jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Livia Hecke Morais | Mark Ladinski
Source:  (preprint, 2024-09-17)
Field: Materials Science — Surfaces, Coatings and Films

Electron Microscopy of Brain Tissue Samples v1 Protocol has been approved by the California Institute of Technology’s Institutional Animal Careand Use Committee (IACUC). Electron microscope ; Brain tissue ; Microscopy ; Materials science ; Biomedical engineering ; Optics ; Physics ; Medicine Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications
Authors: Jingyao Meng | Yuxi Zhang | Rafael M. Fernandes | Tianxing Ma | Richard T. Scalettar
Source: arXiv (Cornell University) (preprint, 2024-07-02)
Field: Materials Science — Materials Chemistry

Supersolid Phase in the Diluted Holstein Model The Holstein model on a square lattice at half-filling has a well-established finite temperature phase transition to an insulating state with long range charge density wave (CDW) order. Because this CDW formation suppresses pairing, a superconducting (SC) phase emerges only with doping. In this work, we study the effects of dilution of the local phonon degrees of freedom in the Holstein model while keeping the system at half filling. We find not only that the CDW remains present up to a dilution fraction $f \sim 0.15$, but also that long range pairing is stabilized with increasing $f$, resulting in a {\it supersolid} regime centered at $f \approx 0.10$, where long range diagonal and off-diagonal correlations coexist. Further dilution results in a purely SC phase, and ultimately in a normal metal. Our results provide a new route to the supersolid phase via the introduction of impurities at fixed positions which both increase quantum fluctuations and also are immune to the competing tendency to phase separation often observed in the doped case. Supersolid ; Phase (matter) ; Statistical physics ; Physics ; Materials science ; Quantum mechanics ; Phase diagram Material Dynamics and Properties ; Theoretical and Computational Physics
Authors: N. Kothari | Chandra Prakash Jain | Deepti Kabra | Chetna Aswani | Hiya Khichi
Source: International Journal of Technical Research & Science (article, 2024-06-25)
Field: Materials Science — Ceramics and Composites

ADVANCED APPLICATIONS AND FUTURE TRENDS OF AR/VR GLASSES Augmented Reality (AR) and Virtual Reality (VR) technologies have undergone remarkable transformations, paving the way for innovative AR/VR glasses. These devices bridge the digital with the physical world, enhancing user engagement across various sectors including education, healthcare, entertainment, and industry. This paper delves into the technological underpinnings, prevalent applications, challenges, and prospective directions of AR/VR glasses, offering insights into their transformative potential and the evolutionary trajectory of immersive technologies. Virtual reality ; Psychology ; Computer science ; Computer graphics (images) ; Human–computer interaction Advanced ceramic materials synthesis ; Intermetallics and Advanced Alloy Properties ; Electronic Packaging and Soldering Technologies
Authors: Yixiu Li | Jingfa Yao | Wenhui Yang | Wei Zhao | Peng Luan | Guifa Teng
Source: Foods (article, 2024-12-16)
Field: Chemistry — Analytical Chemistry

Ripening Study Based on Multi-Structural Inversion of Cherry Tomato qMRI This study introduces a non-destructive, quantitative method using low-field MRI to assess moisture mobility and content distribution in cherry tomatoes. This study developed an advanced 3D non-local mean denoising model to enhance tissue feature analysis and applied an optimized TransUNet model for structural segmentation, obtaining multi-echo data from six tissue types. The structural T2 relaxation inversion was refined by integrating an ACS-CIPSO algorithm. This approach addresses the challenge of low signal-to-noise ratios in multi-echo MRI images from low-field equipment by introducing an innovative solution that effectively reduces voxel noise while retaining structural relaxation variability. The study reveals that there are consistent patterns in the changes in moisture mobility and content across different structures of cherry tomatoes during their ripening process. Mono-exponential analysis reveals the patterns of changes in moisture mobility (T2) and content (A) across various structures. Furthermore, tri-exponential analysis elucidates the patterns of changes in bound water (T21), semi-bound water (T22), and free water (T23), along with their respective contents. These insights offer a novel perspective on the changes in moisture mobility throughout the ripening process of tomato fruit, thereby providing a research pathway for the precise assessment of moisture status and ripening expression in fruits. Ripening ; Water content ; Biological system ; Exponential function ; Inversion (geology) ; Moisture ; Environmental science ; Computer science ; Soil science ; Mathematics ; Materials science ; Chemistry ; Engineering ; Food science ; Geology ; Biology ; Composite material ; Mathematical analysis Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Fermentation and Sensory Analysis
Authors: Ruohong Yang | Peng Hu | Xi Peng | X. Liu | Yunfan Li
Source: arXiv (Cornell University) (preprint, 2024-12-25)
Field: Materials Science — Materials Chemistry

