Cube Framework Research Articles

Enhancing GeoHealth: A step-by-step procedure for spatiotemporal disease mapping.

Cartography, or geographical visualization of disease is an essential aspect of the field of GeoHealth, yet there is limited guidance on the visualization of spatiotemporal disease maps. In order to adequately contribute to understanding disease outbreaks, disease maps should be crafted carefully and according to relevant cartographic guidelines. This article aims to increase the understanding of space-time visualization techniques that are relevant to the field of GeoHealth, by providing a step-by-step framework for the creation of space-time disease visualizations. This study introduces a systematic approach to spatiotemporal disease mapping by integrating operations from the Generalized Space Time Cube (GSTC) Framework with established cartographic symbology guidelines. This resulted in an overview table that contains both the relevant GSTC operations and cartographic guidelines, as well as a step-by-step procedure that guides users through the process of creating informative spatiotemporal disease maps. The practical application of this step-by-step procedure is demonstrated with an example using Dutch COVID-19 data. By providing a clear, practical step by step procedure, this study enhances the capacity of public health professionals, policymakers, and researchers to monitor, understand, and respond to the spatial and temporal dynamics of diseases.

The EL-BIOS Earth Observation Data Cube for Supporting Biodiversity Monitoring in Greece

In recent years, the need to protect and conserve biodiversity has become more critical than ever before, as a prerequisite for both sustainable development and the very survival of the human species. This has made it a priority for the scientific community to develop technological solutions that provide data and information for monitoring, directly or indirectly, biodiversity and the drivers of change. A new era of satellite earth observation upgrades the potential of Remote Sensing (RS) to support, at relatively low cost, but with high accuracy the extraction of information over large areas, at regular intervals, and over extended periods of time. Also, the recent development of the Earth Observation Data Cubes (EODC) framework facilitates EO data management and information extraction, enabling the mapping and monitoring of temporal and spatial patterns on the Earth’s surface. This submission presents the ELBIOS EODC, specifically developed to support the biodiversity management and conservation over Greece. Based on the Open Data Cube (ODC) framework, it exploits multi-spectral optical Copernicus Sentinel-2 data and provides a series of Satellite Earth Observation (SEO) biodiversity products and spectral indices nationwide.

Hydropower Politics in Northeast India: Dam Development Contestations, Electoral Politics and Power Reconfigurations in Sikkim

Around the world, the development of large dams has been increasingly contested. India is no exception and has seen the mobilisation of powerful domestic and transnational socio-environmental movements against dams over more than four decades. In this context, the State of Sikkim in northeast India has been entangled in prolonged hydropower development conflicts since the late 1990s. This article analyses these conflictive entanglements between the Government of India, the State Government of Sikkim, power companies and Sikkim’s autochthonous tribe, the Lepchas. It zooms in on the period of 2011–2017, which saw an abrupt escalation of the conflicts to analyse the messy, deeply political and often unpredictable and contradictory world of dam construction and its contestations. Our analysis is informed by the power cube framework developed by John Gaventa. Our analysis shows how hydropower development is deeply intertwined with local patronage relationships. We show how local elections bring out dam conflict and the operation of power into the open, sometimes leading to abrupt and unexpected switches in positions in relation to hydropower development. We show that these switches should be seen not only as “strategic electoral tactics” but also and importantly as contentious political struggles that (re)configure power in the region. We show how in this process, powerful political actors continuously seek to stabilise power relations among the governing and the governed, choreographing a specific socio-hydraulic order that stretches way beyond simple pro- and anti-dam actors and coalitions as it is embedded in deep hydro(-electro) politics and power plays.

