RESUMO

-The automatic identification of human physical activities, commonly referred to as Human Activity Recognition (HAR), has garnered significant interest and application across various sectors, including entertainment, sports, and notably health. Within the realm of health, a myriad of applications exists, contingent upon the nature of experimentation, the activities under scrutiny, and the methodology employed for data and information acquisition. This diversity opens doors to multifaceted applications, including support for the well-being and safeguarding of elderly individuals afflicted with neurodegenerative diseases, especially in the context of smart homes. Within the existing literature, a multitude of datasets from both indoor and outdoor environments have surfaced, significantly contributing to the activity identification processes. One prominent dataset, the CASAS project developed by Washington State University (WSU) University, encompasses experiments conducted in indoor settings. This dataset facilitates the identification of a range of activities, such as cleaning, cooking, eating, washing hands, and even making phone calls. This article introduces a model founded on the principles of Semi-supervised Ensemble Learning, enabling the harnessing of the potential inherent in distance-based clustering analysis. This technique aids in the identification of distinct clusters, each encapsulating unique activity characteristics. These clusters serve as pivotal inputs for the subsequent classification process, which leverages supervised techniques. The outcomes of this approach exhibit great promise, as evidenced by the quality metrics' analysis, showcasing favorable results compared to the existing state-of-the-art methods. This integrated framework not only contributes to the field of HAR but also holds immense potential for enhancing the capabilities of smart homes and related applications.

RESUMO

The recognition of human activities (HAR) using wearable device data, such as smartwatches, has gained significant attention in the field of computer science due to its potential to provide insights into individuals' daily activities. This article aims to conduct a comparative study of deep learning techniques for recognizing activities of daily living (ADL). A mapping of HAR techniques was performed, and three techniques were selected for evaluation, along with a dataset. Experiments were conducted using the selected techniques to assess their performance in ADL recognition, employing standardized evaluation metrics, such as accuracy, precision, recall, and F1-score. Among the evaluated techniques, the DeepConvLSTM architecture, consisting of recurrent convolutional layers and a single LSTM layer, achieved the most promising results. These findings suggest that software applications utilizing this architecture can assist smartwatch users in understanding their movement routines more quickly and accurately.

Assuntos

RESUMO

Human Activity Recognition (HAR) is a complex problem in deep learning, and One-Dimensional Convolutional Neural Networks (1D CNNs) have emerged as a popular approach for addressing it. These networks efficiently learn features from data that can be utilized to classify human activities with high performance. However, understanding and explaining the features learned by these networks remains a challenge. This paper presents a novel eXplainable Artificial Intelligence (XAI) method for generating visual explanations of features learned by one-dimensional CNNs in its training process, utilizing t-Distributed Stochastic Neighbor Embedding (t-SNE). By applying this method, we provide insights into the decision-making process through visualizing the information obtained from the model's deepest layer before classification. Our results demonstrate that the learned features from one dataset can be applied to differentiate human activities in other datasets. Our trained networks achieved high performance on two public databases, with 0.98 accuracy on the SHO dataset and 0.93 accuracy on the HAPT dataset. The visualization method proposed in this work offers a powerful means to detect bias issues or explain incorrect predictions. This work introduces a new type of XAI application, enhancing the reliability and practicality of CNN models in real-world scenarios.

Assuntos

RESUMO

Research into assisted living environments -within the area of Ambient Assisted Living (ALL)-focuses on generating innovative technology, products, and services to provide medical treatment and rehabilitation to the elderly, with the purpose of increasing the time in which these people can live independently, whether they suffer from neurodegenerative diseases or disabilities. This key area is responsible for the development of activity recognition systems (ARS) which are a valuable tool to identify the types of activities carried out by the elderly, and to provide them with effective care that allows them to carry out daily activities normally. This article aims to review the literature to outline the evolution of the different data mining techniques applied to this health area, by showing the metrics used by researchers in this area of knowledge in recent experiments.