DiFiC: Your Diffusion Model Holds the Secret to Fine-Grained Clustering Fine-grained clustering is a practical yet challenging task, whose essence lies in capturing the subtle differences between instances of different classes. Such subtle differences can be easily disrupted by data augmentation or be overwhelmed by redundant information in data, leading to significant performance degradation for existing clustering methods. In this work, we introduce DiFiC a fine-grained clustering method building upon the conditional diffusion model. Distinct from existing works that focus on extracting discriminative features from images, DiFiC resorts to deducing the textual conditions used for image generation. To distill more precise and clustering-favorable object semantics, DiFiC further regularizes the diffusion target and guides the distillation process utilizing neighborhood similarity. Extensive experiments demonstrate that DiFiC outperforms both state-of-the-art discriminative and generative clustering methods on four fine-grained image clustering benchmarks. We hope the success of DiFiC will inspire future research to unlock the potential of diffusion models in tasks beyond generation. The code will be released. Cluster analysis ; Diffusion ; Computer science ; Statistical physics ; Artificial intelligence ; Physics ; Thermodynamics Machine Learning in Materials Science
Authors: Aurys Šilinga | András Kovács | S. McVitie | Rafal E. Dunin–Borkowski | Kayla Fallon | Trevor P. Almeida
Source: arXiv (Cornell University) (preprint, 2024-11-22)
Field: Materials Science — Surfaces, Coatings and Films

Model-Based Iterative Reconstruction of Three-Dimensional Magnetisation in a Nanowire Structure Using Electron Holographic Vector Field Tomography Methods for characterisation of 3D magnetic spin structures are necessary to advance the performance of 3D magnetic nanoscale technologies. However, as the component dimensions approach the nanometre range, it becomes more challenging to analyse 3D magnetic configurations with the appropriate spatial resolution. In this paper, we present a method based on Lorentz transmission electron microscopy in which model-based iterative reconstruction (MBIR) is used to reconstruct the most probable magnetisation in an exemplar nanostructure. This method is based on relating electron phase measurements to the magnetic configuration of the nanostructure, and therefore, the method is subject to certain limitations. In this proof-of-concept experiment, MBIR was tested on an L-shaped ferromagnetic cobalt nanowire, fabricated using focused electron beam induced deposition. Off-axis electron holography was used to acquire a tomographic tilt series of electron holograms, which were analysed to measure magnetic electron phase shift over two tilt arcs with up to $ \pm 60$ degree tilt range. Then, a 3D magnetisation vector field consistent with the tomographic phase measurements was reconstructed, revealing multiple magnetic domains within the nanowire. The reconstructed magnetisation is accurate for magnetic domains larger than 50 nm, and higher resolution can be achieved by the continued development of tomographic reconstruction algorithms. Electron holography ; Holography ; Field (mathematics) ; Tomography ; Electron tomography ; Nanowire ; Vector field ; Magnetization ; Physics ; Magnetic field ; Optics ; Mathematics ; Mechanics ; Quantum mechanics ; Electron microscope Electron and X-Ray Spectroscopy Techniques ; Magnetic Properties and Applications ; Advanced Electron Microscopy Techniques and Applications
Authors: Boqiang Wang | Xuezeng Zhao | Yiyong Zhang | Zhuogang Wang
Source: PLoS ONE (article, 2024-05-16)
Field: Chemistry — Spectroscopy