C2A-DC: A context-aware adaptive data cube framework for environmental monitoring and climate change crisis management

A context-aware adaptive data cube (C2A-DC) framework based on Earth Observation (EO) data for environmental monitoring to mitigate Climate Change (CC) effects is proposed. It has the property of combining DC formation, calculation of Remote Sensing (RS) operations, deep and machine learning algorithms for classification and data harmonization by updating the layers of the DC using information obtained from the previous operations, all applied to EO data originating from a satellite selected by a stakeholder. Moreover, the proposed framework is context-aware adaptive in the sense that it allows for environmental monitoring tasks according to a stakeholder’s needs, which is in contrast to existing works being constrained to a single type of environmental monitoring. Furthermore, with the proposed framework end-users do not have to make any additional processing action to handle the data as they are given to them as a set of analysis-ready data, harmonically aligned under a unique DC. To showcase the C2A-DC framework’s effectiveness and applicability in environmental monitoring, crisis management, and post-disaster assessment, it is applied to two real-world cases. The first one corresponds to cloud and shadow detection in a flood event, with several Convolutional Neural Network (CNN) architectures being trained on the C2S-MS floods dataset for classification, while k-means clustering and RS product calculations are used for monitoring scenery changes. The second one corresponds to deforestation monitoring from fires, evaluated using RS products.

Competencies for Psychology Practice in Pediatric Palliative Care.

Pediatric psychologists have unique expertise to contribute to the care of youth with serious illnesses yet are not routinely integrated into pediatric palliative care (PPC) teams. To better define the role and unique skillset of psychologists practicing in PPC, support their systematic inclusion as part of PPC teams, and advance trainee knowledge of PPC principles and skills, the PPC Psychology Working Group sought to develop core competencies for psychologists in this subspecialty. A Working Group of pediatric psychologists with expertise in PPC met monthly to review literature and existing competencies in pediatrics, pediatric and subspecialty psychology, adult palliative care, and PPC subspecialties. Using the modified competency cube framework, the Working Group drafted core competencies for PPC psychologists. Interdisciplinary review was conducted by a diverse group of PPC professionals and parent advocates, and competencies were revised accordingly. The six competency clusters include Science, Application, Education, Interpersonal, Professionalism, and Systems. Each cluster includes essential competencies (i.e., knowledge, skills, attitudes, roles) and behavioral anchors (i.e., examples of concrete application). Reviewer feedback highlighted clarity and thoroughness of competencies and suggested additional consideration of siblings and caregivers, spirituality, and psychologists' own positionality. Newly developed competencies for PPC psychologists highlight unique contributions to PPC patient care and research and provide a framework for highlighting psychology's value in this emerging subspecialty. Competencies help to advocate for inclusion of psychologists as routine members of PPC teams, standardize best practices among the PPC workforce, and provide optimal care for youth with serious illness and their families.

Geographic prioritisation in Kenya and Uganda: a power analysis of donor transition

Introduction Donor transition for HIV/AIDS programmes remains sensitive, marking a significant shift away from the traditional investment model of large-scale, vertical investments to control the epidemic and achieve rapid scaling-up...

Throwing and manipulating and cheating with a DNA nano-dice

Artificial molecular machines have captured the imagination of researchers, given their clear potential to mimic and influence human life. Key to behavior simulation is to reproduce the specific properties of physical or abstract systems. Dice throwing, as a stochastic model, is commonly used for result judgment or plan decision in real life. In this perspective we utilize DNA cube framework for the design of a dice device at the nanoscale to reproduce probabilistic events in different situations: equal probability, high probability, and low probability. We first discuss the randomness of DNA cube, or dice, adsorbing on graphene oxide, or table, and then explore a series of events that change the probability through the way in which the energy released from entropy-driven strand displacement reactions or changes in intermolecular forces. As such, the DNA nano-dice system provides guideline and possibilities for the design, engineering, and quantification of behavioral probability simulation, a currently emerging area of molecular simulation research.

Evaluating the benefit of early patient and public involvement for product development and testing with small companies.

