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Inverse design of photonic surfaces on Inconel via multi-fidelity machine learning ensemble framework and high throughput femtosecond laser processing

arXiv

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arXiv 2024年

作者： Grbčić, Luka Park, Minok Elzouka, Mahmoud Prasher, Ravi Müller, Juliane Grigoropoulos, Costas P. Lubner, Sean D. Zorba, Vassilia de Jong, Wibe Albert Applied Mathematics and Computational Research Division Computing Science Area Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Energy Storage and Distributed Resources Division Energy Technologies Area Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Department of Mechanical Engineering University of California at Berkeley BerkeleyCA94709 United States Computational Science Center National Renewable Energy Laboratory GoldenCO80401 United States Department of Mechanical Engineering Division of Materials Science and Engineering Boston University BostonMA02215 United States

We demonstrate a multi-fidelity (MF) machine learning ensemble framework for the inverse design of photonic surfaces, trained on a dataset of 11,759 samples that we fabricate using high throughput femtosecond laser processing. The MF ensemble combines an initial low fidelity model for generating design solutions, with a high fidelity model that refines these solutions through local optimization. The combined MF ensemble can generate multiple disparate sets of laser-processing parameters that can each produce the same target input spectral emissivity with high accuracy (root mean squared errors © 2024, CC BY.

关键词： Machine learning

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Developing growth mindsets in engineering students: Work-in-progress on a systematic literature review 47

Developing growth mindsets in engineering students: Work-in-...

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47th SEFI Annual Conference 2019 - Varietas Delectat: Complexity is the New Normality

作者： Campbell, A.L. Direito, I. Mokhithi, M. Academic Support Programme for Engineering Centre for Research in Engineering Education University of Cape Town Cape Town South Africa Centre for Engineering Education University College London London United Kingdom Department of Mathematics and Applied Mathematics University of Cape Town Cape Town South Africa

ISBN: (纸本)9782873520182

engineering programs can be very demanding, particularly in the first years where students often encounter challenging coursework. Dropout from engineering studies has been linked to 'fixed mindset' beliefs that make students more likely to give up when facing new challenges. Extensive research evidenced that students with a 'fixed mindset' believe that intelligence is an innate and fixed trait. In contrast, students with a 'growth mindset' believe that intelligence can be improved with effort and drive, and are then less likely to disengage when confronting difficult tasks. Interventions to develop 'growth mindsets' have been successfully implemented at primary and secondary schools. However, there seems to be a paucity of interventions with university students studying engineering. In this work-in-progress paper, we will present findings from a systematic literature review of engineering, education and psychology databases to answer the question, 'What interventions to develop growth mindsets have been implemented with engineering students, and what measures have been used to assess the effectiveness of the interventions?' Preliminary findings suggest that the number of interventions aimed at strengthening growth mindsets in engineering students is still small. We present a categorization of interventions together with the measures used to assess the effectiveness of the interventions. The findings will be useful for engineering educators who want to encourage students to have the benefits associated with a growth mindset, such as greater resilience after setbacks and willingness to take on challenges and stick with them when difficulties arise, and support their academic success. © 2020 SEFI 47th Annual Conference: Varietas Delectat... Complexity is the New Normality, Proceedings. All rights reserved.

关键词： Students

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T-cell receptor specificity landscape revealed through de novo peptide design

arXiv

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arXiv 2025年

作者： Visani, Gian Marco Pun, Michael N. Minervina, Anastasia A. Bradley, Philip Thomas, Paul Nourmohammad, Armita Paul G. Allen School of Computer Science and Engineering University of Washington 85 E Stevens Way NE SeattleWA98195 United States Department of Physics University of Washington 3910 15th Avenue Northeast SeattleWA98195 United States St. Jude Children’s Research Hospital MemphisTN United States Fred Hutchinson Cancer Center 1241 Eastlake Ave E SeattleWA98102 United States Department of Applied Mathematics University of Washington 4182 W Stevens Way NE SeattleWA98105 United States

