jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Geetha Birudala | Rajendra Dnyandeo Dighe | Saravanakumar Kasimedu | Sowjanya Pulipati | Hari Veluru | Naveenkumar Gandupally | Gurinderdeep Singh
Source: Asian Journal of Chemistry (article, 2024-08-30)
Field: Chemistry — Organic Chemistry

Exploring Indolyl Triazoles: Synthesis, Computational Profiling and Antimicrobial Assessment In this work, a series of new indolyl-triazole compounds (IT1-8) were synthesized, characterized and evaluated for their in vitro antimicrobial activities. The synthesis involved reactions of indole acetic acid and hydrazine, followed by cyclization with various reagents, yielding triazole compounds elucidated by TLC, IR, NMR, and mass spectral data. Computational profiling using PASS and Molinspiration indicated potential antitubercular and anticancer activities, with compounds IT1 and IT2 showing high bioactivity scores. ProTox 3.0 analysis identified compound IT5 as the safest compound regarding toxicity. The synthesized indolyl-triazoles exhibited significant antimicrobial activity, especially against Mycobacterium tuberculosis and various bacterial strains. The experimental assessments revealed unexpected antibacterial activities, particularly in compounds IT5 and IT6, highlighting the significance of halogenated phenyl rings and indolyl moieties in their efficacy. The study underscores the potential of indolyl-triazoles as antimicrobial agents and the role of computational tools in drug discovery, while also noting the discrepancy between in silico predictions and experimental results, suggesting the need for further exploration of structure-activity relationships (SAR). Antimicrobial ; Chemistry ; Antimycobacterial ; Indole test ; Combinatorial chemistry ; Docking (animal) ; In silico ; Mycobacterium tuberculosis ; Drug discovery ; Stereochemistry ; Organic chemistry ; Biochemistry ; Tuberculosis Click Chemistry and Applications ; Synthesis and biological activity ; Multicomponent Synthesis of Heterocycles
Authors: Joseph P. Molnar | Jiangnan Xia | Rui Zhang | Samuel J. Grauer | Chang Liu
Source: arXiv (Cornell University) (preprint, 2024-12-30)
Field: Chemistry — Spectroscopy

Unsupervised neural-implicit laser absorption tomography for quantitative imaging of unsteady flames This paper presents a novel neural-implicit approach to laser absorption tomography (LAT) with an experimental demonstration. A coordinate neural network is used to represent thermochemical state variables as continuous functions of space and time. Unlike most existing neural methods for LAT, which rely on prior simulations and supervised training, our approach is based solely on LAT measurements, utilizing a differentiable observation operator with line parameters provided in a standard spectroscopy database format. Although reconstructing scalar fields from multi-beam absorbance data is an inherently ill-posed, nonlinear inverse problem, our continuous space-time parameterization supports physics-inspired regularization strategies and enables data assimilation. Synthetic and experimental tests are conducted to validate the method, demonstrating robust performance and reproducibility. We show that our neural-implicit approach to LAT can capture the dominant spatial modes of an unsteady flame from very sparse measurement data, indicating its potential to reveal combustion instabilities in measurement domains with minimal optical access. Tomography ; Absorption (acoustics) ; Laser ; Materials science ; Optics ; Physics Spectroscopy and Laser Applications ; Atmospheric and Environmental Gas Dynamics ; Combustion and flame dynamics
Authors: Dong-Eon Kim | YeongHyeon Park
Source: arXiv (Cornell University) (preprint, 2024-01-09)
Field: Chemistry — Analytical Chemistry

