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Optical Fibers and Sensors for Medical Diagnostics, Treatment, and Environmental Applications XXV 2025

作者： Alexis, Al Anderson, London Markushin, Yuriy Wilson, Emily Khan, Mohammad A. Division of Physics Engineering Mathematics and Computer Science Delaware State University 1200 N. Dupont Hwy DoverDE19901 United States Optical Center for Applied Research Delaware State University 1200 N. Dupont Hwy DoverDE19901 United States GreenbeltMD20771 United States

ISBN: (纸本)9781510683686

Mid-infrared (MIR) spectroscopy and laser-induced breakdown spectroscopy (LIBS) have evolved to be suitable for developing and deploying science instruments for planetary missions. For instance, the NASA Mars rover missions included a suite of instruments (SUPERCAM) with laser spectrometers and LIBS instruments. Both combined can provide a critical chemical composition and quantitative detection of trace gases, solids, and liquids from excited electronics state and rotational, vibrational transitions. LIBS enables rapid in situ detection, spectral information of multi-elemental targets, and profile of lighter elements. In the LIBS technique, a laser is used to ablate a small amount of the sample of interest and turn it into a luminous microplasma, which is then analyzed spectroscopically. In this paper, we show the LIBS study of Lunar regolith, e.g., Lunar Mare and Lunar Highlands simulants. The paper will explore the electrical and physical properties of lunar regolith to enable electrically modified coatings of the lander for dust discharge. In addition, we also investigate the retention of water molecules in these regolith simulants using MIR laser spectroscopy. © 2025 SPIE.

关键词： Laser induced breakdown spectroscopy

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Deep Learning Classification of Subtypes of Luminal Breast Cancer from Immunohistochemistry Slides 5th

Deep Learning Classification of Subtypes of Luminal Breast C...

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5th International Conference on Computing and Network Communications, CoCoNet 2023

作者： Shaji, Vishnu Sharma, Mridul Mathew, Merin Malavika, K. Louis, Dhanya Mary Vijaykumar, Dehannathparambil Kottarathil Nair, Lakshmi Malavika George, Archana Gutjahr, Georg Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Amritapuri India Department of Mathematics Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam India Department of Pharmacy Practice Amrita School of Pharmacy Amrita Institute of Medical Sciences And Research Centre Kerala Kochi India Department of Breast Oncology Amrita Institute of Medical Sciences And Research Center Amrita Vishwa Vidyapeetham Kerala Kochi India Department of Pathology Amrita Institute of Medical Sciences And Research Center Amrita Vishwa Vidyapeetham Kerala Kochi India Amrita Vishwa Vidyapeetham Amritapuri Kerala Kollam India

ISBN: (纸本)9789819747108

This paper proposes deep-learning models to assist in classifying subtypes of luminal breast cancer from immunohistochemistry slides. The classification has traditionally been done by pathologists who evaluate Ki67 indexes by manually counting positive and negative cells that are dyed in different colors in the immunohistochemistry slides. This manual process is error-prone and time-consuming. Therefore, this paper explores the possibility of automatically classifying immunohistochemistry slides. The performances of 4 pre-trained, deep convolutional networks are compared. Based on the good performance of the models, we conclude that the task of Ki67 classification for determining subtypes of luminal breast cancer is well-suited for deep learning models and that for this reason, manual counting of Ki67-positive cells are neither needed nor desirable. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Convolutional neural networks

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A hybrid fractional model for cervical cancer due to human papillomavirus infection

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International Journal of Thermofluids 2025年 26卷

作者： Akgül, Ali Ahmed, Nauman Rano, Sadiya Ali Al-Mdallal, Qasem Department of Electronics and Communication Engineering Saveetha School of Engineering Tamilnadu Chennai India Siirt University Art and Science Faculty Department of Mathematics Siirt56100 Turkey Department of Computer Engineering Biruni University Topkap Istanbul34010 Turkey Near East University Mathematics Research Center Department of Mathematics Near East Boulevard Nicosia/Mersin 10 PC: 99138 Turkey Department of Mathematics and Statistics The University of Lahore Lahore Pakistan Mathematical Sciences Department Bayero University Kano Nigeria Department of Mathematical Sciences UAE University P.O. Box 17551 Al-Ain United Arab Emirates

Numerous scientific and engineering applications exist for thermofluids. The primary cause of cervical cancer is the human papillomavirus (HPV), and thermos-fluid is crucial for identifying, treating, and understanding the cancerous phenomenon. In this work, a hybrid fractional order mathematical model of cervical cancer with modified parameters is studied. The proposed model consists of three fractional order nonlinear differential equations. The Grünwald Letnikov method is used to approximate the hybrid operator. A nonstandard finite difference scheme for the GL approximation is obtained to study the proposed model. Stability analysis of the used method is given, existence and uniqueness of solution to the hybrid model are given. Comparative studies between the two schemes is given. Convergence of the state variables is also shown to converge to true equilibrium points according to the stability conditions of the reproductive number. The effect of the order of fractional derivatives can also be observed in the simulations. In the end, concluding remarks are also given in the conclusion section that reflect the whole current research. © 2025 The Author(s)

