Technologies to improve efficiency

How to do it better is a question that holds a special interest for the human being. It is not enough to carry out a task, it is necessary to carry it out in an optimal way. Technology has played an important role in this search, generating tools to support the operations, analyze the data and automate processes. Technological development has permeated almost all areas of work, from the medical field, through the industrial and commercial sectors and even in the social sciences, seeking to improve current processes or creating new strategies. In the industry, optimization is focused on saving time, resources, and costs. Historically the major evolutions in the industry have all been linked to technological change. The accelerated and growing changes in technology have ushered in the fourth revolution. Industry 4.0 is the digital transformation of manufacturing, production, and the industrial value chain [1]. In the business world to find the best technology solution, managers are thinking of introducing cloud computing, mobile technology, and computer networking to improve efficiency. Information technologies allow building business processes in a new way, namely, they provide automation of the working space of employees and business processes, reduction of production and service times, connectivity between employees and customers, inventory management, and others [2]. Concerning the management of quality control, the introduction of information technology increases the effectiveness of the information process of all stakeholders in the field of quality assurance. In the environmental field, optimization is therefore very critical since this relates to environmental deterioration, economic expenditure, and human health. Green technology innovation efficiency (GTIE) reflects the efficiency of an industry’s use of resources in the green technology innovation process [3]. Studies have demonstrated that the efficiency of global renewable energy generation (REG) is improving [4]. REG inputs are defined as the installed capacity of solar energy, wind power, geothermal energy, and bio-fuel production and define electricity from renewable energy as an output indicator. Then, the optimization, in this case, consists in maximize the electric energy with adequate use of renewable resources. The application of emerging health technologies and digital practices in health care, such as artificial intelligence, telemedicine or telehealth, mobile health, big data, 5G, and the Internet of Things, have become powerful “weapons” to fight against the pandemic and provide strong support in pandemic prevention and control [5]. Likewise, advances in technologies have allowed the development of new treatments and drugs, the analysis of the human body from cell level to the physiological level, and the access to health services has improved. Technological development has become a fundamental tool to improve efficiency in the processes implemented in different areas. It is important to preserve a balance in the elements that are being optimized, such as time, energy, precision, and speed, to get the best out of them. Researches in electronic and computational sciences are making a continuous effort to achieve greater efficiency with optimization of resources

Green Tech: developing a new future

Environmental care is a topic that has aroused special interest in the process of technological development. There is a great concern about the environmental impact of the non-sustainable projects and the interest in green research is growing to create a resilient global society. It is contradictory that while the impact of technology and industrialization has been highly negative on the environment, green technology has contributed to saving the planet. Green technology includes developments and research to protect the environment, improving the performance of the technology in a “green” manner, restoring the damage to the Earth, and optimizing processes conducive to environmental care.El cuidado del medio ambiente es un tema que ha despertado especial interés en el proceso de desarrollo tecnológico. Existe una gran preocupación por el impacto ambiental de los proyectos no sostenibles y el interés en la investigación verde está creciendo para crear una sociedad global resiliente. Es contradictorio que mientras el impacto de la tecnología y la industrialización ha sido muy negativo sobre el medio ambiente, la tecnología verde ha contribuido a salvar el planeta. La tecnología verde incluye desarrollos e investigaciones para proteger el medio ambiente, mejorando el desempeño de la tecnología de manera “verde”, restaurando el daño a la Tierra y optimizando procesos conducentes al cuidado del medio ambiente

Percepción de la consejería de planificación familiar en usuarias atendidas en el Centro de Salud San Sebastián, en el periodo febrero 2017

Identifica la percepción de la consejería de planificación familiar en usuarias atendidas en el centro de salud San Sebastián. Estudio de tipo observacional, transversal y descriptivo con enfoque cuantitativo. De las 319 usuarias del servicio de Planificación Familiar se toman como muestra a 188, a los cuáles se les aplica un cuestionario según Servqual modificado y el Manual de Orientación y Consejería en Salud Sexual y Reproductiva. Los datos son ingresados al programa Spss v.22, para luego hacer un análisis exploratorio, utilizando medidas de tendencia central y dispersión (media y desviación estándar) para las variables cuantitativas, así como frecuencias absolutas y porcentajes para las variables cualitativas. A través de los resultados se observa que la percepción de la consejería de planificación de las usuarias atendidas en el centro de salud San Sebastián durante el periodo de febrero en el año 2017 fue buena en un 81%.Tesi

