jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Zechuan Yin | Jiashen Tang | Connor Hart | John W. Blanchard | Xinyan Xiang | Saipriya Satyajit | Smriti Bhalerao | Tao Tao | Stephen J. DeVience | Ronald L. Walsworth
Source: arXiv (Cornell University) (preprint, 2024-06-06)
Field: Materials Science — Materials Chemistry

Quantum Diamond Microscope for Narrowband Magnetic Imaging with High Spatial and Spectral Resolution The quantum diamond microscope (QDM) is a recently developed technology for near-field imaging of magnetic fields with micron-scale spatial resolution. In the present work, we integrate a QDM with a narrowband measurement protocol and a lock-in camera; and demonstrate imaging of radiofrequency (RF) magnetic field patterns produced by microcoils, with spectral resolution $\approx1$\,Hz. This RF-QDM provides multi-frequency imaging with a central detection frequency that is easily tunable over the MHz-scale, allowing spatial discrimination of both crowded spectral peaks and spectrally well-separated signals. The present instrument has spatial resolution $\approx2\,\mathrm{μm}$, field-of-view $\approx300\times300\,\mathrm{μm^2}$, and per-pixel sensitivity to narrowband fields $\sim{1}\,$nT$\cdot$Hz$^{-1/2}$. Spatial noise can be reduced to the picotesla scale by signal averaging and/or spatial binning. The RF-QDM enables simultaneous imaging of the amplitude, frequency, and phase of narrowband magnetic field patterns at the micron-scale, with potential applications in real-space NMR imaging, AC susceptibility mapping, impedance tomography, analysis of electronic circuits, and spatial eddy-current-based inspection. Narrowband ; Microscope ; Diamond ; Optics ; Image resolution ; Resolution (logic) ; Materials science ; High resolution ; Microscopy ; Physics ; Nuclear magnetic resonance ; Computer science ; Geology ; Remote sensing ; Artificial intelligence Diamond and Carbon-based Materials Research ; Characterization and Applications of Magnetic Nanoparticles ; Advanced Electron Microscopy Techniques and Applications
Authors: И. В. Галкина | Yu. V. Bakhtiyarova | S. R. Romanov
Source:  (article, 2024-12-11)
Field: Chemistry — Organic Chemistry

SYNTHESIS, STRUCTURE AND ANTHELMINTIC ACTIVITY OF TRIPHENYL-(3,5-DI-TERT-BUTYL-4-HYDROXYBENZYL) PHOSPHONIUM NITRATE Anthelmintic ; Phosphonium ; Nitrate ; Chemistry ; Organic chemistry ; Biology ; Ecology Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactivity of Sulfur-Containing Compounds
Authors: Rattanawan Tangporncharoen | Chuleeporn Phanus‐umporn | Supaluk Prachayasittikul | Chanin Nantasenamat | Veda Prachayasittikul | Aungkura Supokawej
Source: PubMed (article, 2024-03-26)
Field: Chemistry — Organic Chemistry

Computer-guided design of novel nitrogen-based heterocyclic sphingosine-1-phosphate (S1P) activators as osteoanabolic agents Osteoanabolic agents, or drugs that promote bone formation, have gained considerable attention for osteoporosis management due to their curative and preventive potentials. Sphingosine-1-phosphate receptor 2 (S1PR2) is an attractive drug target, in which its activation leads to osteogenesis-promoting effect. Nitrogen-containing heterocyclic scaffolds (i.e., quinoxaline and indole) are promising pharmacophores possessing diverse bioactivities and were reported as S1PR2 activators. Quantitative structure-activity relationship (QSAR) modeling is a computational approach well-known as a fundamental tool for facilitating successful drug development. This study demonstrates the discovery of new S1PR2 activators using computational-driven rational design. Herein, an original dataset of nitrogen-containing S1PR2 activators was collected from ChEMBL database. The retrieved dataset was separated into two datasets according to their core scaffolds (i.e., quinoxaline and indole). QSAR modeling was performed using multiple linear regression (MLR) algorithm to successfully obtain two models with good predictive performance. The constructed models also revealed key properties playing essential roles for potent S1PR2 activation, such as Van der Waals volume (R2v+ and E3v), mass (MATS5m and Km), electronegativity (H3e), and number of 5-membered rings (nR05). Subsequently, the constructed models were further employed to guide rational design and predict S1PR2 activating effects of an additional set of 752 structurally modified compounds. Most of the modified compounds were predicted to have higher potency than their parents, and a set of promising potent newly designed compounds was highlighted. Additionally, drug-likeness prediction was performed to reveal that most of the newly designed compounds are druggable compounds with possibility for further development. Sphingosine-1-phosphate ; Sphingosine ; Phosphate ; Biochemistry ; Computational biology ; Chemistry ; Computer science ; Pharmacology ; Bioinformatics ; Medicine ; Biology ; Receptor Synthesis and Reactivity of Sulfur-Containing Compounds ; Organophosphorus compounds synthesis ; Sphingolipid Metabolism and Signaling
Authors: Ankit Kumar Singh | Ranjit Kumar Paul | Anita Sarkar | Md Yeasin | Kanchan Sinha | Soumen Pal | A.K. Paul
Source: Indian Journal Of Agricultural Economics. (article, 2024-12-31)
Field: Chemistry — Analytical Chemistry