Assuntos

RESUMO

Due to wearables' popularity, human activity recognition (HAR) plays a significant role in people's routines. Many deep learning (DL) approaches have studied HAR to classify human activities. Previous studies employ two HAR validation approaches: subject-dependent (SD) and subject-independent (SI). Using accelerometer data, this paper shows how to generate visual explanations about the trained models' decision making on both HAR and biometric user identification (BUI) tasks and the correlation between them. We adapted gradient-weighted class activation mapping (grad-CAM) to one-dimensional convolutional neural networks (CNN) architectures to produce visual explanations of HAR and BUI models. Our proposed networks achieved 0.978 and 0.755 accuracy, employing both SD and SI. The proposed BUI network achieved 0.937 average accuracy. We demonstrate that HAR's high performance with SD comes not only from physical activity learning but also from learning an individual's signature, as in BUI models. Our experiments show that CNN focuses on larger signal sections in BUI, while HAR focuses on smaller signal segments. We also use the grad-CAM technique to identify database bias problems, such as signal discontinuities. Combining explainable techniques with deep learning can help models design, avoid results overestimation, find bias problems, and improve generalization capability.

Assuntos

RESUMO

Introdução: Embora atividade, cotidiano e ocupação sejam objetos centrais da Terapia Ocupacional brasileira, a literatura sugere pouca consistência conceitual sobre o assunto. No entanto, as diferentes teorias psicológicas sobre conceitos podem trazer entendimentos diversos sobre esta questão. Objetivo: Investigar a compreensão de estudantes de Terapia Ocupacional sobre os conceitos de atividade, ocupação e cotidiano. Método: Neste estudo qualitativo, 45 estudantes do último ano de um Curso de Graduação em Terapia Ocupacional participaram de um teste de associação de palavras, sendo os resultados submetidos à análise de conteúdo. Resultados: Os participantes relacionaram a atividade mais diretamente com a prática da Terapia Ocupacional, utilizando termos como recurso, instrumento, objetivo, adaptação e análise. À ocupação, associaram-se termos como trabalho, papéis e participação. Para cotidiano, foram frequentes as categorias rotina, dia a dia e organização/repetição. Houve também a citação cruzada dos conceitos e a coocorrência de algumas categorias, como fazer, exemplos de atividades e atividades da vida diária. Discussão: A partir da visão teórica de conceitos, discute-se que atividade, ocupação e cotidiano se inter-relacionam nas redes conceituais dos graduandos, e suas perspectivas indicam uma pluralidade de influências da literatura da Terapia Ocupacional; os achados se aproximam de outras investigações, sugerindo a percepção de sutilezas que relacionam e ao mesmo tempo diferem tais conceitos. Conclusão: Observa-se que há coerência na diferenciação e nas relações entre os conceitos investigados; por outro lado, salienta-se a necessidade de explicitação das diversas conotações que estes conceitos podem assumir diante dos diferentes referenciais teóricos.

Introducción: Aunque actividad, cotidiano y ocupación sean objetos centrales de la Terapia Ocupacional brasileña, la literatura sugiere poca consistencia conceptual sobre el tema. Sin embargo, las diferentes teorías psicológicas sobre conceptos pueden aportar diferentes entendimientos sobre este tema. Objetivo: Investigar la comprensión de estudiantes de Terapia Ocupacional sobre los conceptos de actividad, ocupación y cotidiano. Método: En este estudio cualitativo, 45 estudiantes del último año del curso de Terapia Ocupacional participaron de una prueba de asociación de palabras y los resultados fueron sometidos a análisis de contenido. Resultados: Los participantes relacionaron actividad con la práctica de la Terapia Ocupacional, utilizando términos como recurso, instrumento, objetivo, adaptación y análisis. La ocupación se asoció con términos como trabajo, roles y participación. Para cotidiano fueron frecuentes las categorías rutina, día a día y organización/repetición. También hubo el cruce de conceptos y la coocurrencia de algunas categorías, como hacer, ejemplos de actividades y actividades de la vida diaria. Discusión: Desde la visión teórica de los conceptos, se discute que actividad, ocupación y cotidiano están interrelacionadas en las redes conceptuales de los estudiantes, y sus perspectivas indican una pluralidad de influencias de la literatura; los hallazgos son similares a otras investigaciones, sugiriendo la percepción de sutilezas que relacionan y al mismo tiempo difieren tales conceptos. Conclusión: Se observa que existe coherencia en la diferenciación y en las relaciones entre los conceptos investigados; por otro lado, se enfatiza la necesidad de explicar las distintas connotaciones que estos conceptos pueden asumir frente a diferentes referencias teóricas.

