jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Amir Hussairi Zaidi | Anis Hazirah ‘Izzati Hasnu Al-Hadi | Noor Aida Syakira Ahmad Sabri | Nik Fasha Edora Nik Kamaruzaman | Nurlaila Ismail | Zakiah Mohd Yusoff | Mohd Nasir Taib
Source: Journal of Electrical & Electronic Systems Research (article, 2024-04-30)
Field: Chemistry — Organic Chemistry

Enhancing Aquilaria Species Classification using Agarwood Oil Analysis and K-Nearest Neighbors (KNN) as Machine Learning Aquilaria species is renowned for the aromatic resinous wood also known as agarwood and 17 accepted species are known can produce it.This tree has been long prized in various cultural and commercial context for its aromatic resinous wood, esteemed for its rich fragrance and versatile applications.The accurate classification of this agarwood-producing plant from Aquilaria species is essential for purposes such as conservation, sustainable resource management, trade regulation, research, and the preservation of cultural and economic traditions.This study employs machine learning, specifically the k-Nearest Neighbors (kNN) algorithm, to classify Aquilaria species based on chemical compounds features extracted from agarwood oil.Agarwood from four Aquilaria (A.) species: A. beccariana, A. crassna, A. malaccensis, and A. subintegra, is being used and the chemical compound is being analysed using the Gas Chromatography-Flame Ionization Detector (GC-FID).Subsequently, classifier performance is assessed using a confusion matrix to measure accuracy.The study not only demonstrates the effectiveness of this technique but also highlights its potential for future research related to Aquilaria and agarwood, reinforcing its relevance and applicability. Agarwood ; Artificial intelligence ; Pattern recognition (psychology) ; Mathematics ; Machine learning ; Computer science ; Medicine Wood and Agarwood Research ; Identification and Quantification in Food ; Advanced Image and Video Retrieval Techniques
Authors: Karandeep Cheema | Pei Han | Hsu‐Lei Lee | Hui Han | Yibin Xie | Anthony Christodoulou | Debiao Li
Source: Proceedings on CD-ROM - International Society for Magnetic Resonance in Medicine. Scientific Meeting and Exhibition/Proceedings of the International Society for Magnetic Resonance in Medicine, Scientific Meeting and Exhibition (article, 2024-08-14)
Field: Materials Science — Materials Chemistry

CEST Mapping from Undersampled Z-spectra in the Brain Using Deep Learning Chemical exchange saturation transfer (CEST) imaging uses radio frequency pulses at different frequency offsets to generate CEST maps. In this work, we used deep learning to calculate CEST maps from steady-state CEST (ss-CEST) images at undersampled frequency offsets, reducing the total scan time by a factor of 3.5. The Z-spectrum was undersampled by selecting the top 15 frequency offsets from Fisher information gain analysis. Fitting results from the proposed method were compared with those from multi-pool fitting with fully sampled Z-spectrum. We showed that it is feasible to reconstruct CEST maps from undersampled, field uncorrected ss-CEST images. Computer science ; Artificial intelligence ; Algorithm Lanthanide and Transition Metal Complexes ; Advanced MRI Techniques and Applications ; Atomic and Subatomic Physics Research
Source: Journal of biotechnology and bioinformatics. (article, 2024-03-29)
Field: Chemistry — Spectroscopy