Research on high performance combustible gas concentration sensor based on pyramid beam splitter matrix Combustible gas concentration detection faces challenges of increasing accuracy, and sensitivity, as well as high reliability in harsh using environments. The special design of the optical path structure of the sensitive element provides an opportunity to improve combustible gas concentration detection. In this study, the optical path structure of the sensitive element was newly designed based on the Pyramidal beam splitter matrix. The infrared light source was modulated by multi-frequency point signal superimposed modulation technology. At the same time, concentration detection results and confidence levels were calculated using the 4-channel combustible gas concentration detection algorithm based on spectral refinement. Through experiment, it is found that the sensor enables full-range measurement of CH4, at the lower explosive limit (LEL, CH4 LEL of 5%), the reliability level is 0.01 parts-per-million (PPM), and the sensor sensitivity is up to 0.5PPM. The sensor is still capable of achieving PPM-level detections, under extreme conditions in which the sensor's optical window is covered by 2/3, and humidity is 85% or dust concentration is 100mg/m3. Those improve the sensitivity, robustness, reliability, and accuracy of the sensor. Detection limit ; Materials science ; Beam splitter ; Sensitivity (control systems) ; Robustness (evolution) ; Optics ; Optical path ; Optoelectronics ; Chemistry ; Electronic engineering ; Physics ; Engineering Spectroscopy and Laser Applications ; Gas Sensing Nanomaterials and Sensors ; Advanced Chemical Sensor Technologies
Authors: Maria Laura VRANIC | Claudio Delrieux | Juan Vorobioff
Source: AJEA (article, 2024-10-08)
Field: Chemistry — Analytical Chemistry

Identificación de Bandas Espectrales claves en Imágenes Hiperespectrales para la Detección de Aflatoxinas en Maní. Las aflatoxinas son micotoxinas producidas por ciertos hongos en alimentos y productos agrícolas, como el maní, especialmente en condiciones cálidas y húmedas. De las 20 aflatoxinas que han sido identificadas, las más relevantes y tóxicas son B1, B2, G1 y G2, siendo la B1 la más peligrosa y asociada a aflatoxicosis en humanos y animales. El cambio climático afecta la producción de estas toxinas al alterar las condiciones ambientales que favorecen el crecimiento de los hongos productores. Las aflatoxinas tienen fluorescencia ultravioleta y distribución superficial, lo que facilita su detección mediante el análisis de imágenes hiperespectrales (HSI). Esta técnica no invasiva permite una evaluación exhaustiva de la calidad de los productos al capturar datos espectrales a lo largo de muchas bandas estrechas, creando hipercubos con información detallada sobre la composición química y características físicas. A pesar de sus ventajas, las HSI enfrentan desafíos como el alto costo, la complejidad de los datos y limitaciones en resolución y velocidad de captura. Este estudio se centra en identificar las longitudes de onda más relevantes para detectar aflatoxinas en imágenes hiperespectrales de maní. Sobre 15 maníes, 5 sin contaminar, 5 contaminadas superficialmente con 2µl de solución de standard de aflatoxina B1 0,001 µg/µl en metanol y 5 contaminados con 4 µl de un Mix de aflatoxinas en acetonitrilo. Se tomaron imágenes hiperespectrales a 194 longitudes de onda en el rango de 500 a 700 nm. Las muestras fueron preprocesadas y clasificadas con la Máquina de Soporte Vectorial (SVM), la precisión global del modelo fue del 97.98%. Para la clasificación de aflatoxina B1 se seleccionaron las longitudes arriba de 640.14 nm. El valor de la precisión global se estabiliza a partir de 104 variables (precisión global = 0,9776). La reducción de longitudes de onda no solo simplifica el proceso de medición, sino que también facilita la integración de la tecnología en líneas de producción existentes. Humanities ; Art Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Advanced Chemical Sensor Technologies
Authors: Dasen Zhang | Ting Ma | Yuqi Peng | Xianjin Liu | Qiwen Bao | Jun‐Jun Xiao
Source:  (preprint, 2024-03-30)
Field: Chemistry — Organic Chemistry

Spectro-polarimetric-depth Imaging by Inverse-Designed Single-Cell Metasurface Metasurface-assisted computational imaging has emerged as a promising route for complex optical field reconstruction. Multi-channel lensless imaging has been proved by leveraging the diverse ingredients of the optical field that can be deliberately ma Polarimetry ; Inverse ; Optics ; Physics ; Materials science ; Mathematics ; Geometry ; Scattering Polydiacetylene-based materials and applications
Authors: Amine Benmoussa | Mohammad-Reza Pendar | José Páscoa
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Numerical Investigation of Electrostatic Coating Using Nitrotherm Spray Technique Materials science ; Coating ; Thermal spraying ; Composite material ; Computer science High voltage insulation and dielectric phenomena ; Electrohydrodynamics and Fluid Dynamics ; Aerosol Filtration and Electrostatic Precipitation
Authors: Adriel Lenner Vinhal Mori | Arlindo Rodrigues Galvão Filho
Source:  (article, 2024-06-18)
Field: Chemistry — Analytical Chemistry

