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Zinc Oxide/Carbon Material-Embedded Supramolecular Drug Delivery System with Photoswitching Properties for Highly Selective and Effective Chemotherapy

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ACS applied Bio Materials 2024年第8期7卷 5506-5518页

作者： Ilhami, Fasih Bintang Munasir, None Gultom, Noto Susanto Cheng, Chih-Chia Department of Natural Science Faculty of Mathematics and Natural Science Universitas Negeri Surabaya Surabaya60231 Indonesia Department of Physics Faculty of Mathematics and Natural Science Universitas Negeri Surabaya Surabaya60231 Indonesia Department of Physics Universitas Padjadjaran Bandung45363 Indonesia Graduate Institute of Applied Science and Technology National Taiwan University of Science and Technology Taipei10607 Taiwan Advanced Membrane Materials Research Center National Taiwan University of Science and Technology Taipei10607 Taiwan

Phototherapy has become a hopeful procedure for the treatment of cancer. Nevertheless, the straightforward creation of a theranostic system that can achieve both tumor localization and production of oxygen species is greatly desired yet remains a challenging endeavor. In this study, we synthesized spherical nanostructures by decorating zinc oxide (ZnO) with peanut shell-based carbon (PNS-C) in an aqueous solution. The PNS-C-decorated ZnO (ZnO/PNS-C)-embedded supramolecular system exhibited spontaneous self-assembly. The nanogels that are produced have several desirable characteristics, including exceptional resistance to degradation by light, highly stable nanostructures that form spontaneously in biological environments, outstanding ability to prevent the destruction of red blood cells, and a high level of sensitivity to changes in pH and light. Under light irradiation, the addition of ZnO/PNS-C-incorporated supramolecular provided high reactive oxygen species production. Moreover, in vitro cellular assays demonstrated ZnO/PNS-C-incorporated supramolecular exhibited highly selective and induced phototoxicity into cancer cells and no effect on the viability of normal cells both before and after irradiation. Overall, the ZnO/PNS-C-incorporated supramolecular system has the potential to stimulate advancements in phototherapy by utilizing highly tumor-selective therapeutic molecules. This can lead to a more effective targeted therapy for cancers. © 2024 American Chemical Society.

关键词： Diseases

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TensorMD: Molecular Dynamics Simulation with Ab Initio Accuracy of 50 Billion Atoms 25

TensorMD: Molecular Dynamics Simulation with Ab Initio Accur...

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Proceedings of the 30th ACM SIGPLAN Annual Symposium on Principles and Practice of Parallel Programming

作者： Yucheng Ouyang Ying Liu Honghui Shang Zhenchuan Chen Jiahao Shan Huimin Cui Xiaobing Feng Xin Chen Xingyu Gao Lifang Wang Haifeng Song Rongfen Lin Fang Li Institute of Computing Technology Chinese Academy of Sciences Beijing China National Research Center of Parallel Computer Engineering and Technology Beijing China Institute of Applied Physics and Computational Mathematics Beijing China

ISBN: (纸本)9798400714436

Molecular dynamics simulation emerges as an important area that HPC+AI helps to investigate the physical properties, with machine-learning interatomic potentials (MLIPs) being used. General-purpose machine-learning (ML) tools have been leveraged in MLIPs, but they are not perfectly matched with each other, since many optimization opportunities in MLIPs have been missed by ML tools. This inefficiency arises from the fact that HPC+AI applications work with far more computational complexity compared with pure AI scenarios. This paper has developed an MLIP, named TensorMD, independently from any ML tool. TensorMD has been evaluated on two supercomputers and scaled to 51.8 billion atoms, i.e., ~ 3× compared with state-of-the-art.

关键词： GPU

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Influence of Heat on Fractionalized Nanofluid with Magnetic Field

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International Journal of applied and Computational mathematics 2025年第1期11卷 1-15页

作者： Ramzan, Muhammad Amir, Muhammad Abbas, Shajar Nazar, Mudassar Jan, Rashid Al Agha, Afnan AL Garalleh, Hakim Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan Department of Civil Engineering College of Engineering Institute of Energy Infrastructure (IEI) Universiti Tenaga National (UNITEN) Putrajaya Campus Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia Mathematics Research Center Near East University TRNC Nicosia 99138 Turkey Department of Mathematical Science College of Engineering Universty of Business and Technology Jeddah 21361 Saudi Arabia

