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Charcoal and firewood use in urban areas of developing countries: Drivers, consequences, and the need for clean cooking solutions
No full textThis study examines the key drivers behind the continued reliance on traditional biomass fuels such as charcoal and firewood in urban areas of developing countries, including the city of Lubumbashi. The paper focuses on economic constraints, health problems associated with the use of these fuels, the environmental consequences of growing use and also looks at the alternatives for cooking and their accessibility. The various reasons behind the growing and constant use of charcoal and firewood are examined in the context of the city of Lubumbashi and other developing countries. However, the continuous supply of charcoal and firewood not only contributes to the degradation of forests and the extinction of species, but also disrupts the livelihoods of forest-dependent families and exacerbates soil erosion. The charcoal production process is intrinsically damaging to both the environment and human well-being. Not only does it emit large quantities of CO 2 , contributing to atmospheric pollution, but it also presents health risks for both producers and users. The smoke and soot generated during charcoal production expose people to harmful substances, leading to adverse health effects and even premature death, particularly among children. This review also discusses the impact of this production and use on the education of women and children, who are responsible for cooking and harvesting firewood, resulting in a higher illiteracy rate among women. Faced with the need to take global action to mitigate the impact of climate change, global carbon dioxide emissions must be drastically reduced to meet the Paris Agreement target of zero net emissions by 2050. A practical and sustainable solution is discussed in this review as an alternative to traditional cooking systems namely solar cooking, which offers enormous potential, provided it is accessible, and is an excellent alternative to the heavy reliance on biomass for household energy needs in developing countries.Funding
This research was financed by the SI funding project Solar Cooker for all (Sc4all) and the BOF-BILA fund of Hasselt University. We would like to thank these organisations for their financial support, which made this study possible.
Acknowledgements
The authors would like to warmly thank the members of the Sc4all team at Hasselt University and the University of Lubumbashi for their invaluable contribution to this project. The authors would also like to thank VLIR-UOS for funding Sylvain Balume's research
Contact patterns of older adults with and without frailty in the Netherlands during the COVID-19 pandemic
No full textBackgroundDuring the COVID-19 pandemic, social distancing measures were imposed to protect the population from exposure, especially older adults and people with frailty, who have the highest risk for severe outcomes. These restrictions greatly reduced contacts in the general population, but little was known about behaviour changes among older adults and people with frailty themselves. Our aim was to quantify how COVID-19 measures affected the contact behaviour of older adults and how this differed between older adults with and without frailty.MethodsIn 2021, a contact survey was carried out among people aged 70 years and older in the Netherlands. A random sample of persons per age group (70-74, 75-79, 80-84, 85-89, and 90 +) and gender was invited to participate, either during a period with stringent (April 2021) or moderate (October 2021) measures. Participants provided general information on themselves, including their frailty, and they reported characteristics of all persons with whom they had face-to-face contact on a given day over the course of a full week.ResultsIn total, 720 community-dwelling older adults were included (overall response rate of 15%), who reported 16,505 contacts. During the survey period with moderate measures, participants without frailty had significantly more contacts outside their household than participants with frailty. Especially for females, frailty was a more informative predictor of the number of contacts than age. During the survey period with stringent measures, participants with and without frailty had significantly lower numbers of contacts compared to the survey period with moderate measures. The reduction of the number of contacts was largest for the eldest participants without frailty. As they interact mostly with adults of a similar high age who are likely frail, this reduction of the number of contacts indirectly protects older adults with frailty from SARS-CoV-2 exposure.ConclusionsThe results of this study reveal that social distancing measures during the COVID-19 pandemic differentially affected the contact patterns of older adults with and without frailty. The reduction of contacts may have led to the direct protection of older adults in general but also to the indirect protection of older adults with frailty.Funding
The SCONE study was fnanced by the Netherlands Organisation for Health Research and Development (ZonMw; grant number 10150511910020).
Acknowledgements
The authors would like to thank all participants of the SCONE (Studying Contacts of Elderly) study for their invaluable input, Inge Besemer for processing the data, and Kylie Ainslie and Brechje de Gier for critically reading the manuscript
EDM-Research/DIMO_ObjectDetection: v1.0
No full textObject detection for the DIMO dataset. Uses the Mask-RCNN model. This is the official implementation of Analysis of Training Object Detection Models with Synthetic Data, published in BMVC: British Machine Vision Conference, 2022.