There is a growing understanding of the benefits of patient and public involvement (PPI), and its evaluation, in research. An online version of the CUBE PPI evaluation framework has been developed. We sought to use the CUBE to evaluate the value of early PPI with two small healthcare companies during product development. Contributors were recruited online and had lived experience of either type 1 diabetes or obesity. Two1-hsessions were run with a company developing a smartphone application to manage diabetes (DEE-EM): one with young people (YP; n = 5) and one with parents (n = 7). Two 1-hsessions were run with a company developing a weight-loss product, both with adults (n = 7 in each session). Sessions were facilitated by an independent University researcher and attended by company representatives, who presented their product. One facilitator led the evaluation of the session by giving a demonstration of the CUBE and asking simple questions in the YP session. A high proportion of contributors completed the CUBE (80.5% DEE-EM; 93% Oxford Medical Products). Responses were positive to all four CUBE dimensions (in italics). Contributors felt there were diverse ways to contribute to the sessions, and that they had a strong voice to add to the discussion. Balance was achieved regarding whose concerns (public or company) led the agenda, and contributors felt that both companies would make changes based on the discussion. The supportive attitude of both companies resulted in most contributors feeling comfortable participating in PPI sessions with the industry, while recognising the profit-making aspect of their work. PPI with small healthcare companies is both feasible and worthwhile. The CUBE framework facilitated the evaluation of the interaction between experts in different knowledge spaces. We provide recommendations for future projects, including considerations of who should participate and the level of implicit endorsement of the product that participation implies. People with lived experience of type 1 diabetes or obesity were invited to contribute to one of four PPI sessions, which they then evaluated. One contributor agreed to contribute to the analysis of the evaluation data and interpretation and preparation of the manuscript.

Decentralized Identity Management Using Blockchain

This article explores the usage of decentralised identity (DID) management using blockchain in global organisations to support secure usage of information resources. Blockchain as technology was initially introduced as a cryptocurrency and there have been challenges in its adoption for enterprise applications such as identity management. DID is emerging as one of the strong blockchain adoption use cases. Industry pioneers and users across domains have started exploring DID use cases, which help better protect their personal data and application access control as compared to traditional, central, or federated identity management models. In this exploratory work, the authors employ qualitative secondary case-based study research methodology to understand the challenges of the current digital identity management landscape and explore the possible benefits of DID as an emerging identity management paradigm. They propose a conceptual cube framework for analysing and studying various DID platforms thereby contributing to both the theory and practice of digitally secure identity.

China Data Cube (CDC) for Big Earth Observation Data: Practices and Lessons Learned

In the face of tight natural resources and complex as well as volatile environments, and in order to meet the pressure brought by population growth, we need to overcome a series of challenges. As a new data management paradigm, the Earth Observation Data Cube simplifies the way that users manage and use earth observation data, and provides an analysis-ready form to access big spatiotemporal data, so as to realize the greater potential of earth observation data. Based on the Open Data Cube (ODC) framework, combined with analysis-ready data (ARD) generation technology, the design and implementation of CDC_DLTool, extending the support for data loading and the processing of international and Chinese imagery data covering China, this study eventually constructs the China Data Cube (CDC) framework. In the framework of this CDC grid, this study carried out case studies of water change monitoring based on international satellite imagery data of Landsat 8 in addition to vegetation change monitoring based on Chinese satellite imagery data of GF-1. The experimental results show that, compared with traditional scene-based data organization, the minimum management unit of this framework is a pixel, which makes the unified organization and management of multisource heterogeneous satellite imagery data more convenient and faster.

