The concept of network resilience has gained increasing attention in the last few decades owing to its great potential in strengthening and maintaining complex systems. From network-based approaches, researchers have explored resilience of real ecological systems comprising diverse types of interactions, such as mutualism, antagonist, and predation, or mixtures of them. In this paper, we propose a dimension-reduction method for analyzing the resilience of hybrid herbivore-plant-pollinator networks. We qualitatively evaluate the contribution of species toward maintaining resilience of networked systems, as well as the distinct roles played by different categories of species. Our findings demonstrate that the strong contributors to network resilience within each category are more vulnerable to extinction. Notably, among the three types of species in consideration, plants exhibit a higher likelihood of extinction, compared to pollinators and herbivores.
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http://dx.doi.org/10.1063/5.0169946 | DOI Listing |
Sci Rep
December 2024
School of Electronic and Nanoscale Engineering, University of Glasgow, Glasgow, G12 8QQ, UK.
In the era of the Internet of Things (IoT), the transmission of medical reports in the form of scan images for collaborative diagnosis is vital for any telemedicine network. In this context, ensuring secure transmission and communication is necessary to protect medical data to maintain privacy. To address such privacy concerns and secure medical images against cyberattacks, this research presents a robust hybrid encryption framework that integrates quantum, and classical cryptographic methods.
View Article and Find Full Text PDFFront Artif Intell
December 2024
School of Industrial Engineering and Management, Oklahoma State University, Stillwater, OK, United States.
The ability to accurately predict the yields of different crop genotypes in response to weather variability is crucial for developing climate resilient crop cultivars. Genotype-environment interactions introduce large variations in crop-climate responses, and are hard to factor in to breeding programs. Data-driven approaches, particularly those based on machine learning, can help guide breeding efforts by factoring in genotype-environment interactions when making yield predictions.
View Article and Find Full Text PDFBMC Health Serv Res
December 2024
School of Nursing, Health, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
Background: The COVID-19 pandemic exposed critical gaps in health system preparedness. This study, guided by a critical ecological model, examines the experiences of primary health and community services in Aotearoa New Zealand during the pandemic, focusing on their response to older people and their unpaid caregivers. The study aims to identify effective strategies for health system resilience.
View Article and Find Full Text PDFExp Dermatol
December 2024
Computer Science & Engineering Department, MNNIT Allahabad, Prayagraj, Uttar Pradesh, India.
Skin cancer remains one of the most common and deadly forms of cancer, necessitating accurate and early diagnosis to improve patient outcomes. In order to improve classification performance on unbalanced datasets, this study proposes a distinctive approach for classifying skin cancer that utilises both machine learning (ML) and deep learning (DL) methods. We extract features from three different DL models (DenseNet201, Xception, Mobilenet) and concatenate them to create an extensive feature set.
View Article and Find Full Text PDFInterdiscip Sci
December 2024
School of Computer Science and Artificial Intelligence, Aliyun School of Big Data, School of Software, Changzhou University, Changzhou, 213164, China.
Cell-Penetrating Peptides (CPPs) are a crucial carrier for drug delivery. Since the process of synthesizing new CPPs in the laboratory is both time- and resource-consuming, computational methods to predict potential CPPs can be used to find CPPs to enhance the development of CPPs in therapy. In this study, EnDM-CPP is proposed, which combines machine learning algorithms (SVM and CatBoost) with convolutional neural networks (CNN and TextCNN).
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