1,721,057 research outputs found
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Towards Transferable Pollution Detection Methods in Aquatic Environments Using Hyperspectral Technology
No full textPrograma de Doctorado en Tecnologías de Telecomunicación e Ingeniería Computacional por la Universidad de Las Palmas de Gran CanariaThis thesis presents a suite of transferable methodologies for environmental
monitoring, focusing on detecting pollutants in aquatic ecosystems. Leveraging
remote sensing with hyperspectral imaging (HSI) and artificial intelligence (AI),
these approaches enable precise pollutant identification and adaptability across
diverse scenarios. Thus, supporting systematic observation over extensive
geographic regions or long-term datasets contributes to developing standardized
solutions for monitoring and protecting Earth’s ecosystems.
Aquatic pollution threatens biodiversity and ecosystem services, especially oil
spills and plastic waste. Oil spills spread quickly across water surfaces, blocking
sunlight and endangering aquatic biodiversity. Plastic waste accumulates
in water bodies, breaking into microplastics that marine organisms ingest,
disrupting food chains. Both pollutants are especially critical to monitor because
they have profound, long-term impacts on ecosystems and food webs. Dye
tracers such as rhodamine can be used as a proxy in oil spill simulations due
to their similar dispersion behaviour in water. This aids in the development of
detection and monitoring techniques for real oil spill events. However, there is
a need for efficient and transferable aquatic pollution monitoring technologies.
HSI is a powerful tool for identifying aquatic pollutants due to its rich spectral
detail. However, the massive data it generates is costly and complex to process,
posing key challenges. Dimensionality reduction techniques—such as spectral
indices and band selection—can trim redundant data, reducing storage and
computational demand while maintaining accuracy. Furthermore, the scarcity of
labelled datasets hinders AI model training. Unsupervised learning approaches
offer a promising solution, enabling models to extract meaningful patterns from
non-labelled data, making HSI more adaptable across diverse environments.
This thesis aims to pioneer efficient and transferable HSI methodologies
for detecting and monitoring critical aquatic pollutants. It focuses on
developing novel approaches that streamline data analysis and improve
transferability across diverse environments. The research progresses from wellestablished, straightforward methods such as spectral indexes to cutting-edge
AI methodologies to enhance pollutant detection. Each chapter focuses on
different HSI technology needs, overcoming the challenges of data complexity,
dimensionality, and the scarcity of labelled datasets. This creates a cohesive
framework that addresses the demands of large-scale environmental monitoring
with adaptable HSI methods. Chapter 2 reduces HSI data complexity by introducing the Normalized
Difference O il I ndex ( NDOI), a n ew s pectral i ndex d esigned t o i mprove oil
spill detection in coastal areas. The study compares the performance of
several spectral indices utilizing images from multiple satellite and airborne
sensors—AVIRIS, HICO, and MERIS—captured during the Deepwater Horizon
disaster in the Gulf of Mexico in 2010. Traditional indices often misclassify
other elements, such as suspended sediments, leading to inaccurate results.
The NDOI avoids sand-in-suspension false positives, offering a m ore reliable
response in coastal areas. NDOI is suitable for detecting oil spills thicker than
50 microns, with an average oil F1-score of 83%, and estimating its thickness
and oil volume exceeding 90% accuracy. It also provides rapid detection of oil
spills due to its simple calculation compared with other spectral indices and
IA models, which is crucial for quick responses to environmental crises. This
development directly contributes to optimizing the use of optical sensors for fast
and efficient pollutant detection.
Chapter 3 tackles the challenge of hyperspectral data’s high dimensionality
by presenting a new dimensionality reduction methodology. The spectral
band selection method identifies t he m ost r elevant b ands f or d etecting specific
pollutants, with testing conducted on plastics and rhodamine. This minimises
redundant or irrelevant bands, reducing computational cost and resource
demands. This methodology has been applied to laboratory images and
outdoor experiments, focusing on analyzing the impact of background effects on
identifying target objects. The methodology successfully transferred influential
spectral bands between datasets with 80-90% accuracy, indicating the potential
for developing specialized sensors with these common bands to enable detection
across various environments. However, the transfer of pre-trained classification
models remains an area for further research, particularly regarding semitransparent objects or solutions influenced by background reflections in complex
environments like optically shallow waters. Refined p ost-processing approaches
suggest that model transfer could be feasible, potentially reducing the need for
labelled data or in-situ validation, thus preserving resources and enabling a more
generalizable classifier.
Chapter 4 addresses the scarcity of labelled data in HSI and AI applications
through the spectral loss function (Sl), which enhances HSI segmentation in
unsupervised neural networks. The loss function is tested on HSI benchmark
datasets, such as Pavia University, Salinas Valley, Indian Pines, and University of
Houston, and a case study using an AVIRIS image from the Deepwater Horizon
catastrophe. Sl was introduced in the currently best-performing unsupervised
segmentation neural network, enhancing evaluation metrics performance by up
to 6%. The proposed method also outperforms well-established techniques,
such as spectral indices. For example, spectral indices rely on few spectral
bands to produce a numerical value for each pixel, requiring an expert to establish a threshold for class determination. In contrast, the unsupervised
neural network can directly assign different classes to varying oil thicknesses
based on their complete spectral response, making them fully transferable across
environments. Therefore, the unsupervised approach can generate ground-truth
data, reducing manual labour. This is especially relevant for remote areas such
as the open ocean, where manual labelling is challenging and resource-intensive.