Numerous researchers have been drawn to the rheology of non-Newtonian fluids because of their diverse uses in the engineering and manufacturing fields, such as lubrication, plastic processing, and mining. Additionally, the characteristics of magnetohydrodynamics non-Newtonian fluids permit its widespread application in computer hard drives, loudspeakers, magnetic resonance imaging, the administration of magnetic medicines, and magnetic hyperthermia. The novelty of present study is the use of fourier’s and fick’s laws. The current work is focused on the analysis of heat and mass transfer in a magnetohydrodynamics Maxwell nanofluid flows across an inclined vertical plate because of these possible uses. The values of angle of inclination is chosen in the range 0 to π3. From Table 1, the values of nanoparticles lies in the range of 0.012–0.18. Using suitable non-dimensional variables, ordinary differential equations are created from the modeling equations, and the Laplace transform method is used to solve these equations. Semi-analytical solutions for temperature, concentration, Bio-Convection, and velocity are found after employing the Laplace transform approach to address the issue. The study highlights several key outcomes likes velocity of fluid is increased with increasing values of Gr but decreased with increasing values of Pr, M, and maxwell parameter. The effect of increasing values of parameters R, L1, and N1 are seen to suppress the concentration profile. The bioconvection concentration profile increases with Φ but reduced with increasing values of L2. Furthermore, the comparison between ordinary and fractionalized maxwell fluid has been drawn. The application of the unsteady flow of nanofluid over a plate with the CPC operator has the potential to impact a wide range of solar energy-related technologies and innovations, leading to increased efficiency, sustainability, and the advancement of renewable energy solutions. © The Author(s), under exclusive licence

关键词： Chemical reaction CPC derivative Free convection Magnetic field Maxwell fluid

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Pose Estimation of Spacecraft Using Dual Transformers and Efficient Bayesian Hyperparameter Optimization

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International Journal of advanced Computer Science and Applications 2025年第4期16卷 629-644页

作者： Raja, N. Kannaiya Ramesh, Janjhyam Venkata Naga El-Ebiary, Yousef A. Baker Muniyandy, Elangovan Reddy, N. Konda Kumar, Vanipenta Ravi Devarasetty, Prasad School of Computing Science and Engineering VIT Bhopal University Madhya Pradesh Bhopal466114 India Department of CSE Graphic Era Hill University Dehradun248002 India Department of CSE Graphic Era Deemed To Be University Uttarakhand Dehradun248002 India Faculty of Informatics and Computing UniSZA University Malaysia Department of Biosciences Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai602 105 India Applied Science Research Center Applied Science Private University Amman Jordan Department of Engineering Mathematics K L University Greenfields Guntur Dist Andhra Pradesh Vaddeswaram522302 India Department of Mathematics Annamacharya University New Boyanapalli Rajampet India Department of Computer Science and Engineering DVR & Dr HS MIC College of Technology Andhra Pradesh Kanchikacherla India

Spacecraft pose estimation is an essential contribution to facilitating central space mission activities like autonomous navigation, rendezvous, docking, and on-orbit servicing. Nonetheless, methods like Convolutional Neural Networks (CNNs), Simultaneous Localization and Mapping (SLAM), and Particle Filtering suffer significant drawbacks when implemented in space. Such techniques tend to have high computational complexity, low domain generalization capacity for varied or unknown conditions (domain generalization problem), and accuracy loss with noise from the space environment causes such as fluctuating lighting, sensor limitations, and background interference. In order to overcome these challenges, this study suggests a new solution through the combination of a Dual-Channel Transformer Network with Bayesian Optimization methods. The innovation is at the center with the utilization of EfficientNet, augmented with squeeze-and-excitation attention modules, to extract feature-rich representations without sacrificing computational efficiency. The dual-channel architecture dissects satellite pose estimation into two dedicated streams—translational data prediction and orientation estimation via quaternion-based activation functions for rotational precision. Activation maps are transformed into transformer-compatible sequences via 1×1 convolutions, allowing successful learning in the transformer's encoder-decoder system. To maximize model performance, Bayesian Optimization with Gaussian Process Regression and the Upper Confidence Bound (UCB) acquisition function makes the optimal hyperparameter selection with fewer queries, conserving time and resources. This entire framework, used here in Python and verified with the SLAB Satellite Pose Estimation Challenge dataset, had an outstanding Mean IOU of 0.9610, reflecting higher accuracy compared to standard models. In total, this research sets a new standard for spacecraft pose estimation, by marrying the versatility of deep le

关键词： Convolutional neural networks

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PHYSICS-INFORMED DEEP INVERSE OPERATOR NETWORKS FOR SOLVING PDE INVERSE PROBLEMS

arXiv

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arXiv 2024年

作者： Cho, Sung Woong Son, Hwijae Stochastic Analysis and Applied Research Center Korea Advanced Institute of Science and Technology Daejeon Korea Republic of Department of Mathematics Konkuk University Seoul Korea Republic of

