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The Role of Universities in AI-Driven Innovation for Sustainable Development

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The Role of Universities in AI-Driven Innovation for Sustain...

Circuit, Systems and Communication (ICCSC), International Conference on

作者： Nasim Salim Ziyad Al Balushi College of Computing and Information Sciences Unversity of Technology and Applied Science Ibri Sultanate of Oman

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

关键词： Technological innovation Government Education Teamwork Sustainable development Artificial intelligence Investment

Multi-functional spin-caloritronic device based on co-doped zigzag silicene nanoribbon heterojunction

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Results in Physics 2024年 67卷

作者： Kharadi, Mubashir A. Mahariq, Ibrahim Department of Electronics and Instrumentation Technology University of Kashmir India GUST Engineering and Applied Innovation Research Center (GEAR) Gulf University for Science and Technology Mishref Kuwait Applied Science Research Center Applied Science Private Unversity Amman Jordan Department of Medical Research China Medical University Hospital China Medical University Taichung Taiwan

The discovery of graphene has sparked significant interest in two-dimensional (2D) materials within the research community. Graphene's discovery has led to the exploration of several 2D-materials with varied properties. Silicene, a silicon-based analogue of graphene, is one of the newer 2D-materials. Silicene is a promising material for electronic applications and novel fields like spintronics. In this study, we propose a spin-caloritronic device based on zigzag silicene nanoribbons (ZSiNRs). The device features a heterojunction between two ZSiNRs, one of which is doped with boron (B) and nitrogen (N) atoms. The ZSiNRs’ edges are terminated with hydrogen (H) atoms for both the nanoribbons in the heterojunction. We utilize the density functional theory (DFT) combined with non-equilibrium Green's function (NEGF) to investigate the thermal-spin transport properties of this device under the influence of a thermal gradient. Notably, the device operates without any external bias, with spin transport driven solely by the thermal gradient. We find that a thermal colossal magnetoresistance (CMR) effect may be produced by modulating thermally generated spin currents by changing the magnetic configuration. Furthermore, the proposed device has superior transport properties, displaying phenomena including the spin-Seebeck diode effect (SSD), spin filtering effect (SFE), and spin-Seebeck effect (SSE). These characteristics position the proposed device as a strong candidate for multi-functional spin-caloritronic applications. © 2024 The Author(s)

关键词： 2D-Materials Co-doping Silicene Spin-caloritronics

Exploring waste activation and mineralization for environmental and economic sustainability in Ghana

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Cleaner Waste Systems

Cleaner Waste Systems 2025年 11卷

作者： Anokye, Kwame Darko, Abigail Okyere Portia, Agyemang Amuah, Ebenezer Ebo Yahans Sodoke, Stephen Agya, Bosompem Ahunoabobirim Douti, Nang Biyogue Kazapoe, Raymond Webrah Bentil, John Department of Environmental Science C. K. Tedam University of Technology and Applied Sciences P. O. Box 24 Navrongo Ghana School of Education Valley View University Accra Ghana College of Engineering Kwame Nkrumah Unversity of Science and Technology Kumasi Ghana Department of Civil Engineering Takoradi Technical University P. O. Box 256 Takoradi Ghana Department of Geomatic Engineering Kwame Nkrumah University of Science and Technology Kumasi Ghana Faculty of Renewable Natural Resources Kwame Nkrumah University of Science and Technology Kumasi Ghana Department of Geological Engineering University for Development Studies Tamale Ghana