T-cells play a key role in adaptive immunity by mounting specific responses against diverse pathogens. An effective binding between T-cell receptors (TCRs) and pathogen-derived peptides presented on Major Histocompatibility Complexes (MHCs) mediate an immune response. However, predicting these interactions remains challenging due to limited functional data on T-cell reactivities. Here, we introduce a computational approach to predict TCR interactions with peptides presented on MHC class I alleles, and to design novel immunogenic peptides for specified TCR-MHC complexes. Our method leverages HERMES, a structure-based, physics-guided machine learning model trained on the protein universe to predict amino acid preferences based on local structural environments. Despite no direct training on TCR-pMHC data, the implicit physical reasoning in HERMES enables us to make accurate predictions of both TCR-pMHC binding affinities and T-cell activities across diverse viral epitopes and cancer neoantigens, achieving up to 72% correlation with experimental data. Leveraging our TCR recognition model, we develop a computational protocol for de novo design of immunogenic peptides. Through experimental validation in three TCR-MHC systems targeting viral and cancer peptides, we demonstrate that our designs—with up to five substitutions from the native sequence—activate T-cells at success rates of up to 50%. Lastly, we use our generative framework to quantify the diversity of the peptide recognition landscape for various TCR-MHC complexes, offering key insights into T-cell specificity in both humans and mice. Our approach provides a platform for immunogenic peptide and neoantigen design, opening new computational paths for T-cell vaccine development against viruses and cancer. © 2025, CC BY-NC-ND.

关键词： T-cells

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Assessing and mapping recreation value as an ecosystem service in central part of Esfahan Province, Iran

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Arabian Journal of Geosciences 2022年第9期15卷 1-11页

作者： Meshram, Sarita Gajbhiye Abdollahi, Sedighe Ildoromi, Ali Reza Salmanmahini, Abdolrassoul Fakheran, Sima Water Resources and Applied Mathematics Research Lab Nagpur India Department of Environmental Science and Engineering Faculty of Natural Resources and Environment Malayer University Malayer Iran Nature Engineering Department Faculty of Natural Resources and Environment Malayer University Malayer Iran Department of Environmental Sciences Faculty of Fisheries and Environment Gorgan University of Agricultural Sciences and Natural Resources Gorgan City Iran Department of Environment Faculty of Natural Resources Isfahan University of Technology Isfahan Iran

Cultural ecosystem services, despite the conceptual and methodological difficulties linked to their evaluation, have received much attention in the scientific literature. We applied a participatory GIS approach for mapping and assessing recreational value for cultural ecosystem services in the central part of Esfahan Province, Iran. Firstly, the objectives of the study were determined, which included a variety of recreational activities. Then, criteria were defined based on these objectives, including slope, land-use types, distances from human infrastructure, and distances from natural and cultural attractions. In the next step, the criteria were standardized and weighted, applying the fuzzy procedure and the analytical hierarchy process (AHP), respectively. After that, the criteria were combined, and eventually, the map of each activity was prepared. Finally, recreational activity maps were used to assign the recreational value of the study area. This was done using a multi-criteria evaluation method. The results showed around 197 km2 of the study area has a high value for recreation, and about 524 km2 has the lowest recreational value, while the highest recreational value was related to walking. The suggested approach can be applied to support landscape planning, recognize areas most demanded by the community, and spatially determine the quantity of recreational value to corroborate political plans.

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Exploring Perfect Fluid Dark Matter with Eht Results of Sgr A* Through Rotating 4d-Egb Black Holes

SSRN

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SSRN 2024年

作者： Atamurotov, Farruh Sarikulov, Furkat Ghosh, Sushant G. Mustafa, G. University of Tashkent for Applied Sciences Str. Gavhar 1 Tashkent100149 Uzbekistan Ulugh Beg Astronomical Institute Astronomy St. 33 Tashkent100052 Uzbekistan Inha University in Tashkent Ziyolilar 9 Tashkent100170 Uzbekistan Centre for Theoretical Physics Jamia Millia Islamia New Delhi110025 India Astrophysics and Cosmology Research Unit School of Mathematics Statistics and Computer Science University of KwaZulu-Natal Private Bag 54001 Durban4000 South Africa Department of Mathematics Shanghai University Shanghai Shanghai200444 China

We investigate the rotating 4D Gauss-Bonnet (GB) black holes surrounded by Perfect Fluid Dark Matter (PFDM) using the Event Horizon Telescope (EHT) observations of Sagittarius A* (Sgr A*). This rotating black hole, influenced by an additional GB parameter $\alpha$ and a DM parameter $\beta$, deviates from the Kerr black hole geometry, offering a more complex horizon structure. Our findings reveal that the shadow size decreases and oblateness increases with higher values of $\alpha$, $\beta$, and the spin parameter $a$. By plotting the shadow area $A$ and oblateness $D$ as functions of $\alpha$ and $\beta$ for various $a$ values, we determine the unique black hole parameters through the intersection points of the $A$ and $D$ curves. We constrain the GB and DM parameters with the EHT shadow observational results of Sgr A* for an inclination angle of $90^\circ$ and $50^\circ$. The results, which align with EHT observations of Sgr A*, suggest that dark matter may exist in significant quantities around the galactic centre, challenging the traditional Kerr black hole model and offering new avenues for understanding complex interactions. Thus, It is essential to consider that rotating 4D GB black holes surrounded by PFDM could be strong candidates for astrophysical black holes. © 2024, The Authors. All rights reserved.