Empirical Analysis of Anomaly Detection on Hyperspectral Imaging Using Dimension Reduction Methods Recent studies try to use hyperspectral imaging (HSI) to detect foreign matters in products because it enables to visualize the invisible wavelengths including ultraviolet and infrared. Considering the enormous image channels of the HSI, several dimension reduction methods-e.g., PCA or UMAP-can be considered to reduce but those cannot ease the fundamental limitations, as follows: (1) latency of HSI capturing. (2) less explanation ability of the important channels. In this paper, to circumvent the aforementioned methods, one of the ways to channel reduction, on anomaly detection proposed HSI. Different from feature extraction methods (i.e., PCA or UMAP), feature selection can sort the feature by impact and show better explainability so we might redesign the task-optimized and cost-effective spectroscopic camera. Via the extensive experiment results with synthesized MVTec AD dataset, we confirm that the feature selection method shows 6.90x faster at the inference phase compared with feature extraction-based approaches while preserving anomaly detection performance. Ultimately, we conclude the advantage of feature selection which is effective yet fast. Hyperspectral imaging ; Dimensionality reduction ; Computer science ; Artificial intelligence ; Anomaly detection ; Pattern recognition (psychology) ; Feature selection ; Feature extraction ; Feature (linguistics) ; Reduction (mathematics) ; Inference ; Mathematics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Remote-Sensing Image Classification
Authors: Elies Gil-Fuster | Jonas R. Naujoks | Grégoire Montavon | Thomas Wiegand | Wojciech Samek | Jens Eisert
Source: arXiv (Cornell University) (preprint, 2024-12-19)
Field: Materials Science — Materials Chemistry

Opportunities and limitations of explaining quantum machine learning A common trait of many machine learning models is that it is often difficult to understand and explain what caused the model to produce the given output. While the explainability of neural networks has been an active field of research in the last years, comparably little is known for quantum machine learning models. Despite a few recent works analyzing some specific aspects of explainability, as of now there is no clear big picture perspective as to what can be expected from quantum learning models in terms of explainability. In this work, we address this issue by identifying promising research avenues in this direction and lining out the expected future results. We additionally propose two explanation methods designed specifically for quantum machine learning models, as first of their kind to the best of our knowledge. Next to our pre-view of the field, we compare both existing and novel methods to explain the predictions of quantum learning models. By studying explainability in quantum machine learning, we can contribute to the sustainable development of the field, preventing trust issues in the future. Quantum ; Computer science ; Cognitive science ; Artificial intelligence ; Psychology ; Physics ; Quantum mechanics Machine Learning in Materials Science
Authors: Dandan DAI | Hui LIU
Source: Frontiers of Agricultural Science and Engineering (article, 2024-09-25)
Field: Chemistry — Analytical Chemistry

An improved multiscale fusion dense network with efficient multiscale attention mechanism for apple leaf disease identification With the development of smart agriculture, accurately identifying crop diseases through visual recognition techniques instead of by eye has been a significant challenge. This study focused on apple leaf disease, which is closely related to the final yield of apples. A multiscale fusion dense network combined with an efficient multiscale attention (EMA) mechanism called Incept_EMA_DenseNet was developed to better identify eight complex apple leaf disease images. Incept_EMA_DenseNet consists of three crucial parts: the inception module, which substituted the convolution layer with multiscale fusion methods in the shallow feature extraction layer; the EMA mechanism, which is used for obtaining appropriate weights of different dense blocks; and the improved DenseNet based on DenseNet_121. Specifically, to find appropriate multiscale fusion methods, the residual module and inception module were compared to determine the performance of each technique, and Incept_EMA_DenseNet achieved an accuracy of 95.38%. Second, this work used three attention mechanisms, and the efficient multiscale attention mechanism obtained the best performance. Third, the convolution layers and bottlenecks were modified without performance degradation, reducing half of the computational load compared with the original models. Incept_EMA_DenseNet, as proposed in this paper, has an accuracy of 96.76%, being 2.93%, 3.44%, and 4.16% better than Resnet50, DenseNet_121 and GoogLeNet, respectively, proved to be reliable and beneficial, and can effectively and conveniently assist apple growers with leaf disease identification in the field. Identification (biology) ; Mechanism (biology) ; Scale (ratio) ; Computer science ; Fusion ; Fusion mechanism ; Artificial intelligence ; Botany ; Biology ; Physics ; Cartography ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: Soo‐Yeon Hwang | Kyung‐Hwa Jeon | Hwa-Jong Lee | Inhye Moon | Sehyun Jung | Seul-Ah Kim | Hyunji Jo | Seojeong Park | Misun Ahn | Soo-Yeon Kwak | Younghwa Na | Youngjoo Kwon
Source: eLife (article, 2024-06-03)
Field: Chemistry — Organic Chemistry

