检索结果-内蒙古大学图书馆

arXiv 2025年

作者： Li, Honglin Liu, Chuhao Guo, Yongfeng Luo, Xiaoshan Chen, Yijie Liu, Guangsheng Li, Yu Wang, Ruoyu Wang, Zhenyu Wu, Jianzhuo Ma, Cheng Xie, Zhuohang Lv, Jian Ding, Yufei Zhang, Huabin Luo, Jian Zhong, Zhicheng Li, Mufan Wang, Yanchao Li, Wan-Lu Key Laboratory of Material Simulation Methods and Software Ministry of Education College of Physics Jilin University Changchun China Aiiso Yufeng Li Family Department of Chemical and Nano Engineering University of California La Jolla San DiegoCA United States Institute of Molecular Engineering Plus College of Chemistry Fuzhou University Fuzhou China College of Chemistry and Molecular Engineering Peking University Beijing China School of Physics Nankai University Tianjin China Institute of Modern Physics Fudan University Shanghai China Program in Materials Science and Engineering University of California La Jolla San DiegoCA United States School of Artificial Intelligence and Data Science University of Science and Technology of China Hefei230026 China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou215123 China International Center of Future Science Jilin University Changchun China Department of Computer Science and Engineering University of California La Jolla San DiegoCA United States Center for Renewable Energy and Storage Technologies Physical Science and Engineering Division King Abdullah University of Science and Technology Thuwal Saudi Arabia Suzhou Lab Suzhou215123 China

Amorphous multi-element materials offer unprecedented tunability in composition and properties, yet their rational design remains challenging due to the lack of predictive structure-property relationships and the vast configurational space. Traditional modeling struggles to capture the intricate short-range order that dictates their stability and functionality. We here introduce ApolloX, a pioneering predictive framework for amorphous multi-element materials, establishing a new paradigm by integrating physics-informed generative modeling with particle swarm optimization, using chemical short-range order as an explicit constraint. By systematically navigating the disordered energy landscape, ApolloX enables the targeted design of thermodynamically stable amorphous configurations. It accurately predicts atomic-scale arrangements, including composition-driven metal clustering and amorphization trends, which are well-validated by experiments, while also guiding synthesis by leveraging sluggish diffusion to control elemental distribution and disorder. The resulting structural evolution, governed by composition, directly impacts catalytic performance, leading to improved activity and stability with increasing amorphization. This predictive-experimental synergy transforms the discovery of amorphous materials, unlocking new frontiers in catalysis, energy storage, and functional disordered systems. Copyright © 2025, The Authors. All rights reserved.

关键词： Amorphization

来源：评论

学校读者我要写书评

暂无评论

Medical Image Segmentation by Improved Nested Unet 2nd

Medical Image Segmentation by Improved Nested Unet

引用

2nd International Conference on Intelligent Systems and Data science, ISDS 2024

作者： Tran, Song-Toan Le, Minh-Hai Nguyen, Thai-Son Nghi, Vinh-Khanh Nguyen, Thanh-Nguyen School of Engineering and Technology Tra Vinh University Tra Vinh87000 Viet Nam Resource Development Institute Tra Vinh University Tra Vinh87000 Viet Nam PhD program in Computer Science Tra Vinh University Tra Vinh87000 Viet Nam Facilities Management Department Tien Giang University Tien Giang84000 Viet Nam

ISBN: (纸本)9789819796120

Deep learning is revolutionizing the field of medical image segmentation. The U-shaped (Unet) model, with its encoder-decoder architecture and skip connections, has become the dominant architecture for this task. However, variations like Nested-Unet are necessary to address the challenge of adapting network size for datasets of varying scales. This study proposes a novel model based on a multilayer Unet architecture that leverages all output features within nodes of a Nested Unet network. We evaluate the proposed model on four key medical image datasets: electron microscopy (EM), nuclei, polyps (CVC-ClinicDB), and skin lesions. The model achieves impressive Dice scores: 94.08% for EM, 92.68% for nuclei, 87.20% for skin lesions, and 93.02% for the CVC-ClinicDB dataset. These outstanding results demonstrate the superior performance of our proposed model compared to recent advancements in the field. Remarkably, the model achieves this performance while maintaining a size that is half that of a traditional Unet model. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Image segmentation

来源：评论

学校读者我要写书评

暂无评论

Combining flow virometry with tree-based machine learning models for rapid virus particle estimation in different wastewater matrices

