jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Alexander Chalenkov
Source: Russia and America in the 21st Century (article, 2024-01-01)
Field: Materials Science — General Materials Science

Advanced automotive technologies of the US and their impact on the development of the industry A significant acceleration in the development of promising fundamental technologies in recent years is one of the most significant factors in increasing the competitiveness of all manufacturing sectors of all countries of the world as a whole. This is especially true for the automotive industry, since its products are extremely dependent on new developments that provide higher safety standards, environmental impact, and are also a locomotive for the development of transport and energy infrastructure in developed and developing countries. In addition, proactively creating, testing, and implementing such technologies in the automotive industry is critical to the entire US economy and its sustainability due to the significant export focus of local automakers and competition from leading foreign automakers who are also focused on introducing new technologies into their own auto industry products. This article analyzes the most innovative achievements of American automotive and related companies, advanced software developers, semiconductor industry and environmental companies that aim to achieve global leadership in achieving sustainable development and carbon neutrality, as well as their impact on the local economy, road safety, transport and energy infrastructure of the United States at the present stage. Automotive industry ; Manufacturing engineering ; Engineering ; Business ; Aerospace engineering Material Properties and Applications ; Engineering Technology and Methodologies ; Technology Assessment and Management
Authors: Jiale Huang | Xiangjian Qian | Mingpu Qin
Source: arXiv (Cornell University) (preprint, 2024-12-31)
Field: Chemistry — Spectroscopy

Clifford circuits Augmented Matrix Product States for fermion systems Clifford circuits Augmented Matrix Product States (CAMPS) was recently proposed to leverage the advantages of both Clifford circuits and Matrix Product States (MPS). Clifford circuits can support large entanglement and can be efficiently simulated classically according to the Gottesman-Knill theorem. So in CAMPS, MPS needs only to handle the so-called Non-stabilizerness Entanglement Entropy which significantly improves the simulation accuracy for a given bond dimension. In this work, we generalize CAMPS to study the Fermion system by taking advantage of the Jordan-Wigner transformation which can map the studied Fermion system to a spin system. We benchmark the method on both the spinless $t-V$ model and the spinful Hubbard model. Our test results show significant improvement of the accuracy of CAMPS over MPS, especially when the interactions are strong. Fermionic CAMPS provides a useful tool for the accurate study of many-body fermion systems in the future and has the potential to help resolve long-standing issues. Product (mathematics) ; Fermion ; Matrix (chemical analysis) ; Matrix multiplication ; Electronic circuit ; Physics ; Computer science ; Mathematics ; Quantum mechanics ; Materials science ; Quantum Advanced NMR Techniques and Applications ; Quantum and electron transport phenomena ; Inorganic Chemistry and Materials
Authors: L. Kh. Yussupova | G.K. Yeldiyar | G.B. Jumabekova | S. E. Munasipov | Б. Абзалбекулы
Source: Mechanics and Technologies (article, 2024-09-30)
Field: Materials Science — Polymers and Plastics

ANALYSING THE CONSUMER CHARACTERISTICS OF A PACKAGE OF MATERIALS FOR THE UPPER OF TEENAGE SHOES The article presents the results of research of consumer properties of materials of footwear for teenagers. The operational and ergonomic characteristics of the package of materials for shoe upper parts are investigated. Package design ; Advertising ; Psychology ; Engineering drawing ; Engineering ; Business Textile materials and evaluations
Authors: G.G. Khaidarov
Source:  (article, 2024-04-26)
Field: Chemistry — Organic Chemistry