High-Throughput Extraction of Interfacial Electrostatic Features from GLP-1- GLP-1R Complex Structures: A GLP-1-GLP-1R Based Mini GIBAC Perspective From a structural point of view, understanding interfacial electrostatic features of ligand-receptor complexes is crucial for biophysics-based design of therapeutics with improved efficacy and safety. Here, with a high-throughput structural feature extraction approach, this article reports a set of electrostatic structural features from glucagon-like peptide-1 (GLP-1) and GLP-1 receptor (GLP-1R) complex structures determined using experimental tools. Leveraging a set of computational structural biophysical analyses, this article systematically characterized the electrostatic interactions within the GLP-1- GLP-1R complexes with two sets of criteria to identify interfacial salt bridges and hydrogen bonds. Overall, the results here reveal intricate details of electrostatic interactions critical for GLP-1 binding to its receptor GLP-1R, providing biophysical insights into the stabilization of the GLP-1-GLP-1R complex structure and the molecular basis of GLP-1/GLP-1R signaling. Furthermore, the electrostatic structural features extracted from GLP-1 and GLP-1R complex structures are useful in the development of a machine learning-based ligand-receptor binding affinity calculating model, i.e., a GLP-1-GLP-1R-specific general intermolecular binding affinity calculator (GIBAC), paving the way for accelerated discovery and structure-based design of drug candidates targeting the GLP-1/GLP-1R system with improved efficacy and safety Perspective (graphical) ; Throughput ; Materials science ; Nanotechnology ; Computer science ; Artificial intelligence Mass Spectrometry Techniques and Applications ; Innovative Microfluidic and Catalytic Techniques Innovation ; Advanced biosensing and bioanalysis techniques
Authors: A. T. Herath | Rhowell N. Tiozon | Tobias Kretzschmar | Peter J. Mahon | Nese Sreenivasulu | Vito M. Butardo
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Machine Learning Approach for High-Throughput Phenolic Antioxidant Screening in Black Rice Germplasm Collection Based on Surface Ftir Germplasm ; Fourier transform infrared spectroscopy ; Throughput ; Antioxidant ; High-throughput screening ; Chemistry ; Biotechnology ; Computer science ; Horticulture ; Agricultural engineering ; Food science ; Biology ; Engineering ; Chemical engineering ; Biochemistry ; Telecommunications Spectroscopy and Chemometric Analyses
Authors: Oliver Snow | Amirreza Kazemi | Forum Bhanshali | Alyas Nasiri | Annett Rozek | Martin Ester
Source: Journal of Chemical Information and Modeling (article, 2024-07-20)
Field: Chemistry — Analytical Chemistry

Identifying Synergistic Components of Botanical Fungicide Formulations Using Interpretable Graph Neural Networks Botanical formulations are promising candidates for developing new biopesticides that can protect crops from pests and diseases while reducing harm to the environment. These biopesticides can be combined with permeation enhancer compounds to boost their efficacy against pests and fungal diseases. However, finding synergistic combinations of these compounds is challenging due to the large and complex chemical space. In this paper, we propose a novel deep learning method that can predict the synergy of botanical products and permeation enhancers based on in vitro assay data. Our method uses a weighted combination of component feature vectors to represent the input mixtures, which enables the model to handle a variable number of components and to interpret the contribution of each component to the synergy. We also employ an ensemble of interpretation methods to provide insights into the underlying mechanisms of synergy. We validate our method by testing the predicted synergistic combinations in wet-lab experiments and show that our method can discover novel and effective biopesticides that would otherwise be difficult to find. Our method is generalizable and applicable to other domains, where predicting mixtures of chemical compounds is important. Fungicide ; Graph ; Artificial neural network ; Computer science ; Artificial intelligence ; Machine learning ; Theoretical computer science ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Computational Drug Discovery Methods ; Fungal Plant Pathogen Control
Authors: Willie J. Padilla
Source:  (article, 2024-03-11)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Deep inverse methods in electromagnetic metamaterials Deep learning (DL) has enabled the development of deep inverse models (DIMs) to solve inverse problems in artificial electromagnetic materials (AEMs). DIMs often outperform conventional optimization approaches, but their performance has not been thoroughly compared. We evaluated eight state-of-the-art DIMs on three unique AEM design problems, quantitatively comparing their solution time and accuracy. We found that modern DIMs can be decomposed into independent modules, and that interchanging these modules can create novel higher performing DIMs. We taxonomized the unique modules of current state-of-the-art DIMs into three categories: initializers, filters, and optimizers. We conclude by discussing some important outstanding issues of deep inverse design of AEMs, and presenting an outlook of this exciting field. Inverse ; Metamaterial ; Inverse problem ; Computer science ; Deep learning ; Field (mathematics) ; Electromagnetics ; Artificial intelligence ; Algorithm ; Electronic engineering ; Physics ; Mathematics ; Engineering ; Optics ; Geometry ; Mathematical analysis Metamaterials and Metasurfaces Applications ; Underwater Acoustics Research ; Electromagnetic Scattering and Analysis
Authors: Xianluo Hu | Weihao Tie | Yu Shao | Yifan Meng | Yang Li | Jinyong Fang
Source:  (article, 2024-06-16)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetism design of the Halbach cylinders based on artificial neural network The Halbach cylinder is a widely used structure of the Halbach array, which aims to generate the strongest magnetic field using the least amount of material. In this paper, a novel magnetism design for the Halbach cylinder based on 2D analytical theory and BP artificial neural network (BP-ANN) is proposed. Compared with the commonly used approaches, the influence of the thickness direction on the 2D analytical solution is corrected. At the same time, the time-consuming cost has obvious advantages over the finite element method, which is very important for large-scale and multi-parameter magnet optimization. As an important element in the beam optical system, the Halbach cylinder can deflect and focus the beam to achieve the desired effect. The proposed method in this article can be directly integrated into the beam optical design, and the influence of magnet parameters on the beam can be directly reflected, which is of great significance to the engineering processing and manufacturing of magnets. Halbach array ; Magnetism ; Artificial neural network ; Computer science ; Physics ; Mechanical engineering ; Engineering ; Artificial intelligence ; Magnet ; Condensed matter physics Magnetic Properties and Applications
Authors: Haoqi Zhu | Zhongyi Wang | Luofei Li | Dong Liang | Minghao Yin | Bin Xue | Yi Cao
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Spectroscopy