The inefficiency of waste management systems in Ghana, characterized by inadequate infrastructure and unsustainable practices such as open dumping and landfilling, resulted in significant environmental, social, and economic challenges. This narrative review evaluates waste activation (pyrolysis) and CO₂ mineralization as solutions to repurpose Ghana's 12,710 tons/day of waste, with organic matter constituting 53.91 % of municipal solid waste, offering innovative solutions to these challenges. Drawing on global best practices and contextualizing them within Ghana's socio-economic and environmental landscape, the study examined the applicability, benefits, and implementation strategies of these advanced technologies. Key findings revealed that waste activation and mineralization mitigated pollution, reduced greenhouse gas emissions, enhanced public health, generated renewable energy and supported industrial growth. Studies show pyrolysis of low-density polyethylene sachets produces fuel with approximately 10 % lower global warming potential than conventional fossil fuels, while fly ash use in cement decreased CO₂ emissions by 15 million tons. The study also identified gaps in Ghana's waste management policies and provided actionable recommendations for integrating these technologies into the national framework. Notwithstanding growing academic attention to sustainable waste management, the specific roles of waste activation and CO₂ mineralization remain underexplored within the Ghanaian context. Existing policies and research continue to emphasize conventional approaches such as landfilling and incineration, offering limited empirical synthesis that aligns these advanced technologies with Ghana's unique waste profile, institutional realities, and socio-environmental conditions. This review bridges this critical gap by critically examining the feasibility, benefits, and implementation strategies of waste activation and mineralization in Ghana, thereby contributing to i

关键词： Biochar Circular economy Ghana Mineralization Waste-to-energy

Spiderweb nanomechanical resonators via Bayesian optimization: inspired by nature and guided by machine learning

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arXiv 2021年

作者： Shin, Dongil Cupertino, Andrea de Jong, Matthijs H.J. Steeneken, Peter G. Bessa, Miguel A. Norte, Richard A. Delft University of Technology Faculty of Mechanical Maritime and Materials Engineering Department of Materials Science and Engineering Delft University of Technology Faculty of Mechanical Maritime and Materials Engineering Department of Precision and Microsystems Engineering Delft Unversity of Technology Faculty of Applied Sciences Department of Quantum Nanoscience Kavli Institute of Nanoscience

From ultra-sensitive detectors of fundamental forces to quantum networks and sensors, mechanical resonators are enabling next-generation technologies to operate in room temperature environments. Currently, silicon nitride nanoresonators stand as a leading microchip platform in these advances by allowing for mechanical resonators whose motion is remarkably isolated from ambient thermal noise. However, to date, human intuition has remained the driving force behind design processes. Here, inspired by nature and guided by machine learning, a spiderweb nanomechanical resonator is developed that exhibits vibration modes which are isolated from ambient thermal environments via a novel"torsional soft-clamping" mechanism discovered by the data-driven optimization algorithm. This bio-inspired resonator is then fabricated;experimentally confirming a new paradigm in mechanics with quality factors above 1 billion in room temperature environments. In contrast to other state-of-the-art resonators, this milestone is achieved with a compact design which does not require sub-micron lithographic features or complex phononic bandgaps, making it significantly easier and cheaper to manufacture at large scales. Here we demonstrate the ability of machine learning to work in tandem with human intuition to augment creative possibilities and uncover new strategies in computing and nanotechnology. © 2021, CC BY.

