176 research outputs found

    Time for a Nappy Change: beliefs and attitudes towards modern cloth nappies.

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    The United Nations Environment Programme highlights how the use of disposable nappies has become unsustainable, yet the practice of using modern cloth nappies (MCN) is niche. This study uses mixed methods of survey, story completion and focus group methods to explore how behaviour beliefs and attitudes to behaviour contribute to families’ decision making regarding the nappy system they use for their children. 1588 responded to the survey; 38 completed story completion activity; 24 participated in groups. This study finds that beliefs about the performance as a nappy, environmental credentials, financial considerations, laundry, effort, and hygiene differ according to the level of personal experience of using MCN. While beliefs about the environmentalcredentials of MCN create powerful drivers for the intention to use MCN, other beliefs about the upfront costs, laundry and effort contribute a negative attitude to MCN overall if their support network of other MCN users is not established. Current MCN users found using cloth nappy retailer websites, nappy libraries, and social media groups, including pre-loved and-sell groups, to be beneficial in improving attitude to MCN. This study concludes that interventions that simultaneously reduce or remove perceived barriers such as upfront costs, financial risks and too much effort, paired with campaigns which increase the likelihood of finding support, are more likely, than individual interventions, to be effective in increasing the number of families using MCN.Further study is needed to investigate the potential of interventions which reduce the financial risks such as, easy to access hire kits, spread the cost of MCN and pre-natal and newborn public services such as midwives and health visitors being well informed and encouraging of the use of MCN.<br/

    Illuminating Hidden Harvests – The contributions of small-scale fisheries to sustainable development

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    Small-scale fisheries account for at least 40 percent of the global catch from capture fisheries and provide employment across the value chain for an estimated 60.2 million people, about 90 percent of the total number employed in fisheries globally. The economic value of these fisheries, however, is only a part of their importance: for example, nearly 53 million additional people were estimated to be engaged in subsistence activities in 2016. Rightly considered from a holistic and integrated perspective, small-scale fisheries define the livelihoods, nutrition and culture of a substantial and diverse segment of humankind. This study, Illuminating Hidden Harvests: the contributions of small-scale fisheries to sustainable development (hereinafter Illuminating Hidden Harvests, or IHH), uncovers the contributions and impacts of small-scale fisheries through a multidisciplinary approach to data collection and analysis. The study provides information that quantifies and improves understanding of the crucial role of small-scale fisheries in the areas of food security and nutrition, sustainable livelihoods, poverty eradication and healthy ecosystems. It also examines gender equality as well as the nature and scope of governance in small-scale fisheries, and how this differs between different countries and fishery units. The IHH study was carried out in support of the implementation of the Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries in the Context of Food Security and Poverty Eradication (SSF Guidelines), themselves developed in recognition of the plight of small-scale fishers, fishworkers and associated communities. The SSF Guidelines provide advice and direction for the enhancement of responsible and sustainable small-scale fisheries, through the development and implementation of participatory, ecosystem-friendly policies, strategies and legal frameworks

    Novel deep learning architectures for marine and aquaculture applications

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    Alzayat Saleh's research was in the area of artificial intelligence and machine learning to autonomously recognise fish and their morphological features from digital images. Here he created new deep learning architectures that solved various computer vision problems specific to the marine and aquaculture context. He found that these techniques can facilitate aquaculture management and environmental protection. Fisheries and conservation agencies can use his results for better monitoring strategies and sustainable fishing practices

    Machine learning in marine ecology: an overview of techniques and applications

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    Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets.Machine learning in marine ecology: an overview of techniques and applicationspublishedVersio

    Exploring Animal Behavior Through Sound: Volume 1

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    This open-access book empowers its readers to explore the acoustic world of animals. By listening to the sounds of nature, we can study animal behavior, distribution, and demographics; their habitat characteristics and needs; and the effects of noise. Sound recording is an efficient and affordable tool, independent of daylight and weather; and recorders may be left in place for many months at a time, continuously collecting data on animals and their environment. This book builds the skills and knowledge necessary to collect and interpret acoustic data from terrestrial and marine environments. Beginning with a history of sound recording, the chapters provide an overview of off-the-shelf recording equipment and analysis tools (including automated signal detectors and statistical methods); audiometric methods; acoustic terminology, quantities, and units; sound propagation in air and under water; soundscapes of terrestrial and marine habitats; animal acoustic and vibrational communication; echolocation; and the effects of noise. This book will be useful to students and researchers of animal ecology who wish to add acoustics to their toolbox, as well as to environmental managers in industry and government