Source code for the following scientific publication:
Vanherle, B., Moonen, S., Van Reeth, F., and Michiels, N. (2022). Analysis of Training Object Detection Models with Synthetic Data. 33rd British Machine Vision Conference 2022, BMVC 2022, London, UK, November 21-24, 2022. Retrieved from https://bmvc2022.mpi-inf.mpg.de/0833.pdfPILS SBO: Product Inspection with Little Supervision. Flanders Make (Belgium). awardNumber:null. 02ndjfz59BOF Special Research Fund. Hasselt University. awardNumber:null. 10.13039/50110000955
A critique of current approaches to privacy in machine learning
No full textAccess to large datasets, the rise of the Internet of Things (IoT) and the ease of collecting personal data, have led to significant breakthroughs in machine learning. However, they have also raised new concerns about privacy data protection. Controversies like the Facebook-Cambridge Analytica scandal highlight unethical practices in today's digital landscape. Historical privacy incidents have led to the development of technical and legal solutions to protect data subjects' right to privacy. However, within machine learning, these problems have largely been approached from a mathematical point of view, ignoring the larger context in which privacy is relevant. This technical approach has benefited data-controllers and failed to protect individuals adequately. Moreover, it has aligned with Big Tech organizations' interests and allowed them to further push the discussion in a direction that is favorable to their interests. This paper reflects on current privacy approaches in machine learning and explores how various big organizations guide the public discourse, and how this harms data subjects. It also critiques the current data protection regulations, as they allow superficial compliance without addressing deeper ethical issues. Finally, it argues that redefining privacy to focus on harm to data subjects rather than on data breaches would benefit data subjects as well as society at large.This research received funding from the Netherlands Organization for Scientifc Research (NWO): Coronary ARtery disease: Risk estimations and Interventions for prevention and EaRly detection (CARRIER): project nr. 628.011.212
An exploration of the relationship between perceived injustice and pain severity in breast cancer survivors: a structural equation model
No full textPurpose The construct of perceived injustice is receiving increased attention in pain research due to its relationship with adverse pain outcomes. Even though cancer survivors face a 30% risk of developing pain, there is a notable lack of research exploring this relationship within this population. Therefore, this study aims to explore the relationship of perceived injustice with pain severity in breast cancer survivors (BCS). Methods A directed acyclic graph (DAG) (i.e., path model) was multidisciplinary created a priori, positioning perceived injustice -measured with the Injustice Experienced Questionnaire (IEQ)- as the main exposure variable and pain severity -measured with the Brief Pain Inventory (BPI)- as the main outcome variable, with the addition of explanatory variables within the relationship of IEQ and BPI. Data from 156 female BCS with pain and perceived injustice were analysed using structural equation modelling, with confidence intervals estimated through Montecarlo simulation. Results Perceived injustice did not have a significant univariate direct relationship with pain severity (beta = 0.186, p = 0.102). However, in the complete path model including explanatory outcomes, a significant relationship was observed (beta = 0.304, 95% CI [0.136; 0.477]), explaining 22,5% of the variance in pain severity. In the complete model, the greatest proportion of the effect was mediated through pain catastrophizing (beta = 0.226, 95% CI [0.101; 0.376]). Conclusion The findings indicate that perceived injustice has an important relationship with pain severity levels of BCS experiencing pain and perceived injustice. Multimodal intervention studies are suggested for future investigation as a treatment for pain in BCS.Registration prior to recruitment atClinicalTrials.gov NCT04730154.This work was supported by Stand up to Cancer [grant numbers ANI251 +ANI394]
DAPCy: a Python package for the discriminant analysis of principal components method for population genetic analyses
No full textThe Discriminant Analysis of Principal Components method is a pivotal tool in population genetics, combining principal component analysis and linear discriminant analysis to assess the genetic structure of populations using genetic markers, focusing on the description of variation between genetic clusters. Despite its utility, the original R implementation in the adegenet package faces computational challenges with large genomic datasets. To address these limitations, we introduce DAPCy, a Python package leveraging the scikit-learn library to enhance the method's scalability and efficiency. DAPCy supports large datasets by utilizing compressed sparse matrices and truncated singular value decomposition for dimensionality reduction, coupled with training-test cross-validation for robust model evaluation. It also includes modules for de novo genetic clustering and extensive visualization and reporting capabilities. Compared to the original R implementation, DAPCy can process genomic datasets with thousands of samples and features in less computational time and with reduced memory usage. To show DAPCy's computational capabilities, we benchmarked it with the R implementation using the Plasmodium falciparum dataset from MalariaGEN and the 1000 Genomes Project.Availability and implementation DAPCy can be installed as a Python package through pip. Source code is available on https://gitlab.com/uhasselt-bioinfo/dapcy. Documentation and a tutorial can be found on https://uhasselt-bioinfo.gitlab.io/dapcy/.This work was supported by the Flemish Special Research Fund (BOF) [BOF21DOC23]
Informatieverstrekking over rechtsmiddelen aan de gefailleerde op aangifte van staking van betaling : het Grondwettelijk Hof komt (zijdelings) tussen
No full textChronic low dose 90Sr contamination in Lemna minor: from transcriptional dynamics of epigenetic regulators to population level effects
No full textThe ecotoxicology model plant Lemna minor was exposed for 6 weeks to 90Sr, simulating the dose rates present in the Chernobyl Exclusion Zone (CEZ), in order to understand the effects of chronic low dose ionising radiation exposure. The data suggest that the plant may exhibit temporally variable acclimation responses that can be interpreted as early-, mid-, and long-term phases. Morphological changes included increased area and frond number, while molecular adjustments encompassed variations in pigment levels, glutathione metabolism, and expression modulation of telomerase-related and DNA methylation machinery genes. Physiological parameters and 90Sr uptake remained relatively stable, yet fluctuations indicate a continuous adjustment to the chronic stress, suggesting L. minor's potential for phytoremediation. The interplay between transcriptional regulation of DNA methylation and the examined endpoints suggests a potential involvement of epigenetic mechanisms in L. minor's acclimation to chronic low dose-rate 90Sr stress. This work provides knowledge on L. minor's abiotic stress responses and contributes to our understanding of plant adaptation to low-level ionising radiation (IR). The findings contribute to the development of adverse outcome pathways (AOPs) for L. minor exposed to IR, improving environmental risk assessment approaches.Funding
The author(s) declare that financial support was received for the research and/or publication of this article. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 956009.
