Towards robotic process automation implementation: an end-to-end perspective

Purpose: Robotic process automation (RPA) seeks to automate business processes, using software robots that interact with systems through their user interface, improving efficiency and reducing costs. However, some critical steps, such as identifying processes suitable for RPA automation, can have a tremendous impact in organizations if a wrong process is selected. Therefore, the purpose of this paper is to provide an approach for analyzing RPA development in business organizations. Design/methodology/approach: This research presents a cohesive literature review about RPA, in order to identify RPA main concepts, which should be reported and considered in all RPA case studies. A model connecting the main elicited RPA concepts is presented as well as its evaluation and applicability grounded of past RPA case study (CS) analysis, using design science research. Findings: The results from this research show that most of the RPA main concepts gathered in the literature review are not reported in the selected RPA CSs. Originality/value: As RPA is a recent topic, literature lacks a synthetization of RPA main topics. This research aims to fill the gap on that, by identifying and synthesize the main topics related to RPA and proposing a model that connects the main RPA concepts, which can be used by researchers as a schema for conducting and writing RPA case studies.info:eu-repo/semantics/acceptedVersio

Efeito da somatotropina bovina recombinante (bst) sobre a produção de leite de cabras Saanen e Anglo Nubiana.

O presente trabalho, realizado na Embrapa Caprinos, Sobral-CE, avaliou o efeito da somatotropina bovina recombinante (bST) sobre a produção de leite de cabras Saanen e Anglo Nubiana, recebendo dois níveis de concentrado. Foram usadas 30 cabras, entre a segunda e quarta ordem de parição, divididas em dois grupos, de acordo com a quantidade de concentrado fornecida: 1,0 kg/animal/dia e 1,25 kg/animal/dia. Cada grupo foi subdividido em dois tratamentos, sendo fornecido 0 mg/kg.PV e 3,0 mg/kg.PV de bST. As análises estatísticas foram realizadas por meio do procedimento GLM, utilizando o modelo que incluiu os efeitos fixos de raça, níveis de concentrado, interação raça x níveis de concentrado, bST e suas interações e a variável dia de lactação (efeitos linear e quadrático). Houve efeito (P<0,01) de raça, níveis de concentrado e da aplicação do bST sobre a produção de leite, sendo significativa (P<0,01) apenas a interação raça x níveis de concentrado. A produção de leite foi maior (P<0,05) para a Saanen em relação a Anglo Nubiana apenas no tratamento com menor nível de concentrado. Para a Anglo Nubiana, a quantidade de concentrado fornecida afetou (P<0,05) a produção de leite. A produção de leite aumentou (P<0,05) com a aplicação de bST, de 1,73 kg/aniamal/dia para 2,04 kg/animal/dia. Conclui-se, portanto, que a produção de leite de cabras Saanen e Anglo Nubiana pode ser incrementada com a aplicação de bST. Effect of recombinant bovine somatotropin (bst) on milk production of Saanen and Anglo-Nnubian goats. Abstract: This work was carried out at Embrapa Caprinos, in Sobral, Ceará, to evaluate the effect of recombinant bovine somatotropin (bST) on milk production of Saanen ang Anglo-Nubian goats fed two levels of concentrate. Thirty animals, between the second and the fouth parturition order, were divided into two groups according with the the amount of concentrate: 1 kg/animal/day and 1.25 kg/animal/day. Each group was splited in two treatments, being fed 0 mg/kg BW and 3mg/kg BW of bST. Statistical analysis were done by GLM procedure, by utilizing a model which included the fixed effects of breeds, levels of concentrate, interactions of breeds and levels of concentrate, bST and its interactions, and the variable days of lactation (linear and squared effects). It was found effect (P<0.01) of breed, levels of concentrate and application of bST on milk production, being signifficant (P<0.01) only the interaction breed x levels of concentrate. Milk production was higher (P<0.05) for Saanen in relation to Anglo-Nubian only in the treatment with the lowest level of concentrate. For Anglo-nubian the amount of concentrate influenced (P<0.05) milk production. Milk production increased (P<0.05) with bST application from 1.73 kg/animal/day to 2.04 kg/animal/day. It was concluded that milk production in the studyed breeds can be improved with BST application

Predictive analysis in healthcare: emergency wait time prediction

Emergency departments are an important area of a hospital, being the major entry point to the healthcare system. One of the most important issues regarding patient experience are the emergency department waiting times. In order to help hospitals improving their patient experience, the authors will perform a study where the Random Forest algorithm will be applied to predict emergency department waiting times. Using data from a Portuguese hospital from 2013 to 2017, the authors discretized the emergency waiting time in 5 different categories: “Really Low”, “Low”, “Average”, “High”, “Really High”. Plus, the authors considered as waiting time, the time from triage to observation. The authors expect to correctly evaluate the proposed classification algorithm efficiency and accuracy in order to be able to conclude if it is valuable when trying to predict ED waiting times.info:eu-repo/semantics/acceptedVersio

Emergency waiting times data analysis

The Emergency Departments (ED) are a complex and important area of a hospital. With limited resources, it is mandatory to focus on efficiency. When hospitals are unable to deal with high demand, problems may rise leading to longer waiting times and more dissatisfaction. In this research, the authors extracted knowledge from a hospital ED, through data analysis and data mining, applying Random Forest and Naïve Bayes to study the ED patient waiting time and diseases.info:eu-repo/semantics/publishedVersio

Melhoramento genético participativo: uma nova perspectiva para o manejo da diversidade genética do semi-árido nordestino.

bitstream/item/52153/1/Midia-Melhoramento-genetico.pd

Extracting clinical information from electronic medical records

As the adoption of Electronic Medical Records (EMRs) rises in the healthcare institutions, these resources are each day more important because of the clinical data they contain about patients. However, the unstructured textual data in the form of narrative present in those records, makes it hard to extract and structure useful clinical information. This unstructured text limits the potential of the EMRs, because the clinical data these records contain, can be used to perform important operations inside healthcare institutions such as searching, summarization, decision support and statistical analysis, as well as be used to support management decisions or serve for research. These operations can only be done if the clinical data from the narratives is properly extracted and structured. Usually this extraction is made manually by healthcare practitioners, what is not efficient and is error-prone. The present work uses Natural Language Processing (NLP) and Information Extraction(IE) techniques in order to develop a pipeline system that can extract clinical information directly from unstructured texts present in Portuguese EMRs, in an automated way, in order to help EMRs to fulfil their potential.info:eu-repo/semantics/acceptedVersio

AutoFB: Automating Fetal Biometry Estimation from Standard Ultrasound Planes

During pregnancy, ultrasound examination in the second trimester can assess fetal size according to standardized charts. To achieve a reproducible and accurate measurement, a sonographer needs to identify three standard 2D planes of the fetal anatomy (head, abdomen, femur) and manually mark the key anatomical landmarks on the image for accurate biometry and fetal weight estimation. This can be a time-consuming operator-dependent task, especially for a trainee sonographer. Computer-assisted techniques can help in automating the fetal biometry computation process. In this paper, we present a unified automated framework for estimating all measurements needed for the fetal weight assessment. The proposed framework semantically segments the key fetal anatomies using state-of-the-art segmentation models, followed by region fitting and scale recovery for the biometry estimation. We present an ablation study of segmentation algorithms to show their robustness through 4-fold cross-validation on a dataset of 349 ultrasound standard plane images from 42 pregnancies. Moreover, we show that the network with the best segmentation performance tends to be more accurate for biometry estimation. Furthermore, we demonstrate that the error between clinically measured and predicted fetal biometry is lower than the permissible error during routine clinical measurements