引用

Water Research 2025年

作者： Yevhen Myshkevych Ibrahima N'Doye Julie Sanchez Medina Fahad K. Aljehani Yanghui Xiong Taous-Meriem Laleg-Kirati Pei-Ying Hong Environmental Science and Engineering Program Division of Biological and Environmental Science and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia 23955 KAUST Center of Excellence on Smart Health King Abdullah University of Science and Technology Thuwal Saudi Arabia 23955 Electrical and Computer Engineering Program Division of Computer Electrical and Mathematical Science and Engineering King Abdullah University of Science and Technology Thuwal Saudi Arabia 23955 National Institute for Research in Digital Science and Technology (INRIA) Paris-Saclay France

Enumerating virus particles (VPs) at different stages of the wastewater treatment process or along the distribution network is essential for ensuring high performance and reducing public health risks. Herein, we aimed to (i) optimize the flow virometry (FVM) protocol for use in wastewater matrices, (ii) correlate FVM data with specific virus genera of interest, and (iii) develop machine learning (ML) models for determining total VP concentration. We identified and tested a comprehensive set of parameters to determine the optimal conditions for wastewater FVM. Specifically, we tested various sample preprocessing steps to enhance FVM detection sensitivity, including the use of different nucleic acid staining dyes, surfactant addition and concentration optimization, glutaraldehyde fixation, and the effect of sample freezing before FVM analysis. Spearman's rank correlation of FVM data with virus genera concentration using a conventional qPCR-based method in 206 samples showed a positive correlation for all five virus genera, ranging from 0.21 to 0.44 (p < 0.01). The extreme gradient-boosting (XGB) model using easily accessible physiochemical water parameters (such as turbidity, electroconductivity, total dissolved solids, total suspended solids, pH, chemical oxygen demand, and concentrations of nitrate nitrogen, nitrite nitrogen, and ammonium nitrogen) as input data outperformed the random forest (RF) model and can be used to estimate total virus count across all types of wastewater matrices as output data. Furthermore, XGB achieved a better root mean square error in the four treatment processes (influent, aerobic, sand, and MBR) by a mean of 23% than RF in model development. This study demonstrates that FVM, combined with ML, can significantly enhance monitoring capabilities by accurately estimating VP concentrations across diverse wastewater matrices.

关键词： flow virometry virus enumeration water reuse machine learning

来源：评论

学校读者我要写书评

暂无评论

A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach

arXiv

引用

arXiv 2025年

作者： Kaur, Navreet Gonzales, Manuel Alcaraz, Cristian Garcia Gong, Jiaqi Wells, Kristen J. Barnes, Laura E. Department of Systems and Information Engineering University of Virginia CharlottesvilleVA United States SDSU/UC San Diego Joint Doctoral Program in Clinical Psychology San DiegoCA United States Department of Computer Science The University of Alabama TuscaloosaAL United States Department of Psychology San Diego State University San DiegoCA United States

Non-adherence to medications is a critical concern since nearly half of patients with chronic illnesses do not follow their prescribed medication regimens, leading to increased mortality, costs, and preventable human distress. Amongst stage 0-3 breast cancer survivors, adherence to long-term adjuvant endocrine therapy (i.e., Tamoxifen and aromatase inhibitors) is associated with a significant increase in recurrence-free survival. This work aims to develop multi-scale models of medication adherence to understand the significance of different factors influencing adherence across varying time frames. We introduce a computational framework guided by Social Cognitive Theory for multi-scale (daily and weekly) modeling of longitudinal medication adherence. Our models employ both dynamic medication-taking patterns in the recent past (dynamic factors) as well as less frequently changing factors (static factors) for adherence prediction. Additionally, we assess the significance of various factors in influencing adherence behavior across different time scales. Our models outperform traditional machine learning counterparts in both daily and weekly tasks in terms of both accuracy and specificity. Daily models achieved an accuracy of 87.25% (Precision – 92.04%, Recall – 93.15%, Specificity – 77.50%), and weekly models, an accuracy of 76.04% (Precision – 75.83%, Recall – 85.80%, Specificity – 72.30%). Notably, dynamic past medication-taking patterns prove most valuable for predicting daily adherence, while a combination of dynamic and static factors is significant for macro-level weekly adherence patterns. While our models exhibit strong predictive performance, they are constrained by potential cohort-specific biases, reliance on self-reported adherence data, and a limited understanding of the context around non-adherence. Future research will focus on external validation across diverse populations and explore the real-world implementation of sensor-rich systems for a more compre