关键词： Lung cancer

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On Metzler Positive Systems on Hypergraphs

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IEEE Transactions on Control of Network Systems 2025年

作者： Cui, Shaoxuan Zhang, Guofeng Jardon-Kojakhmetov, Hildeberto Cao, Ming Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen9747 AG Netherlands Department of Applied Mathematics The Hong Kong Polytechnic University Kowloon999077 Hong Kong The Hong Kong Polytechnic University Shenzhen Research Institute Shenzhen Guang Dong518057 China Engineering and Technology Institute Groningen University of Groningen Groningen9747 AG Netherlands

In graph-theoretical terms, an edge in a graph connects two vertices while a hyperedge of a hypergraph connects any more than one vertices. If the hypergraph's hyperedges further connect the same number of vertices, it is said to be uniform. In algebraic graph theory, a graph can be characterized by an adjacency matrix, and similarly, a uniform hypergraph can be characterized by an adjacency tensor. This similarity enables us to extend existing tools of matrix analysis for studying dynamical systems evolving on graphs to the study of a class of polynomial dynamical systems evolving on hypergraphs utilizing the properties of tensors. To be more precise, in this paper, we first extend the concept of a Metzler matrix to a Metzler tensor and then describe some useful properties of such tensors. Next, we focus on positive systems on hypergraphs associated with Metzler tensors. More importantly, we design control laws to stabilize the origin of this class of Metzler positive systems on hypergraphs. In the end, we apply our findings to two classic dynamical systems: a higher-order Lotka-Volterra population dynamics system and a higher-order SIS epidemic dynamic process. The corresponding novel stability results are accompanied by ample numerical examples. © 2025 IEEE.

关键词： Tensors

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AI-Powered Healthcare with Advanced Graph Convolutional Neural Networks and SVM for Diagnosis

AI-Powered Healthcare with Advanced Graph Convolutional Neur...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： A. Mahendran Sridhar Babu Nalla Lalit Mohan Trivedi S Mohankumar D.R Prince Williams Manne Renuka Department of Computer Science Sona College of Arts and Science Salem India Department of Computer Science and Engineering SR University Hyderabad India Department of Ash Mathematics Moradabad Institute of Technology Moradabad India Department of Computer Science and Engineering Erode Sengunthar Engineering College Perundurai India Department of Information Technology College of Computing and Information Sciences University of Technology and Applied Science- Sohar Sohar Sultanate of Oman Department of Electronics and Communication Engineering Vidya Jyothi Institute of Technology Hyderabad India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

Medical treatment costs are rising for many reasons. These include an increasing world population, an aging population, greater healthcare needs, more diseases, and inflation. With AI's emergence in navigation, voice recognition, image lesion segmentation, ride-sharing, and smartphone personal assistants, the healthcare industry has quickly adopted it. Initial preprocessing includes standardization, missing values, and discarding anomalies. Next, the model is trained using the obtained features. Spearman correlation, a rank-based nonparametric statistic, can quantify monotonic correlation strength and direction. Even with missing values, the Graph Convolutional Network (GCN) performs well in model training. The suggested model outperformed state-of-the-art alternatives with 93.48% accuracy. It outperformed TransLSTM and GraphSAGE on complex datasets. This research stresses the system's ability to improve healthcare forecast accuracy through preprocessing, feature extraction, and GCN model training. Results show that AI can improve healthcare analytics despite lacking data.

关键词： Training Support vector machines Correlation Accuracy Graph convolutional networks Medical services Feature extraction Integrated circuit modeling Artificial intelligence Stress

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Fashion Design Transformation using Text-Guided Diffusion Models

Fashion Design Transformation using Text-Guided Diffusion Mo...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Arjun Adityan Kabeleswar Karthikeyan Vaiapury T Palanisamy Senthil Kumar Tangavel Department of Mathematics Amrita Vishwa Vidyapeetham Coimbatore India TCS Research and Innovation IITM Research Park Chennai India Department of Mathematics Amrita School of Physical Sciences Amrita Vishwa Vidyapeetham Coimbatore India Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

We provide a novel approach for altering existing fashion designs through the integration of text-guided diffusion models with perceptual loss optimization. Effective technology for design evolution and modification is crucial for the rapid design iteration and prototyping of the fashion industry. We propose a pipeline that combines a LoRA-fine-tuned Stable Diffusion model with a perceptual loss to guarantee structural and stylistic faithfulness. The initial part is training a dedicated text-to-image generation model using fashion-specific data, subsequently refining the outputs through a perceptual loss mechanism to maintain structural integrity and improve stylistic coherence. After training with the DeepFashion-MultiModal dataset and evaluating performance on a custom dataset of essential garments, our method demonstrates exceptional preservation of stylistic coherence and structural integrity. Our approach offers a viable alternative for the automated transformation of fashion design while enhancing creative workflows within the fashion sector.