Synthesis and biological assessment of chalcone and pyrazoline derivatives as novel inhibitor for ELF3-MED23 interaction HER2 overexpression significantly contributes to the aggressive nature and recurrent patterns observed in various solid tumors, notably gastric cancers. Trastuzumab, HER2-targeting monoclonal antibody drug, has shown considerable clinical success; however, readily emerging drug resistance emphasizes the pressing need for improved interventions in HER2-overexpressing cancers. To address this, we proposed targeting the protein-protein interaction (PPI) between ELF3 and MED23 as an alternative therapeutic approach to trastuzumab. In this study, we synthesized a total of 26 compounds consisting of 10 chalcones, 7 pyrazoline acetyl, and 9 pyrazoline propionyl derivatives, and evaluated their biological activity as potential ELF3-MED23 PPI inhibitors. Upon systematic analysis, candidate compound <b>10</b> was selected due to its potency in downregulating reporter gene activity of <i>ERBB2</i> promoter confirmed by SEAP activity and its effect on HER2 protein and mRNA levels. Compound <b>10</b> effectively disrupted the binding interface between the ELF3 TAD domain and the 391-582 amino acid region of MED23, resulting in successful inhibition of the ELF3-MED23 PPI. This intervention led to a substantial reduction in HER2 levels and its downstream signals in the HER2-positive gastric cancer cell line. Subsequently, compound <b>10</b> induced significant apoptosis and anti-proliferative effects, demonstrating superior in vitro and in vivo anticancer activity overall. We found that the anticancer activity of compound <b>10</b> was not only restricted to trastuzumab-sensitive cases, but was also valid for trastuzumab-refractory clones. This suggests its potential as a viable therapeutic option for trastuzumab-resistant gastric cancers. In summary, compound <b>10</b> could be a novel alternative therapeutic strategy for HER2-overexpressing cancers, overcoming the limitations of trastuzumab. Chalcone ; Pyrazoline ; Chemistry ; Computational biology ; Pharmacology ; Biology ; Stereochemistry ; Organic chemistry Synthesis and biological activity ; Synthesis and Biological Evaluation
Authors: Robert Allen Roach
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-10-09)
Field: Materials Science — Materials Chemistry