关键词： Endocrinology

来源：评论

学校读者我要写书评

暂无评论

CIWARS: A Web Server for Antibiotic Resistance Surveillance Using Longitudinal Metagenomic Data

引用

Journal of Molecular Biology 2025年

作者： Emon, Muhit Islam Cheung, Yat Fei Stoll, James Rumi, Monjura Afrin Brown, Connor Choi, Joung Min Moumi, Nazifa Ahmed Ahmed, Shafayat Song, Haoqiu Sein, Justin Yao, Shunyu Khan, Ahmad Gupta, Suraj Kulkarni, Rutwik Butt, Ali Vikesland, Peter Pruden, Amy Zhang, Liqing Department of Computer Science Virginia Tech Blacksburg 24060 VA United States Fralin Life Science Institute Virginia Tech Blacksburg 24060 VA United States Department of Civil and Environmental Engineering Virginia Tech Blacksburg 24060 VA United States The Interdisciplinary PhD Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg 24060 VA United States

The rise of antibiotic resistance (AR) poses a substantial threat to human and animal health, food security, and economic stability. Wastewater-based surveillance (WBS) has emerged as a powerful strategy for population-level AR monitoring, providing valuable data to guide public health and policy decisions. Metagenomic sequencing is especially promising, as it can yield comprehensive profiles of antibiotic resistance genes (ARGs) and other genes relevant to AR in a single run. However, online analytical platforms to facilitate analysis of longitudinal metagenomic data are lacking. To address this, we introduce CyberInfrastructure for Waterborne Antibiotic Resistance Surveillance (CIWARS), a web server configured for characterizing key AR trends from longitudinal metagenomic WBS data. CIWARS offers comprehensive profiling of ARGs and taxonomic profiling of pathogen-associated bacterial taxonomic groups, identifies potential associations of ARGs with mobile genetic elements (MGEs) and pathogen-containing taxa, and assesses resistome risk based on the co-occurrence of ARGs, MGEs, and pathogen-like sequences. Additionally, it detects anomalous AR indicators over time, aiding in identifying potential events of concern, such as the emergence of resistant strains or outbreaks. Through interactive temporal data visualization, CIWARS enables AR monitoring and can serve as a tool to inform effective and timely interventions to mitigate the spread and transmission of AR. Here, CIWARS is demonstrated using longitudinal metagenomic data from a wastewater treatment plant (WWTP) influent and effluent, but it can be extended to any environment. CIWARS provides a valuable tool to support global efforts to combat the evolution and spread of AR, while also guiding agricultural and public health efforts aimed at optimizing antibiotic use. The web server is freely available at https://***/. © 2025 Elsevier Ltd

关键词： anomaly detection antibiotic resistance (AR) AR indicator profiling longitudinal metagenomics wastewater-based surveillance

来源：评论

学校读者我要写书评

暂无评论

Artificial visual perception neural system using a solution-processable MoS_(2)-based in-memory light sensor

引用

Light(science & Applications) 2023年第8期12卷 1583-1594页

作者： Dayanand Kumar Lana Joharji Hanrui Li Ayman Rezk Ammar Nayfeh Nazek El-Atab Smart Advanced Memory Devices and Applications(SAMA)LaboratoryElectrical and Computer Engineering ProgramComputer Electrical Mathematical Science and Engineering DivisionKing Abdullah University of Science and Technology(KAUST)Thuwal 23955Kingdom of Saudi Arabia Department of Electrical Engineering and Computer Science Khalifa UniversityAbu Dhabi 127788United Arab Emirates

Optoelectronic devices are advantageous in in-memory light sensing for visual information processing,recognition,and storage in an energy-efficient ***,in-memory light sensors have been proposed to improve the energy,area,and time efficiencies of neuromorphic computing *** study is primarily focused on the development of a single sensing-storage-processing node based on a two-terminal solution-processable MoS2 metal-oxide-semiconductor(MOS)charge-trapping memory structure—the basic structure for charge-coupled devices(CCD)—and showing its suitability for in-memory light sensing and artificial visual *** memory window of the device increased from 2.8 V to more than 6V when the device was irradiated with optical lights of different wavelengths during the program ***,the charge retention capability of the device at a high temperature(100 ℃)was enhanced from 36 to 64%when exposed to a light wavelength of 400 *** larger shift in the threshold voltage with an increasing operating voltage confirmed that more charges were trapped at the Al_(2)O_(3)/MoS_(2) interface and in the MoS_(2) layer.A small convolutional neural network was proposed to measure the optical sensing and electrical programming abilities of the *** array simulation received optical images transmitted using a blue light wavelength and performed inference computation to process and recognize the images with 91%*** study is a significant step toward the development of optoelectronic MOS memory devices for neuromorphic visual perception,adaptive parallel processing networks for in-memory light sensing,and smart CCD cameras with artificial visual perception capabilities.

