ESS - SA - Saúde Ambiental
Permanent URI for this community
Browse
Browsing ESS - SA - Saúde Ambiental by Title
Now showing 1 - 10 of 396
Results Per Page
Sort Options
- 3rd International Congress of Environmental Health: Proceedings BookPublication . Vieira Da Silva, Manuela; Oliveira, Rui S.; Rodrigues, Matilde; Nunes, Mafalda; Santos, Joana; Carvalhais, C.; Rebelo, Andreia; Freitas, Marisa; Xavier, AnaThis third edition addresses to ‘Emerging risks and challenges for the environment, health and safety’ and intended as a guide to the various symposiums and workshops, to provide all present the most recent scientific and technological advances in the areas: Occupational Health and Toxicology; Exposure to Nanoparticles; Assessment and Risk Management; Occupational Safety; Exposure to Bioaerosols; Environment and Sustainability; Food Safety and Public Health.
- Acceptance of industrial collaborative robots: Preliminar results of appliction of portuguese version of the Frankenstein Syndrome Questionnaire (FSQ)Publication . Pinto, Ana; Ferraz, Mariana; Nomura, Tatsuya; Santos, JoanaCobots are highly flexible and able to operate in the same workspace and at the same time with the worker. The use of these technologies allows for increased production performance while ensuring comfort and confidence for the worker. Robot acceptance is still a controversial topic with various approaches and methods to measure acceptance of humanoid robots. This study aimed to evaluate cobots acceptance after a motor assembly task in a collaborative workstation. 30 university students were divided into two groups, with group 1 having read the assembly instructions before the usage of the assembly workstation and group 2 without having any previous knowledge about the car engine. All participants completed the Portuguese version of the Frankenstein Syndrome Questionnaire (FSQ). Data analyses were carried out using descriptive and inferential statistics using IBM SPSS Statistics software, version 28.0. One correlations was found between the scales of the FSQ (p < 0.05). It was possible to conclude that the acceptance of robots by the participants in group 1 and group 2 was the same. This study can contribute to understanding which factors explain the acceptance of collaborative robots, to improve human-robot intercation.
- Acidentes de trabalho no setor do comércio a retalho: um estudo de caso supermercados de conveniênciaPublication . Correia, Ana; Balazeiro, Márcia; Cavadas, Maria I.; Campos, Joana R.; Costa, Nelson; Rodrigues, João; Rodrigues, MatildeThis study aims to characterize the occupational accidents in convenience supermarkets, identifying the personal factors related to its frequency and severity. A total of 443 accidents that occurred between 2015 and September 2017, in a group of supermarkets from a Portuguese company, were analyzed. Different company databases were gathered and compiled for the purpose of this study. Frequency and severity rates were determined for the total accidents and for employees age and gender. Results showed that both accident rates increased during the analyzed period. Higher frequency and severity rates were found for female. Data also denote that accidents with higher severity occurred in workers with more than 46 years old and among the ones with basic education.
- Acidentes de trabalho que resultam em lesões musculoesqueléticas no setor do retalho alimentarPublication . Balazeiro, Márcia; Correia, Ana; Cavadas, Inês; Costa, Nelson; Rodrigues, João; Rodrigues, MatildeOs acidentes de trabalho que resultam em lesões musculoesqueléticas (LME) têm uma elevada expressão sobre o total de acidentes. No entanto, existe pouca evidência sobre os acidentes ocorridos no setor do retalho alimentar, apesar da sua relevância face às atividades desenvolvidas pelos trabalhadores. O presente estudo teve como objetivo analisar os acidentes por sobre-esforço físico em duas tipologias de supermercados (hipermercados e supermercados de conveniência). Para tal foram analisados os dados presentes nas bases de dados da empresa relativos aos acidentes ocorridos entre 2015 e o primeiro semestre de 2017. Os resultados mostraram que as LME ainda são significativas relativamente ao total de acidentes notificados, tendo-se observado um aumento do Índice de Gravidade ao longo do período em estudo nas duas tipologias de supermercados. O Índice de Frequência baixou ligeiramente, mas mantendo uma considerável expressão face ao total de acidentes. As partes do corpo mais afetadas foram os membros superiores, as costas e os membros inferiores, tendo-se verificado uma relação entre a atividade física e a parte do corpo afetada. O mecanismo que esteve na origem de um maior número de acidentes foi a movimentação manual de cargas realizada de modo inadequado. Os resultados remetem para a necessidade de intervenção de forma a reduzir os índices de sinistralidade por LME.