Revolutionizing Supramolecular Materials Design with Artificial Intelligence Supramolecular chemistry ; Nanotechnology ; Computer science ; Engineering ; Artificial intelligence ; Materials science ; Chemistry ; Crystallography Aerogels and thermal insulation ; Diatoms and Algae Research ; Polymer composites and self-healing
Authors: Matheus Santos Macedo | Tarso Vilela Ferreira | Jugurta Montalvão | Charles A. C. Araujo | Ulisses D. E. S. Lebre
Source: Research Square (preprint, 2024-08-17)
Field: Materials Science — Materials Chemistry

Insulation Hydrophobicity Classification Based on Fourier Transform and Shallow Neural Networks Artificial neural network ; Computer science ; Artificial intelligence ; Pattern recognition (psychology) ; Segmentation ; Degradation (telecommunications) ; Feature extraction ; Fourier transform ; Mathematics High voltage insulation and dielectric phenomena ; Infrastructure Maintenance and Monitoring ; Water Systems and Optimization
Authors: Siti Wardah | Mohamad Yani | Taufik Djatna | Marimin Marimin
Source: International Journal of Information and Decision Sciences (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Hybrid of machine learning-based multiple criteria decision making and mass balance analysis in the new coconut agro-industry product development Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249. Balance (ability) ; Product (mathematics) ; New product development ; Computer science ; Operations management ; Business ; Operations research ; Manufacturing engineering ; Marketing ; Economics ; Mathematics ; Engineering Coconut Research and Applications
Authors: Matteo Orlandi | Piero Procacci | Marina Macchiagodena | Fabrizio Carta | Claudiu T. Supuran | Marco Pagliai
Source:  (article, 2024-09-20)
Field: Chemistry — Organic Chemistry