Acknowledgments
Acknowledgment is given to Dr. Jordi Vives i Batlle from the Biosphere Impact Studies group at SCK CEN for guidance with the dosimetric analysis and the use of the ERICA tool, and to prof.dr.ir. Nico Van Den Brink for his supervision and the facilities he made available during the carrying out of telomere length measurements at Wageningen University & Research. Furthermore, we are grateful to Dr. Federico Picerni from Alma Mater Studiorum University of Bologna for his assistance in reviewing this paper
Multi-Dimensional Internet of Drones Framework for Smart City Applications
No full textAs urban populations grow, smart cities face increasing demands for efficient management of transportation, public safety, and resource allocation. The use of drone technology can lower delivery costs, especially in rural or hard-to-reach areas where traditional delivery methods are expensive. Drones can provide real-time traffic data, help to monitor traffic events like congestion and enforce traffic rules. In rescue operations drones can be quickly deployed to search for missing persons in large or difficult-to-access areas, such as forests, mountains, or disaster zones. Also, they can be used to provide aerial views of disaster-affected areas and help to assess damage and plan rescue operations. However, realizing their full potential requires addressing technical collaboration challenges.
The collaboration between robots and drones represents transformative opportunity. However, this potential comes with a high degree of complexity. The success of collaborative drones relies on addressing critical challenges in autonomy, coordination, information management, regulations, etc. Efficient joint planning of tasks among drones requires advanced algorithms to ensure optimal performance and avoid conflicts. At the same time, drones need a smart system to be able to decide when and how to collaborate and at the same time consider its profitability from the collaboration. In addition, drones’ collaboration opens the opportunities to have real-time situational awareness which can also take into consideration the uncertainty and the trustworthiness of shared information. Equally important drones need to collaborate with existing RWTOs (Real Word Transportation Objects) and comply with norms and regulations related to its operation and collaboration.
This doctoral thesis introduces a collaboration framework for drones enabling them to autonomously balance individual and collective objectives while coordinating action plans within a peer network. The framework address challenges related to group awareness via the management of group beliefs. Furthermore, norms between drones and Real Word Transportation Objects (RWTOs) are investigated to enhance drone applications’ flexibility and compliance with regulations.
This research proposes a five-stage methodology to develop a collaborative drone architecture, enabling drones to switch between selfish and collaborative behaviors while addressing uncertainty, dynamic regulations, and mission efficiency. The first stage defines the global collaboration architecture and implements a participatory planning approach to demonstrate its effectiveness. The second stage introduces ASBAF (Assessment, Setup, Bidding, Agreement, Feedback), a multi-stage collaboration approach using bio-inspired algorithms and cost/benefit analysis for optimized task allocation. The third stage focuses on group awareness under uncertainty, proposing a belief management approach with belief fusion operators selected via Hierarchical Analysis Process (HAP). The fourth stage enhances flexibility through a runtime norm regulation framework using Multi-Agent Systems (MAS), where Cloud of Norms dynamically adjusts access rules based on environmental factors. The final stage validates the framework via a search and rescue case study, developing a simulation tool with configurable parameters (e.g., drone count, charging stations) and a linear model to assess surveillance effectiveness. The methodology integrates AI, multi-agent systems, and optimization techniques to improve drone collaboration in dynamic environments.
This doctoral thesis is composed of six chapters. In Chapter 1, a general introduction is provided, where the research statements, objectives, questions, and methodology are presented. In Chapter 2, the proposed architecture adopted in the collaborative framework for drones is introduced, and its advantages are demonstrated through an implementation example in a transport scenario. Chapter 3 is dedicated to the joint planning of operational tasks within a group of drones. In Chapter 4, the management of group awareness and its impact on collaboration are addressed. Chapter 5 focuses on norm management to enhance the flexibility of drone applications in smart cities, and in Chapter 6, a collaborative drone surveillance system is implemented for search and rescue operations to improve the operational effectiveness of surveillance tasks through better resource management