关键词： Black holes

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Levenberg-Marquardt Backpropagation for Numerical Treatment of Micropolar Flow in a Porous Channel with Mass Injection

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Complexity 2021年第1期2021卷

作者： Ullah, Hakeem Khan, Imran Alsalman, Hussain Islam, Saeed Asif Zahoor Raja, Muhammad Shoaib, Muhammad Gumaei, Abdu Fiza, Mehreen Ullah, Kashif Mizanur Rahman, Sk. Md. Ayaz, Muhammad Department of Mathematics Abdul Wali Khan University Mardan MardanKP 23200 Pakistan Department of Computer Science College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Douliou Yunlin64002 Taiwan Department of Mathematics COMSATS University Islamabad Attock Campus Attock43600 Pakistan Department of Computer Science Faculty of Applied Sciences Taiz University Taiz6803 Yemen Information and Communication Engineering Technology School of Engineering Technology and Applied Science Centennial College Toronto Canada

In this research work, an effective Levenberg-Marquardt algorithm-based artificial neural network (LMA-BANN) model is presented to find an accurate series solution for micropolar flow in a porous channel with mass injection (MPFPCMI). The LMA is one of the fastest backpropagation methods used for solving least-squares of nonlinear problems. We create a dataset to train, test, and validate the LMA-BANN model regarding the solution obtained by optimal homotopy asymptotic (OHA) method. The proposed model is evaluated by conducting experiments on a dataset acquired from the OHA method. The experimental results are obtained by using mean square error (MSE) and absolute error (AE) metric functions. The learning process of the adjustable parameters is conducted with efficacy of the LMA-BANN model. The performance of the developed LMA-BANN for the modelled problem is confirmed by achieving the best promise numerical results of performance in the range of E-05 to E-08 and also assessed by error histogram plot (EHP) and regression plot (RP) measures. © 2021 Hakeem Ullah et al.

关键词： Mean square error

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Insight into mechanism of excellent visible-light photocatalytic activity of CuO/MgO/ZnO nanocomposite for advanced solution of environmental remediation

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Chemosphere 2024年 359卷 142224页

作者： Imran, Muhammad Raza, Mohsin Noor, Hadia Faraz, Sadia Muniza Raza, Ali Farooq, Umar Khan, Mohammad Ehtisham Ali, Syed Kashif Bakather, Omer Y. Ali, Wahid Bashiri, Abdullateef H. Zakri, Waleed Centre of Excellence in Solid State Physics University of Punjab Lahore54590 Pakistan Additive Manufacturing Institute Shenzhen University Shenzhen518060 China Department of Electronic Engineering NED University of Engineering & Technology Karachi75270 Pakistan Department of Chemistry The Islamia University of Bahawalpur Baghdad-ul-Jadeed Campus Bahawalpur63100 Pakistan Department of Chemical Engineering Technology College of Applied Industrial Technology Jazan University Jazan45142 Saudi Arabia Department of Physical Sciences Chemistry Division College of Science Jazan University P.O. Box. 114 Jazan45142 Saudi Arabia Nanotechnology Research Unit College of Science Jazan University P.O. Box. 114 Jazan45142 Saudi Arabia Chemical Engineering Department College of Engineering and Computer Sciences Jazan University Jazan45142 Saudi Arabia Department of Mechanical Engineering College of Engineering Jazan University P. O. Box 114 Jazan45142 Saudi Arabia

Environmental remediation has sought several innovative ways for the treatment of wastewater and captivated researchers around the globe towards it. Through this study, we aim to proceed with the efforts to foster sustainable and feasible ways for the treatment of wastewater. In this work, we report the sol-gel synthesis of CuO/MgO/ZnO nanocomposite and carry out their systematic characterization with the help of state-of-the-art analytical techniques, such as FTIR, SEM, TEM, PL, XRD, Raman, and AFM. The SEM along with TEM and AFM provided useful insights into the surface morphology of the synthesized nanocomposite on both 2D and 3D surfaces and concluded the well-dispersed behavior of the nanocomposite. The characteristic functional groups responsible for carrying out the reaction of Cu–O, Mg–O, and Zn–O were identified by FTIR spectroscopy. On the other hand, crystal size, dislocation density, and microstrain of the nanocomposite were calculated by XRD. For optical studies, photoluminescence spectroscopy was performed. Once the characterization of the nanocomposite was done, they were eventually treated against the toxic organic dye, methylene blue. The calculated rate constant values of k for CuO was 2.48 × 10−3 min−1, for CuO/MgO (2.04 × 10−3 min−1), for CuO/ZnO (1.82 × 10−3 min−1) and CuO/MgO/ZnO was found to be 2.00 × 10−3 min−1. It has become increasingly evident that nanotechnology can be used in various facets of modern life, and its implementation in wastewater treatment has recently received much attention. © 2024 Elsevier Ltd