Introduction: Although activity, everyday life and occupation are central objects of the Brazilian Occupational Therapy, there is little conceptual consistency on these themes in the literature. However, different psychological theories about concepts can bring a diversity of understandings on this issue. Aim: To investigate the understanding of Occupational Therapy undergraduates, on the concepts of activity, occupation, and everyday life. Method: In this qualitative study, a word association test was applied to 45 senior undergraduates of an Occupational Therapy course and the results were subjected to content analysis. Results: Participants related activity to Occupational Therapy practice using terms such as resource, instrument, objective, adaptation, and analysis. Occupation was associated with terms such as work, roles, and participation. As for everyday life, the (daily) routine and organization/repetition categories were frequently found. Cross-citation of concepts and co-occurrence of some categories, such as examples of activities, doing, and activities of daily living were also observed. Discussion: From a theoretical view of concepts, this study discusses that activity, occupation, and everyday life are interrelated in the undergraduates' conceptual networks, and their perspectives indicate a plurality of influences from the Occupational Therapy literature. Findings corroborate those reported in other inquiries, suggesting the perception of subtleties that simultaneously associate and differentiate such concepts. Conclusion: There is consistency in the way undergraduates distinguish and relate the concepts investigated; in contrast, the need to explain the different connotations that these concepts can assume according to different theoretical frameworks is highlighted.

Assuntos

RESUMO

Indoor localization and human activity recognition are two important sources of information to provide context-based assistance. This information is relevant in ambient assisted living (AAL) scenarios, where older adults usually need supervision and assistance in their daily activities. However, indoor localization and human activity recognition have been mostly considered isolated problems. This work presents and evaluates a framework that takes advantage of the relationship between location and activity to simultaneously perform indoor localization, mapping, and human activity recognition. The proposed framework provides a non-intrusive configuration, which fuses data from an inertial measurement unit (IMU) placed in the person's shoe, with proximity and human activity-related data from Bluetooth low energy beacons (BLE) deployed in the indoor environment. A variant of the simultaneous location and mapping (SLAM) framework was used to fuse the location and human activity recognition (HAR) data. HAR was performed using data streaming algorithms. The framework was evaluated in a pilot study, using data from 22 people, 11 young people, and 11 older adults (people aged 65 years or older). As a result, seven activities of daily living were recognized with an F1 score of 88%, and the in-door location error was 0.98 ± 0.36 m for the young and 1.02 ± 0.24 m for the older adults. Furthermore, there were no significant differences between the groups, indicating that our proposed method works adequately in broad age ranges.

Assuntos

RESUMO

The Assisted Living Environments Research Area-AAL (Ambient Assisted Living), focuses on generating innovative technology, products, and services to assist, medical care and rehabilitation to older adults, to increase the time in which these people can live. independently, whether they suffer from neurodegenerative diseases or some disability. This important area is responsible for the development of activity recognition systems-ARS (Activity Recognition Systems), which is a valuable tool when it comes to identifying the type of activity carried out by older adults, to provide them with assistance. that allows you to carry out your daily activities with complete normality. This article aims to show the review of the literature and the evolution of the different techniques for processing this type of data from supervised, unsupervised, ensembled learning, deep learning, reinforcement learning, transfer learning, and metaheuristics approach applied to this sector of science. health, showing the metrics of recent experiments for researchers in this area of knowledge. As a result of this article, it can be identified that models based on reinforcement or transfer learning constitute a good line of work for the processing and analysis of human recognition activities.

Assuntos

RESUMO

In this article, we introduce explainable methods to understand how Human Activity Recognition (HAR) mobile systems perform based on the chosen validation strategies. Our results introduce a new way to discover potential bias problems that overestimate the prediction accuracy of an algorithm because of the inappropriate choice of validation methodology. We show how the SHAP (Shapley additive explanations) framework, used in literature to explain the predictions of any machine learning model, presents itself as a tool that can provide graphical insights into how human activity recognition models achieve their results. Now it is possible to analyze which features are important to a HAR system in each validation methodology in a simplified way. We not only demonstrate that the validation procedure k-folds cross-validation (k-CV), used in most works to evaluate the expected error in a HAR system, can overestimate by about 13% the prediction accuracy in three public datasets but also choose a different feature set when compared with the universal model. Combining explainable methods with machine learning algorithms has the potential to help new researchers look inside the decisions of the machine learning algorithms, avoiding most times the overestimation of prediction accuracy, understanding relations between features, and finding bias before deploying the system in real-world scenarios.