Metalloproteins Investigation for the Development of new Inhibitors Nowadays, drug-resistant bacteria are becoming a real threat to our society. The chance of dangerous pathogens developing resistance to classical antibiotics and the risk of being unable to fight this impending menace without innovative drugs is drawing ever closer. Our study aims to develop a computational protocol to speed up the drug discovery pipeline for a new class of antibiotics, capable of inhibiting the specific metalloproteins crucial for bacteria survival. Every year drug-resistant tuberculosis causes the deaths of hundreds of thousands of people worldwide. The main targets of our new generation of antitubercular drugs are the β Carbonic Anhydrases, which are selectively expressed by the pathogen. To accomplish this drug discovery task, we started from scratch to build completely new chemical entities. The plumb of the available chemical space, which is esteemed to contain around 1023 drug like compounds, for the “de novo” discovery of a new candidate is a chimerical challenge, so efficient methods are needed to explore it. Deep learning algorithms, like Generative Adversarial Networks, can speed up this process, enabling the opportunity to accelerate the search of the chemical space. This was how we intended to take advantage of AI, to gain access to a reasonable number of promising starting points for our drug design purpose. Then, we performed a comparative virtual screening procedure, that involved different Docking suites that explicitly consider metal-ligand interactions, to assess the binding affinity of the generated and optimized hit compounds. Lastly, we managed to set up an alchemical free energy calculation protocol to obtain more accurate free energy predictions of the metal-binding ligands, to guide the hit-to-lead optimization. Regarding the theoretical framework necessary to simulate the nuanced dynamic behaviour of the metalloproteins target, we are involved in the setup of tailored Force Fields and QM/MM MD simulations, to analyze the coordination bond formation mechanisms and the energy. Indeed, we managed to design a low nanomolar inhibitor, selective for the pathogenβ2 Carbonic Anhydrase, whose activity is going to be tested in vivo. Metalloprotein ; Computer science ; Chemistry ; Biochemistry ; Enzyme Ferrocene Chemistry and Applications ; Nanoparticle-Based Drug Delivery
Authors: Marimin Marimin | Mohamad Yani | Taufik Djatna | Siti Wardah
Source: International Journal of Information and Decision Sciences (article, 2024-01-01)
Field: Chemistry — Inorganic Chemistry

Hybrid of machine learning-based multiple criteria decision making and mass balance analysis in the new coconut agro-industry product development Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249. Balance (ability) ; Product (mathematics) ; New product development ; Operations management ; Business ; Computer science ; Manufacturing engineering ; Industrial organization ; Marketing ; Economics ; Mathematics ; Psychology ; Engineering Coconut Research and Applications
Authors: Mao-Ken Hsu | Boyu Huang | Chi‐Hua Yu
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Integration of Deep Learning and Reinforcement Learning for Efficient Woven Composite Design Reinforcement ; Reinforcement learning ; Composite number ; Artificial intelligence ; Computer science ; Materials science ; Composite material Textile materials and evaluations ; Additive Manufacturing and 3D Printing Technologies
Authors: Qian Peng | Mengyuan He | Liyun Ma | Xinxin Li | Keran Chen | Zhenyu Lu | Ning Wang
Source:  (article, 2024-08-28)
Field: Materials Science — Polymers and Plastics