Ultra-thin graphene cube framework@TiO2 heterojunction as high-performance anode materials for lithium ion batteries

Here, we proposed a new strategy to build the integrated graphene cube (Gr) framework@TiO2 composite to improve the ion transport kinetics and electrical conductivity of TiO2 as a long-life and high-capacity anode for lithium ion batteries. Combined with the salt template method for ultra-thin framework, the distinct structure of Gr@TiO2 shows an excellent electrochemical performance, e.g., initial coulombic efficiency (ICE), rate performance and specific capacity, due to the increased kinetics of lithium ions. Through this method, the integrity is dramatically improved and the pulverization and agglomeration of the anode after long-term cycles are restrained. The optimized Gr@TiO2 displays a high stable reversible capacity of 179.5 mAh g−1 after 4000 cycles at 1 A g−1, excellent rate performance (125.5 mAh g−1 at 5 A g−1). Kinetic studies through Electrochemical Impedance Spectra, Galvanostatic Intermittent Titration Technique and Linear Sweep Voltammetry confirm that the electrical conductivity and ion transport kinetics are dramatically improved through the ultra-thin graphene cube framework as a heterojunction structure of Gr@TiO2.

Solving software project scheduling problem using grey wolf optimization

In this paper, we will explore the application of grey wolf optimization (GWO) methodology in order to solve the software project scheduling problem (SPSP) to seek an optimum solution via applying different instances from two datasets. We will focus on the effects of the quantity of employees as well as the number of tasks which will be accomplished. We concluded that increasing employee number will decrease the project’s duration, but we could not find any explanation for the cost values for all instances that studied. Also, we concluded that, when increasing the number of the tasks, both the cost and duration will be increased. The results will compare with a max-min ant system hyper cube framework (MMAS-HC), intelligent water drops algorithm (IWD), firefly algorithm (FA), ant colony optimization (ACO), intelligent water drop algorithm standard version (IWDSTD), and intelligent water drop autonomous search (IWDAS). According to these study and comparisons, we would like to say that GWO algorithm is a better optimizing tool for all instances, except one instance that FA is outperform the GWO.

Integrating Open Data Cube and Brazil Data Cube Platforms for Land Use and Cover Classifications

The potential to perform spatiotemporal analysis of the Earth's surface, fostered by a large amount of Earth Observation (EO) open data provided by space agencies, brings new perspectives to create innovative applications. Nevertheless, these big datasets pose some challenges regarding storage and analytical processing capabilities. The organization of these datasets as multidimensional data cubes represents the state-of-the-art in analysis-ready data regarding information extraction. EO data cubes can be defined as a set of time-series images associated with spatially aligned pixels along the temporal dimension. Some key technologies have been developed to take advantage of the data cube power. The Open Data Cube (ODC) framework and the Brazil Data Cube (BDC) platform provide capabilities to access and analyze EO data cubes. This paper introduces two new tools to facilitate the creation of land use and land over (LULC) maps using EO data cubes and Machine Learning techniques, and both built on top of ODC and BDC technologies. The first tool is a module that extends the ODC framework capabilities to lower the barriers to use Machine Learning (ML) algorithms with EO data. The second tool relies on integrating the R package named Satellite Image Time Series (sits) with ODC to enable the use of the data managed by the framework. Finally, water mask classification and LULC mapping applications are presented to demonstrate the processing capabilities of the tools.

A harmful religio-cultural practice (Chhaupadi) during menstruation among adolescent girls in Nepal: Prevalence and policies for eradication.