关键词： Resonators

DeePMD-kit v3: A Multiple-Backend Framework for Machine Learning Potentials

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arXiv 2025年

作者： Zeng, Jinzhe Zhang, Duo Peng, Anyang Zhang, Xiangyu He, Sensen Wang, Yan Liu, Xinzijian Bi, Hangrui Li, Yifan Cai, Chun Zhang, Chengqian Du, Yiming Zhu, Jia-Xin Mo, Pinghui Huang, Zhengtao Zeng, Qiyu Shi, Shaochen Qin, Xuejian Yu, Zhaoxi Luo, Chenxing Ding, Ye Liu, Yun-Pei Shi, Ruosong Wang, Zhenyu Bore, Sigbjørn Løland Chang, Junhan Deng, Zhe Ding, Zhaohan Han, Siyuan Jiang, Wanrun Ke, Guolin Liu, Zhaoqing Lu, Denghui Muraoka, Koki Oliaei, Hananeh Singh, Anurag Kumar Que, Haohui Xu, Weihong Xu, Zhangmancang Zhuang, Yong-Bin Dai, Jiayu Giese, Timothy J. Jia, Weile Xu, Ben York, Darrin M. Zhang, Linfeng Wang, Han School of Artificial Intelligence and Data Science Unversity of Science and Technology of China Hefei China AI for Science Institute Beijing100080 China DP Technology Beijing100080 China Academy for Advanced Interdisciplinary Studies Peking University Beijing100871 China State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences Beijing100871 China University of Chinese Academy of Sciences Beijing China Baidu Inc. Beijing China Department of Computer Science University of Toronto TorontoON Canada Department of Chemistry Princeton University PrincetonNJ08540 United States University of Chinese Academy of Sciences Beijing100871 China State Key Laboratory of Physical Chemistry of Solid Surfaces iChEM College of Chemistry and Chemical Engineering Xiamen University Xiamen361005 China College of Integrated Circuits Hunan University Changsha410082 China State Key Laboratory of Advanced Technology for Materials Synthesis and Processing Center for Smart Materials and Device Integration School of Material Science and Engineering Wuhan University of Technology Wuhan430070 China College of Science National University of Defense Technology Changsha410073 China Hunan Key Laboratory of Extreme Matter and Applications National University of Defense Technology Changsha410073 China ByteDance Research Beijing100098 China Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China College of Materials Science and Opto-Electronic Technology University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Theoretical and Computational Photochemistry of Ministry of Education College of Chemistry Beijing Normal University Beijing100875 China Department of Geosciences Princeton University PrincetonNJ08544 United States Department of Applied Physics and Applied Mathematics Columbia University New YorkNY10027 United States IKKEM Fujian Xiamen361005 China Graduate

In recent years, machine learning potentials (MLPs) have become indispensable tools in physics, chemistry, and materials science, driving the development of software packages for molecular dynamics (MD) simulations and related applications. These packages, typically built on specific machine learning frameworks such as TensorFlow, PyTorch, or JAX, face integration challenges when advanced applications demand communication across different frameworks. The previous TensorFlow-based implementation of DeePMD-kit exemplified these limitations. In this work, we introduce DeePMD-kit version 3, a significant update featuring a multi-backend framework that supports TensorFlow, PyTorch, JAX, and PaddlePaddle backends, and demonstrate the versatility of this architecture through the integration of other MLPs packages and of Differentiable Molecular Force Field. This architecture allows seamless backend switching with minimal modifications, enabling users and developers to integrate DeePMD-kit with other packages using different machine learning frameworks. This innovation facilitates the development of more complex and interoperable workflows, paving the way for broader applications of MLPs in scientific research. Copyright © 2025, The Authors. All rights reserved.

关键词： Molecular dynamics

The pivot energy of Solar Energetic Particles Affecting the Martian surface radiation environment

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arXiv 2020年

作者： Guo, Jingnan Wimmer-Schweingruber, Robert F. Wang, Yuming Grande, Manuel Matthiä, Daniel Zeitlin, Cary Ehresmann, Bent Hassler, Donald M. School of Earth and Space Sciences University of Science and Technology of China Hefei China CAS Center for Excellence in Comparative Planetology Hefei China Institute of Experimental and Applied Physics Christian-Albrechts-University Kiel Germany Unversity of Aberystwyth Aberystwyth United Kingdom German Aerospace Agency Cologne Germany Leidos Innovations Corporation HoustonTX United States Planetary Science Division Southwest Research Institute BoulderCO United States

Space radiation is a major risk for humans, especially on long-duration missions to outer space, e.g., a manned mission to Mars. Galactic cosmic rays (GCR) contribute a predictable radiation background, the main risk is due to the highly variable and currently unpredictable flux of solar energetic particles (SEPs). Such sporadic SEP events may induce acute health effects and are thus considered a critical mission risk for future human exploration of Mars. Therefore, it is of utmost importance to study, model, and predict the surface radiation environment during such events. It is well known that the deep-space SEP differential energy spectrum at high energies is often given by a power law. We use a measurement-validated particle transport code to show that, for large SEP events with proton energy extending above ∼ 500 MeV with a power-law distribution, it is sufficient to measure the SEP flux at a pivot energy of ∼ 300 MeV above the Martian atmosphere to predict the dose rate on the Martian surface. In conjunction with a validation by in-situ measurements from the Martian surface, this remarkable simplification and elegant quantification could enable instant predictions of the radiation environment on the surface of Mars upon the onset of large SEP events. Copyright © 2020, The Authors. All rights reserved.