Assuntos

RESUMO

The celestial mechanics of the Sun, Moon, and Earth dominate the variations in gravitational force that all matter, live or inert, experiences on Earth. Expressed as gravimetric tides, these variations are pervasive and have forever been part of the physical ecology with which organisms evolved. Here, we first offer a brief review of previously proposed explanations that gravimetric tides constitute a tangible and potent force shaping the rhythmic activities of organisms. Through meta-analysis, we then interrogate data from three study cases and show the close association between the omnipresent gravimetric tides and cyclic activity. As exemplified by free-running cyclic locomotor activity in isopods, reproductive effort in coral, and modulation of growth in seedlings, biological rhythms coincide with temporal patterns of the local gravimetric tide. These data reveal that, in the presumed absence of rhythmic cues such as light and temperature, local gravimetric tide is sufficient to entrain cyclic behaviour. The present evidence thus questions the phenomenological significance of so-called free-run experiments.

Assuntos

RESUMO

Worldwide demographic projections point to a progressively older population. This fact has fostered research on Ambient Assisted Living, which includes developments on smart homes and social robots. To endow such environments with truly autonomous behaviours, algorithms must extract semantically meaningful information from whichever sensor data is available. Human activity recognition is one of the most active fields of research within this context. Proposed approaches vary according to the input modality and the environments considered. Different from others, this paper addresses the problem of recognising heterogeneous activities of daily living centred in home environments considering simultaneously data from videos, wearable IMUs and ambient sensors. For this, two contributions are presented. The first is the creation of the Heriot-Watt University/University of Sao Paulo (HWU-USP) activities dataset, which was recorded at the Robotic Assisted Living Testbed at Heriot-Watt University. This dataset differs from other multimodal datasets due to the fact that it consists of daily living activities with either periodical patterns or long-term dependencies, which are captured in a very rich and heterogeneous sensing environment. In particular, this dataset combines data from a humanoid robot's RGBD (RGB + depth) camera, with inertial sensors from wearable devices, and ambient sensors from a smart home. The second contribution is the proposal of a Deep Learning (DL) framework, which provides multimodal activity recognition based on videos, inertial sensors and ambient sensors from the smart home, on their own or fused to each other. The classification DL framework has also validated on our dataset and on the University of Texas at Dallas Multimodal Human Activities Dataset (UTD-MHAD), a widely used benchmark for activity recognition based on videos and inertial sensors, providing a comparative analysis between the results on the two datasets considered. Results demonstrate that the introduction of data from ambient sensors expressively improved the accuracy results.

Assuntos

RESUMO

Este estudo teórico-metodológico teve por objetivo sistematizar o conceito de unidade afetivo-cognitiva a partir da Psicologia Histórico-Cultural. Esse objetivo se justifica pela escassez de estudos acerca do tema na Psicologia e pela observância de que há uma predominância de visões dualistas acerca da razão e da emoção nos estudos psicológicos. Nesse sentido, o estudo da unidade afetivo-cognitiva pode trazer acréscimos à Psicologia por discutir os aspectos metodológicos da cisão razão/emoção e por evidenciar a união entre esses processos como parte essencial do processo humano de apreensão da realidade. Sendo assim, buscou-se discutir a constituição da consciência humana e da estrutura da atividade como unidades de análise da unidade afetivo-cognitiva. A partir de Leontiev e Vigotski, analisou-se a estrutura da atividade humana e sua expressão pelos significados sociais e sentidos pessoais como unidade afetivo-cognitiva. Essa investigação resultou na constatação de que a estrutura da atividade e a constituição da consciência humana demandam funções afetivo-cognitivas para formar a imagem subjetiva da realidade objetiva no psiquismo humano. Por isso, indicou-se, conforme afirma Vigotski, que entender a unidade afetivo-cognitiva como sistema semântico da consciência demanda o destrinchamento da relação entre a atividade humana e a forma como o ser torna essa atividade consciente.