A Novel Ensemble Machine Learning Approach for Forecasting Oilseeds Prices in India The oilseed crops hold significant agricultural importance globally, particularly in India, where they contribute significantly to economic growth and food security.However, predicting its prices accurately remains challenging due to complex factors such as supply-demand dynamics, weather fluctuations, and global economic conditions.This study investigates price forecasting for major oilseed crops using traditional statistical models like autoregressive integrated moving average (ARIMA) and simple exponential smoothing (SES), alongside machine learning (ML) algorithms including artificial neural networks (ANN), random forest (RF), support vector regression (SVR), and K-nearest neighbors (KNN).The research employs a weighted ensemble (WE) approach based on the cuckoo search (CS) algorithm for weight optimization and model confidence set (MCS) for superior model selection.The ensemble model integrates predictions from multiple models, aiming to enhance forecast accuracy and reliability.Monthly wholesale price data of major oilseed crops from January 2010 to June 2024 across various markets from different states of India, sourced from AGMARKNET, have been analyzed.Results show that the proposed CS-WE model outperformed all the candidate models and suggest using an ensemble approach rather than a single model in managing risks and improving decision-making in the oilseed sector. Ensemble learning ; Economics ; Agricultural economics ; Machine learning ; Econometrics ; Computer science ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Agricultural Economics and Practices
Authors: S. Ogita | Yoshiki Ishii | Go Watanabe | Hitoshi Washizu | Kang Kim | Nobuyuki Matubayasi
Source: arXiv (Cornell University) (preprint, 2024-10-07)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Atomistic analysis of nematic phase transition in 4-cyano-4$^{\prime}$-$n$-alkyl biphenyl liquid crystals: Sampling for the first-order phase transition and the free-energy decomposition Molecular dynamics (MD) simulations were conducted using the generalized replica exchange method (gREM) on the 4-cyano-4$^{\prime}$-$n$-alkylbiphenyl ($n$CB) system with $n=5$, 6, 7, and 8, which exhibits a nematic-isotropic (NI) phase transition. Sampling near the phase transition temperature in systems undergoing first-order phase transitions, such as the NI phase transition, is demanding due to the substantial energy gap between the two phases. To address this, gREM, specifically designed for first-order phase transitions, was utilized to enhance sampling near the NI phase transition temperature. Free-energy calculations based on the energy representation (ER) theory were employed to characterize the NI phase transition. ER evaluates the insertion free energy of $n$CB molecule for both nematic and isotropic phases, revealing a change in the temperature dependence across the NI phase transition. Further decomposition into intermolecular interaction energetic and entropic terms shows quantitatively the balance between these contributions at the NI phase transition temperature. Liquid crystal ; Biphenyl ; Alkyl ; Phase transition ; Phase (matter) ; Decomposition ; Prime (order theory) ; Materials science ; Condensed matter physics ; Chemical physics ; Chemistry ; Organic chemistry ; Physics ; Mathematics ; Combinatorics Liquid Crystal Research Advancements ; Surfactants and Colloidal Systems ; Material Dynamics and Properties
Authors: Vilém Neděla | Eva Tihlaříková | Petr Cápal | Jaroslav Doležel
Source: Research Square (preprint, 2024-03-07)
Field: Materials Science — Surfaces, Coatings and Films

