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The minimal projective bundle dimension and toric 2-Fano manifolds
Get PDFA preprint version of this article is available at: arXiv:2301.00883v2 [math.AG], https://arxiv.org/abs/2301.00883 under a CC BY-SA licence (https://creativecommons.org/licenses/by-sa/4.0/) . It has not been certified by peer review.Motivated by the problem of classifying toric 2-Fano manifolds, we introduce a new invariant for smooth projective toric varieties, the minimal projective bundle dimension. This invariant m(X)∈{1,…,dim(X)} captures the minimal degree of a dominating family of rational curves on X or, equivalently, the minimal length of a centrally symmetric primitive relation for the fan of X. We classify smooth projective toric varieties with m(X)≥dim(X)−2, and show that projective spaces are the only 2-Fano manifolds among smooth projective toric varieties with m(X)∈{1,dim(X)−2,dim(X)−1,dim(X)}.Carolina Araujo was partially supported by CAPES/COFECUB, CNPq and FAPERJ Research Fellowships. Roya Beheshti was supported by NSF grant DMS-2101935. Ana-Maria Castravet was partially supported by the ANR 20-CE40-0023 grant FanoHK and the ANR-22-CE40-0009-01 grant FRACASSO. Enrica Mazzon was supported by the col-laborative research center SFB 1085 Higher Invariants - Interactions between Arithmetic Geometry and Global Analysis funded by the Deutsche Forschungsgemeinschaft. Nivedita Viswanathan was supported by the EPSRC New Horizons Grant No.EP/V048619/1
Experimental Investigation of Crack Tip Constraint Effects on Fracture Assessment of API 5L X65 Steel Grade for Low-Temperature Applications (−120°C)
Get PDFSupplementary information: Appendixes for this paper are available at https://doi.org/10.6084/m9.figshare.25188575.Crack tip constraint is a significant issue in engineering components’ design and repair decisions. The main reason is that fracture assessment procedures, such as BS 7910, rely on lower-bound fracture toughness test data from deeply cracked bend specimens. This can generate stress states under various loading conditions with an appropriate crack tip stress triaxiality for metallic structures. Many real components (e.g., oil and gas pipelines) have small in-plane (shallow cracks) and out-of-plane (thin-wall thickness) dimensions that can cause a reduction in crack tip constraint to a considerable amount, thereby increasing the fracture toughness. As such, the structural assessment of low-constraint structural components using fracture toughness data obtained from deeply notched specimens may be safe but overly conservative, resulting in unnecessary repair shutdowns and costs. Consequently, relating fracture toughness values determined from laboratory specimens to real structural components becomes an issue in structural integrity assessments based on the two-parameter fracture mechanics methodology. This study investigates the applicability of the constraint-based failure assessment diagram (FAD) approach for the evaluation of cracked pin-loaded single-edge notched tension and three-point single-edge notched bend specimens at low (−120°C) and room temperatures. The analyses reveal that the experimentally measured toughness values, J0, depend on the crack sizes for the considered specimen geometries (a/W = 0.1, 0.3, 0.5). The results show the benefits of using constraint-modified FAD approach for the assessment of shallow cracks. Therefore, the enhanced toughness associated with constraint reduction indicated an increased margin and allows realistic design and repair decision-making that can help prevent catastrophic failures.This work is supported by The Welding Institute, Ltd., National Structural Integrity Research Centre, Brunel
University London, and Lloyd’s Register Foundation as part of their PhD grant. Lloyd’s Register Foundation helps to protect life and property by supporting engineering-related education, public engagement, and the
application of research (https://www.lrfoundation.org.uk)
Thermoelectric generator efficiency: An experimental and computational approach to analysing thermoelectric generator performance
Get PDFData availability: Data will be made available on request.TEGs are devices that convert heat directly into electricity through the Seebeck effect, offering a promising solution for waste heat recovery in various industries. In this research, COMSOL Multiphysics 6.0 was used to conduct a comprehensive 3-dimensional computational study of TEGs. Integrating thermal and electrical models in COMSOL facilitates a detailed understanding of the thermoelectric phenomenon. Applying six distinct temperature gradients, temperature and electrical distribution, power output, and efficiency of the TEG was thoroughly analysed. Experimental validation confirms strong agreement between simulation and experimental data, emphasizing accuracy. The average efficiency for the TEG at 1 Ω load is 3.12 %, increasing to 3.62 % for a 2 Ω load. The relative error between the computational model and the experimental model was 5 % for open circuit, 12.56 % for closed circuit at 1 Ω, and 12.14 % for closed circuit at 2 Ω, affirming the accuracy of the computational approach. Therefore, the computational model is validated by experimental results.
