Month: April 2024

Abstract

This paper reports exploratory research that considers two challenges recognised in the disaster risk reduction (DRR) community in recent years: one is the reinforcement of community-based DRR and the other is experts' prioritising high-impact/low-frequency hazards. Inquiries into stakeholders’—community members’ in particular—understandings of disaster risks have been scarce. The research aimed to address these gaps by investigating communities’ perceptions around community-based DRR and disaster risks. The research focused on natural water hazards, such as floods and typhoons generated due to atmospheric forcing factors, as well as tsunamis in four communities in Japan and England. A field survey of major structural mitigation solutions, non-structural measures, and community interviews revealed that community members did not necessarily find the often-used impact/frequency description of hazards helpful in developing and implementing community-based DRR activities. Such hazard-based scientific language does not necessarily correspond with the general public. The paper attempted ‘the number of affected people’, which was recognised by the research participants, to be applied as a tool for understanding disaster risks.

Abstract

Protecting patient privacy has become a top priority with the introduction of Healthcare 5.0 and the growth of the Internet of Things. This study provides a revolutionary strategy that makes use of blockchain technology, information fusion, and federated illness prediction and deep extreme machine learning to meet the difficulties with regard to healthcare privacy. The suggested framework integrates several innovative technologies to make healthcare systems safe and privacy-preserving. The framework leverages the blockchain system, a distributed and unchangeable ledger, to secure the integrity, traceability and openness of private medical information. Patient privacy is better protected as a result, and there is less chance of data breaches or unauthorized access. The system makes use of the Linear Discriminant Analysis (LDA), Decision Tree, Extra Tree Classifier, AdaBoost, and Federated Deep Extreme Machine Learning algorithms to increase the accuracy and efficacy of illness prediction. This method allows for collaborative learning across many healthcare organizations without disclosing raw data, protecting privacy. The system obtains a thorough awareness of patient health, allowing for the early diagnosis of diseases and the development of individualized treatment suggestions. To further detect and reduce possible security risks in the IoMT environment, the framework also includes intrusion detection methods. Protecting patient data and infrastructure, the system can quickly identify and react to unauthorized actions or threats. High accuracy and privacy protection are shown by the results, making it appropriate for Healthcare 5.0 applications. The findings have important ramifications for researchers, politicians, and healthcare professionals who are seeking to develop safe and privacy-conscious healthcare systems.

Abstract

Floods cause substantial losses to life and property, especially in flood-prone regions like northwestern Bangladesh. Timely and precise evaluation of flood impacts is critical for effective flood management and decision-making. This research demonstrates an integrated approach utilizing machine learning and Google Earth Engine to enable real-time flood assessment. Synthetic aperture radar (SAR) data from Sentinel-1 and the Google Earth Engine platform were employed to generate near real-time flood maps of the 2020 flood in Kurigram and Lalmonirhat. An automatic thresholding technique quantified flooded areas. For land use/land cover (LULC) analysis, Sentinel-2’s high resolution and machine learning models like artificial neural networks (ANN), random forests (RF) and support vector machines (SVM) were leveraged. ANN delivered the best LULC mapping with 0.94 accuracy based on metrics like accuracy, kappa, mean F1 score, mean sensitivity, mean specificity, mean positive predictive value, mean negative value, mean precision, mean recall, mean detection rate and mean balanced accuracy. Results showed over 600,000 people exposed at peak inundation in July—about 17% of the population. The machine learning-enabled LULC maps reliably identified vulnerable areas to prioritize flood management. Over half of croplands flooded in July. This research demonstrates the potential of integrating SAR, machine learning and cloud computing to empower authorities through real-time monitoring and accurate LULC mapping essential for effective flood response. The proposed comprehensive methodology can assist stakeholders in developing data-driven flood management strategies to reduce impacts.

Abstract

This study introduces a novel methodology for the detection and classification of fission track (FT) clusters in microscope images, employing state-of-the-art deep learning techniques for segmentation and classification (Elgad in nuclear forensics—fission track analysis—star segmentation and classification using deep learning, Ben-Gurion University, 2022). The U-Net model, a fully convolutional network, was used to carry out the segmentation of various star-like patterns in both single-class and multi-class scenarios.

Abstract

This article analyses the determinants of international migration flows within the European Union and specifically focuses on the role of cultural and linguistic differences in explaining the size of these flows. For that purpose, a set of indicators of cultural distance is controlled for along with economic, demographic, geographical, political and network variables using data from 28 member states of the EU over the period 1998–2018. Economic factors play an important role in examining migration flows, but economic differentials alone may be insufficient to explain the uneven real-life migration pattern in the EU. The results suggest strong evidence of the importance of linguistic distance in explaining the direction of migration flows across the EU.

Abstract

The foamed structure of the central layer, predominantly governed by the addition levels of TiH₂ and the composition of the precursor, in addition to the parameters associated with the foaming process, constitutes the pivotal determinant impacting the spectrum of applications for aluminum foam sandwiches. Systematic research on the foaming behavior of the AlMg₄Si₈ matrix and pure Al matrix precursors in closed cavities with different TiH₂ addition levels was conducted. The results reveal that with the increasing amount of TiH₂, specifically at a 200% expansion rate of AlMg₄Si₈ foams, the number of large pores decreased, the average pore size decreased from 0.67 mm² (0.6 wt%) to 0.43 mm² (1.0 wt%), and the total number of pores increased from 125 (0.6 wt%) to 156 (1.0 wt%). This trend of pore size consistency was evident not only at the mentioned 200% expansion rate but also across different expansion rates of AlMg₄Si₈ foams and across all expansion rates of pure Al foams. The pore structures observed in the AlMg₄Si₈ matrix and pure aluminum matrix foams within a closed cavity exhibited significantly superior characteristics compared to those achieved through free foaming processes. The circularity of pure Al foams was mainly distributed in the range of 1–2, being higher than the AlMg₄Si₈ foams (1–1.5) due to their low viscosity and foam stability. The compressive curve of AlMg₄Si₈ foams at a 400% expansion rate reveals that as the average cross-sectional area increased, there was a higher likelihood of experiencing initial collapse conditions. Additionally, the fluctuation of the platform area became more pronounced. Further, the slope corresponding to the elastic stage of the compressive curve increased as the overall stress levels increased.