Advanced Environmental Scanning Electron Microscopy Reveals 3D Organization of Condensed Mitotic Chromosomes in Their Native State Abstract The challenge of in-situ handling and high-resolution, low-dose imaging of intact, sensitive and moist samples in their native state at the nanometer scale or live samples is met by Advanced Environmental Scanning Electron Microscopy (A-ESEM). This new generation of ESEM uses machine learning-based optimization of thermodynamic conditions with respect to sample specifics to employ a low temperature method and an ionization secondary electron detector with an electrostatic separator in an in-house modified electron microscope equipped with temperature, humidity and gas pressure sensors for in-situ and real-time monitoring of the sample. An invisible thin film of ionic liquid is used to increase the thermal and electrical conductivity of the samples and minimize damage from free radicals. To validate the power of the new method, we analyze condensed mitotic metaphase chromosomes to reveal new structural features of their perichromosomal layer, and the organization of chromatin fibers, not directly observed before by any microscopic technique. Mitosis ; Scanning electron microscope ; State (computer science) ; Electron microscope ; Electron ; Nanotechnology ; Microscopy ; Materials science ; Evolutionary biology ; Biology ; Biophysics ; Chemical physics ; Cell biology ; Physics ; Optics ; Computer science ; Quantum mechanics Electron and X-Ray Spectroscopy Techniques ; Advanced Electron Microscopy Techniques and Applications ; Genomics and Chromatin Dynamics
Authors: Lars Nieradzik | Henrike Stephani | Jördis Sieburg-Rockel | Stephanie Helmling | Andrea Olbrich | Stephanie Wrage | Janis Keuper
Source: arXiv (Cornell University) (preprint, 2024-11-18)
Field: Chemistry — Organic Chemistry

WoodYOLO: A Novel Object Detector for Wood Species Detection in Microscopic Images Wood species identification plays a crucial role in various industries, from ensuring the legality of timber products to advancing ecological conservation efforts. This paper introduces WoodYOLO, a novel object detection algorithm specifically designed for microscopic wood fiber analysis. Our approach adapts the YOLO architecture to address the challenges posed by large, high-resolution microscopy images and the need for high recall in localization of the cell type of interest (vessel elements). Our results show that WoodYOLO significantly outperforms state-of-the-art models, achieving performance gains of 12.9% and 6.5% in F2 score over YOLOv10 and YOLOv7, respectively. This improvement in automated wood cell type localization capabilities contributes to enhancing regulatory compliance, supporting sustainable forestry practices, and promoting biodiversity conservation efforts globally. Detector ; Object (grammar) ; Computer vision ; Artificial intelligence ; Object detection ; Computer science ; Pattern recognition (psychology) ; Optics ; Physics Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Zakiah Mohd Yusoff | Nurlaila Nurlaila | Siti Mariatul Hazwa Mohd Huzir | Siti Aminah Nordin
Source: Research Square (preprint, 2024-01-16)
Field: Chemistry — Organic Chemistry

Examining the Trend in Agarwood Oil Studies for the 5 Recent Years (2019-2023): A Bibliometric Analysis Abstract This bibliometric article investigates the burgeoning research landscape surrounding Agarwood oil, derived from the Aquilaria tree species. The historical and cultural significance of Agarwood, coupled with its diverse applications in perfumery, traditional medicine, and religious practices, has fueled a surge in scientific interest. However, a comprehensive understanding of the current state of Agarwood oil research is lacking. This study addresses this gap through a systematic review and bibliometric analysis of scholarly databases. By employing techniques such as co-citation analysis and keyword co-occurrence, the research aims to quantify patterns, relationships, and collaborations within the literature. The methodology involves a focused exploration of a specified timeframe to capture recent developments, with the anticipation that the outcomes will include the identification of key contributors, thematic clusters, and emerging trends in Agarwood oil research. The study is poised to contribute valuable insights for researchers, policymakers, and industry stakeholders, offering a holistic perspective on the interconnectedness of Agarwood oil research and guiding future exploration in this dynamic field. Agarwood ; Field (mathematics) ; Social science ; Data science ; Sociology ; Computer science ; Mathematics Wood and Agarwood Research ; Forensic Fingerprint Detection Methods ; Cultural Heritage Materials Analysis
Authors: Yunqiao Yang | Long-Kai Huang | Shengzhuang Chen | Kede Ma | Ying Wei
Source: arXiv (Cornell University) (preprint, 2024-11-04)
Field: Materials Science — Materials Chemistry