Moreover, the findings highlight the relationship between external load resistance and power output, revealing that the maximum output power was achieved when the external load resistance matched the internal load resistance at 2 Ω. This work also significantly contributes to advancing the computational modelling of TEGs, validated through rigorous experimental analysis
Recent advances and applications of artificial intelligence in 3D bioprinting
Get PDF3D bioprinting techniques enable the precise deposition of living cells, biomaterials, and biomolecules, emerging as a promising approach for engineering functional tissues and organs. Meanwhile, recent advances in 3D bioprinting enable researchers to build in vitro models with finely controlled and complex micro-architecture for drug screening and disease modeling. Recently, artificial intelligence (AI) has been applied to different stages of 3D bioprinting, including medical image reconstruction, bioink selection, and printing process, with both classical AI and machine learning approaches. The ability of AI to handle complex datasets, make complex computations, learn from past experiences, and optimize processes dynamically makes it an invaluable tool in advancing 3D bioprinting. The review highlights the current integration of AI in 3D bioprinting and discusses future approaches to harness the synergistic capabilities of 3D bioprinting and AI for developing personalized tissues and organs.B.Z. would like to thank Royal Society Research Grant (The Royal Society 10.13039/501100000288, RG\R1\241133) for supporting the research work and collaborations
Therapeutic Effect of Superficial Scalp Hypothermia on Chemotherapy-Induced Alopecia in Breast Cancer Survivors
Get PDFAlopecia is a common adverse effect of neoadjuvant or adjuvant chemotherapy in patients with early breast cancer. While hair typically regrows over time, more than 40% of patients continue to suffer from permanent partial alopecia, significantly affecting body image, psychological well-being, and quality of life. This concern is a recognized reason why some breast cancer patients decline life-saving chemotherapy. It is critical for healthcare professionals to consider the impact of this distressing side effect and adopt supportive measures to mitigate it. Among the various strategies investigated to reduce chemotherapy-induced alopecia (CIA), scalp cooling has emerged as the most effective. This article reviews the pathophysiology of CIA and examines the efficacy of different scalp cooling methods. Scalp cooling has been shown to reduce the incidence of CIA, defined as less than 50% hair loss, by 50% in patients receiving chemotherapy. It is associated with high patient satisfaction and does not significantly increase the risk of scalp metastasis or compromise overall survival. Promising new scalp cooling technologies, such as cryogenic nitrogen oxide cryotherapy, offer the potential to achieve and maintain lower scalp temperatures, potentially enhancing therapeutic effects. Further investigation into these approaches is warranted. Research on CIA is hindered by significant heterogeneity and the lack of standardised methods for assessing hair loss. To advance the field, further interdisciplinary research is crucial to develop preclinical models of CIA, establish a uniform, internationally accepted and standardised classification system, and establish an objective, personalised prognosis monitoring system.Air Products PLC under the grant agreement 216-206-P-F. Grant holder: Professor Hussam Jouhara
High Performance Breast Cancer Diagnosis from Mammograms Using Mixture of Experts with EfficientNet Features (MoEffNet)
Get PDFData Statement: In this study, we use three publicly available datasets: MIAS (Mammographic Image Analysis Society database) (https://www.repository.cam.ac.uk/items/b6a97f0c-3b9b40ad-8f18-3d121eef1459 ), CBIS-DDSM (Curated Breast Imaging Subset of the Digital Database for Screening Mammography) (https://www.cancerimagingarchive.net/collection/cbisddsm/ ), and INbreast (https://medicalresearch.inescporto.pt/breastresearch/index.p hp/Get_INbreast_Database ).As breast cancer is a leading cause of death for women globally, there is a critical need for better diagnostic tools. To address this challenge, we propose MoEffNet, a cutting-edge framework that offers high-performance breast cancer diagnosis. MoEffNet is characterised by its innovative hybrid integration of EfficientNet and Mixture of Experts (MoEs), two powerful techniques developed to enhance accuracy and efficiency. EfficientNet, known for its robust feature extraction capabilities, utilises compound scaling and depth-wise separable convolutions to capture image features across multiple levels of abstraction. This is combined with MoEs framework, which employs specialised expert networks to analyse distinct aspects of