Gesture Capture and Recognition of Wearable Systems Based on Smart Textile Materials Wearable systems are gaining popularity across various domains, offering opportunities for innovative applications in human-robot interaction and healthcare. This paper explores the development of a wearable system utilizing smart textile materials for gesture capture and recognition. The device, constructed using core–shell-structured composite yarns, seamlessly integrates with the user’s wrist, enabling swift and accurate detection of wrist movements. By leveraging technologies such as Leap Motion for gesture capture and signal processing techniques including variational mode extraction and Hilbert transform, the device is capable of extracting features and classifying gestures. Experimental results demonstrate the effectiveness of the device in capturing and recognizing various wrist movements, the accuracy rate reaches 93.86%. The integration of wearable technology, signal processing, and machine learning provides effective avenues for enhancing human-robot interaction and advancing our understanding of human physiology and behavior. Gesture ; Wearable computer ; Gesture recognition ; Computer science ; Textile ; Wearable technology ; Human–computer interaction ; Artificial intelligence ; Embedded system ; Materials science Textile materials and evaluations ; Hand Gesture Recognition Systems ; Digital Media and Visual Art
Authors: Md Ali, Syaril Azrad | Norhellme, Nornajme | Ahmad Rashidi, Syazwan | Mahmud Zuhudi, Nurul Zuhairah | Abd Aziz, Noor Zuhaira
Source: Universiti Putra Malaysia Institutional Repository (Universiti Putra Malaysia) (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Segmentation of openings in non-crimp fabric using deep learning Non-Crimp Fabrics (NCF) are widely used in industries like aerospace due to their lightweight and high-strength properties. Accurate characterization of openings in NCF is essential for maintaining material quality and performance. Traditional machine vision methods, such as histogram-based and edge detection approaches, often struggle with complex NCF patterns and rely heavily on manual feature engineering. This study applies deep learning models U-Net, LinkNet, and DeepLabv3+ for the binary segmentation of openings in carbon fiber NCF. These models use a ResNet18 encoder, pre-trained on ImageNet, to automatically extract robust features and improve generalization. To address data limitations, we augmented the collected NCF dataset to increase variability. The results show that U-Net, LinkNet, and DeepLabv3+ achieved Intersection over Union (IoU) scores of 0.7419, 0.7228, and 0.7172, respectively, demonstrating their effectiveness in segmenting openings in NCF. This study presents a method that enhances segmentation accuracy and improves ability to generalize across varying data conditions, providing a more adaptable approach to NCF characterization with potential applications for other advanced materials. Segmentation ; Deep learning ; Intersection (aeronautics) ; Feature (linguistics) ; Market segmentation ; Image segmentation ; Pattern recognition (psychology) ; Feature extraction Textile materials and evaluations ; Mechanical Behavior of Composites ; Fiber-reinforced polymer composites
Authors: Yanis Zirem | Léa Ledoux | Nina Ogrinc | Laurine Lagache | Roland P. Bourette | Chann Lagadec | Paul Chaillou | Michel Salzet | Isabelle Fournier
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2024-12-12)
Field: Chemistry — Spectroscopy

Development of Molecular Digital Twins Based on Ambient Ionization Mass Spectrometry Imaging for Application in Cancer Surgery Summary Cancer surgery is a fundamental component of oncology treatment, its quality significantly impacts patient outcomes, influencing both relapse rates and survival. However, achieving this customization is contingent upon early collection of robust molecular data during surgery, providing accurate information for diagnosis, prognosis, and delineating surgical margins. The introduction of digital twin (DT) technology has recently opened a new era of precision and effectiveness in cancer surgery. Expanding from its successful implementations in the industrial sector, DT concept has evolved into a highly promising breakthrough in healthcare. Therefore, our study goal is on creating DT by using accurate and high-throughput molecular data obtained through mass spectrometry imaging. We developed a machine-learning-based pipeline that allow to depict infiltration of cancer cells into normal tissue that offer precise delineation of tumor margins thanks to SpiderMass. This process also enables the prediction of relative presence of bacterial strains in tumoral and healthy mammary glands. Personalization ; Mass customization ; Implementation ; Mass spectrometry imaging ; Molecular imaging ; Computer science ; Infiltration (HVAC) ; Medicine ; Medical physics ; Mass spectrometry ; Chemistry ; Biology ; Geography ; Software engineering Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Advanced Biosensing Techniques and Applications
Authors: Rosalía López‐Ruiz | Ana María Jiménez Carvelo | Luis Cuadros-Rodrı́guez
Source: SSRN Electronic Journal (preprint, 2024-01-01)
Field: Chemistry — Organic Chemistry

The Phospholipid Chromatographic Fingerprint: An Analytical Cutting-Edge Strategy in the Distinguished Characterization of Edible Vegetable Oils Fingerprint (computing) ; Characterization (materials science) ; Chromatography ; Phospholipid ; Chemistry ; Materials science ; Computer science ; Artificial intelligence ; Nanotechnology ; Biochemistry Edible Oils Quality and Analysis ; Essential Oils and Antimicrobial Activity ; Metabolomics and Mass Spectrometry Studies