Review
Agriculture, Dairy & Animal Science
Giovanni Franzo, Matteo Legnardi, Giulia Faustini, Claudia Maria Tucciarone, Mattia Cecchinato
Summary: In the future, the demand for poultry meat and eggs is predicted to increase with population growth. This expansion brings both opportunities and challenges such as pollution, competition for resources, animal welfare concerns, and infectious diseases. Optimization and increased efficiency are needed in poultry production, and the use of big data offers the opportunity to develop tools to maximize farm profitability and reduce impacts. Sensor technologies and advanced statistical techniques are discussed, as well as the progress in pathogen genome sequencing and analysis.
Review
Chemistry, Multidisciplinary
Alexander Hinderhofer, Alessandro Greco, Vladimir Starostin, Valentin Munteanu, Linus Pithan, Alexander Gerlach, Frank Schreiber
Summary: This paragraph discusses the status, opportunities, challenges, and limitations of machine learning applied to X-ray and neutron scattering techniques, focusing on surface scattering. Typical strategies and potential pitfalls are outlined. The applications to reflectometry and grazing-incidence scattering are critically discussed. Comment is also given on the availability of training and test data for machine learning applications, such as neural networks, and a large reflectivity data set is provided as reference data for the community.
JOURNAL OF APPLIED CRYSTALLOGRAPHY
(2023)
Article
Materials Science, Characterization & Testing
Aksel S. Obdrup, D. C. Florian Wieland, Mathias K. Huss-Hansen, Matthias M. L. Arras, Matti Knaapila
Summary: In this study, wide-angle X-ray scattering combined with linear discriminant analysis was used to distinguish between healthy and damaged UHMWPE ropes, providing a new method for nondestructive testing.
Review
Clinical Neurology
Yuzhe Liu, Yuan Luo, Andrew M. Naidech
Summary: Significant advances in medical data accumulation, computational techniques, and management have been made in the last decade. Big data and computational methods can address gaps in patient selection, complications prediction, and outcome understanding. Automated neuroimaging analysis can help triage patients, and data-intensive techniques enable accurate risk calculations for timely prediction of adverse events.
Review
Chemistry, Multidisciplinary
Hadas Shalit Peleg, Anat Milo
Summary: The chemistry community is experiencing a surge in scientific discoveries in organic chemistry with the support of machine learning. However, the use of machine learning techniques is often limited by small datasets in experimental organic chemistry. This article highlights the limitations of small data in machine learning and emphasizes the impact of bias and variance on constructing reliable predictive models. It aims to raise awareness of these potential pitfalls and provides an introductory guideline for good practice. Ultimately, the article stresses the great value of statistical analysis of small data and advocates for a holistic data-centric approach in chemistry.
ANGEWANDTE CHEMIE-INTERNATIONAL EDITION
(2023)
Article
Chemistry, Analytical
Theodoros Alexakis, Nikolaos Peppes, Konstantinos Demestichas, Evgenia Adamopoulou
Summary: The increasing needs for data acquisition, storage and analysis in transportation systems have led to the adoption of new technologies and methods, such as big data techniques and analytics tools. This study aims to provide a distributed architecture platform that addresses the deficiencies in data gathering, storage, and analysis for intelligent transportation systems (ITS). The proposed system utilizes big data frameworks and tools as well as analytics tools to offer continuous collection, storage, and data analysis capabilities, providing a comprehensive solution for ITS applications.
Article
Health Care Sciences & Services
K. C. Santosh, Sourodip Ghosh
Summary: The paper analyzes medical imaging tools in the context of Covid-19, finding that dataset size and data augmentation are not suitable for Covid-19 screening.
JOURNAL OF MEDICAL SYSTEMS
(2021)
Article
Chemistry, Multidisciplinary
Murat Dener, Gokce Ok, Abdullah Orman
Summary: The study suggests using memory data in malware detection and applying deep learning and machine learning approaches in a big data environment. Results show that the Logistic Regression algorithm achieved the most successful malware detection in memory analysis.