RESULTS OF APPLYING THE CONCEPT OF “UNPACKING” A SUBSTANCE AND ITS CONNECTION WITH THE INTERNAL ENERGY OF A SUBSTANCE The development of the concept of internal energy of a substance is outlined, taking into account the spatial arrangement of molecules (atoms) in a substance. As a result of the application of this unified concept of “unpacking”, the physical meaning of previously known isolated physical concepts such as: surface tension, internal energy, enthalpy, melting point, boiling point, critical temperature was explained from a unified point of view. Thus, it was possible to combine seven physical phenomena into a single concept called the “unpacking” of matter. Mathematical formulas for the relationship for each of these physical phenomena are obtained. The formulas are checked against tables of physical values of substances from physics reference books. The formulas are obtained under certain assumptions specified in published works. Connection (principal bundle) ; Internal energy ; Point (geometry) ; Energy (signal processing) ; Physical system ; Calculus (dental) ; Meaning (existential) Chemical Thermodynamics and Molecular Structure ; Advanced Physical and Chemical Molecular Interactions ; Complex Systems and Dynamics
Authors: A. V. Abramov | Mikhail Yu. Sarkisov | Margarita V. Rodicheva | Elena B. Demokratova
Source: Technologies & Quality (article, 2024-06-17)
Field: Materials Science — Polymers and Plastics

NEW APPROACHES TO JUSTIFICATION OF MATERIALS FOR GARMENTS USING THE EXAMPLE OF A MEN’S T-SHIRT The possibility of using modern methods of virtual fitting of clothes on an avatar of the human body in the process of forming the quality of garments is shown. A general scheme for calculating the me-chanical processes developed in garments when fitting the human body in statics and dynamics is presented, as well as the main directions for automating the calculation process. Based on the chosen numerical method, the problem of selecting textile materials to ensure the required quality of the product was solved using the example of a men’s T-shirt. The mechanical properties of several samples of knitted fabrics for such products were determined. Based on the obtained values, the calculation of stress fields in the parts of a men’s T-shirt in various body avatar poses was carried out. An experimental verification of the modelling results was carried out. Regularities have been established between the mechanical properties of knitted fabric and the nature of stress in the structure of the product. The possibility of their use when choosing textile materials is shown from the point of view of ensuring the required quality of products at the design stage. Clothing ; Political science ; Law Textile materials and evaluations ; Manufacturing Process and Optimization ; Mechanical Behavior of Composites
Authors: Yalei Tang
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-10-18)
Field: Materials Science — Materials Chemistry

Quantitative Insight to Fission Gas Bubble Distribution in Irradiated Annular U-10Zr Metallic Fuel Using Deep Learning U-10Zr Metal fuel is a promising nuclear fuel candidate for next-generation sodium-cooled fast spectrum reactors. Porosity is one of the key facts to impact the performance of metallic fuel. Additionally, a mechanical understanding of fission gas bubbles evolution behavior is a prerequisite for fuel development and qualification. Previous study of fission gas bubbles relied on a simple threshold method working on low resolution optical microscopy images, which has challenges in recognizing bubble boundaries, and caused inaccurate statistics of bubble properties. In this paper, a pre-trained deep learning model on Scanning Electron Microscopy (SEM) images from an annular U-10Zr fuel (AF1), was applied to another U-10Zr annular fuel (AF2). More accurate fission gas bubble segmentation results were generated, which leads to more precise qualitative analysis on the morphology, size, density, and orientation of bubbles. Furthermore, we investigated the lanthanide movement along the radial temperature gradient and obtained conclusive findings. Fission ; Bubble ; Irradiation ; Distribution (mathematics) ; Materials science ; Nuclear physics ; Nuclear engineering ; Mechanics ; Physics ; Engineering ; Neutron ; Mathematics ; Mathematical analysis Nuclear Materials and Properties ; Nuclear reactor physics and engineering ; Nuclear Physics and Applications
Authors: Susannah Kate Conroy
Source: arXiv (Cornell University) (preprint, 2024-10-28)
Field: Materials Science — Materials Chemistry