Inverse problems involving partial differential equations (PDEs) can be seen as discovering a mapping from measurement data to unknown quantities, often framed within an operator learning approach. However, existing methods typically rely on large amounts of labeled training data, which is impractical for most real-world applications. Moreover, these supervised models may fail to capture the underlying physical principles accurately. To address these limitations, we propose a novel architecture called Physics-Informed Deep Inverse Operator Networks (PI-DIONs), which can learn the solution operator of PDE-based inverse problems without labeled training data. We extend the stability estimates established in the inverse problem literature to the operator learning framework, thereby providing a robust theoretical foundation for our method. These estimates guarantee that the proposed model, trained on a finite sample and grid, generalizes effectively across the entire domain and function space. Extensive experiments are conducted to demonstrate that PI-DIONs can effectively and accurately learn the solution operators of the inverse problems without the need for labeled *** Codes 65M32, 68T99 © 2024, CC BY-NC-ND.

关键词： Inverse problems

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Dual-mode frequency multiplier in graphene-base hot electron transistor

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Nanoscale 2023年第6期15卷 2586-2594页

作者： Liang, Bor-Wei Li, Min-Fang Lin, Hung-Yu Li, Kai-Shin Chen, Jyun-Hong Shieh, Jia-Min Wu, Chien-Ting Simbulan, Kristan Bryan Su, Ching-Yuan Kuan, Chieh-Hsiung Lan, Yann-Wen Graduate Institute of Electronics Engineering National Taiwan University Taipei10617 Taiwan Taiwan Semiconductor Research Institute National Applied Research Laboratories Hsinchu30078 Taiwan Department of Mathematics and Physics University of Santo Tomas Manila1008 Philippines Graduate Institute of Energy Engineering National Central University No. 300 Jhongda Rd. Jhongli Taoyuan320317 Taiwan Department of Physics National Taiwan Normal University Taipei11677 Taiwan Advanced Materials and Green Energy Research Center National Taiwan Normal University Taipei11677 Taiwan

Since quantum computers have been gradually introduced in countries around the world, the development of the many related quantum components that can operate independently of temperature has become more important for enabling mature products with low power dissipation and high efficiency. As an alternative to studying cryo-CMOSs (complementary metal-oxide-semiconductors) to achieve this goal, quantum tunneling devices based on 2D materials can be examined instead. In this work, a vertical graphene-based quantum tunneling transistor has been used as a frequency modulator. The transistor can operate via different quantum tunneling mechanisms and generates, by applying the appropriate bias, voltage-resistance curves characteristic of variable nonlinear resistance for both base and emitter voltages. We experimentally demonstrate frequency modulation from input signals over the range of 100 kHz to 10 MHz, enabling a tunable frequency doubler or tripler in just a single transistor. This frequency multiplication with a tunneling mechanism makes the graphene-based tunneling device a promising option for frequency electronics in the emerging field of quantum technologies. © 2023 The Royal Society of Chemistry.

关键词： Graphene

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Computation of Thermal Radiative Flux from a Porous Medium by Using Fractal–Fractional Operator

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International Journal of applied and Computational mathematics 2024年第6期10卷 1-14页

作者： Garalleh, Hakim AL Ramzan, Muhammad Shafique, Ahmad Abbas, Shajar Nazar, Mudassar Jan, Rashid Department of Mathematical Science College of Engineering University of Business and Technology Jeddah Saudi Arabia Centre for Advanced Studies in Pure and Applied Mathematics Bahauddin Zakariya University Multan Pakistan Department of Civil Engineering College of Engineering Institute of Energy Infrastructure (IEI) Universiti Tenaga Nasional (UNITEN) Putrajaya Campus Jalan IKRAM-UNITEN Selangor Kajang 43000 Malaysia Mathematics Research Center Near East University Mersion 10 Nicosia 99138 Turkey

Fractal–fractional derivative has numerous significance in real world problems. The main goal of this derivative is the use of memory effects to formulate the model. Due to extensive applications of fractional derivatives, in the present article fractional order model of Brinkman fluid has been analyzed. The governing equations of the present flow model are solved numerically by using Cranck-Nicolson method to obtain the solutions for mass, energy, and momentum equations. In additions the physical aspects of material and flow parameters like as Prandtl number, magnetic force, Schmidt number, Grashof thermal number, Grashof mass number, especially the fractional operators are studied by drawing the graphs of field of interest. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Chemical reaction Heat flux Magnetic field Nanofluid

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An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

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Computers, Materials & Continua 2022年第1期70卷 817-829页