Una metodología basada en espiral aplicada al análisis de células en una imagen

The advances in technology, microscopy and computing have allowed the development of new fields in cell image analysis. However, the usability of these platforms is adequate to expert users only. Many software tools are oriented to expert users in image processing, likewise the use of bioinformatics require a basic knowledge in programming. The development of research in cell imaging requires the joint work of computer Scientifics and biologist. In this paper we present a methodology to develop a software solution applied to the analysis of cell images.Los avances en tecnología, microscopía y computación han permitido el desarrollo de nuevos campos en el análisis de imágenes celulares. Sin embargo, la usabilidad de estas plataformas es adecuada solo para usuarios expertos. Muchas herramientas software están orientadas a usuarios expertos en el procesamiento de imágenes y así mismo el uso de herramientas bioinformáticas requiere un conocimiento básico en programación. El desarrollo de investigaciones en imágenes celulares requiere el trabajo conjunto de biólogos y de expertos en computación. En este artículo se presenta una metodología para desarrollar una solución de software aplicada al análisis de imágenes celulares

Spiral-Based model for software architecture in bio-image analysis: A case study in RSV cell infection

The advancement in biological and medical image acquisitions has allowed the development of numerous investigations in different fields supported by image analysis, from cell to physiological level. The complexity in the treatment of data, generated by image analysis, requires a structured methodology for software development. In this paper we proposed a framework to develop a software solution with a Service-Oriented Architecture (SOA) applied to the analysis of biological images. The framework is completed with a novel image analysis methodology that would help researchers to achieve better results in their image analysis projects. We evaluate our proposal in a scientific project related to cell image analysis

Las percepciones del IGV, en las Bodegas Acogidas al Nuevo RUS del Distrito de San Sebastián, Cusco, 2021

El informe de investigación sobre Las Percepciones del IGV, en las Bodegas Acogidas al Nuevo RUS, tiene como objetivo general analizar en qué consiste las percepciones del IGV en el Nuevo RUS del distrito de San Sebastián, la aplicación de la normatividad tributaria y el cumplimiento de la misma, con el objetivo de extender el entendimiento de la realidad no experimental, porque describe el proceso de las percepciones del IGV en los bodegueros del Distrito de San Sebastián acogidos al nuevo RUS, donde se obtendrá información relevante para el conocimiento del problema ubicada en el lugar de la realidad, la metodología es de tipo descriptiva simple, con diseño no experimental y transversal, la acción es cuantitativo, donde se utilizará información recopilada en el periodo 2021, para la reunir los datos se usó la técnica de la encuesta mediante el instrumento del cuestionario, con una población de muestra de 50 bodegueros, el procedimiento que se empleo fue mediante el aplicativo estadístico SPSS, en donde se determinó que los contribuyentes de los negocios de las bodegas tienen un bajo conocimiento de la normatividad tributaria que implica el pago por adelantado de las percepciones del IGV

MC-Kmeans: an approach to cell image segmentation using clustering algorithms

Digital image processing has been a fundamental tool for the diagnostic and treatment of diseases. Several techniques have been used to analyze microscopic images in cell-level processes. Different methods for the segmentation task are recognized for its contribution in the image processing. Nevertheless, not all are useful in the studies at a microscopic level. In most of the biomedical images, cells are visually clustered and this makes that, simple and fast algorithms which are used in the other cases, may fail. This research proposes the development of a segmentation algorithm in HEp-2 cells type, using the marker-controlled watershed and k-means methods. This approach achieves an improvement in the cell segmentation, which allows obtaining effective information in the posterior analysis. We obtained a precision of 82.3% in the performance and in the qualitative analysis the method reached an outstanding performance in comparison with the other segmentation techniques used in the experiments. Finally, we concluded that the algorithm proposed, is suitable for the segmentation of the studied cells

Intelligent video anomaly detection and classification using faster RCNN with deep reinforcement learning model