Spotlight Session on Autonomous Weapons Systems at ICRC 34th International Conference Autonomous weapons systems (AWS) change the way humans make decisions, the effect of those decisions and who is accountable for decisions made. We must remain vigilant, informed and human-centred as we tackle our deliberations on developing norms regarding their development, use and justification. Ways to enhance compliance in international humanitarian law (IHL) include: Training weapons decision makers in IHL; developing best practice in weapons reviews including requirements for industry to ensure that any new weapon, means or method of warfare is capable of being used lawfully; develop human-centred test and evaluation methods; invest in digital infrastructure to increase knowledge of the civilian environment in a conflict and its dynamics; invest in research on the real effects and consequences of civilian harms to the achievement of military and political objectives; improve secure communications between stakeholders in a conflict; and finally to upskill governments and NGOs in what is technically achievable with emerging technologies so that they can contribute to system requirements, test and evaluation protocols and operational rules of use and engagement. Governments are responsible for setting requirements for weapons systems. They are responsible for driving ethicality as well as lethality. Governments can require systems to be made and used to better protect civilians and protected objects. The UN can advocate for compliance with IHL, human rights, human-centred use of weapons systems and improved mechanisms to monitor and trace military decision making including those decisions affected by autonomous functionality. Session (web analytics) ; Political science ; Aeronautics ; Computer security ; Computer science ; Engineering ; World Wide Web High-Velocity Impact and Material Behavior
Authors: Cheng Zeng | Eleni Dilma | Jason J. Xu | Leo L. Duan
Source: arXiv (Cornell University) (preprint, 2024-03-01)
Field: Chemistry — Analytical Chemistry

Bridged Posterior: Optimization, Profile Likelihood and a New Approach to Generalized Bayes Optimization is widely used in statistics, and often efficiently delivers point estimates on useful spaces involving structural constraints or combinatorial structure. To quantify uncertainty, Gibbs posterior exponentiates the negative loss function to form a posterior density. Nevertheless, Gibbs posteriors are supported in high-dimensional spaces, and do not inherit the computational efficiency or constraint formulations from optimization. In this article, we explore a new generalized Bayes approach, viewing the likelihood as a function of data, parameters, and latent variables conditionally determined by an optimization sub-problem. Marginally, the latent variable given the data remains stochastic, and is characterized by its posterior distribution. This framework, coined bridged posterior, conforms to the Bayesian paradigm. Besides providing a novel generative model, we obtain a positively surprising theoretical finding that under mild conditions, the $\sqrt{n}$-adjusted posterior distribution of the parameters under our model converges to the same normal distribution as that of the canonical integrated posterior. Therefore, our result formally dispels a long-held belief that partial optimization of latent variables may lead to underestimation of parameter uncertainty. We demonstrate the practical advantages of our approach under several settings, including maximum-margin classification, latent normal models, and harmonization of multiple networks. Bayes' theorem ; Maximum likelihood ; Computer science ; Mathematics ; Bayesian probability ; Artificial intelligence ; Statistics Spectroscopy and Chemometric Analyses
Authors: Noviana Pratiwi | Dedi Rosadi | Abdurakhman Abdurakhman
Source: ITM Web of Conferences (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Identifying Possible Socio-Demographic Factors in Infant Mortality Rate Classification With Orthogonal Projections to Latent Structures Discriminant Analysis The measurement of infant mortality rate (IMR) stands as a crucial and complex indicator within global public health. Particularly for countries with constrained resources necessitating easily calculable and accurate gauges of population health, IMR retains its relevance. Several studies investigating infant mortality rates adopt a multilevel perspective and connect underlying factors with proximate variables. Conversely, interdisciplinary viewpoints, such as socio-demographic, offer a comprehensive overview of the intricate connections between socio-demographic disparities and mortality rates. This research focuses on the analysis of infant mortality rates, aiming to identify the sociodemographic factors influencing them to facilitate the formulation of more targeted intervention strategies. Employing Orthogonal Projection to Latent Structures - Discriminant Analysis (OPLS-DA) as the analytical method is motivated by its capability to navigate complex multivariable structures and elucidate the relationships between socio-demographic variables and infant mortality rates. The OPLS-DA model is used to extract significant patterns and relationships in the data so that it is possible for socio-demographic factors to have a significant impact on infant mortality rates. It is hoped that the results of the classification will provide insight and become the basis for developing policies by considering socio-demographic aspects in efforts to prevent infant mortality. Linear discriminant analysis ; Discriminant ; Statistics ; Mathematics ; Econometrics ; Artificial intelligence ; Computer science Spectroscopy and Chemometric Analyses ; Nutritional Studies and Diet ; Advanced Chemical Sensor Technologies
Authors: Aditya S Baheti | Ankush D. Sawarkar | Umayr Shaikh | Deepti D. Shrimankar
Source: Cureus Journal of Computer Science. (article, 2024-11-12)
Field: Chemistry — Analytical Chemistry