Overview of Nuclear Materials Discovery &amp; Qualification initiative (NMDQi) The Nuclear Materials Discovery and Qualification Initiative (NMDQi) is a program launched in 2020 to address the need for development of new nuclear materials on shorter timelines. The NMDQi has a goal to accelerate nuclear materials qualification to fulfill the promises of early and advanced reactor technologies as a safe, clean, and low-cost baseload energy. Materials development and qualification in the nuclear industry is by definition challenging due to stringent safety requirements, limited availability of specialized facilities for materials irradiation and testing, and the challenging high-temperature, high-radiation environment. Outside the nuclear industry, options exist for increasing the efficiency and rate of materials discovery for accelerated technology commercialization, which can also be applied for our use through the NMDQi. Focus will be on generating tools and capabilities that integrate experimental and computational techniques, allowing materials to be selected prior to fabrication, and providing crucial data to improve upon modeling. Engineering Nuclear Materials and Properties ; Nuclear Physics and Applications ; Nuclear reactor physics and engineering
Authors: Christina Glaubitz | Amélie Bazzoni | Christoph Neururer | R. Locher | Kata Dorbić | Jessica Caldwell | Miguel Spuch‐Calvar | Laura Rodríguez‐Lorenzo | Sandor Balog | Vincent Serneels | Barbara Rothen‐Rutishauser | Alke Petri‐Fink
Source: Current Research in Food Science (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Correct labelling? A full analytical pathway for silica and titania particles in food products Introducing particles as additives, specifically engineered nanoparticles, in the food industry has improved food properties. Since 2014, alongside the presence of these added particles, there has been a mandatory requirement to disclose if those additives are nanomaterials in the ingredient list of food products. However, detecting and characterizing nanomaterials is time-consuming due to their small sizes, low concentrations, and diverse food matrices. We present a streamlined analytical process to detect the presence of silica and titania particles in food, applicable for food regulation and control. Using X-ray Fluorescence Spectrometry for screening enables quick categorization of inorganic particles labeling accuracy, distinguishing products with and without them. For the former, we develop matrix-independent digestion and introduce time-effective statistics to evaluate the median particle size using a reduced number of particles counted, ensuring accurate "nano" labeling. Through the implementation of this work, our objective is to simplify and facilitate verifying the proper labeling of food products. Nanomaterials ; Food industry ; Ingredient ; Labelling ; Food products ; Nanoparticle ; Food labelling ; Particle size ; Food additive ; Complex matrix ; Matrix (chemical analysis) ; Process engineering ; Food processing ; Nanotechnology ; Particle (ecology) ; Biochemical engineering ; Materials science ; Chemistry ; Chromatography ; Food science ; Engineering Analytical chemistry methods development ; Heavy metals in environment ; X-ray Spectroscopy and Fluorescence Analysis
Authors: Lili Nurliyana Abdullah | Fatimah Sidi | Ildar Kurmashev | Kainizhamal Iklassova | Mohamad Yusnisyahmi Yusof | Iskandar Ishak
Source: Vestnik of M Kozybayev North Kazakhstan University (article, 2024-12-10)
Field: Chemistry — Analytical Chemistry

Ensemble deep learning approach for apple fruitlet detection from digital images Agriculture commodities are commodities that have a high economic worth and the potential to be developed further. The green and red apple, in instance, is one type of fruit that has the potential to be cultivated as part of agriculture. The apple economy is reasonably steady, particularly with regard to the supply of production to the market. The purpose of this research is to enhance the performance of the CNN-based model and make it capable of precise detection of the green and red apple fruitlet. To enhance the overall performance of the model, the revised CNN-based YOLOv5 ensemble model was implemented with the SiLU (Sigmoid Linear Units activation function), Batch Normalization, and SGD (Stochastic Gradient Descent) algorithms. The combination of activation function, optimization, batch normalization, and ensemble technique can be later used to enhance the YOLOv5 ensemble model and used to detect the green and red apple fruitlet with the benefits of utilizing limited resources. This is possible thanks to the combination of the activation function, optimization, batch normalization, and ensemble technique. According to the findings of the comprehensive research, the accuracy of the updated yolo ensemble model has climbed into 97.8%, 92.1%, 95% percent of accuracy mAP for green, red and both apples together compared to previous model. Artificial intelligence ; Computer science ; Ensemble learning ; Deep learning ; Pattern recognition (psychology) ; Machine learning Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing in Agriculture
Authors: Tejas Chaudhari
Source: International Journal for Research in Applied Science and Engineering Technology (article, 2024-03-31)
Field: Chemistry — Analytical Chemistry