- Actinobacteria from arctic and atlantic deep-sea sediments—biodiversity and bioactive potentialPublication . Ribeiro, Inês ; Antunes, Jorge T. ; Alexandrino, Diogo A. M.; Tomasino, Maria Paola ; Almeida, Eduarda ; Hilário, Ana ; Urbatzka, Ralph ; Leão, Pedro N. ; Mucha, Ana P. ; Carvalho, Maria F.The deep-sea covers over 70% of the Earth’s surface and harbors predominantly uncharacterized bacterial communities. Actinobacteria are the major prokaryotic source of bioactive natural products that find their way into drug discovery programs, and the deep-sea is a promising source of biotechnologically relevant actinobacteria. Previous studies on actinobacteria in deep-sea sediments were either regionally restricted or did not combine a community characterization with the analysis of their bioactive potential. Here we characterized the actinobacterial communities of upper layers of deep-sea sediments from the Arctic and the Atlantic (Azores and Madeira) ocean basins, employing 16S rRNA metabarcoding, and studied the biosynthetic potential of cultivable actinobacteria retrieved from those samples. Metabarcoding analysis showed that the actinobacterial composition varied between the sampled regions, with higher abundance in the Arctic samples but higher diversity in the Atlantic ones. Twenty actinobacterial genera were detected using metabarcoding, as a culture-independent method, while culture-dependent methods only allowed the identification of nine genera. Isolation of actinobacteria resulted on the retrieval of 44 isolates, mainly associated with Brachybacterium, Microbacterium, and Brevibacterium genera. Some of these isolates were only identified on a specific sampled region. Chemical extracts of the actinobacterial isolates were subsequently screened for their antimicrobial, anticancer and anti-inflammatory activities. Extracts from two Streptomyces strains demonstrated activity against Candida albicans. Additionally, eight extracts (obtained from Brachybacterium, Brevibacterium, Microbacterium, Rhodococcus, and Streptomyces isolates) showed significant activity against at least one of the tested cancer cell lines (HepG2 and T-47D). Furthermore, 15 actinobacterial extracts showed anti-inflammatory potential in the RAW 264.4 cell model assay, with no concomitant cytotoxic response. Dereplication and molecular networking analysis of the bioactive actinobacterial extracts showed the presence of some metabolites associated with known natural products, but one of the analyzed clusters did not show any match with the natural products described as responsible for these bioactivities. Overall, we were able to recover taxonomically diverse actinobacteria with different bioactivities from the studied deep-sea samples. The conjugation of culture-dependent and -independent methods allows a better understanding of the actinobacterial diversity of deep-sea environments, which is important for the optimization of approaches to obtain novel chemically-rich isolates.
- Advancing the understanding of pupil size variation in occupational safety and health: A systematic review and evaluation of open-source methodologiesPublication . Ferreira, Filipa; Ferreira, Simão; Mateus, Catarina; Rocha, Nuno; Coelho, Luís; Rodrigues, MatildePupil size can be used as an important biomarker for occupational risks. In recent years, there has been an increase in the development of open-source tools dedicated to obtaining and measuring pupil diameter. However, it remains undetermined determined whether these tools are suitable for use in occupational settings. This study explores the significance of pupil size variation as a biomarker for occupational risks and evaluates existing open-source methods for potential use in both research and occupational settings, with the goal of to prevent occupational accidents and improve the health and performance of workers. To this end, a two-phase systematic literature review was conducted in the Web of Science™, ScienceDirect®, and Scopus® databases. For the relevance of monitoring pupil size variation in occupational settings, 15 articles were included. The articles were divided into three groups: mental workload, occupational stress, and mental fatigue. In most cases, pupil dilation increased with workload enhancement and with higher levels of stress. Regarding fatigue, it was noted that an increase in this condition corresponded with a decrease in pupil size. With respect to the open-source methodologies, 16 articles were identified, which were categorized into two groups: algorithms and software. Convolutional neural networks (CNN)1 have exhibited superior performance among the various algorithmic approaches studied. Building on this insight, and considering the evaluations of software options, MEYE emerges as the premier open-source system for deployment in occupational settings due to its compatibility with a standard computer webcam. This feature positions MEYE as a particularly practical tool for workers in stable environments, like those of developers and administrators.
- An eco-friendly approach for analysing sugars, minerals, and colour in brown sugar using digital image processing and machine learningPublication . Alves, Vandressa; Santos, Jeferson M. dos; Viegas, Olga; Pinto, Edgar; Ferreira, Isabel M.P.L.V.O.; Lima, Vanderlei Aparecido; Felsner, Maria L.Brown sugar is a natural sweetener obtained by thermal processing, with interesting nutritional characteristics. However, it has significant sensory variability, which directly affects product quality and consumer choice. Therefore, developing rapid methods for its quality control is desirable. This work proposes a fast, environmentally friendly, and accurate method for the simultaneous analysis of sucrose, reducing sugars, minerals and ICUMSA colour in brown sugar, using an innovative strategy that combines digital image processing acquired by smartphone cell with machine learning. Data extracted from the digital images, as well as experimentally determined contents of the physicochemical characteristics and elemental profile were the variables adopted for building predictive regression models by applying the kNN algorithm. The models achieved the highest predictive capacity for the Ca, ICUMSA colour, Fe and Zn, with coefficients of determination (R2) ≥ 92.33 %. Lower R2 values were observed for sucrose (81.16 %), reducing sugars (85.67 %), Mn (83.36 %) and Mg (86.97 %). Low data dispersion was found for all the predictive models generated (RMSE < 0.235). The AGREE Metric assessed the green profile and determined that the proposed approach is superior in relation to conventional methods because it avoids the use of solvents and toxic reagents, consumes minimal energy, produces no toxic waste, and is safer for analysts. The combination of digital image processing (DIP) and the kNN algorithm provides a fast, non-invasive and sustainable analytical approach. It streamlines and improves quality control of brown sugar, enabling the production of sweeteners that meet consumer demands and industry standards.