mammograms. MoEffNet analyses features at various levels: low-level for basic patterns, mid-level for detailed analyses, and high-level for complex contents. Features extracted from various EfficientNet model stages are assigned to specialised experts to optimise diagnostic precision. A dynamic gating mechanism (EffiGate) is introduced to ensure that the most relevant experts contribute to each diagnostic decision, by dynamically adjusting their influence based on input data characteristics. This approach ensures that the most effective experts are utilised for each case, resulting in superior accuracy. The scalability of MoEffNet is highlighted by its ability to adapt to various computational constraints and accuracy requirements, using EfficientNet’s architecture, which ranges from B0 to B7 models. We have validated MoEffNet’s effectiveness on three mammographic datasets (MIAS, CBIS-DDSM, and INbreast) achieving outstanding results (AUC > 0.99 across all datasets), outperforming existing methods. Particularly, EfficientNet B1 and B2 models with three or four experts achieved the highest accuracy, demonstrating MoEffNet’s potential as a robust diagnostic tool for early breast cancer detection. Through its innovative hybrid model, robust feature extraction, dynamic gating, and specialised expert networks, MoEffNet sets a new benchmark in automated mammogram analysis, offering a powerful tool for more accurate and reliable breast cancer diagnosis.10.13039/501100007914-Brunel University London “This work was supported in part by Brunel University London research funding scheme.”
Comparing third-party responsibility with intention attribution: An fMRI investigation of moral judgment
Get PDFData availability: Data will be made available on request.Supplementary material (Appendix A) is available online at: https://www.sciencedirect.com/science/article/pii/S1053810024001296?via%3Dihub#s0120 .Neuroimaging studies demonstrate that moral responsibility judgments activate the social cognition network, presumably reflecting mentalising processes. Conceptually, establishing an agent’s intention is a sub-process of responsibility judgment. However, the relationship between both processes on a neural level is poorly understood. To date, neural correlates of responsibility and intention judgments have not been compared directly. The present fMRI study compares neural activation elicited by third-party judgments of responsibility and intention in response to animated pictorial stimuli showing harm events. Our results show that the social cognition network, in particular Angular Gyrus (AG) and right Temporo-Parietal Junction (RTPJ), showed stronger activation during responsibility vs. intention evaluation. No greater activations for the reverse contrast were observed. Our imaging results are consistent with conceptualisations of intention attribution as a sub-process of responsibility judgment. However, they question whether the activation of the social cognition network, particularly AG/RTPJ, during responsibility judgment is limited to intention evaluation.Summer Seminars in Neuroscience and Philosophy, Duke University. S.B. was further supported by the ‘Subjectivity, Agency and Social Cognition’ Grant to P.H. from the John Templeton Foundation, by the PRIN [grant 20175YZ855] from the Italian government, and by a fellowship at the Paris Institute for Advanced Study (France), with the financial support of the French State, programme “Investissements d’avenir” managed by the Agence Nationale de la Recherche [ANR-11-LABX-0027-01 Labex RFIEA+]. P.H. and S.B. were additionally supported by a AHRC Science in Culture grant to P.H. [Award number: 162746]. P.H. and E.K. were further supported by a European Research Council Advanced Grant [HUMVOL 323943] to P.H. P.H. was further funded by a Chaire de Recherche Jean D’Alembert Paris-Saclay/Institute of Advanced Studies, Paris, and benefitted from a fellowship at the Paris Institute for Advanced Study (France), with the financial support of the French State, programme “Investissements d’avenir” managed by the Agence Nationale de la Recherche (ANR-11-LABX-0027-01 Labex RFIEA+)
Association between blood-based protein biomarkers and brain MRI in the Alzheimer’s disease continuum: a systematic review