APPLIED SCIENCES-BASEL
(2022)
Review
Engineering, Biomedical
Jacob Kerner, Alan Dogan, Horst von Recum
Summary: Machine learning has been widely utilized in various fields, including biomaterials, optimizing data collection and analysis. Recent advances in biomaterials have focused on quantitative structure properties relationships, introducing four basic models for rapid development and addressing the lack of machine learning implementation in the field. This article aims to spark greater interest and awareness in utilizing computational methods for biomaterials research.
ACTA BIOMATERIALIA
(2021)
Review
Medicine, General & Internal
Ana F. Pina, Maria Joao Meneses, Ines Sousa-Lima, Roberto Henriques, Joao F. Raposo, Maria Paula Macedo
Summary: This review examines the relationship between cluster analysis and T2D and highlights the complexity and heterogeneity of diabetes. It proposes an integrative model for understanding individual pathology. To achieve precision medicine and prevent complications, more factors such as etiological factors, pathophysiological mechanisms, and environmental factors should be considered.
EUROPEAN JOURNAL OF CLINICAL INVESTIGATION
(2023)
Article
Computer Science, Hardware & Architecture
Mohammad Hassan Almaspoor, Ali A. Safaei, Afshin Salajegheh, Behrouz Minaei-Bidgoli
Summary: This paper presents a novel distributed method for SVM training to address the efficiency issue in large-scale datasets. The method uses a small subset of training samples for classification, reducing the problem size and required resources. It also works effectively on unbalanced datasets.
JOURNAL OF SUPERCOMPUTING
(2023)
Article
Chemistry, Physical
Nathan J. Szymanski, Christopher J. Bartel, Yan Zeng, Mouhamad Diallo, Haegyeom Kim, Gerbrand Ceder
Summary: Machine learning is a valuable tool in materials characterization, allowing for automated interpretation of experimental results and on-the-fly decision making to improve measurement effectiveness. By combining an ML algorithm with a physical diffractometer, we developed an autonomous and adaptive XRD method that leverages early experimental information to enhance the identification of crystalline phases. Our results demonstrate the effectiveness of ML-driven XRD in detecting trace materials and identifying short-lived intermediate phases, showcasing the advantages of in-line ML for materials characterization and indicating the potential for adaptive experimentation.
NPJ COMPUTATIONAL MATERIALS
(2023)
Review
Green & Sustainable Science & Technology
Sara Barja-Martinez, Monica Aragues-Penalba, Ingrid Munne-Collado, Pau Lloret-Gallego, Eduard Bullich-Massague, Roberto Villafafila-Robles
Summary: This paper provides a comprehensive analysis of artificial intelligence applications in distribution power systems, covering various aspects such as operation, monitoring, maintenance, and planning. It identifies potential AI techniques for power system applications and needed data sources. The study also examines data-driven services for distribution networks, highlighting interdependencies between different services and the importance of enhanced sensorization for better service outcomes.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2021)
Review
Nanoscience & Nanotechnology
Lihao Chen, Chuwen Lan, Ben Xu, Ke Bi
Summary: Material characterization is essential for high-throughput computational material science under big data environment. By analyzing element composition, molecular structure, and energy band distribution, researchers can predict the physical properties of materials, improve the quality of machine learning models, save computing resources, and enhance the understanding of the correlation of material attributes.
ADVANCED COMPOSITES AND HYBRID MATERIALS
(2021)
Article
Physics, Multidisciplinary
Peco Myint, Karl F. Ludwig, Lutz Wiegart, Yugang Zhang, Andrei Fluerasu, Xiaozhi Zhang, Randall L. Headrick
Summary: In investigating the self-organized ion-beam nanopatterning of silicon using coherent x-ray scattering, a relationship similar to de Gennes narrowing is discovered. However, unlike the classic phenomenon, the dynamic surface exhibits a wide range of behaviors with compressed exponential relaxation at lengths corresponding to the dominant structural motif. This behavior is attributed to the morphological persistence of the self-organized surface ripple patterns.
PHYSICAL REVIEW LETTERS
(2021)
Article
Physics, Multidisciplinary
Yuri D. Lensky, Kostyantyn Kechedzhi, Igor Aleiner, Eun-Ah Kim
Summary: Stabilizer codes allow for non-local encoding and processing of quantum information. Deformations of stabilizer surface codes introduce new and non-trivial geometry, leading to emergence of long sought after objects known as projective Ising non-Abelian anyons. We present a simple and systematic approach to construct effective unitary protocols for braiding, manipulation and readout of non-Abelian anyons.
