Time-series analysis based on machine learning for occupational risk evaluation in public administration

Occupational diseases are currently a concerning problem for office workers, who spend long periods of time seated in static positions. Musculoskeletal disorders, specifically, have the highest prevalence among workers, contributing negatively by 17% to the Years Lived with Disability worldwide. This work is part of the PrevOccupAI project, which monitors office workers through wearable sensors and questionnaires, in order to provide them reports that bring to their attention some risk factors that can potentiate occupational diseases. During this work, a study with 40 subjects working in a real environment was carried out. After data pre-processing and synchronization, as it was only intended to analyze sitting data, the periods in which the participants were not seated were removed from the acquired signals. For this purpose, a machine learning model was developed, which uses features from the smartphone’s accelerometer signal to distinguish between sitting and walking. The best model reached an accuracy of 100.0%. Additionally, a model capable of partially predicting the participants’ answers to daily pain questionnaires was developed. Using the electromyography signals and personal information gathered from other questionnaires, it was possible to train a model that predicts if the subject reported pain or not, both at the beginning and end of the working day. Using the Random Forest algorithm, it was possible to achieve a mean accuracy of 86.3%. For each acquisition performed by the 40 participants, a relative ergonomic occupa- tional risk was assigned through variables that characterize postural variability. Using machine learning algorithms, models were trained to attempt to predict the modelled risk. A mean accuracy of 65.7% was achieved for the classification model, and a mean absolute error of 0.84 for the regression model.As doenças ocupacionais são, atualmente, um problema preocupante em trabalhadores de escritório, que passam muito tempo sentados em posições estáticas. As doenças muscu- loesqueléticas, especificamente, são as que têm maior prevalência entre os trabalhadores, contribuindo negativamente em 17% para os Anos Vividos com Incapacidade. Esta dissertação é parte do projeto PrevOccupAI, que monitoriza trabalhadores de escritório através de sensores e questionários, de forma a fornecer-lhes relatórios que cha- mem à sua atenção alguns dos fatores de risco que podem potenciar doenças ocupacionais. Durante este trabalho, foi realizado um estudo em 40 sujeitos a trabalhar em contexto real. Depois de pré-processamento e sincronização dos dados, como só se pretendia analisar dados de trabalhadores sentados, os períodos em que os participantes não estiveram sentados foram retirados dos sinais adquiridos. Para isso, foi desenvolvido um modelo de aprendizagem automática, que usa características do sinal do acelerómetro do telemóvel para distinguir entre sentado e a andar. O melhor modelo atingiu uma exatidão de 100,0%. Adicionalmente, foi desenvolvido um modelo capaz de prever parcialmente as respos- tas dos participantes a questionários diários de dor. Através dos sinais de eletromiografia e informação pessoal retirada de outros questionários, foi possível treinar um modelo que prevê se o sujeito reportou dor ou não, tanto no início como no fim do dia de trabalho. Utilizando o algoritmo de Floresta Aleatória, foi possível atingir uma exatidão média de 86,3%. A cada aquisição realizada pelos 40 participantes foi atribuído um risco ocupacional ergonómico relativo, através de variáveis que caracterizam a variabilidade postural. Uti- lizando algoritmos de aprendizagem automática, foram treinados modelos para tentar prever o risco modelado. Para o modelo de classificação, atingiu-se uma exatidão média de 65,7%, enquanto que para o modelo de regressão se conseguiu que o erro médio absoluto não ultrapassasse 0,84

The future is now: the impact of digital transformation on business models and corporate communication

We are living in a world of rapid evolving technologies that are fusing the physical, digital and biological worlds and impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human (WEF, n.d.). Amidst the so-called 4th industrial revolution, every company’s success is now more than ever dependent on its capacity to adjust to this new era and make smart investments in the future. The aim of the present work is to demonstrate how Digital Transformation (DT) plays a key role in that process, shaping today’s business models, customer relationship and even the way we communicate. To accomplish that goal, examples of DT from Amazon, Santander and Emel shall be explored in order to illustrate this trend and showcase how these organizations, in particular, are progressing along the digital maturity path.Not Publishe

The minimum cost network upgrade problem with maximum robustness to multiple node failures

The design of networks which are robust to multiple failures is gaining increasing attention in areas such as telecommunications. In this paper, we consider the problem of upgrading an existent network in order to enhance its robustness to events involving multiple node failures. This problem is modeled as a bi-objective mixed linear integer formulation considering both the minimization of the cost of the added edges and the maximization of the robustness of the resulting upgraded network. As the robustness metric of the network, we consider the value of the Critical Node Detection (CND) problem variant which provides the minimum pairwise connectivity between all node pairs when a set of c critical nodes are removed from the network. We present a general iterative framework to obtain the complete Pareto frontier that alternates between the minimum cost edge selection problem and the CND problem. Two different approaches based on a cover model are introduced for the edge selection problem. Computational results conducted on different network topologies show that the proposed methodology based on the cover model is effective in computing Pareto solutions for graphs with up to 100 nodes, which includes four commonly used telecommunication networks.publishe

Opportunistic predation of a colony of Polybia platycephala (Richards) (Hymenoptera, Vespidae) by Labidus praedator (Smith) (Hymenoptera, Formicidae)

Social wasps have developed several defense mechanisms, especially against ants. Predator attacks are the main threat to their nests. The strategy adopted by the wasps, when attacked by ants, is to abandon the nest, thus preserving the adult population for future nesting. The present study reports in detail the predation of a colony of Polybia platycephala by Labidus praedator

Muscle residual force enhancement: a brief review

Muscle residual force enhancement has been observed in different muscle preparations for more than half a century. Nonetheless, its mechanism remains unclear; to date, there are three generally accepted hypotheses: 1) sarcomere length non-uniformity, 2) engagement of passive elements, and 3) an increased number of cross-bridges. The first hypothesis uses sarcomere non-homogeneity and instability to explain how "weak" sarcomeres would convey the higher tension generated by an enhanced overlap from "stronger" sarcomeres, allowing the whole system to produce higher forces than predicted by the force-length relationship; non-uniformity provides theoretical support for a large amount of the experimental data. The second hypothesis suggests that passive elements within the sarcomeres (i.e., titin) could gain strain upon calcium activation followed by stretch. Finally, the third hypothesis suggests that muscle stretch after activation would alter cross-bridge kinetics to increase the number of attached cross-bridges. Presently, we cannot completely rule out any of the three hypotheses. Different experimental results suggest that the mechanisms on which these three hypotheses are based could all coexist

Temporal activity patterns and foraging behavior by social wasps (Hymenoptera, Polistinae) on fruits of Mangifera indica L.(Anacardiaceae)

This research was done in Juiz de Fora, Minas Gerais, Brazil on february 2012, with objective was to determine which species of social wasps visiting mango fruits, their behaviors displayed by them while foraging and verify which the species of wasps visitors offer risk of accidents to farmers. The studied area was monitored during February 2012, from 8:00 to 17:00. in a 144 hour effort, and the data collected included the time of activity, diversity, aggressiveness and the general behavior of social wasps around the fruits. There were registered a total of 175 individuals of 12 different species, healthy fruits were damaged during the day, and we registered the abundance and richness peaks throughout the day. This study indicated the needs for special care during the harvest, as aggressive wasps are indeed present and the abundant, resulting in a possible increase of the risk for the workers