Chhaupadi is a deeply rooted tradition and a centuries-old harmful religio-cultural practice. Chhaupadi is common in some parts of Karnali and Sudurpaschim Provinces of western Nepal, where women and girls are considered impure, unclean, and untouchable in the menstrual period or immediately following childbirth. In Chhaupadi practice, women and girls are isolated from a range of daily household chores, social events and forbidden from touching other people and objects. Chhaupadi tradition banishes women and girls into menstruation huts', or Chhau huts or livestock sheds to live and sleep. These practices are guided by existing harmful beliefs and practices in western Nepal, resulting in poor menstrual hygiene and poor physical and mental health outcomes. This study examined the magnitude of Chhaupadi practice and reviewed the existing policies for Chhaupadi eradication in Nepal. We used both quantitative survey and qualitative content analysis of the available policies. First, a quantitative cross-sectional survey assessed the prevalence of Chhaupadi among 221 adolescent girls in Mangalsen Municipality of Achham district. Second, the contents of prevailing policies on Chhaupadi eradication were analysed qualitatively using the policy cube framework. The current survey revealed that most adolescent girls (84%) practised Chhaupadi in their most recent menstruation. The Chhaupadi practice was high if the girls were aged 15-17 years, born to an illiterate mother, and belonged to a nuclear family. Out of the girls practising Chhaupadi, most (86%) reported social and household activities restrictions. The policy content analysis of identified higher-level policy documents (constitution, acts, and regulations) have provisioned financial resources, ensured independent monitoring mechanisms, and had judiciary remedial measures. However, middle (policies and plans) and lower-level (directives) documents lacked adequate budgetary commitment and independent monitoring mechanisms. Chhaupadi remains prevalent in western Nepal and has several impacts to the health of adolescent girls. Existing policy mechanisms lack multilevel (individual, family, community, subnational and national) interventions, including financial and monitoring systems for Chhaupadi eradication. Eradicating Chhaupadi practice requires a robust multilevel implementation mechanism at the national and sub-national levels, including adequate financing and accountable systems up to the community level.

ACCESSING AND PROCESSING BRAZILIAN EARTH OBSERVATION DATA CUBES WITH THE OPEN DATA CUBE PLATFORM

Abstract. Recently, several technologies have emerged to address the need to process and analyze large volumes of Earth Observations (EO) data. The concept of Earth Observations Data Cubes (EODC) appears, in this context, as the paradigm of technologies that aim to structure and facilitate the way users handle this type of data. Some projects have adopted this concept in developing their technologies, such as the Open Data Cube (ODC) framework and the Brazil Data Cube (BDC) platform, which provide open-source tools capable of managing, processing, analyzing, and disseminating EO data. This work presents an approach to integrate these technologies through the access and processing of data products from the BDC platform in the ODC framework. For this, we developed a tool to automate the process of searching, converting, and indexing data between these two systems. Besides, four ODC functional modules have been customized to work with BDC data. The tool developed and the changes made to the ODC modules expand the potential for other initiatives to take advantage of the features available in the ODC.

Creativity and Register

The discussion on how creativity can be described in terms of register, field, tenor, and mode has been established in Law’s (2019b) creativity-in-register cube framework (CIRCF). This article is a sequel to that discussion by demonstrating how the CIRCF can be used in the quantitative analysis of Carter’s (2004) pattern-forming creativity, using the dialogues of TV drama House, M.D. (Shore, 2005) as examples. This article begins with an introductory review of background information and relevant literature on linguistic creativity and systemic functional linguistics, before focusing on verbal repetition as a resource for pattern-forming creativity. The methodology section includes a brief description of the CIRCF and the procedures for the computer-assisted extraction of pattern-forming creativity. The analysis section provides a walk-through of the CIRCF in the multidimensional semiotic analysis of pattern-forming creativity, both numerically and graphically, involving different registers, and variables under field, tenor and mode. Possible future research topics are included in the conclusion section.

Application of Deep Learning Algorithm to Build an Automated Cloud Segmentation Model Based on Open Data Cube Framework

Cloud detection is a significant task in optical remote sensing to reconstruct the contaminated cloud area from multi-temporal satellite images. Besides, the rapid development of machine learning techniques, especially deep learning algorithms, can detect clouds over a large area in optical remote sensing data. In this study, the method based on the proposed deep-learning method called ODC-Cloud, which was built on convolutional blocks and integrating with the Open Data Cube (ODC) platform. The results showed that our proposed model achieved an overall 90% accuracy in detecting cloud in Landsat 8 OLI imagery and successfully integrated with the ODC to perform multi-scale and multi-temporal analysis. This is a pioneer study in techniques of storing and analyzing big optical remote sensing data.