- An empirical study about the variables that influence the social acceptability on the biogasPublication . Dias, Telma; Rodrigues, Matilde; Pereira, Ilídio; Leão, Celina P.Due to the excessive use of conventional energy sources, renewable energy technologies have becoming a potential alternative to provide a sustainable development of society. Biogas has been considered as one of the most environmental beneficial source of energy that contributes for reducing greenhouse gas emissions and global warming (Jiang et al., 2011). Furthermore, the production of biogas can be highly important in rural areas, where there are many treatment stations of organic wastes, being a good way to solve problems related to release gases into the atmosphere, as well as related to the bad smells. However, the society opposition, disinterest and lack of knowledge about the advantages of biogas production and its use, can be a barrier to the availability of the implementation of these renewable technologies. In this context, more studies are necessary to clarify the public acceptability of these technologies (Zyadin et al ., 2012), in order to get further information to help into design an intervention strategy.
- An integration of intelligent approaches and economic criteria for predictive analytics of occupational accidentsPublication . Gholamizadeh, Kamran; Zarei, Esmaeil; Yazdi, Mohammad; Rodrigues, Matilde A.; Shirmohammadi-Khorram, Nasrin; Mohammadfam, IrajOccupational accidents are a significant concern, resulting in human suffering, economic crises, and social issues. Despite ongoing efforts to comprehend their causes and predict their occurrences, the use of machine learning models in this domain remains limited. This study aims to address this gap by investigating intelligent approaches that incorporate economic criteria to predict occupational accidents. Four machine learning algorithms, Random Forest (RF), Support Vector Machine (SVM), Multivariate Adaptive Regression Spline (MARS), and M5 Tree Model (M5), were employed to predict occupational accidents, considering three economic criteria: basic income (BI), inflation index (II), and price index (PI). The study focuses on identifying the most suitable model for predicting the frequency of occupational accidents (FOA) and determining the economic criteria with the greatest influence. The results reveal that the RF model accurately predicts accidents across all income levels. Additionally, among the economic criteria, II had the most significant impact on accidents. The findings suggest that a reduction in FOA is unlikely in the coming years due to the increasing growth of II and PI, coupled with a slight annual increase in BI. Implementing appropriate countermeasures to enhance workers’ economic welfare, particularly for low-income employees, is crucial for reducing occupational accidents. This research underscores the potential of machine learning models in predicting and preventing occupational accidents while highlighting the critical role of economic factors. It contributes valuable insights for scholars, practitioners, and policymakers to develop effective strategies and interventions to improve workplace safety and workers’ economic well-being
- An unobtrusive multimodal stress detection model & recommender systemPublication . Ferreira, Simão; Correia, Hugo; Rodrigues, Fátima; Rodrigues, Matilde; Rocha, NunoStudies estimate that about 50% of all lost workdays are related to occupational stress. In recent years, several solutions for mental health management, including biofeedback applications, have emerged as a way to enhance employee mental health. Solutions to mitigate risk factors related to the working settings present an enormous potential and a clear contribution. However, most of the work that has been developed is limited to laboratory environments and does not suit real-life needs. Our study proposes an unobtrusive multimodal approach for detecting work-related stress combining videoplethysmography and self-reported measures for stablishing the ground truth in real-life settings. The study involved 28 volunteers over a two-month period. Various physiological signals were collected through a videopletismography solution, while users were performing daily working, for approximately eight hours a day. In parallel, selfreported measures were collected via a pop-up application (developed by the research team) that periodically retrieved the user's perceived stress (amongst other variables) in order to label the physiological data. In order to develop the stress detection model, we pre-processed the data and performed Heart Rate Variability (HRV) feature extraction. Then, we experimented with several machine learning models, utilizing both individual and combined physiological signals to explore all available alternatives. After rigorous evaluation, the best-trained model achieved an accuracy of over 80% and an F1 Score of over 85%. With the stress detection model in place, we are developing a structured intervention model to help reduce stress. This intervention model integrates two interconnected dimensions through digital coaching, which prioritizes personalized recommendations based on user preferences. Our top priority is to ensure user engagement, and we believe that adherence to and adoption of recommended interventions are more likely when users receive recommendations that align with their preferences. Thus, we prioritize personalized recommendations that are tailored to each individual's unique model. After detecting immediate stress peaks and providing real-time feedback on stress levels, our alarm system goes a step further by offering customized recommendations for brief stress relief. The digital coach (intervention model) offers various recommendations and active lifestyle changes such as exercise, task management, weight management, better sleep habits, structured pauses, and other critical interventions. These critical interventions are also based on user preferences, allowing our system to prevent future stress-related incidents and, most importantly, mitigate long-term stress. This project and its methodology demonstrate that truly unobtrusive stress detection is possible and can be performed within the scope of ethical demands. In future work, we will evaluate the responses and beneficial outcomes of implementing a recommender system.