关键词： Morphology

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Individualized brain mapping for navigated neuromodulation

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Chinese Medical Journal 2024年第5期137卷 508-523页

作者： Chaohong Gao Xia Wu Xinle Cheng Kristoffer Hougaard Madsen Congying Chu Zhengyi Yang Lingzhong Fan Sino–Danish College University of Chinese Academy of SciencesBeijing 100190China Brainnetome Center National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190China Department of Applied Mathematics and Computer Science Technical University of DenmarkKongens Lyngby 2800Denmark Danish Research Centre for Magnetic Resonance Centre for Functional and Diagnostic Imaging and ResearchCopenhagen University Hospital Amager and HvidovreHvidovre 2650Denmark CAS Center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Health and Life Sciences University of Health and Rehabilitation SciencesQingdaoShandong 266000China

The brain is a complex organ that requires precise mapping to understand its structure and *** atlases provide a powerful tool for studying brain circuits,discovering biological markers for early diagnosis,and developing personalized treatments for neuropsychiatric *** techniques,such as transcranial magnetic stimulation and deep brain stimulation,have revolutionized clinical therapies for neuropsychiatric ***,the lack of fine-scale brain atlases limits the precision and effectiveness of these *** in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation ***,the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized *** article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles,advantages,disadvantages,and future trends of these *** article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.

关键词： Brain atlas Individualization Navigated neuromodulation Multimodal magnetic resonance imaging Transcranial magnetic stimulation Deep brain stimulation

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Optimal Qubit Mapping Search for Encoding Classical Data into Matrix Product State Representation with Minimal Loss

arXiv

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arXiv 2024年

作者： Jeon, Hyungjun Lee, Kyungmin Lee, Dongkyu Kim, Bongsang Kim, Taehyun Department of Computer Science and Engineering Seoul National University Seoul08826 Korea Republic of Quantum AI Dept AI Lab CTO LG Electronics Seoul06772 Korea Republic of Automation and System Research Institute Seoul National University Seoul08826 Korea Republic of Inter-university Semiconductor Research Center Seoul National University Seoul08826 Korea Republic of Institute of Computer Technology Seoul National University Seoul08826 Korea Republic of Institute of Applied Physics Seoul National University Seoul08826 Korea Republic of

Matrix product state (MPS) offers a framework for encoding classical data into quantum states, enabling the efficient utilization of quantum resources for data representation and processing. This research paper investigates techniques to enhance the efficiency and accuracy of MPS representations specifically designed for encoding classical data. Based on the observations that MPS truncation error depends on the pattern of the classical data, we devised an algorithm that finds optimal qubit mapping for given classical data, thereby improving the efficiency and fidelity of the MPS representation. Furthermore, we evaluate the impact of the optimized MPS in the context of quantum classifiers, demonstrating their enhanced performance compared to the conventional mapping. This improvement confirms the efficacy of the proposed techniques for encoding classical data into quantum states. MPS representation combined with optimal qubit mapping can pave a new way for more efficient and accurate quantum data representation and processing. © 2024, CC BY-NC-ND.

关键词： Mapping

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How we can use text classification in the Back-Office environment of a bank as ‘business as usual’ solution

How we can use text classification in the Back-Office enviro...

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International Symposium on applied Computational Intelligence and Informatics ( SACI)

作者： Zsolt Krutilla Attila Kovari Doctoral School of Applied Informatics and Applied Mathematics Óbuda University Budapest Hungary Informatics Institution University of Dunaújváros Dunaújváros Hungary Institute of Engineering Alba Regia Technical Faculty Óbuda University Budapest Hungary Institute of Digital Technology Faculty of Informatics Eszterházy Károly Catholic University Eger Hungary Department of Natural Science Institute of Engineering University of Dunaújváros Dunaújváros Hungary GAMF Faculty of Engineering and Computer Science John von Neumann University Kecskemét Hungary

Natural Language Processing nowadays provides scientists with many research areas and opportunities, but as with most applied sciences, our goal in Natural Language Processing is to refine the underlying science and technology until it can be used reliably for business purposes. Transformer models, such as GPT or BERT, are currently showing outstanding results in the field of natural-language processing, but they require huge computational power and data to teach, but these conditions can only be met by larger research centers and large companies, and their inaccuracy makes them unsuitable for use as a ‘business as usual’ (BAU) solution. In this paper, we present a solution that is able to overcome these problems by focusing on accuracy and usability, and that can also bring a new perspective to the process of teaching deep learning models.

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