关键词： Forecasting

Preparation of high sphericity Li2TiO3 tritium breeder by polymer assisted sedimentation method

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Preparation of high sphericity Li2TiO3 tritium breeder by po...

Chinese Materials Conference, 2018

作者： Chen, Zhipeng Lu, Wei Chu, Delin Wang, Weihua Pu, Wenjing Jiang, Haiyan Pan, Zihao Ma, Shubing Deng, Haifei Qi, Junli Institute of Physical Science and Information Technology Anhui Unversity Heifei China Institute of Applied Physics PLA Army Academy of Artillery & Air Defense Heifei China College of materials science and engineering Hefei University of Technology Heifei China

ISBN: (纸本)9783035713848

The tritium breeder materials used in solid tritium breeding blanket of fusion reactor are lithium-based ceramics, like lithium titanate (Li2 TiO3), lithium orthosilicate (Li4 SiO4), lithium aluminate (LiA1O2) and lithium zirconate (Li8 ZrO6). Among them, Li2 TiO3 has the advantages of stable chemical property, high mechanical strength and high lithium content, so it has become one of the preferred materials. Based on the design of solid blanket and tritium extraction process, breeder materials need to be made into a certain shape of pebbles. Spherical breeder is widely used in the design of fusion reactor for the larger specific surface area, more channels between the pebbles to diffuse and release tritium, and easier loading or unloading. However, due to the high requirements of the breeder pebbles, the production control are not complete. In this paper, a new preparation method of ceramic pellets with high sphericity, polymer assisted sedimentation method, was used to prepare ceramic pebbles. The slurry consisted of micro-scaled ceramic powder and organic monomers. Spherical droplets of ceramic slurry were obtained by sedimentation method in viscous liquids. The organic monomer contained in the droplets was heated to polymerize into small pellet preforms during the sedimentation process. By optimizing the sintering process, the fabricated pebbles could perform high compressive load (>18 N), spherical shape (0.95~1.02), uniform particle size (0.8~1.2mm) and porosity (15~20 %). The main advantages of this method is the mild preparation conditions (50~110 ℃), the simplified transfer and purification process and convenient industrialization. The obtained product has high spherical shape, uniform particle size, high compactness and mechanical strength. © 2019 Trans Tech Publications, Switzerland.