Alcohol Quality Analysis Using Machine Learning Regression Technique This research paper investigates the predictive modeling of alcohol quality machine learning regression analysis. The study employs a comprehensive approach to preprocessing, modeling, and evaluation to achieve accurate predictions. Initial data exploration reveals the dataset's structure and identifies potential issues such as missing values and outliers. Preprocessing steps include outlier removal using the interquartile range method and feature scaling to ensure uniformity. The dataset is split into two parts, i.e., training and testing, for development and evaluation. Regression models are built using the ordinary least squares method, incorporating features transformed to address skewness and scaled to improve model performance. Model diagnostics are conducted to assess assumptions including multicollinearity and normality of residuals. The final model exhibits strong predictive capability, as evidenced by high R-squared values and low mean squared error. Visualization techniques illustrate the relationship between actual and predicted values, providing insight into model accuracy. Overall, this paper demonstrates the effectiveness of linear regression in predicting alcohol content with an accuracy of 92%, offering valuable implications for quality control and production optimization in the alcohol industry along with other regression models such as gradient boosting with an accuracy of 90% and decision tree regressor with an accuracy of 83%. Regression analysis ; Quality (philosophy) ; Computer science ; Machine learning ; Regression ; Artificial intelligence ; Statistics ; Mathematics ; Philosophy Spectroscopy and Chemometric Analyses
Authors: Amelia R. Klein | Nader Engheta | Lee C. Bassett
Source: arXiv (Cornell University) (preprint, 2024-06-12)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Designing metasurface optical interfaces for solid-state qubits using many-body adjoint shape optimization We present a general strategy for the inverse design of metasurfaces composed of elementary shapes. We use it to design a structure that collects and collimates light from nitrogen-vacancy centers in diamond. Such metasurfaces constitute scalable optical interfaces for solid-state qubits, enabling efficient photon coupling into optical fibers and eliminating free-space collection optics. The many-body shape optimization strategy is a practical alternative to topology optimization that explicitly enforces material and fabrication constraints throughout the optimization, while still achieving high performance. The metasurface is easily adaptable to other solid-state qubits, and the optimization method is broadly applicable to fabrication-constrained photonic design problems. Qubit ; Solid-state ; State (computer science) ; Physics ; Computer science ; Optics ; Quantum mechanics ; Quantum ; Engineering physics ; Algorithm Metamaterials and Metasurfaces Applications ; Plasmonic and Surface Plasmon Research ; Semiconductor Quantum Structures and Devices
Authors: Manon Monier | Jean-Noël Lorenzi | Sunitha Narasimha | Flora Borne | Vincent Contremoulins | Louis Mevel | Romane Petit | Youssef El Hachem | François Graner | Virginie Courtier‐Orgogozo
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-05-10)
Field: Materials Science — Biomaterials

Adhesive and mechanical properties of the glue produced by 25 Drosophila species Abstract Drosophila glue, a bioadhesive produced by fly larvae to attach themselves to a substrate for several days, has recently gained attention for its peculiar adhesive and mechanical properties. Although Drosophila glue production was described more than 50 years ago, a general survey of the adhesive and mechanical properties of this proteinaceous gel across Drosophila species is lacking. To measure adhesion, we present here a protocol that is robust to variations in protocol parameters, pupal age and calculation methods. We find that the glue, which covers the entire pupal surface, increases the animal rigidity and plasticity when bound to a glass slide. Our survey of pupal adhesion in 25 Drosophilidae species reveals la wide range of phenotypes, from species that produce no or little glue and adhere little, to species that produce high amounts of glue and adhere strongly. One species, D. hydei , stands out from the rest and emerges as a promising model for the development of future bioadhesives, as it has the highest detachment force per glue area and produces relatively large amounts of glue relative to its size. We also observe that species that invest more in glue tend to live in more windy and less rainy climates, suggesting that differences in pupal adhesion properties across species are shaped by ecological factors. Our present survey provides a basis for future biomimetic studies based on Drosophila glue. GLUE ; Drosophila (subgenus) ; Adhesive ; Evolutionary biology ; Biology ; Composite material ; Materials science ; Genetics Silk-based biomaterials and applications ; Adhesion, Friction, and Surface Interactions
Authors: Taher Yacoub | Н А Зарубина | Camille Depenveiller | H-P Nguyen | V-T Vong | M-T Tran | Yusuke Kagaya | Nakamura, Tsukasa | Daisuke Kihara | Florent Langenfeld | Matthieu Montès
Source: SPIRE - Sciences Po Institutional REpository (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