Classificação de Espécies de Fungos por Imagens Hiperespectrais utilizando Aprendizagem de Máquina Este trabalho propõe a classificação de quatro espécies de fungos usando Hyperspectral imaging (HSI) e Machine Learning (ML). A técnica HSI, que adquire dados espectrais de forma rápida e não destrutiva, é usada para melhorar a identificação de espécies fúngicas e automatizar processos manuais em produtos microbiológicos. O estudo se concentra na diferenciação de espécies de fungos usando HSI e aprendizado de máquina supervisionado, alcançando uma acurácia de 97,12% com o classificador de rede neural Multilayer Perceptron (MLP). Os resultados destacam o potencial do uso de ML e HSI na diferenciação de espécies de fungos em ambientes clínicos e microbiológicos. Computer science Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote-Sensing Image Classification
Authors: Emmanuel Chinedu Onuoha
Source: Asian Journal of Biological Sciences (article, 2024-11-01)
Field: Chemistry — Inorganic Chemistry

Boosting Blood Health: The Role of Tender Coconut Water in Leukocyte and Thrombocyte Modulation in Rats Background and Objective: Tender coconut water (TCW) is a natural beverage rich in bioactive compounds, known for its potential health benefits, including immune modulation and blood health improvement.The TCW from the Malayan green dwarf hybrid was administered in varying doses to assess its impact on blood cell parameters.This study explores the effects of tender coconut water on leukocyte and thrombocyte modulation in Wistar rats, highlighting its potential benefits for immune function and blood health.Materials and Methods: Forty Wistar rats were divided into four groups: Group A (control) received no treatment, while Groups B, C and D were treated with 10, 20 and 30 mL/kg of TCW, respectively.Blood samples were analyzed using a hematology analyzer and statistical evaluation was performed using SPSS version 26.Results: The results showed a significant increase in total white blood cell counts, including lymphocytes, eosinophils, basophils and platelets in the treated groups.A significant decrease in neutrophil counts was also observed (p<0.05).Notably, the group that received 20 mL/kg of TCW exhibited the highest platelet count, surpassing the effects observed at the 30 mL/kg dose. Conclusion:This study suggests that tender coconut water, particularly at a dose of 20 mL/kg, may have immunomodulatory and hemostatic effects, making it a promising natural supplement for enhancing immune function and blood health in Wistar rats.Further research is warranted to explore its potential applications in clinical settings. Boosting (machine learning) ; Platelet ; Food science ; Biology ; Chemistry ; Immunology ; Computer science Coconut Research and Applications ; Fatty Acid Research and Health ; Curcumin's Biomedical Applications
Authors: Svetlana S. Hurkovskaya | Д. Ю. МІХЄЄНКО
Source: Вісник Херсонського національного технічного університету (article, 2024-12-30)
Field: Materials Science — Materials Chemistry

АВТОМАТИЗОВАНА ПОБУДОВА 2D-КРЕСЛЕНЬ З 3D-МОДЕЛЕЙ ІЗ ВИКОРИСТАННЯМ ІНСТРУМЕНТІВ КОМП’ЮТЕРНОЇ ГРАФІКИ У статті досліджується ефективність сучасного програмного забезпечення для автоматизації створення кресленників із тривимірних моделей, що є актуальним завданням для інженерної та архітектурної галузей. Основна увага приділена аналізу можливостей програм AutoCAD, SolidWorks, Fusion 360 та алгоритмів, заснованих на згорткових нейронних мережах (Convolutional Neural Networks, CNN). Дослідження спрямоване на визначення рівня ефективності цих інструментів за такими ключовими параметрами, як швидкість обробки, адаптація до складних форм і гнучкість у налаштуванні кресленників. Дослідження показало, що AutoCAD забезпечує високу швидкість обробки простих моделей, проте має значні обмеження у роботі зі складною геометрією, що знижує його ефективність у завданнях із високим рівнем деталізації. SolidWorks демонструє кращу адаптацію до криволінійних форм завдяки використанню алгоритмів сплайнів, а також забезпечує автоматичне оновлення креслень при зміні тривимірної моделі. Fusion 360 ефективно працює з органічними формами завдяки обчислювальним методам і хмарній обробці, але залежність від інтернет-з’єднання може уповільнити роботу з великими обсягами даних. Алгоритми CNN виявилися найефективнішими для аналізу складних форм, забезпечуючи високу деталізацію, однак потребують значних обчислювальних ресурсів і часу. Зроблено висновок, що адаптація програмного забезпечення до складних форм залежить від рівня розвитку алгоритмів і можливостей гнучкого налаштування креслень відповідно до стандартів, таких як ISO, ANSI або ГОСТ. Програмні продукти SolidWorks і Fusion 360 виявилися найбільш збалансованими за критеріями точності, адаптивності та функціональності. Проте розробка і впровадження нейронних мереж відкривають нові перспективи для автоматизації, особливо в задачах із нестандартними формами та високою складністю. Materials science Machine Learning in Materials Science ; Advanced machining processes and optimization ; Advanced Theoretical and Applied Studies in Material Sciences and Geometry
Authors: Dmitry Rudoy | Victor Chigvintsev | Anastasiya Olshevskaya | Mary Оdabashyan | Danila Donskoy | Evgeniy Ivliev
Source:  (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