关键词： visual neural process

来源：评论

学校读者我要写书评

暂无评论

engineering Andreev Bound States for Thermal Sensing in Proximity Josephson Junctions

arXiv

引用

arXiv 2025年

作者： Jung, Woochan Arnault, Ethan G. Huang, Bevin Park, Jinho Jang, Seong Watanabe, Kenji Taniguchi, Takashi Englund, Dirk Fong, Kin Chung Lee, Gil-Ho Physics Department Pohang University of Science and Technology Pohang37673 Korea Republic of Research Laboratory of Electronics Massachusetts Institute of Technology CambridgeMA02139 United States Intelligence Community Postdoctoral Research Fellowship Program Massachusetts Institute of Technology CambridgeMA02139 United States Department of Mechanical Engineering Columbia University New YorkNY10027 United States Research Center for Functional Materials National Institute for Materials Science Tsukuba Japan International Center for Materials Nanoarchitectonics National Institute for Materials Science Tsukuba Japan Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Computer Engineering Northeastern University BostonMA02115 United States Department of Physics Northeastern University BostonMA02115 United States Quantum Materials and Sensing Institute BurlingtonMA01803 United States

The thermal response of proximity Josephson junctions (JJs) is governed by the temperature (T)- dependent occupation of Andreev bound states (ABS), making them promising candidates for sensitive thermal detection. In this study, we systematically engineer ABS to enhance the thermal sensitivity of critical current (lc) of proximity JJs, quantified as |dlc/dT/| for threshold readout scheme and |dlc/***−1| for inductive readout scheme. Using a gate-tunable graphene-based JJ platform, we explore the impact of key parameters—including channel length, transparency, carrier density, and superconducting material—on the thermal response. Our results reveal that the proximity-induced superconducting gap plays a crucial role in optimizing thermal sensitivity. Notably, we see a maximum |dlc/***−1| value of 0.6 K−1 at low temperatures with titanium-based graphene JJs. By demonstrating a systematic approach to engineering ABS in proximity JJs, this work establishes a versatile framework for optimizing thermal sensors and advancing the study of ABS-mediated transport. © 2025, CC BY.

关键词： Superconducting materials

来源：评论

学校读者我要写书评

暂无评论

AgriField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel

arXiv

引用

arXiv 2025年

作者： Kimara, Elvis Hadadi, Mozhgan Godbersen, Jackson Balu, Aditya Jubery, Talukder Li, Yawei Krishnamurthy, Adarsh Schnable, Patrick S. Ganapathysubramanian, Baskar Department of Computer Science Iowa State University Ames United States Translational AI Research and Education Center Ames United States Department of Mechanical Engineering Iowa State University Ames United States Plant Science Institute Iowa State University Ames United States Interdepartmental Genetics and Genomics Graduate Program Iowa State University Ames United States Department of Agronomy Iowa State University Ames United States

The application of artificial intelligence (AI) in three-dimensional (3D) agricultural research, particularly for maize, has been limited by the scarcity of large-scale, diverse datasets. While 2D image datasets are abundant, they fail to capture essential structural details such as leaf architecture, plant volume, and spatial arrangements that 3D data provide. To address this limitation, we present AgriField3D (website), a curated dataset of 3D point clouds of field-grown maize plants from a diverse genetic panel, designed to be AI-ready for advancing agricultural research. Our dataset comprises over 1,000 high-quality point clouds collected using a Terrestrial Laser Scanner, complemented by procedural models that provide structured, parametric representations of maize plants. These procedural models, generated using Non-Uniform Rational B-Splines (NURBS) and optimized via a two-step process combining Particle Swarm Optimization (PSO) and differentiable programming, enable precise, scalable reconstructions of leaf surfaces and plant architectures. To enhance usability, we performed graph-based segmentation to isolate individual leaves and stalks, ensuring consistent labeling across all samples. We also conducted rigorous manual quality control on all datasets, correcting errors in segmentation, ensuring accurate leaf ordering, and validating metadata annotations. The dataset further includes metadata detailing plant morphology and quality, alongside multi-resolution subsampled versions (100k, 50k, 10k points) optimized for various computational needs. By integrating point cloud data of field grown plants with high-fidelity procedural models and ensuring meticulous manual validation, AgriField3D provides a comprehensive foundation for AI-driven phenotyping, plant structural analysis, and 3D applications in agricultural research. © 2025, CC BY-NC-SA.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Conceptual Exploration of Generative AI-Induced Cognitive Dissonance and its Emergence in University-Level Academic Writing