SHREC2024: Non-rigid Complementary Shapes Retrieval in Protein-protein Interactions The aim of this SHREC 2024 track is to compare different algorithms for retrieving non-rigid complementary shape pairs, applied in the context of 3D objects being more complex (e.g. with many folds and roughness) such as proteins. The dataset used for this benchmark is based on 52 selected protein-protein complexes for which an experimental structure is publicly available. One of the main difficulties of this challenge is the non-inclusion of the shapes derived from the ground truth conformations in the dataset. Different metrics were used to evaluate the retrieval performance (nearest-neighbor, first-tier, second-tier, and true positives) and to evaluate the quality of the predicted poses (TM-score, lDDT, ICS, IPS and DockQ - those metrics are classically used in the Critical Assessment of PRediction of Interactions challenges). Two teams took part in this challenge and were able to return the expected results. This paper discusses these results and prospects of retrieval methods based only on the protein shape information in the absence of atomic data, in a large context of protein-protein docking. Computer science ; Protein–protein interaction ; Computational biology ; Chemistry ; Biochemistry ; Biology Enzyme Structure and Function ; Protein Structure and Dynamics ; Machine Learning in Bioinformatics
Authors: Әбілқас Сағынов атындағы Қарағанды техникалық университеті, Қарағанды қ., Қазақстан | Beibit Akhmetov | Р. Ф. Серова | С.A. Жаутикова
Source: Mechanics and Technologies (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

INTEGRATED METHOD FOR ASSESSING THE RELIABILITY OF REINFORCED CONCRETE STRUCTURES Мақалада тұрғын үйлердегі темірбетон конструкцияларының эксплуатациялық сенімділігін бақылау әдістемесі ұсынылған, ол бұзылмайтын бақылаудың, математикалық модельдеудің және болжаудың заманауи әдістерін кешенді қолдануға негізделген. Зерттеу барысында визуалды тексеру, аспаптық өлшеулер, математикалық модельдеу және Монте-Карло әдісі арқылы конструкциялардың техникалық жағдайы бағаланды. Нәтижесінде, визуалды тексеру кезінде 30% жағдайда ақаулар анықталды, ал аспаптық өлшеулер бетонның орташа беріктігі 25 МПа, арматураның беріктігі 400 МПа деңгейінде екендігін көрсетті. Математикалық модельдеу нәтижесінде конструкциялардың жүктемеге төзімділігі 7,5 МПа деңгейінде бағаланды, бұл бетонның беріктігінен төмен. Монте-Карло әдісі арқылы жүргізілген статистикалық талдау 10000 модельдеуден 1500 жағдайда құрылымның бұзылу ықтималдығын анықтады, бұл 15% құрады. Ұсынылған мониторинг әдістемесі ақауларды анықтауға, техникалық жағдайды бағалауға және қалдық қызмет мерзімін болжауға мүмкіндік берді, бұл тұрғын үйлердің қауіпсіздігі мен сенімділігін қамтамасыз ету үшін маңызды. Тұрғын үй ғимараттарының темірбетон конструкцияларының эксплуатациялық сенімділігін бақылаудың кешенді әдістемесі заманауи талаптарға жауап беретін конструкциялардың сенімділігін бақылау және бағалау саласына елеулі үлес қосады. Reliability (semiconductor) ; Reliability engineering ; Structural engineering ; Reinforced concrete ; Structural reliability ; Forensic engineering ; Engineering ; Computer science ; Artificial intelligence ; Physics ; Probabilistic logic Structural mechanics and materials ; Geodetic Measurements and Engineering Structures