Learning Where to Edit Vision Transformers Model editing aims to data-efficiently correct predictive errors of large pre-trained models while ensuring generalization to neighboring failures and locality to minimize unintended effects on unrelated examples. While significant progress has been made in editing Transformer-based large language models, effective strategies for editing vision Transformers (ViTs) in computer vision remain largely untapped. In this paper, we take initial steps towards correcting predictive errors of ViTs, particularly those arising from subpopulation shifts. Taking a locate-then-edit approach, we first address the where-to-edit challenge by meta-learning a hypernetwork on CutMix-augmented data generated for editing reliability. This trained hypernetwork produces generalizable binary masks that identify a sparse subset of structured model parameters, responsive to real-world failure samples. Afterward, we solve the how-to-edit problem by simply fine-tuning the identified parameters using a variant of gradient descent to achieve successful edits. To validate our method, we construct an editing benchmark that introduces subpopulation shifts towards natural underrepresented images and AI-generated images, thereby revealing the limitations of pre-trained ViTs for object recognition. Our approach not only achieves superior performance on the proposed benchmark but also allows for adjustable trade-offs between generalization and locality. Our code is available at https://github.com/hustyyq/Where-to-Edit. Transformer ; Artificial intelligence ; Computer science ; Computer vision ; Engineering ; Electrical engineering Machine Learning in Materials Science
Authors: Nazifa Sahani Barbhuiya | Sabina Begum Choudhury | Monjur Ahmed Laskar | Abhishek Chowdhury | Anupam Das Talukdar | Manabendra Dutta Choudhury
Source: Journal Of Advanced Zoology (article, 2024-02-10)
Field: Chemistry — Organic Chemistry

In Silico Evaluation Of Flacourtia Jangomas Compounds For Hepatoprotective Activity: A Molecular Docking Study The liver is prone to damage induced by xenobiotics, which significantly contributes to the widespread prevalence of liver diseases. To explore potential therapeutic avenues, this in silico study utilizes molecular docking techniques to elucidate interactions between bioactive compounds derived from Flacourtia jangomas and their target proteins, particularly the androgen receptor (AR) and acetyl cholinesterase, both implicated in hepatocellular carcinogenesis. While pharmaceutical drugs for liver diseases exist, their limitations necessitate the exploration of alternative options. Plant-derived compounds have garnered attention for their potential beneficial effects, with Flacourtia jangomas emerging as a promising candidate due to its known pharmacological activities. Through our investigation, we identified several compounds, including catechin, limonin, jangomolide, rutin hydrate, hydnocarpic acid, and chaulmoogric acid, which exhibited notable interactions with AR and acetyl cholinesterase. Enzalutamide, an AR inhibitor, demonstrated a docking score lower than catechin but higher than other compounds, indicating its potential therapeutic efficacy. Catechin exhibited the highest binding affinity, supported by more favorable scores, signifying strong interactions with AR. Rutin hydrate displayed superior docking parameters against acetyl cholinesterase compared to neostigmine. Considering various scoring parameters such as lipo, ambig, clash, and rot scores, catechin and rutin hydrate emerged as favorable options over enzalutamide and neostigmine, respectively. However, experimental validation is essential to confirm these findings. The compounds identified in this study hold promise for the development of clinically effective hepatoprotective agents In silico ; Docking (animal) ; Computational biology ; Chemistry ; Traditional medicine ; Biology ; Biochemistry ; Medicine ; Veterinary medicine ; Gene Plant-Derived Bioactive Compounds ; Food Science and Nutritional Studies
Authors: Lei Yan | Cen Liu | Muhammad Zain | Minghan Cheng | Zhonhyang Huo | Chenming Sun
Source: Preprints.org (preprint, 2024-09-23)
Field: Chemistry — Analytical Chemistry

Identification of Rice Varieties Based on Nutritional Quality Estimation Through UAV Hyperspectral Imaging Identification of nutritious rice variety through non-destructive detection technology is necessary for high quality seed production. With the development in technology, rapid and non-destructive identification methods based on unmanned aerial vehicle (UAV) remote sensing technology are increasingly gaining scientific community attention. This study utilized hyperspectral imaging technology to acquire spectral reflectance data from the rice canopy during the grain-filling stage. Different models (Stepwise Multiple Linear Regression, SMLR and the Back Propagation Neural Network, BPNN) for estimating rice nutritional quality indicators based on canopy spectral information were constructed using both multiple stepwise regression and BP neural networks. The results showed that the model based on BPNN estimation performed best for predicting grain protein content, with an R2 =0.9516 and RMSE=0.3492, indicating high accuracy and stability of the model. Overall, hyperspectral imaging technology combined with various model could significantly help to identify rice varieties. Further, the current findings provide a technical reference for the selection of high-quality rice varieties in a non-destructive manner. Hyperspectral imaging ; Identification (biology) ; Estimation ; Quality (philosophy) ; Computer vision ; Artificial intelligence ; Computer science ; Remote sensing ; Biology ; Geography ; Botany ; Engineering Spectroscopy and Chemometric Analyses
Authors: A. A. Kalinichenko | N. Bârsan | Björn H. Junker | André Sackmann | Udo Weimar
Source: Poster (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