This contribution expands the scope of AI-driven detection techniques to operate
without labelled datasets, thereby enhancing adaptability.
This thesis concludes with a synthesis of the main insights from each
chapter, a reflection on the implications and limitations identified, and
suggestions for future research directions. The thesis successfully develops
new transferable HSI methods that enhance the detection and monitoring
of aquatic pollutants, overcoming critical knowledge gaps. Spectral indices,
such as NDOI, provide a quick and efficient solution for rapid, low-resource
decision-making but rely on manual thresholding. Band selection methods
help identify critical spectral bands, which can improve model transfer and
generalization across environments. Unsupervised methods complement these
techniques by addressing non-labelled datasets, providing a foundation for
large-scale monitoring. This research contributes to more efficient, adaptable,
and transferable environmental monitoring technologies by addressing critical
challenges related to data complexity, dimensionality reduction, and the scarcity
of labelled datasets. The strengths and weaknesses of this suite of methods
should be carefully considered to select the most appropriate approach based on
each study’s specific characteristics.
These advancements hold promise for designing next-generation sensors for
UAVs and space missions, prioritizing data efficiency and precision. Moreover,
the techniques directly apply to environmental management, including early
spill detection, beach cleanup coordination, and supporting data-driven policies.
Future work will focus on further automating hyperspectral monitoring
techniques to minimize manual intervention. Efforts will be directed toward
improving algorithm transferability by incorporating more variability in training
data and advancing post-processing techniques. Additionally, the scalability of
emerging tools, such as cloud computing, will be explored to improve efficient
large-scale monitoring
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Urban Material Flow Analysis of Plastics: A Case Study for Leiden
No full textGlobal plastic production increased from 200 million metric tons in 2002, to 359 million metric tons in 2019. It is estimated that plastics in the marine environment weigh more than 150 million metric tons and by 2050 could even outweigh the mass of fish in the ocean. Over half of the world population (55%) nowadays lives in urban areas and consumes large amounts of materials and turns them into waste, including plastics. Thus, it necessary to understand the plastic mass balance of a city. The goal of this thesis is to assess plastic flows and stocks for an urban area, using the well-established method of Material Flow Analysis (MFA). By using MFA, material flows through society can be assessed in a systematic way. This research answers the following research question: “How can the plastic mass balance of an urban area be modelled using Material Flow Analysis?” This is done by developing a generic framework, which includes eight consumption sectors, three distinguishable littering processes and two subsystems (surface water system and soil). A bottom-up approach is applied and the municipality of Leiden in the Netherlands is used as a case study. The surface water system is analysed as a subsystem, which stores plastics but also transports them through physical forces, like water flow outside of the system. Crowd-sourced data is analysed and its usage for urban MFA assessed. The analysis shows that the biggest plastic flows are packaging flows (4 146 t), plastics in building waste (1 342 t) and plastics in End-of-life transportation systems (570 t). The highest plastic stocks are in buildings (86 080 t), transportation (14 020 t) and electronics devices (10 326 t). It is concluded that the crowd-sourced data set Litterati includes for 2019 for the case of Leiden to little data points in order to represent the plastic litter quantity in the system accurately. This thesis adds to MFA theory by investigating the surface water system and its connection to the environment through physical forces like water flow and wind.Industrial Ecolog
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
Four Reasons Why All Geoscientists Should Do Fieldwork
No full textAs geoscientists we often visit strange and distant places to collect data that will help answer science questions and interrogate hypotheses. In an airport, we are easily recognized: hiking shoes, too many checked bags (read: cases with equipment), malaria medication, and a letter from your professor stating that all your activities will be strictly in the name of science. Fieldwork can be exciting, horrible, enlightening, disappointing, surprising, or simply a complete failure
Below the surface: A laboratorial research to the vertical distribution of buoyant plastics in rivers
No full textRivers are identified as main sources of plastic litter in oceans. About 65% of the plastic litter is buoyant in fresh waters, meaning it has the capability to float, making transport over rivers relatively easy. A better understanding of how plastic litter is transported via rivers is crucial. Both for quantification and mitigation of the plastic problem. Most research on quantification of the plastic flux is based on surface-measurements only, up till about 50 cm water-depth. Thereby, most cleaning strategies focus on skimming only the surface. This research investigates the distribution of buoyant plastic litter over the water depth in rivers. Given the wide use of marginal buoyant plastics, it is hypothesized that a significant share of the plastic in rivers is transported below the first 50 cm surface-water, due to the mixing ability of turbulent flow. In such case, a great share of the plastic litter is overlooked in both flux estimates and riverine removal strategies. The question arises: How is plastic distributed over the water depth in rivers and how is this distribution related to prevailing flow conditions? The research is based on four pillars: Knowledge on the hydraulic plastic parameters (1), in combination with experimental observations (2), might lead to an explanation of the distribution with a theoretical approximation (3), based on existing literature from neighboring research fields. Lastly, manipulation (4) of this plastic distribution by hydraulic interventions is investigated. With this research, a first insight is created on the vertical behavior of plastic in relation to the flow conditions. This study shows that marginal buoyant plastics can be sensitive to turbulent motions in flow and a significant amount of plastic might be transported below the surface. In order to create a complete picture of the behavior of different kinds of plastic in stream flows, more extensive research is needed
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