关键词： Spheres

Technologies for the ELGAR large scale atom interferometer array

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arXiv 2020年

作者： Canuel, B. Abend, S. Amaro-Seoane, P. Badaracco, F. Beaufils, Q. Bertoldi, A. Bongs, K. Bouyer, P. Braxmaier, C. Chaibi, W. Christensen, N. Fitzek, F. Flouris, G. Gaaloul, N. Gaffet, S. Garrido Alzar, C.L. Geiger, R. Guellati-Khelifa, S. Hammerer, K. Harms, J. Hinderer, J. Holynski, M. Junca, J. Katsanevas, S. Klempt, C. Kozanitis, C. Krutzik, M. Landragin, A. Làzaro Roche, I. Leykauf, B. Lien, Y.-H. Loriani, S. Merlet, S. Merzougui, M. Nofrarias, M. Papadakos, P. Pereira dos Santos, F. Peters, A. Plexousakis, D. Prevedelli, M. Rasel, E.M. Rogister, Y. Rosat, S. Roura, A. Sabulsky, D.O. Schkolnik, V. Schlippert, D. Schubert, C. Sidorenkov, L. Siemß, J.-N. Sopuerta, C.F. Sorrentino, F. Struckmann, C. Tino, G.M. Tsagkatakis, G. Viceré, A. von Klitzing, W. Woerner, L. Zou, X. LP2N Laboratoire Photonique Numérique et Nanosciences Université Bordeaux-IOGS-CNRS:UMR 5298 rue F. Mitterrand TalenceF-33400 France Leibniz Universität Hannover Institut für Quantenoptik Welfengarten 1 HannoverD-30167 Germany 08193 Spain Carrer del Gran Capità 2-4 Edifici Nexus despatx 201 Barcelona08034 Spain Kavli Institute for Astronomy and Astrophysics Beijing100871 China Institute of Applied Mathematics Academy of Mathematics and Systems Science CAS Beijing100190 China Zentrum für Astronomie und Astrophysik TU Berlin Hardenbergstraße 36 Berlin10623 Germany L’AquilaI-67100 Italy INFN Laboratori Nazionali del Gran Sasso AssergiI-67100 Italy LNE-SYRTE Observatoire de Paris Université PSL CNRS Sorbonne Université 61 avenue de l’Observatoire PARISF-75014 France Midlands Ultracold Atom Research Centre School of Physics and Astronomy University of Birmingham BirminghamB15 2TT United Kingdom ZARM Unversity of Bremen Am Fallturm 2 Bremen28359 Germany DLR German Aerospace Center Linzer Strasse 1 Bremen28359 Germany ARTEMIS Université Côte d’Azur Observatoire de la Côte d’Azur CNRS NiceF-06304 France Leibniz University Hannover Appelstrasse 2 Hannover30167 Germany Institute of Computer Science Foundation for Research and Technology - Hellas Heraklion70013 Greece LSBB Laboratoire Souterrain Bas Bruit CNRS Avignon University - La grande combe Rustrel84400 France Laboratoire Kastler Brossel Sorbonne Université CNRS ENS-PSL Research University Collège de France 4 place Jussieu Paris75005 France Institut de Physique du Globe de Strasbourg UMR 7516 Université de Strasbourg/EOST CNRS 5 rue Descartes Strasbourg67084 France I-56021 Italy Humboldt-Universität zu Berlin Institute of Physics Newtonstrasse 15 Berlin12489 Germany Computer Science Department University of Crete Heraklion70013 Greece Dept. of Physics and Astronomy Univ. of Bologna Via Berti-Pichat 6/2 BolognaI-40126 Italy Söflinger Str. 100 Ulm

We proposed the European Laboratory for Gravitation and Atom-interferometric Research (ELGAR), an array of atom gradiometers aimed at studying space-time and gravitation with the primary goal of observing gravitational waves (GWs) in the infrasound band with a peak strain sensitivity of 3.3×10−22/√Hz at 1.7 Hz. In this paper we detail the main technological bricks of this large scale detector and emphasis the research pathways to be conducted for its realization. We discuss the site options, atom optics, and source requirements needed to reach the target sensitivity. We then discuss required seismic isolation techniques, Gravity Gradient Noise reduction strategies, and the metrology of various noise couplings to the detector. Copyright © 2020, The Authors. All rights reserved.

关键词： Atoms

Author Correction: Multi-ancestry genome-wide association analyses improve resolution of genes and pathways influencing lung function and chronic obstructive pulmonary disease risk

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Nature genetics 2023年第10期55卷 1778-1779页