PT5.149 - AI-enabled rapid method for complex quality assessment of edible oils The research aimed to replace a set of standard methods for edible oil quality control by developing an AI-enabled rapid headspace analysis procedure.The use of a ceramic sensor array confirmed the feasibility of assessment of oil oxidation under static conditions, however, it was unable accurately predicting individual quality parameters.Due to the impracticality of applying selective enhancement approaches to ceramic sensors, an array of commercially available microsensors were used in combination with different temperature modulation modes. Edible oil ; Quality assessment ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Reliability engineering ; Chemistry ; Evaluation methods ; Engineering ; Food science Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Ryota Ono | Rajiv Kaushik | Sergey Artyukhin | Martin Jansen | I. V. Solovyev | R. A. Ewings
Source: arXiv (Cornell University) (preprint, 2024-11-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Entangled orbital, spin, and ferroelectric orders in $p$-electron magnet CsO$_2$ Alkali superoxides differ from conventional transition metal magnets, exhibit magnetism from partially occupied oxygen molecular $π^*$-orbitals. Among them, CsO$_2$ stands out for its potential to exhibit novel quantum collective phenomena, such as an orbital order induced Tomonaga-Luttinger liquid state. Using ab-initio Hubbard models, superexchange theory, and experimental spin wave measurements, we propose that CsO$_2$ exhibits unconventional magnetoelectric characteristics at low temperature. Our analysis confirms a canted antiferromagnetic ground state and a spin-flop transition, with ferroelectricity is induced by breaking inversion and time-reversal symmetry in the spin-flop phase. Consequently, our analysis reveals a strong interplay not only between exchange interactions but also among magnetically-induced polarization and orbital order. The magnetic structure, stabilized by orbital order, induces magnetically-induced polarization through an antisymmetric mechanism. Overall, our results reveal the coexistence of three highly entangled orders in CsO$_2$, namely, orbital, spin and ferroelectricity. Magnet ; Condensed matter physics ; Ferroelectricity ; Spin (aerodynamics) ; Electron ; Physics ; Quantum mechanics Magnetic and transport properties of perovskites and related materials ; Advanced Condensed Matter Physics ; Inorganic Fluorides and Related Compounds
Authors: Nguyễn Thị Thắm | Đoàn Văn Thuần | Trần Bích Thủy
Source: Journal of Science and Technology (article, 2024-01-25)
Field: Chemistry — Inorganic Chemistry

Tổng hợp và đánh giá động học hấp phụ của vật liệu graphit tróc trong xử lí nước thải dệt nhuộm Vật liệu graphit tróc được tổng hợp bằng phương pháp chiếu xạ vi sóng. Cấu trúc vật liệu được đánh giá bằng các phương pháp phân tích nhiễu xạ tia X (XRD), quang phổ hồng ngoại chuyển đổi Fourier FT-IR và đẳng nhiệt hấp phụ - khử hấp phụ N2. Kết quả phân tích nhiễu xạ tia X cho cường độ peak, chứng tỏ graphit tróc (Exfoliated Graphite - EG) được hình thành với độ tinh thể cao, phù hợp với kết quả phân tích phổ hồng ngoại chuyển đổi FT-IR. Diện tích bề mặt riêng của BET (Brunauer-Emmett-Teller) và thể tích lỗ rỗng của vật liệu EG được đo tương ứng là 40,95m2/g và 0,16176cm3/g. Đánh giá động học hấp phụ Congo Red thông qua các mô hình động học như: mô hình giả kiến bậc 1, mô hình giả kiến bậc 2, Elovich và Bangham trên EG chỉ ra sự hấp phụ CR lên EG tuân theo mô hình động học giả kiến bậc 2 với hệ số tương quan R2&gt; 0,98. Chemistry Pigment Synthesis and Properties