A note on tunnel number of composite knots

AbstractLet K be a knot in a sphere S3. We denote by t(K) the tunnel number of K. For two knots K1 and K2, we denote by K1♯K2 the connected sum of K1 and K2. In this paper, we will prove that if one of K1 and K2 has high distance while the other has distance at least 3 then t(K1♯K2)=t(K1)+t(K2)+1

Green synthesis of iron silicide nanoparticles for photothermal cancer therapy

Over the last few decades, the interest in photothermal therapy increased dramatically. A wide variety of photothermal agents have been reported since 2003. Improved photothermal agents with good efficacy, safety and scalable synthesis are required. This thesis focuses on the fabrication, characterization, photothermal performance, photothermal therapy $\textit{in vivo}$ and biodegradability test of a novel photothermal agent. The thesis is arranged in the manner as follows. The motivation and the background topics (chapter 1) were firstly introduced. Then the synthesis of materials and basic characterization (chapter 2), $\textit{in-vitro}$ photothermal performance (chapters 3), photothermal therapy $\textit{in vivo}$ (chapter 4) and then $\textit{in-vitro}$ biodegradability (chapter 5). Mesoporous iron silicide nanoparticles (FeSi NPs) were synthesized by a simple green method of magnesiothermic co-reduction. Starting from biogenic mesoporous silica (“tabasheer”) extracted from bamboo and Fe$_{2}$O$_{3}$, the resultant FeSi NPs of a small band gap showed a good optical absorption with a high photothermal conversion efficiency of 76.2%, indicating a good photothermal performance. The weight extinction coefficient of the FeSi NPs was 13.3 L g$^{−1}$ cm$^{−1}$ at 1064 nm (second near-infrared window, NIR-II), which surpassed the performance of other competitive Si-based and Fe-based photothermal agents. $\textit{In vivo}$ results on mice showed clearly an efficient suppression of tumour growth by photothermal treatment with the synthesized FeSi NPs. Biodegradability test shows that fabricated FeSi NPs are slowly biodegradable. The results from our thesis indicate that FeSi NPs are a new class of promising photothermal agents (PTAs) for photothermal therapy of cancer

A Chebyshev Confidence Guided Source-Free Domain Adaptation Framework for Medical Image Segmentation

Source-free domain adaptation (SFDA) aims to adapt models trained on a labeled source domain to an unlabeled target domain without the access to source data. In medical imaging scenarios, the practical significance of SFDA methods has been emphasized due to privacy concerns. Recent State-of-the-art SFDA methods primarily rely on self-training based on pseudo-labels (PLs). Unfortunately, PLs suffer from accuracy deterioration caused by domain shift, and thus limit the effectiveness of the adaptation process. To address this issue, we propose a Chebyshev confidence guided SFDA framework to accurately assess the reliability of PLs and generate self-improving PLs for self-training. The Chebyshev confidence is estimated by calculating probability lower bound of the PL confidence, given the prediction and the corresponding uncertainty. Leveraging the Chebyshev confidence, we introduce two confidence-guided denoising methods: direct denoising and prototypical denoising. Additionally, we propose a novel teacher-student joint training scheme (TJTS) that incorporates a confidence weighting module to improve PLs iteratively. The TJTS, in collaboration with the denoising methods, effectively prevents the propagation of noise and enhances the accuracy of PLs. Extensive experiments in diverse domain scenarios validate the effectiveness of our proposed framework and establish its superiority over state-of-the-art SFDA methods. Our paper contributes to the field of SFDA by providing a novel approach for precisely estimating the reliability of pseudo-labels and a framework for obtaining high-quality PLs, resulting in improved adaptation performance

Superconductivity and metallic behavior in heavily doped bulk single crystal diamond and graphene/diamond heterostructure

Owing to extremely large band gap of 5.5 eV and high thermal conductivity, diamond is recognized as the most important semiconductor. The superconductivity of polycrystalline diamond has always been reported, but there are also many controversies over the existence of superconductivity in bulk single crystal diamond and it remains a question whether a metallic state exists for such a large band gap semiconductor. Herein, we realize a single crystal superconducting diamond with a Hall carrier concentration larger than 3*1020 cm-3 by co-doped of boron and nitrogen. Furthermore, we show that diamond can transform from superconducting to metallic state under similar carrier concentration with tuned carrier mobility degrading from 9.10 cm2 V-1 s-1 or 5.30 cm2 V-1 s-1 to 2.66 cm2 V-1 s-1 or 1.34 cm2 V-1 s-1. Through integrating graphene on a nitrogen and boron heavily co-doped diamond, the monolayer graphene can be superconducting through combining Andreev reflection and exciton mediated superconductivity, which may intrigue more interesting superconducting behavior of diamond heterostructure

Design of Edge Computing System for Photovoltaic Panel Hot Spot Detection Based on Machine Learning

The hot spot effect of photovoltaic panel refers to the local heating phenomenon caused by the photovoltaic panel being covered, which not only seriously affects the power generation efficiency of photovoltaic panel, but also is one of the most important factors threatening the service life of photovoltaic panel. In this paper, an edge computing system was designed to detect hot spot effect based on real-time sensing data such as current, voltage and illuminance. The system consists of three parts: data acquisition side, data processing side and data display side. The hot spot detection algorithm model based on machine learning is deployed on the edge side, which can detect the degree of hot spot effect and locate the hot spot according to the sensor data of each photovoltaic panel in real time. Additionally, this system could push the data to the cloud management platform and each user terminal to realize remote operation and maintenance

Estimating Brain Age with Global and Local Dependencies

The brain age has been proven to be a phenotype of relevance to cognitive performance and brain disease. Achieving accurate brain age prediction is an essential prerequisite for optimizing the predicted brain-age difference as a biomarker. As a comprehensive biological characteristic, the brain age is hard to be exploited accurately with models using feature engineering and local processing such as local convolution and recurrent operations that process one local neighborhood at a time. Instead, Vision Transformers learn global attentive interaction of patch tokens, introducing less inductive bias and modeling long-range dependencies. In terms of this, we proposed a novel network for learning brain age interpreting with global and local dependencies, where the corresponding representations are captured by Successive Permuted Transformer (SPT) and convolution blocks. The SPT brings computation efficiency and locates the 3D spatial information indirectly via continuously encoding 2D slices from different views. Finally, we collect a large cohort of 22645 subjects with ages ranging from 14 to 97 and our network performed the best among a series of deep learning methods, yielding a mean absolute error (MAE) of 2.855 in validation set, and 2.911 in an independent test set

A survey on computational intelligence approaches for predictive modeling in prostate cancer

Predictive modeling in medicine involves the development of computational models which are capable of analysing large amounts of data in order to predict healthcare outcomes for individual patients. Computational intelligence approaches are suitable when the data to be modelled are too complex forconventional statistical techniques to process quickly and eciently. These advanced approaches are based on mathematical models that have been especially developed for dealing with the uncertainty and imprecision which is typically found in clinical and biological datasets. This paper provides a survey of recent work on computational intelligence approaches that have been applied to prostate cancer predictive modeling, and considers the challenges which need to be addressed. In particular, the paper considers a broad definition of computational intelligence which includes evolutionary algorithms (also known asmetaheuristic optimisation, nature inspired optimisation algorithms), Artificial Neural Networks, Deep Learning, Fuzzy based approaches, and hybrids of these,as well as Bayesian based approaches, and Markov models. Metaheuristic optimisation approaches, such as the Ant Colony Optimisation, Particle Swarm Optimisation, and Artificial Immune Network have been utilised for optimising the performance of prostate cancer predictive models, and the suitability of these approaches are discussed