作者： Prachi Agrawal Khalid Alnowibet Talari Ganesh Adel F.Alrasheedi Hijaz Ahmad Ali Wagdy Mohamed Department of Mathematics and Scientific Computing National Institute of Technology HamirpurHimachal Pradesh177005India Statistics and Operations Research Department College of ScienceKing Saud UniversityRiyadh11451Kingdom of Saudi Arabia Section of Mathematics International Telematic University UninettunoRoma00186Italy Operations Research Department Faculty of Graduate Studies for Statistical ResearchCairo UniversityGiza12613Egypt Wireless Intelligent Networks Center(WINC) School of Engineering and Applied SciencesNile UniversityGizaEgypt

Recent years witness a great deal of interest in artificial intelligence(AI)tools in the area of *** has developed a large number of tools to solve themost difficult search-and-optimization problems in computer science and operations ***,metaheuristic-based algorithms are a sub-field of *** study presents the use of themetaheuristic algorithm,that is,water cycle algorithm(WCA),in the transportation problem.A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull *** the parameters are stochastic,the corresponding constraints are *** are converted into deterministic constraints using the stochastic programming *** this study,we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization *** is influenced by the water cycle process of how streams and rivers flow toward the sea(optimal solution).WCA is applied to the stochastic transportation problem,and obtained results are compared with that of the new metaheuristic optimization algorithm,namely the neural network algorithm which is inspired by the biological nervous *** is concluded that WCA presents better results when compared with the neural network algorithm.

关键词： Artificial intelligence metaheuristic algorithm stochastic programming transportation problem water cycle algorithm weibull distribution

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Incommensurate broken helix induced by nonstoichiometry in the axion insulator candidate EuIn2As2

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Physical Review B 2025年第8期111卷 L081109-L081109页

作者： Masaki Gen Yukako Fujishiro Kazuki Okigami Satoru Hayami Max T. Birch Kiyohiro Adachi Daisuke Hashizume Takashi Kurumaji Hajime Sagayama RIKEN Center for Emergent Matter Science (CEMS) Wako 351-0198 Japan Institute for Solid State Physics University of Tokyo Kashiwa Chiba 277-8581 Japan RIKEN Cluster for Pioneering Research (CPR) Wako 351-0198 Japan Department of Applied Physics University of Tokyo Tokyo 113-8656 Japan Graduate School of Science Hokkaido University Sapporo 060-0810 Japan Materials Characterization Support Team RIKEN Center for Emergent Matter Science (CEMS) Wako 351-0198 Japan Department of Advanced Materials Science University of Tokyo Kashiwa 277-8561 Japan Division of Physics Mathematics and Astronomy California Institute of Technology Pasadena California 91125 USA Institute of Materials Structure Science High Energy Accelerator Research Organization Tsukuba 305-0801 Japan

Zintl phase EuIn2As2 has garnered growing attention as an axion insulator candidate, triggered by the identification of a commensurate double-Q broken-helix state in previous studies, however, its periodicity and symmetry remain subjects of debate. Here, we perform resonant x-ray scattering experiments on EuIn2As2, revealing an incommensurate nature of the broken-helix state, where both the wave number and the amplitude of the helical modulation exhibit systematic sample dependence. Furthermore, the application of an in-plane magnetic field brings about a fanlike state that appears to preserve the double-Q nature, which might be attributed to multiple-spin interactions in momentum space. We propose that the itinerant character of EuIn2As2, most likely induced by Eu deficiency, gives rise to the helical modulation and impedes the realization of a theoretically predicted axion state with the collinear antiferromagnetic order.

关键词： X ray scattering

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Self-Supervised Entity Alignment Based on Multi-Modal Contrastive Learning

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 2031-2033页

作者： Bo Liu Ruoyi Song Yuejia Xiang Junbo Du Weijian Ruan Jinhui Hu the School of Software Engineering Xi’an Jiaotong UniversityXi’an 710000 the China Electronics Technology Group Corporation(CETC)Key Laboratory of Smart City Model Simulation and Intelligent Technology the Smart City Research Institute of CETC and National Center for Applied Mathematics Shenzhen(NCAMS)Shenzhen 518000China the Chinese University of Hong Kong Shenzhen 518000China Tencent Shenzhen 518000China the CETC Key Laboratory of Smart City Model Simulation and Intelligent Technology the Smart City Research Institute of CETC and National Center for Applied Mathematics Shenzhen(NCAMS)Shenzhen 518000China the Shenzhen Institute of Advanced Technology Chinese Academy of SciencesShenzhen 518055

Dear Editor,This letter proposes an unsupervised entity alignment method,which realizes integration of multiple multi-modal knowledge graphs *** recent years,Large-scale multi-modal knowledge graphs(LMKGs),containing text and image,have been widely applied in numerous knowledge-driven topics,such as question answering,entity linking,information extraction,reasoning and recommendation.

关键词： modal linking letter

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