arXiv

引用

arXiv 2025年

作者： Seran, Carl Errol Tan, Myles Joshua Toledo Karim, Hezerul Abdul AlDahoul, Nouar Biology Program College of Arts and Sciences University of St. La Salle Bacolod Philippines Department of Natural Sciences College of Arts and Sciences University of St. La Salle Bacolod Philippines Faculty of Education University of the Philippines Open University Los Baños Philippines Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering University of Florida GainesvilleFL United States Department of Epidemiology College of Public Health and Health Professions College of Medicine University of Florida GainesvilleFL United States Department of Chemical Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Department of Electronics Engineering College of Engineering and Technology University of St. La Salle Bacolod Philippines Yo-Vivo Corporation Bacolod Philippines Faculty of Engineering Multimedia University Persiaran Multimedia Cyberjaya Malaysia Department of Computer Science Division of Science New York University Abu Dhabi Abu Dhabi United Arab Emirates

The integration of Generative Artificial Intelligence (GenAI) intouniversity-level academic writing presents both opportunities and challenges,particularly in relation to cognitive dissonance (CD). This work explores howGenAI serves as both a trigger and amplifier of CD, as students navigateethical concerns, academic integrity, and self-efficacy in their writingpractices. By synthesizing empirical evidence and theoretical insights, weintroduce a hypothetical construct of GenAI-induced CD, illustrating thepsychological tension between AI-driven efficiency and the principles oforiginality, effort, and intellectual ownership. We further discuss strategiesto mitigate this dissonance, including reflective pedagogy, AI literacyprograms, transparency in GenAI use, and discipline-specific task *** approaches reinforce critical engagement with AI, fostering a balancedperspective that integrates technological advancements while safeguarding humancreativity and learning. Our findings contribute to ongoing discussions on AIin education, self-regulated learning, and ethical AI use, offering aconceptual framework for institutions to develop guidelines that align AIadoption with academic values. © 2025, CC BY.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

On the particle approximation of lagged Feynman–Kac formulae

引用

Stochastic Processes and their Applications 2025年 188卷

作者： Awadelkarim, Elsiddig Caffarel, Michel Del Moral, Pierre Jasra, Ajay Applied Mathematics and Computational Science Program Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology Thuwal 23955-6900 Saudi Arabia University of Toulouse CNRS-Lab. de Chimie et Physique Quantiques Toulouse 31062 France Centre de Recherche Inria Bordeaux Sud-Ouest Talence 33405 France School of Data Science The Chinese University of Hong Kong Shenzhen Shenzhen China

In this paper we examine the numerical approximation of the limiting invariant measure associated with Feynman–Kac formulae. These are expressed in a discrete time formulation and are associated with a Markov chain and a potential function. The typical application considered here is the computation of eigenvalues associated with non-negative operators as found, for example, in physics or particle simulation of rare-events. We focus on a novel lagged approximation of this invariant measure, based upon the introduction of a ratio of time-averaged Feynman–Kac marginals associated with a positive operator iterated l∈N times;a lagged Feynman–Kac formula. This estimator and its approximation using Diffusion Monte Carlo (DMC) are commonly used in the physics literature. In short, DMC is an iterative algorithm involving N∈N particles or walkers simulated in parallel, that undergo sampling and resampling operations. In this work, it is shown that for the DMC approximation of the lagged Feynman–Kac formula, one has an almost sure characterization of the L₁-error as the time parameter (iteration) goes to infinity and this is at most of O(exp{−κl}/N), for κ>0. In addition a non-asymptotic in time, and time uniform L₁−bound is proved which is O(l/N). We also prove a novel central limit theorem to give a characterization of the exact asymptotic in time variance. This analysis demonstrates that the strategy used in physics, namely, to run DMC with N and l small and, for long time enough, is mathematically justified. Our results also suggest how one should choose N and l in practice. We emphasize that these results are not restricted to physical applications;they have broad relevance to the general problem of particle simulation of the Feynman–Kac formula, which is utilized in a great variety of scientific and engineering fields. © 2025 Elsevier B.V.

关键词： Diffusion Monte Carlo Eigenvalue approximation Feynman–Kac formula

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：