laboratory cell detection using images from a CytoSMART Exact FL microscope. In this context, a dataset of 21 expert-labeled cell images was created, along with an extra Sperm DetectionV dataset of 1024 images for transfer learning. The dataset was trained using the pretrained YOLOv5 algorithm with the Sperm DetectionV database. A laboratory test was also performed to confirm result's viability. Compared to YOLOv4, the current YOLOv5 model had accuracy, precision, recall, and F1 scores of 92%, 84%, 91%, and 87%, respectively. The YOLOv5 algorithm was also used for cell counting and compared to the current segmentation-based U-Net and OpenCV model that has been implemented. In conclusion, the proposed model successfully recognizes and counts the different types of cells present in the laboratory. [ABSTRACT FROM AUTHOR]

Copyright of Lecturas de Economia is the property of Universidad de Antioquia, Facultad de Ciencias Economicas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Visión Electrónica is the property of Fondo de Universidad Distrital Francisco Jose de Caldas and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction. Sauce and conserve industries are a part of the food sector. They elaborate, transform, prepare, conserve and pack human consumption food, in this case pineapple sauces, pulps and marmalades, from agricultural raw materials 1-2. The solid organic waste from this industrial sector's productive activity must be treated in a proper way, in order to reduce its negative environmental impact. Objective. The implementation of an integral solid waste management plan in an agricultural industry of sauce and conserve production. Materials and methods. An internal plan for the integral management of solid waste was proposed, including campaigns of waste reduction in the sources, with flyers with information of consciousness and training. Besides, colorful bins were installed in order to incentivize the classification of materials. The possibilities of commercialization of the recyclable materials obtained were evaluated and, finally, a better model for the disposal of pineapple peel was proposed. This is an organic remain generated in the cut process and it was usually sent to the landfills. Results. The plan was implemented in the company, creating a new environmental consciousness. A better use of the organic waste was achieved by sending them to a worm culture. Besides, along with the efficient separation of materials, a monthly earning of COL $ 176.920 was obtained. Conclusion. A reduction of negative environmental impacts was achieved. Besides, the plan brought new economic benefits for the company and the first steps were taken for executing cleaner production policies and management systems which can be useful for more certifications in the future. [ABSTRACT FROM AUTHOR]