Article
Chemistry, Multidisciplinary
Valerie Hsieh, Dorri Halbertal, Nathan R. . Finney, Ziyan Zhu, Eli Gerber, Michele Pizzochero, Emine Kucukbenli, Gabriel R. Schleder, Mattia Angeli, Kenji Watanabe, Takashi Taniguchi, Eun-Ah Kim, Efthimios Kaxiras, James Hone, Cory R. Dean, D. N. Basov
Summary: Twisted van der Waals multilayers are regarded as a rich platform for accessing novel electronic phases. This study proposes that naturally formed stacking domains due to relative twist between layers can act as an additional control knob. The researchers observe selective adhesion of metallic nanoparticles and liquid water at domains with specific stacking configurations and demonstrate the manipulation of nanoparticles can locally reconfigure the moire superlattice.
Article
Multidisciplinary Sciences
T. I. Andersen, Y. D. Lensky, K. Kechedzhi, I. K. Drozdov, A. Bengtsson, S. Hong, A. Morvan, X. Mi, A. Opremcak, R. Acharya, R. Allen, M. Ansmann, F. Arute, K. Arya, A. Asfaw, J. Atalaya, R. Babbush, D. Bacon, J. C. Bardin, G. Bortoli, A. Bourassa, J. Bovaird, L. Brill, M. Broughton, B. B. Buckley, D. A. Buell, T. Burger, B. Burkett, N. Bushnell, Z. Chen, B. Chiaro, D. Chik, C. Chou, J. Cogan, R. Collins, P. Conner, W. Courtney, A. L. Crook, B. Curtin, D. M. Debroy, A. Del Toro Barba, S. Demura, A. Dunsworth, D. Eppens, C. Erickson, L. Faoro, E. Farhi, R. Fatemi, V. S. Ferreira, L. F. Burgos, E. Forati, A. G. Fowler, B. Foxen, W. Giang, C. Gidney, D. Gilboa, M. Giustina, R. Gosula, A. G. Dau, J. A. Gross, S. Habegger, M. C. Hamilton, M. Hansen, M. P. Harrigan, S. D. Harrington, P. Heu, J. Hilton, M. R. Hoffmann, T. Huang, A. Huff, W. J. Huggins, L. B. Ioffe, S. V. Isakov, J. Iveland, E. Jeffrey, Z. Jiang, C. Jones, P. Juhas, D. Kafri, T. Khattar, M. Khezri, M. Kieferova, S. Kim, A. Kitaev, P. V. Klimov, A. R. Klots, A. N. Korotkov, F. Kostritsa, J. M. Kreikebaum, D. Landhuis, P. Laptev, K. -M. Lau, L. Laws, J. Lee, K. W. Lee, B. J. Lester, A. T. Lill, W. Liu, A. Locharla, E. Lucero, F. D. Malone, O. Martin, J. R. McClean, T. McCourt, M. McEwen, K. C. Miao, A. Mieszala, M. Mohseni, S. Montazeri, E. Mount, R. Movassagh, W. Mruczkiewicz, O. Naaman, M. Neeley, C. Neill, A. Nersisyan, M. Newman, J. H. Ng, A. Nguyen, M. Nguyen, M. Y. Niu, T. E. O'Brien, S. Omonije, A. Petukhov, R. Potter, L. P. Pryadko, C. Quintana, C. Rocque, N. C. Rubin, N. Saei, D. Sank, K. Sankaragomathi, K. J. Satzinger, H. F. Schurkus, C. Schuster, M. J. Shearn, A. Shorter, N. Shutty, V. Shvarts, J. Skruzny, W. C. Smith, R. Somma, G. Sterling, D. Strain, M. Szalay, A. Torres, G. Vidal, B. Villalonga, C. V. Heidweiller, T. White, B. W. K. Woo, C. Xing, Z. J. Yao, P. Yeh, J. Yoo, G. Young, A. Zalcman, Y. Zhang, N. Zhu, N. Zobrist, H. Neven, S. Boixo, A. Megrant, J. Kelly, Y. Chen, V. Smelyanskiy, E. -A. Kim, I. Aleiner, P. Roushan
Summary: Indistinguishability of particles is a fundamental principle in quantum mechanics. While braiding of Abelian anyons leaves the system unchanged, braiding of non-Abelian anyons can change the observables of the system without violating the principle of indistinguishability. Experimental observation of non-Abelian anyons' exchange statistics has remained elusive, but using quantum processors, it is now possible to manipulate and braid them, allowing for the verification of their fusion rules and statistics. This work provides insights into non-Abelian braiding and its potential application in fault-tolerant quantum computing with the inclusion of error correction.