作者： Nick Shrine Abril G Izquierdo Jing Chen Richard Packer Robert J Hall Anna L Guyatt Chiara Batini Rebecca J Thompson Chandan Pavuluri Vidhi Malik Brian D Hobbs Matthew Moll Wonji Kim Ruth Tal-Singer Per Bakke Katherine A Fawcett Catherine John Kayesha Coley Noemi Nicole Piga Alfred Pozarickij Kuang Lin Iona Y Millwood Zhengming Chen Liming Li Sara R A Wijnant Lies Lahousse Guy Brusselle Andre G Uitterlinden Ani Manichaikul Elizabeth C Oelsner Stephen S Rich R Graham Barr Shona M Kerr Veronique Vitart Michael R Brown Matthias Wielscher Medea Imboden Ayoung Jeong Traci M Bartz Sina A Gharib Claudia Flexeder Stefan Karrasch Christian Gieger Annette Peters Beate Stubbe Xiaowei Hu Victor E Ortega Deborah A Meyers Eugene R Bleecker Stacey B Gabriel Namrata Gupta Albert Vernon Smith Jian'an Luan Jing-Hua Zhao Ailin F Hansen Arnulf Langhammer Cristen Willer Laxmi Bhatta David Porteous Blair H Smith Archie Campbell Tamar Sofer Jiwon Lee Martha L Daviglus Bing Yu Elise Lim Hanfei Xu George T O'Connor Gaurav Thareja Omar M E Albagha Karsten Suhre Raquel Granell Tariq O Faquih Pieter S Hiemstra Annelies M Slats Benjamin H Mullin Jennie Hui Alan James John Beilby Karina Patasova Pirro Hysi Jukka T Koskela Annah B Wyss Jianping Jin Sinjini Sikdar Mikyeong Lee Sebastian May-Wilson Nicola Pirastu Katherine A Kentistou Peter K Joshi Paul R H J Timmers Alexander T Williams Robert C Free Xueyang Wang John L Morrison Frank D Gilliland Zhanghua Chen Carol A Wang Rachel E Foong Sarah E Harris Adele Taylor Paul Redmond James P Cook Anubha Mahajan Lars Lind Teemu Palviainen Terho Lehtimäki Olli T Raitakari Jaakko Kaprio Taina Rantanen Kirsi H Pietiläinen Simon R Cox Craig E Pennell Graham L Hall W James Gauderman Chris Brightling James F Wilson Tuula Vasankari Tarja Laitinen Veikko Salomaa Dennis O Mook-Kanamori Nicholas J Timpson Eleftheria Zeggini Josée Dupuis Caroline Hayward Ben Brumpton Claudia Langenberg Stefan Weiss Georg Homuth Carsten Oliver Schmidt Nicole Probst-Hensch Marjo-Riitta Jarvelin Alanna C Morrison Ozren Polasek Igor Department of Population Health Sciences University of Leicester Leicester UK. nick.shrine@leicester.ac.uk. Department of Population Health Sciences University of Leicester Leicester UK. Division of Respiratory Medicine and NIHR Nottingham Biomedical Research Centre University of Nottingham Nottingham UK. Leicester National Institute for Health and Care Research Biomedical Research Centre Glenfield Hospital Leicester UK. Channing Division of Network Medicine Division of Pulmonary and Critical Care Medicine Department of Medicine Brigham and Women's Hospital Boston MA USA. Harvard Medical School Boston MA USA. COPD Foundation Washington DC USA. Department of Clinical Science Unversity of Bergen Bergen Norway. Nuffield Department of Population Health University of Oxford Oxford UK. MRC Population Health Research Unit University of Oxford Oxford UK. Department of Epidemiology and Biostatistics School of Public Health Peking University Health Science Center Beijing China. Department of Respiratory Diseases Ghent Universital Hospital Ghent Belgium. Department of Bioanalysis Faculty of Pharmaceutical Sciences Ghent University Ghent Belgium. Department of Epidemiology Eramus Medical Center Rotterdam The Netherlands. Department of Internal Medicine Eramus Medical Center Rotterdam The Netherlands. Center for Public Health Genomics University of Virginia Charlottesville VA USA. Department of Medicine Columbia University Medical Center New York NY USA. Medical Research Council Human Genetics Unit Institute of Genetics and Cancer University of Edinburgh Edinburgh UK. Human Genetics Center Department of Epidemiology Human Genetics and Environmental Sciences School of Public Health The University of Texas Health Science Center at Houston Houston TX USA. MRC Centre for Environment and Health Department of Epidemiology and Biostatistics School of Public Health Imperial College London London UK. Department of Epidemiology and Public Health Swiss Tropical