    University of Maine Undergraduate Catalog, 2022-2023

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    The University of Maine undergraduate catalog for the 2022-2023 academic year includes an introduction, the academic calendars, general information about the university, and sections on attending, facilities and centers, and colleges and academic programs including the Colleges of Business, Public Policy and Health, Education and Development, Engineering, Liberal Arts and Sciences, and Natural Sciences, Forestry and Agriculture

    Machine learning in marine ecology: an overview of techniques and applications

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    Machine learning covers a large set of algorithms that can be trained to identify patterns in data. Thanks to the increase in the amount of data and computing power available, it has become pervasive across scientific disciplines. We first highlight why machine learning is needed in marine ecology. Then we provide a quick primer on machine learning techniques and vocabulary. We built a database of ∼1000 publications that implement such techniques to analyse marine ecology data. For various data types (images, optical spectra, acoustics, omics, geolocations, biogeochemical profiles, and satellite imagery), we present a historical perspective on applications that proved influential, can serve as templates for new work, or represent the diversity of approaches. Then, we illustrate how machine learning can be used to better understand ecological systems, by combining various sources of marine data. Through this coverage of the literature, we demonstrate an increase in the proportion of marine ecology studies that use machine learning, the pervasiveness of images as a data source, the dominance of machine learning for classification-type problems, and a shift towards deep learning for all data types. This overview is meant to guide researchers who wish to apply machine learning methods to their marine datasets

    3-я Міжнародна конференція зі сталого майбутнього: екологічні, технологічні, соціальні та економічні аспекти (ICSF 2022) 24-27 травня 2022 року, м. Кривий Ріг, Україна

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    Матеріали 3-ої Міжнародної конференції зі сталого майбутнього: екологічні, технологічні, соціальні та економічні аспекти (ICSF 2022) 24-27 травня 2022 року, м. Кривий Ріг, Україна.Proceedings of the 3rd International Conference on Sustainable Futures: Environmental, Technological, Social and Economic Matters (ICSF 2022) 24-27 May 2022, Kryvyi Rih, Ukraine

    Passive acoustic monitoring for assessment of natural and anthropogenic sound sources in the marine environment using automatic recognition

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    In the marine environment, sound can be an efficient source of information. Indeed, several marine species, including fish, use sound to navigate, select habitats, detect predators and prey, and to attract mates. Therefore, all the abiotic, biotic and manmade sounds that comprise the soundscape, have the potential to be used to assess and monitor species and marine environments. Passive acoustic monitoring (PAM) involves the use of acoustic sensors to record sound in the environment, from which relevant ecological information can be inferred. This thesis studied marine soundscapes, with special attention on fish communities, anthropogenic noise, and applied several methods to analyse acoustic recordings. Most of the focus was on the Tagus estuary, where the presence of two highly vocal species is known: the Lusitanian toadfish (Halobatrachus didactylus) and the meagre (Argyrosomus regius). Azorean and Mozambique soundscapes were also analysed. Several methods were applied to extract information and to visualize soundscape characteristics, including sound recognition systems based on hidden Markov models to recognize fish sounds and boat passages. Analysis of several types of marine environments and time scales showed several advantages and disadvantages of different methods. The use of sound pressure level on different frequency bands allowed the quantification of daily and seasonal patterns. Ecoacoustic indices appear to be cost-effective tools to monitor biodiversity in some marine environments. Using automatic recognition, vocal rhythms (diel and seasonal patterns) and vocal interactions among individuals were also characterized. Furthermore, boat noise effects on fish were studied: we encountered impacts on the audition, vocal behaviour and reproduction. Overall, we used PAM as a tool to remotely assess and monitor soundscapes, biodiversity, fish communities’ seasonal patterns, fish behaviour, species presence, and the effect of anthropogenic noise aiming to contribute for the management and conservation of marine ecosystems

    Remote Sensing Applications in Coastal Environment

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    Coastal regions are susceptible to rapid changes, as they constitute the boundary between the land and the sea. The resilience of a particular segment of coast depends on many factors, including climate change, sea-level changes, natural and technological hazards, extraction of natural resources, population growth, and tourism. Recent research highlights the strong capabilities for remote sensing applications to monitor, inventory, and analyze the coastal environment. This book contains 12 high-quality and innovative scientific papers that explore, evaluate, and implement the use of remote sensing sensors within both natural and built coastal environments
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