关键词： Training Deep learning Sentiment analysis Refining Pipelines Text to image Coherence Diffusion models Data models Optimization

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable Systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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Suicide ideation detection based on documents dimensionality expansion

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computers in Biology and Medicine 2025年第Pt B期192卷 110266-110266页

作者： Esmi, Nima Shahbahrami, Asadollah Gaydadjiev, Georgi de Jonge, Peter Bernoulli Institute for Mathematics Computer Science and Artificial Intelligence University of Groningen Groningen Netherlands Intelligent Systems Research Center Khazar University Baku Azerbaijan Department of Computer Engineering University of Guilan Rasht Iran Computer Engineering Laboratory Delft University of Technology Delft Netherlands Faculty of Behavioural and Social Sciences University of Groningen Groningen Netherlands

Accurate and secure classifying informal documents related to mental disorders is challenging due to factors such as informal language, noisy data, cultural differences, personal information and mixed emotions. Conventional deep learning models often struggle to capture patterns in informal text, as they miss long-range dependencies, explain words and phrases literally, and have difficulty processing non-standard inputs like emojis. To address these limitations, we expand data dimensionality, transforming and fusing textual data and signs from a 1D to a 2D space. This enables the use of pre-trained 2D CNN models, such as AlexNet, Restnet-50, and VGG-16 removing the need to design and train new models from scratch. We apply this approach to a dataset of social media posts to classify informal documents as either related to suicide or non-suicide content. Our results demonstrate high classification accuracy, exceeding 99%. In addition, our 2D visual data representation conceals individual private information and helps explainability. © 2025 The Authors

关键词： Convolutional neural networks

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Theory of phase reduction for systems with higher-order interactions: a route to Kuramoto models on hypergraphs

arXiv

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arXiv 2025年

作者： León, Iván Muolo, Riccardo Hata, Shigefumi Nakao, Hiroya Department of Applied Mathematics and Computer Science Universidad de Cantabria Santander39005 Spain Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo152-8552 Japan Graduate School of Science and Engineering Kagoshima University Kagoshima890-0065 Japan Research Center for Autonomous Systems Materialogy Institute of Innovative Research Tokyo Institute of Technology Yokohama226-8501 Japan

Phase reduction is a powerful technique in the study of nonlinear oscillatory systems. Under certain assumptions, it allows us to describe each multidimensional oscillator by a single phase variable, giving rise to simple phase models such as the Kuramoto model. Classically, the method has been applied in the case where the interactions are only pairwise (two-body). However, increasing evidence shows that interactions in real-world systems are not pairwise but higher-order, i.e., many-body. Although synchronization in higher-order systems has received much attention, analytical results are scarce because of the highly nonlinear nature of the framework. In this paper, we fill the gap by presenting a general theory of phase reduction for the case of higher-order interactions. We show that the higher-order topology is preserved in the phase reduced model at the first order and that only odd couplings have an effect on the dynamics when certain symmetries are present. Additionally, we show the power and ductility of the phase reduction approach by applying it to a population of Stuart-Landau oscillators with an all-to-all configuration and with a ring-like hypergraph topology;in both cases, only the analysis of the phase model can provide insights and analytical results. © 2025, CC BY-NC-ND.

关键词： Topology

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STAF: Sinusoidal Trainable Activation Functions for Implicit Neural Representation

arXiv

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arXiv 2025年

作者： Morsali, Alireza Vaez, MohammadJavad Soltani, Hossein Kazerouni, Amirhossein Taati, Babak Mohammad-Noori, Morteza Department of Electrical Engineering McGill University Canada School of Mathematics Statistics and Computer Science University of Tehran Iran Department of Computer Science University of Toronto Canada Vector Institute Canada KITE Research Institute University Health Network Canada

Implicit Neural Representations (INRs) have emerged as a powerful framework for modeling continuous signals. The spectral bias of ReLU-based networks is a well-established limitation, restricting their ability to capture fine-grained details in target signals. While previous works have attempted to mitigate this issue through frequency-based encodings or architectural modifications, these approaches often introduce additional complexity and do not fully address the underlying challenge of learning high-frequency components efficiently. We introduce Sinusoidal Trainable Activation Functions (STAF), designed to directly tackle this limitation by enabling networks to adaptively learn and represent complex signals with higher precision and efficiency. STAF inherently modulates its frequency components, allowing for self-adaptive spectral learning. This capability significantly improves convergence speed and expressivity, making STAF highly effective for both signal representations and inverse problems. Through extensive evaluations, we demonstrate that STAF outperforms state-of-the-art (SOTA) methods in accuracy and reconstruction fidelity with superior Peak Signal-to-Noise Ratio (PSNR). These results establish STAF as a robust solution for overcoming spectral bias and the capacity-convergence gap, making it valuable for computer graphics and related fields. Our codebase is publicly accessible on the https://***/AlirezaMorsali/STAF. © 2025, CC BY.

关键词： Signal to noise ratio

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