Correction
Multidisciplinary Sciences
Michael Matty, Eun-Ah Kim
NATURE COMMUNICATIONS
(2023)
Article
Physics, Multidisciplinary
Y. Shen, J. Sears, G. Fabbris, J. Li, J. Pelliciari, M. Mitrano, W. He, Junji Zhang, J. F. Mitchell, V. Bisogni, M. R. Norman, S. Johnston, M. P. M. Dean
Summary: Charge order is a common phenomenon in both cuprate superconductors and low-valence nickelate superconductors, but their electronic characteristics differ. In this study, using resonant inelastic x-ray scattering, researchers identified the involvement of Ni 3dx2-y2, 3d3z2-r2, and O 2p sigma orbitals in the formation of diagonal charge order in an overdoped low-valence nickelate. The results reveal that the low-energy physics and ground-state character of these nickelates are more complex than those in cuprates.
Article
Multidisciplinary Sciences
G. Fabbris, D. Meyers, Y. Shen, V. Bisogni, J. Zhang, J. F. Mitchell, M. R. Norman, S. Johnston, J. Feng, G. S. Chiuzbaian, A. Nicolaou, N. Jaouen, M. P. M. Dean
Summary: Ruddlesden-Popper and reduced Ruddlesden-Popper nickelates are potential substitutes for high-temperature superconducting cuprates, but the extent of their similarity has been contested. Resonant inelastic x-ray scattering (RIXS) has been crucial in studying their electronic and magnetic excitations, but inconsistent results among different samples and the absence of publicly available data have hindered detailed comparisons. To address this, we present open RIXS data on La4Ni3O10 and La4Ni3O8.
Article
Chemistry, Physical
Elena Krivyakina, Bisham Poudel, Cheng Li, Stanislaw Kolesnik, Bogdan Dabrowski, J. F. Mitchell, Stephan Rosenkranz, Omar Chmaissem
Summary: The synthesis and structural properties of Sr0.4Ba0.6Mn0.94Ti0.06O3-δ with polycrystalline cubic and hexagonal structures were investigated using in situ neutron diffraction and thermogravimetric analysis. The experiments replicated and explained the key synthesis processes involved in switching between different reducing and oxidizing atmospheres, transforming a hexagonal phase into an oxygen-deficient cubic perovskite phase. The partial substitution of large Ba ions at the Sr sites enhanced strains and created wide channels, facilitating rapid oxidation of the oxygen-deficient cubic phase, resulting in phase separation between oxygen-rich and oxygen-poor phases.
CHEMISTRY OF MATERIALS
(2023)
Article
Physics, Multidisciplinary
Anjana M. Samarakoon, J. Strempfer, Junjie Zhang, Feng Ye, Yiming Qiu, J. -w. Kim, H. Zheng, S. Rosenkranz, M. R. Norman, J. F. Mitchell, D. Phelan
Summary: Quantum materials, especially low-dimensional ones, often exhibit competing or intertwining magnetic, electronic, and structural ordering phenomena. In the case of quasi-2D materials R4Ni3O10 (R = La, Pr), intertwined charge-and spin-density waves have been identified. However, for R = Pr, exchange coupling between the transition -metal and rare-earth sublattices leads to a dimensional crossover into a fully 3D-ordered and coupled SDW state, altering the structure of SDW on the Ni sublattice.