This theoretical-methodological study aimed to systematize the concept of affective-cognitive unity in Historical-Cultural Psychology. This objective is justified by the scarcity of studies on the subject in Psychology and based on the observation that there is a predominance of dualistic views on reason and emotion in psychological studies. In this sense, the study of the affective--cognitive unit can present improvements to Psychology by discussing the methodological aspects of the reason/emotion split and for making evident the union between these processes as an essential part of the human process of apprehension of reality. Thus, we sought to discuss the constitution of human consciousness and the structure of activity as units of analysis of the affective--cognitive unity. Based on Leontiev and Vygotsky, we analyzed the structure of human activity and its expression by social meanings and personal senses as an affective-cognitive unit. This investigation resulted in the realization that the structure of activity and the constitution of human consciousness demand affective-cognitive functions to form the subjective image of objective reality in the human psyche. Therefore, it was pointed out, as Vygotsky states, that understanding the affective-cognitive unity as a semantic system of consciou-sness requires the study of how human activity relates to the way that the human being is aware and conscious of this activity.

Este estudio, teórico-metodológico, tuvo como objetivo sistematizar el concepto de unidad afectivo-cognitiva a partir de la Psicología Histórico-Cultural. Este objetivo se justifica por la escasez de estudios acerca del tema en la Psicología y por la observancia de que hay un predominio de visiones dualistas acerca de la razón y la emoción en los estudios psicológicos. En este sentido, el estudio de la unidad afectivo-cognitiva puede incrementar a la Psicología por discutir los aspectos metodológicos de la escisión razón / emoción y por evidenciar, la unión entre estos procesos como parte esencial del proceso humano de aprehensión de la realidad. Siendo así, se buscó discutir la constitución de la conciencia humana y de la estructura de la actividad como unidades de análisis de la unidad afectivo-cognitiva. A partir de Leontiev y Vigotski, se analizó la estructura de la actividad humana y su expresión por los significados sociales y sentidos personales como unidad afectivo-cognitiva. Esta investigación resultó en la constatación de que la estructura de la actividad y la constitución de la conciencia humana demandan funciones afectivo-cognitivas para formar la imagen subjetiva de la realidad objetiva en el psiquismo humano. Por eso, se indica, según afirma Vigotski, que entender la unidad afectivo-cognitiva como sistema semántico de la conciencia, demanda desmenuzar la relación entre la actividad humana y la forma como el ser hace esa actividad consciente.

Assuntos

RESUMO

Currently, many applications have emerged from the implementation of software development and hardware use, known as the Internet of things. One of the most important application areas of this type of technology is in health care. Various applications arise daily in order to improve the quality of life and to promote an improvement in the treatments of patients at home that suffer from different pathologies. That is why there has emerged a line of work of great interest, focused on the study and analysis of daily life activities, on the use of different data analysis techniques to identify and to help manage this type of patient. This article shows the result of the systematic review of the literature on the use of the Clustering method, which is one of the most used techniques in the analysis of unsupervised data applied to activities of daily living, as well as the description of variables of high importance as a year of publication, type of article, most used algorithms, types of dataset used, and metrics implemented. These data will allow the reader to locate the recent results of the application of this technique to a particular area of knowledge.

Assuntos

RESUMO

Smartphones have emerged as a revolutionary technology for monitoring everyday life, and they have played an important role in Human Activity Recognition (HAR) due to its ubiquity. The sensors embedded in these devices allows recognizing human behaviors using machine learning techniques. However, not all solutions are feasible for implementation in smartphones, mainly because of its high computational cost. In this context, the proposed method, called HAR-SR, introduces information theory quantifiers as new features extracted from sensors data to create simple activity classification models, increasing in this way the efficiency in terms of computational cost. Three public databases (SHOAIB, UCI, WISDM) are used in the evaluation process. The results have shown that HAR-SR can classify activities with 93% accuracy when using a leave-one-subject-out cross-validation procedure (LOSO).

Assuntos

RESUMO

In this work, authors address workload computation combining human activity recognition and heart rate measurements to establish a scalable framework for health at work and fitness-related applications. The proposed architecture consists of two wearable sensors: one for motion, and another for heart rate. The system employs machine learning algorithms to determine the activity performed by a user, and takes a concept from ergonomics, the Frimat's score, to compute the corresponding physical workload from measured heart rate values providing in addition a qualitative description of the workload. A random forest activity classifier is trained and validated with data from nine subjects, achieving an accuracy of 97.5%. Then, tests with 20 subjects show the reliability of the activity classifier, which keeps an accuracy up to 92% during real-time testing. Additionally, a single-subject twenty-day physical workload tracking case study evinces the system capabilities to detect body adaptation to a custom exercise routine. The proposed system enables remote and multi-user workload monitoring, which facilitates the job for experts in ergonomics and workplace health.