Get PDFSupplementary Information is available online at: https://link.springer.com/article/10.1007/s00415-024-12674-w#Sec15 .Blood-based biomarkers (BBM) are becoming easily detectable tools to reveal pathological changes in Alzheimer’s disease (AD). A comprehensive and up-to-date overview of the association between BBM and brain MRI parameters is not available. This systematic review aimed to summarize the literature on the associations between the main BBM and MRI markers across the clinical AD continuum. A systematic literature search was carried out on PubMed and Web of Science and a total of 33 articles were included. Hippocampal volume was positively correlated with Aβ42 and Aβ42/Aβ40 and negatively with Aβ40 plasma levels. P-tau181 and p-tau217 concentrations were negatively correlated with temporal grey matter volume and cortical thickness. NfL levels were negatively correlated with white matter microstructural integrity, whereas GFAP levels were positively correlated with myo-inositol values in the posterior cingulate cortex/precuneus. These findings highlight consistent associations between various BBM and brain MRI markers even in the pre-clinical and prodromal stages of AD. This suggests a possible advantage in combining multiple AD-related markers to improve accuracy of early diagnosis, prognosis, progression monitoring and treatment response.This research was supported by funding obtained under the National Recovery and Resilience Plan (NRRP), Mission 4 Component 2 Investment 1.3—Call for tender No. 341 of 15/03/2022 of the Italian Ministry of University and Research funded by the European Union—NextGenerationEU, Project code PE0000006, Concession Decree No. 1553 of 11/10/2022 adopted by the Italian Ministry of University and Research, CUP D93C22000930002, “A multiscale integrated approach to the study of the nervous system in health and disease” (MNESYS)
An Efficient Human Activity Recognition In-Memory Computing Architecture Development for Healthcare Monitoring
Get PDFHuman activity recognition has played a crucial role in healthcare information systems due to the fast adoption of artificial intelligence (AI) and the internet of thing (IoT). Most of the existing methods are still limited by computational energy, transmission latency, and computing speed. To address these challenges, we develop an efficient human activity recognition in-memory computing architecture for healthcare monitoring. Specifically, a mechanism-oriented model of Ag/a-Carbon/Ag memristor is designed, serving as the core circuit component of the proposed in-memory computing system. Then, one-transistor-two-memristor (1T2M) crossbar array is proposed to perform high-efficiency multiply-accumulate (MAC) operation and high-density memory in the proposed scheme. To facilitate understanding of the proposed efficient human activity recognition in-memory computing design, self-attention ConvLSTM module, multi-head convolutional attention module, and recognition module are proposed. Furthermore, the proposed system is applied to perform human activity recognition, which contains eleven different human activities, including five different postural falls, and six basic daily activities. The experimental results show that the proposed system has advantages in recognition performance (≥ 0.20% accuracy, ≥ 1.10% F1-score) and time consumption (approximately 8∼10 times speed up) compared to existing methods, indicating an advancement in smart healthcare applications.This work was supported in part by the National Postdoctoral Researcher Support Program under Grant GZB20230356, the Shuimu Tsinghua Scholar program under Grant 2023SM035, the National Natural Science Foundation of China under Grant 62206062, and the Fundamental Research Funds for the Provincial University of Zhejiang under Grant GK229909299001-06
God, witchcraft, and beliefs about illness in Mauritius
Get PDFSupplemental material is available online at: https://www.tandfonline.com/doi/full/10.1080/2153599X.2024.2363748#supplemental-material-section .Why do people use supernatural concepts to explain and treat illness? In a Mauritian sample, we examined how uncertainty around the cause of symptoms, illness severity, and knowledge about past moral behavior, influenced participants’ tendency to attribute illnesses to God and/or witchcraft. We employed a preregistered vignette-based experiment to manipulate these variables in four illnesses and a combination of scaled and open-ended freelist questions about the causes and cures for each illness. Participants (N = 530) gave supernatural causes and cures for all illnesses. High uncertainty around the cause of the symptoms increased participants’ claims that the illness was caused by God. When the sick person had a history of immoral behavior, participants were more willing to attribute their illness to God and witchcraft and offered up 4 times more supernatural causes in freelists compared with a person with no such history. We found no evidence that severity influenced participants’ likelihood of suggesting supernatural causes or cures. Finally, when participants gave a supernatural cause, they were more likely to also indicate that the illness needed a supernatural cure (e.g., consulting a spiritual healer), suggesting that supernatural causes increase the need for supernatural cures.This work was supported by The Issachar Fund and the Templeton Religion Trust (grant number TRT0207)
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