Article
Materials Science, Multidisciplinary
Xingyu Liao, Michael R. Norman, Hyowon Park
Summary: Infinite-layer nickelates (RNiO2) show distinct differences compared to cuprate superconductors, leading to a debate about the role of rare-earth ions (R = La, Pr, Nd) in low-energy many-body physics. In this study, we investigate the role of Pr 4 f orbitals and the correlated electronic structure of PrNiO2 using density functional plus dynamical mean-field theory. Our findings suggest that the Pr 4 f states are insulating and do not exhibit Kondo or Zhang-Rice physics upon hole doping. These results have implications for understanding other reduced valence nickelates.
Article
Materials Science, Multidisciplinary
M. R. Norman, A. S. Botana, J. Karp, A. Hampel, H. LaBollita, A. J. Millis, G. Fabbris, Y. Shen, M. P. M. Dean
Summary: This paper presents a simple formalism for calculating x-ray absorption and resonant inelastic x-ray scattering, which uses the density of states as input from a single-particle or many-body ab initio calculation and is designed to capture itinerant like features. The formalism is applied to calculate XAS and RIXS for reduced valence nickelates, cuprate, and unreduced nickelate compounds. The results show strong orbital polarization in reduced valence nickelates and reproduce key aspects of a recent RIXS experiment for R4Ni3O8. Implications for the nature of 3d electrons in reduced valence nickelates are discussed.
Article
Materials Science, Multidisciplinary
J. Sears, Y. Shen, M. J. Krogstad, H. Miao, E. S. Bozin, I. K. Robinson, G. D. Gu, R. Osborn, S. Rosenkranz, J. M. Tranquada, M. P. M. Dean
Summary: The interaction between charge density wave (CDW) and lattice plays a crucial role in La1.875Ba0.125CuO4, inducing periodic modulation of Cu-Cu spacing within the CuO2 planes and out-of-plane breathing modulation of lanthanum layers. The CDW-related structural distortions propagate through the crystal, leading to overlapping structural modulations in adjacent layers and within the same layer. This effect could facilitate the coupling of CDWs between adjacent planes.
Article
Physics, Multidisciplinary
Cole Miles, Rhine Samajdar, Sepehr Ebadi, Tout T. Wang, Hannes Pichler, Subir Sachdev, Mikhail D. Lukin, Markus Greiner, Kilian Q. Weinberger, Eun-Ah Kim
Summary: Machine learning is a promising approach for studying complex phenomena with rich datasets. This study introduces a hybrid-correlation convolutional neural network (hybrid-CCNN) and applies it to experimental data generated by a programmable quantum simulator. The hybrid-CCNN is able to discover and identify new quantum phases on square lattices with programmable interactions. This combination of programmable quantum simulators with machine learning provides a powerful tool for exploring correlated quantum states of matter.
PHYSICAL REVIEW RESEARCH
(2023)
Article
Materials Science, Multidisciplinary
Yu Li, P. G. LaBarre, D. M. Pajerowski, A. P. Ramirez, S. Rosenkranz, D. Phelan
Summary: The anisotropic spin-glass transition in pseudobrookite Fe2TiO5, where spin freezing is observed only along the c axis, has been a long-standing puzzle. Recent neutron diffraction experiments reveal the coalescence of surfboard-shaped antiferromagnetic nanoregions above the glass transition temperature. In this study, we conducted high resolution inelastic neutron scattering measurements to understand the temperature dependence of spin dynamics within the surfboard regions on picosecond timescales. The results show that strong quasi-elastic magnetic scattering is observed well above Tg, indicating the presence of strong exchange interactions. The presence of fluctuating intrasurfboard spin correlations is also observed even at base temperature.
Article
Materials Science, Multidisciplinary
J. Sears, Y. Shen, M. J. Krogstad, H. Miao, Jiaqiang Yan, Subin Kim, W. He, E. S. Bozin, I. K. Robinson, R. Osborn, S. Rosenkranz, Young-June Kim, M. P. M. Dean
Summary: In this study, structural disorder in alpha-RuCl3 is investigated using x-ray diffuse scattering and three-dimensional difference pair distribution function analysis. A quantitative model is developed to describe the disorder in terms of rotational twinning and intermixing of the high and low-temperature structural layer stacking. This disorder may have important implications for the magnetic and electronic properties of alpha-RuCl3.