Abstract

Cell segregation allows the compartmentalization of cells with similar fates during morphogenesis, which can be enhanced by cell fate plasticity in response to local molecular and biomechanical cues. Endothelial tip cells in the growing retina, which lead vessel sprouts, give rise to arterial endothelial cells and thereby mediate arterial growth. Here, we have combined cell type-specific and inducible mouse genetics, flow experiments in vitro, single-cell RNA sequencing and biochemistry to show that the balance between ephrin-B2 and its receptor EphB4 is critical for arterial specification, cell sorting and arteriovenous patterning. At the molecular level, elevated ephrin-B2 function after loss of EphB4 enhances signaling responses by the Notch pathway, VEGF and the transcription factor Dach1, which is influenced by endothelial shear stress. Our findings reveal how Eph-ephrin interactions integrate cell segregation and arteriovenous specification in the vasculature, which has potential relevance for human vascular malformations caused by EPHB4 mutations.

Abstract

The Belt and Road Initiative (BRI), spearheaded by China, is reshaping the global economy, particularly in the Middle East and North Africa (MENA). This article focuses on Iran and the Kingdom of Saudi Arabia (KSA), scrutinizing how the BRI has catalyzed economic changes in these countries. Using qualitative and quantitative data, it analyzes their unique responses to the BRI, considering their economic challenges and political goals. Employing the theory of complex interdependence, it examines how both countries leverage the BRI to diversify their economies and enhance cooperation, altering their global positions. This analysis highlights the BRI’s profound impact on Middle Eastern geopolitics beyond its economic implications. This study offers a more analytical understanding of its significance by providing insights into the BRI’s role in global economic dynamics.

Abstract

Untangling flow and mass transport in aquifers is essential for effective water management and protection. However, understanding the mechanisms underlying such phenomena is challenging, particularly in highly heterogeneous natural aquifers. Past research has been limited by the lack of dense data series and experimental models that provide precise knowledge of such aquifer characteristics. To bridge this gap and advance our current understanding, we present the findings of a pioneering experimental investigation that characterizes a unique, strongly heterogeneous, laboratory-constructed phreatic aquifer at an intermediate scale under radial flow conditions. This strong heterogeneity was achieved by randomly distributing 2527 cells across 7 layers, each filled with one of 12 different soil mixtures, with their textural characteristics, porosity, and saturated hydraulic conductivity measured in the laboratory. We placed 37 fully penetrating piezometers radially at varying distances from the central pumping well, allowing for an extensive pumping test campaign to obtain saturated hydraulic conductivity values for each piezometer location and scaling laws along eight directions. Results reveal that the aquifer’s strong heterogeneity led to significant vertical and directional anisotropy in saturated hydraulic conductivity. Furthermore, we experimentally demonstrated for the first time that the porous medium tends toward homogeneity when transitioning from the scale of heterogeneity to the scale of investigation. These novel findings, obtained on a uniquely highly heterogeneous aquifer, contribute to the field and provide valuable insights for researchers studying flow and mass transport phenomena. The comprehensive dataset obtained will serve as a foundation for future research and as a tool to validate findings from previous studies on strongly heterogeneous aquifers.

Abstract

Mitochondria are critical modulators of antiviral tolerance through the release of mitochondrial RNA and DNA (mtDNA and mtRNA) fragments into the cytoplasm after infection, activating virus sensors and type-I interferon (IFN-I) response^1–4. The relevance of these mechanisms for mitochondrial diseases remains understudied. Here we investigated mitochondrial recessive ataxia syndrome (MIRAS), which is caused by a common European founder mutation in DNA polymerase gamma (POLG1)⁵. Patients homozygous for the MIRAS variant p.W748S show exceptionally variable ages of onset and symptoms⁵, indicating that unknown modifying factors contribute to disease manifestation. We report that the mtDNA replicase POLG1 has a role in antiviral defence mechanisms to double-stranded DNA and positive-strand RNA virus infections (HSV-1, TBEV and SARS-CoV-2), and its p.W748S variant dampens innate immune responses. Our patient and knock-in mouse data show that p.W748S compromises mtDNA replisome stability, causing mtDNA depletion, aggravated by virus infection. Low mtDNA and mtRNA release into the cytoplasm and a slow IFN response in MIRAS offer viruses an early replicative advantage, leading to an augmented pro-inflammatory response, a subacute loss of GABAergic neurons and liver inflammation and necrosis. A population databank of around 300,000 Finnish individuals⁶ demonstrates enrichment of immunodeficient traits in carriers of the POLG1 p.W748S mutation. Our evidence suggests that POLG1 defects compromise antiviral tolerance, triggering epilepsy and liver disease. The finding has important implications for the mitochondrial disease spectrum, including epilepsy, ataxia and parkinsonism.