Space-time demand cube for spatial-temporal coverage optimization model of shared bicycle system: A study using big bike GPS data

As a sustainable transport mode, bicycle sharing is increasingly popular and the number of bike-sharing services has grown significantly worldwide in recent years. The locational configuration of bike-sharing stations is a basic issue and an accurate assessment of demand for service is a fundamental element in location modeling. However, demand in conventional location-based models is often treated as temporally invariant or originated from spatially fixed population centers. The neglect of the temporal and spatial dynamics in current demand representations may lead to considerable discrepancies between actual and modeled demand, which may in turn lead to solutions that are far from optimal. Bike demand distribution varies in space and time in a highly complex manner due to the complexity of urban travel. To generate better results, this study proposed a space-time demand cube framework to represent and capture the fine-grained spatiotemporal variations in bike demand using a large shared bicycle GPS dataset in the “China Optics Valley” in Wuhan, China. Then, a more spatially and temporally accurate coverage model that maximizes the space-time demand coverage and minimizes the distance between riders and bike stations is built for facilitating bike stations location optimization. The results show that the space-time demand cube framework can finely represent the spatiotemporal dynamics of user demand. Compared with conventional models, the proposed model can better cover the dynamic needs of users and yields ‘better’ configuration in meeting real-world bike riding needs.

Governance and Power Dynamics in a Small-Scale Hilsa Shad (Tenualosa ilisha) Fishery: A Case Study from Bangladesh

This paper considers the hilsa shad (Tenualosa ilisha) fishery of southern Bangladesh as a case study regarding governance and power dynamics at play in a small-scale fishery, and the relevance of these for the sustainable management of coastal fisheries. Qualitative methods, involving in-depth individual interviews (n = 128) and focus group discussions (n = 8) with key stakeholders in the hilsa fishery, were used to capture multiple perspectives on governance from those in different positions in the relative power structures studied, while facilitating insightful discussions and reflections. The analysis here is based on a power cube framework along three power dimensions (levels, spaces, and forms) in Bangladesh’s hilsa fishery. The study displays an imbalance in the present hilsa governance structure, with some stakeholders exercising more power than others, sidelining small-scale fishers, and encouraging increasing illegal fishing levels that ultimately harm both the fisheries and those dependent on them. To overcome this, we propose a co-management system that can play a vital role in equalizing power asymmetry among hilsa fishery stakeholders and ensure effective hilsa fishery governance. Our results suggest that recognizing analyzed power dynamics has substantial implications for the planning and implementation of such co-management and the long-term sustainability of the hilsa fishery.

New Urbanization paths in mineral resource abundant regions in China: A three-dimensional cube framework

The proposal of the New Urbanization strategy is not only the result of introspection of extensive urbanization practices in recent decades in China, but also the result of application of the ancient Chinese traditional philosophy “unity of man and nature”, which coincides with modern international idea of sustainable development. Mineral resource abundant regions (MRARs) in China face unique difficulties and challenges in advancing the New Urbanization. It is of great importance to construct a systematic framework of New Urbanization paths for the MRARs in China to effectively guide the New Urbanization there. According to the requirements of strong sustainability, with consideration for the characteristics of MRARs in China, and through systematic summary of the mechanisms of urbanization and regional development discovered by researchers, this paper finds the determining factors for the paths of New Urbanization in MRARs, which are environmental carrying capacity, social and economic foundations, and geographical location conditions. Based on the three determining factors, a three-dimensional cube framework of New Urbanization paths for MRARs is constructed. According to the three-dimensional cube framework, there are two types of path for New Urbanization in MRARs: simple urbanization and intensive urbanization, and each type includes four paths: resource-based, comprehensive, location-based, and central-city urbanization. As a result, there are eight paths for New Urbanization in MRARs in China. These paths differ significantly in their characteristics and advancing points. Under certain conditions, the two types and four urbanization paths are inter-transformable. When formulating advancement policy, it is vital to respect the laws of the nature and the laws of social and economic development, apply cleaner production and circular economy, and adhere to people-oriented principles.