Assuntos

RESUMO

The ubiquity of smartphones and the growth of computing resources, such as connectivity, processing, portability, and power of sensing, have greatly changed people's lives. Today, many smartphones contain a variety of powerful sensors, including motion, location, network, and direction sensors. Motion or inertial sensors (e.g., accelerometer), specifically, have been widely used to recognize users' physical activities. This has opened doors for many different and interesting applications in several areas, such as health and transportation. In this perspective, this work provides a comprehensive, state of the art review of the current situation of human activity recognition (HAR) solutions in the context of inertial sensors in smartphones. This article begins by discussing the concepts of human activities along with the complete historical events, focused on smartphones, which shows the evolution of the area in the last two decades. Next, we present a detailed description of the HAR methodology, focusing on the presentation of the steps of HAR solutions in the context of inertial sensors. For each step, we cite the main references that use the best implementation practices suggested by the scientific community. Finally, we present the main results about HAR solutions from the perspective of the inertial sensors embedded in smartphones.

Assuntos

RESUMO

Pollution is a growing environmental problem throughout the world, and the impact of human activities on biodiversity and the genetic variability of natural populations is increasingly preoccupying, given that adaptive processes depend on this variability, in particular that found in the repetitive DNA. In the present study, the mitochondrial DNA (COI) and the distribution of repetitive DNA sequences (18S and 5S rDNA) in the fish genome were analysed in fish populations inhabiting both polluted and unpolluted waters in the northern Amazon basin. The results indicate highly complex ribosomal sequences in the fish genome from the polluted environment because these sequences are involved primarily in the maintenance of genome integrity, mediated by a systematic increase in the number of copies of the ribosomal DNA in response to changes in environmental conditions.

Assuntos

RESUMO

Motivated by the importance of studying the relationship between habits of students and their academic performance, daily activities of undergraduate participants have been tracked with smartwatches and smartphones. Smartwatches collect data together with an Android application that interacts with the users who provide the labeling of their own activities. The tracked activities include eating, running, sleeping, classroom-session, exam, job, homework, transportation, watching TV-Series, and reading. The collected data were stored in a server for activity recognition with supervised machine learning algorithms. The methodology for the concept proof includes the extraction of features with the discrete wavelet transform from gyroscope and accelerometer signals to improve the classification accuracy. The results of activity recognition with Random Forest were satisfactory (86.9%) and support the relationship between smartwatch sensor signals and daily-living activities of students which opens the possibility for developing future experiments with automatic activity-labeling, and so forth to facilitate activity pattern recognition to propose a recommendation system to enhance the academic performance of each student.

Assuntos

RESUMO

Raptor species conservation should consider a landscape perspective in order to include habitat requirements associated to large home ranges around nesting sites. Landscape analysis can help to better understand raptor habitat requirements and the degree of tolerance to habitat changes at different scales.We used a landscape ecology perspective to determine the nesting habitat selection of endemic and endangered Cuban Black Hawk, and using ecological niche modeling, we obtained the potential distribution of nests to evaluate the effectiveness of protected areas (PAs) for raptor conservation.Nesting habitat selection was related to breeding success at a landscape scale using data from 27 different nesting sites during 2012-2013 breeding seasons. The potential nesting areas distribution was compared with current officially PAs design in the central region of Cuba.All nests were located in mangrove swamp. Pairs chose nesting sites with low soil-vegetation moisture and low soil reflectance. At the landscape level, they selected low shape complexity of patches and few patches of coastal vegetation around nesting sites which contained similar mangrove patch size and shape. The potential distribution of nests increased close to the coastline. The model predicted a suitable narrow area of 556 km2, and the most favorable nesting area represented 2% of this total. 33% of nests were located within officially natural protected areas while 27% were close to or inside highly threatened areas. A 16% of high to medium suitable nesting habitat overlaps with urban areas. Currently, PAs contain 23% of the nesting area distribution.Our study shows landscape ecology and nest-site selection approach is crucial to evaluate the persistence of Cuban Black Hawk, as environmental variables and human activity can be related to its productivity. This approach can be applied in conservation strategies of island raptors.

RESUMO

The present study aimed to evaluate the distribution and origin of 16 Priority PAHs in surficial sediment samples of Todos os Santos Bay (TSB, Brazil). Total PAHs concentrations ranged from below the method detection limit (

Assuntos