Recently, intelligent video surveillance applications have become essential in public security by the use of computer vision technologies to investigate and understand long video streams. Anomaly detection and classification are considered a major element of intelligent video surveillance. The aim of anomaly detection is to automatically determine the existence of abnormalities in a short time period. Deep reinforcement learning (DRL) techniques can be employed for anomaly detection, which integrates the concepts of reinforcement learning and deep learning enabling the artificial agents in learning the knowledge and experience from actual data directly. With this motivation, this paper presents an Intelligent Video Anomaly Detection and Classification using Faster RCNN with Deep Reinforcement Learning Model, called IVADC-FDRL model. The presented IVADC-FDRL model operates on two major stages namely anomaly detection and classification. Firstly, Faster RCNN model is applied as an object detector with Residual Network as a baseline model, which detects the anomalies as objects. Besides, deep Q-learning (DQL) based DRL model is employed for the classification of detected anomalies. In order to validate the effective anomaly detection and classification performance of the IVADC-FDRL model, an extensive set of experimentations were carried out on the benchmark UCSD anomaly dataset. The experimental results showcased the better performance of the IVADC-FDRL model over the other compared methods with the maximum accuracy of 98.50% and 94.80% on the applied Test004 and Test007 dataset respectively

Unsupervised deep learning based variational autoencoder model for COVID-19 diagnosis and classification

At present times, COVID-19 has become a global illness and infected people has increased exponentially and it is difficult to control due to the non-availability of large quantity of testing kits. Artificial intelligence (AI) techniques including machine learning (ML), deep learning (DL), and computer vision (CV) approaches find useful for the recognition, analysis, and prediction of COVID-19. Several ML and DL techniques are trained to resolve the supervised learning issue. At the same time, the potential measure of the unsupervised learning technique is quite high. Therefore, unsupervised learning techniques can be designed in the existing DL models for proficient COVID-19 prediction. In this view, this paper introduces a novel unsupervised DL based variational autoencoder (UDL-VAE) model for COVID-19 detection and classification. The UDL-VAE model involved adaptive Wiener filtering (AWF) based preprocessing technique to enhance the image quality. Besides, Inception v4 with Adagrad technique is employed as a feature extractor and unsupervised VAE model is applied for the classification process. In order to verify the superior diagnostic performance of the UDL-VAE model, a set of experimentation was carried out to highlight the effective outcome of the UDL-VAE model. The obtained experimental values showcased the effectual results of the UDL-VAE model with the higher accuracy of 0.987 and 0.992 on the binary and multiple classes respectively

Hydrolysis of raw fish proteins extracts by Carnobacterium maltaromaticum strains isolated from Argentinean freshwater fish

Lactic acid bacteria (LAB) isolated from freshwater fish (hatcheries and captures) from Paraná river (Argentina) were analyzed by using culture-dependent approaches. The species belonging to Carnobacterium (C.) divergens, C. inhibens, C. maltaromaticum, C. viridans and Vagococcus (V.) salmoninarum were identify as predominant by RAPD-PCR and 16 s rRNA gene sequencing. C. maltaromaticum (H-17, S-30, B-42 and S-44) grew in raw fish extract and slightly reduced the medium pH (5.81–5.91). These strains exhibited moderate fish sarcoplasmic protein degradation (≤ 73 %) releasing small peptides and free amino acids, being alanine, glycine, asparagine and arginine concentrations increased in a higher extent (17.84, 1.47, 1.26 and 0.47 mg/100 mL, respectively) by S-44 strain at 96 h incubation. Interestingly C. maltaromaticum H-17 was able to inhibit Listeria monocytogenes. Results suggest that these strains would contribute to the development of new safe and healthy fishery products with improved nutritional and sensory characteristics.Fil: Dallagnol, Andrea Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Nordeste. Instituto de Materiales de Misiones. Universidad Nacional de Misiones. Facultad de Ciencias Exactas Químicas y Naturales. Instituto de Materiales de Misiones; ArgentinaFil: Pescuma, Micaela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; ArgentinaFil: Gamarra Espínola, Natalia. Universidad Nacional de Misiones; ArgentinaFil: Vera, Mariela Natalia. Universidad Nacional de Misiones; ArgentinaFil: Vignolo, Graciela Margarita. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; Argentin