USING NEURAL NETWORKS FOR AGROCHEMICAL SOIL ANALYSIS In a neural network, it is possible, based on the analysis of several million photographs, to teach in a few milliseconds to distinguish cultivated crops from weeds. By analyzing the effectiveness of herbicides, the neural network is able to make the right decisions to reduce crop contamination. A neural network can distinguish a healthy plant from a sick one. The system is able to determine the degree of threat to future crops and suggest ways to solve the problem. In the agro-industrial complex of the Russian Federation, there are a large number of tasks that can be solved using artificial intelligence. The trained neural network is able to adapt to the agro-climatic features of regions and individual market agents. Agrochemical ; Artificial neural network ; Computer science ; Environmental science ; Agricultural engineering ; Artificial intelligence ; Agriculture ; Engineering ; Ecology ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Computational Techniques and Applications
Authors: Asena Kumsal Şen Bayram | Yekta Özgüven | Nadide Ebru Yazar | Erincik Edgü | Sebahat Sevde Sağlam
Source: Journal of Computational Design (article, 2024-09-30)
Field: Materials Science — Materials Chemistry

Malzeme Odaklı Yapay Zeka Destekli Bir Tasarım Süreci Önerisi: Doğal Malzemeden Yapay Zekâya Malzeme/yapay zeka etkileşiminin tasarım sürecinde kısıtlayıcı mı yoksa özgürleştirici mi bir rol oynadığının araştırılacağı bu proje, nesnelerin tasarlanma ve üretim süreçlerindeki malzeme ve form ilişkisinin deneyim yoluyla incelenmesine odaklanmaktadır. Bu deneyim ortamı, bir çalıştay kapsamında, malzemenin temel özelliklerinin tanımlanması, bu tanım çerçevesinde formun dijital olarak tasarlanması, tasarlanan formun söz konusu malzeme ile üretilmesi ve bu üretim sürecinde malzeme ve/veya forma ilişkin karşılaşılan sorunların gereken revizyonlar ile çözülmeye çalışması gibi çeşitli aşamalardan oluşmaktadır. Malzemelerin çevre dostu biyo-polimer baz kısmına tekstil, kestane kabuğu veya talaş eklenmesiyle oluşturulacak kişisel reçeteler, yapay zeka destekli Midjourney uygulamasında forma dönüştürülecektir. [Hakem incelemesi için çıkarıldı] [Hakem incelemesi için çıkarıldı] Fakültesi bünyesinde toplam 43 öğrencinin tasarladıkları malzemelerin belirleyecekleri duyusal, anlamsal, duygusal, performatif ve potansiyel özelliklerini yapay zeka girdisi olarak kullanacakları araştırmada, enformel bir ortamda yaparak öğrenme hedeflenmektedir. Dijital olarak üretilen formun, malzeme deneyleri ile fiziksel olarak üretilebilirliklerinin deneyimlenmesi, üretim sürecindeki zorluklar ve bunların nasıl çözümlendiği araştırmanın önemli aşamalarındandır. Öğrencilerin süreci çeşitli, yazılı ve görsel verilerle kaydedeceği çalıştayda, elde edilecek veriler sınıflandırılarak, çalışmada anahtar kelime olarak tariflenen ifadelerinin değişimleri analiz edilmiştir. Yenilenebilir biyo-polimer malzeme kullanarak güncel bir dijital tasarım yöntemi ile form oluşturmaya temellenen bu proje, bir yandan sürdürülebilirlik ve tasarımda yapay zeka kullanımı üzerine farkındalık oluştururken, bir yandan da deneyim yoluyla enformel öğrenme süreçlerini yeniden gündeme getirmektedir. Physics ; Humanities ; Gynecology ; Philosophy ; Medicine Material Selection and Properties ; Additive Manufacturing and 3D Printing Technologies ; Design Education and Practice