Fruit Scan - Disease Identification in Fruits Using Image Processing Abstract: The agricultural industry plays a crucial role in sustaining global food security, and the health of fruit crops is paramount in ensuring a steady food supply. Fruit diseases pose a significant threat to crop yield and quality, making their early detection and management essential. In recent years, the integration of technology and artificial intelligence has transformed fruit disease detection, offering more accurate and efficient solutions. This abstract provides an overview of the techniques and challenges associated with fruit disease detection. This review highlights various methodologies employed in fruit disease detection, including computer vision, machine learning, and sensor-based approaches. Computer vision, powered by deep learning algorithms, has enabled the automated identification of disease symptoms based on image analysis. Machine learning models, such as neural networks and support vector machines, have been deployed to classify disease types, predict disease severity, and assist in decision-making for disease management. Sensor-based techniques, like hyperspectral imaging and electronic nose systems, offer non-invasive and real-time monitoring of fruit health. Despite the progress in fruit disease detection techniques, several challenges persist. These include data acquisition and labelling, the need for robust and transferable models, scalability, and the integration of multiple technologies. Furthermore, the deployment of these technologies in the field may require addressing issues related to resource constraints, infrastructure, and the digital divide in agricultural communities Identification (biology) ; Image processing ; Computer science ; Computer vision ; Artificial intelligence ; Pattern recognition (psychology) ; Horticulture ; Image (mathematics) ; Biology ; Botany Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food
Authors: Rajendra Maruti Ardalkar | Sandesh Mane | A. Goswami | Mahesh P. Gaonkar | Bhavik Pradeep Joshi | Hemlata K. Bagla | Α. V. R. Reddy
Source: Research Square (preprint, 2024-07-11)
Field: Materials Science — Materials Chemistry

Spectroscopic Evaluation of Colour Grading of Type Ia Diamonds†. Grading (engineering) ; Geology ; Computer science ; Mathematics ; Engineering ; Civil engineering Diamond and Carbon-based Materials Research ; High-pressure geophysics and materials ; Geological and Geochemical Analysis
Authors: Priya Vij | Patil Manisha Prashant
Source: Journal of Angiotherapy (article, 2024-09-01)
Field: Chemistry — Analytical Chemistry

Enhancing Ovarian Cancer Detection Using Self-Organizing Maps and Improved Recurrent Neural Networks with Extended Harmony Search Optimization Background: Ovarian cancer (OC) is a highly fatal malignancy of the female reproductive system, characterized by its high mortality rate and the challenges associated with clinical research due to the disease's complexity and late-stage diagnosis. Advances in technology, such as the Internet of Medical Things (IoMT), offer new opportunities for improving OC detection and diagnosis. Objective: This study aimed to develop and evaluate a novel method for OC detection using IoMT data, leveraging Self-Organizing Maps (SOM) and Improved Recurrent Neural Networks (IRNN) enhanced with the Extended Harmony Search Optimization (EHSO) algorithm to improve feature selection and classification accuracy. Methods: The study utilized OC data from the IoMT and applied SOM for feature selection, which helps in managing and classifying large datasets. SOM was employed to improve data representation and address challenges in labeling and classifying data. The IRNN model, optimized using the EHSO algorithm, was developed to enhance classification performance. The model was tested using a dataset from Kaggle comprising 179 benign and 172 malignant OC images with 50 attributes. Results: The IRNN model with EHSO demonstrated superior performance compared to other methods. In the training dataset, it achieved an accuracy of 95.72% and a Root Mean Square Error (RMSE) of 4.8%. For the testing dataset, the model maintained a high accuracy of 90.4% and an RMSE of 6.8%. The IRNN with EHSO outperformed alternative methods in terms of specificity and sensitivity, while the Genetic Algorithm (GA) showed the lowest performance across all metrics. Conclusion: The proposed method using SOM and IRNN with EHSO significantly improves the detection of ovarian cancer by optimizing feature selection and classification accuracy. This approach offers a promising advancement in utilizing IoMT data for early and accurate OC diagnosis, potentially enhancing patient outcomes through more effective detection and treatment strategies. Harmony search ; Harmony (color) ; Computer science ; Artificial neural network ; Artificial intelligence ; Machine learning ; Pattern recognition (psychology) ; Art Spectroscopy and Chemometric Analyses ; Infrared Thermography in Medicine
Authors: Xin Li | Guodong Ren | Yun Yu | Arashdeep Singh Thind | Anjana K. Shah | Abbey Bowers | Rohan Mishra | Xiaoshan Xu
Source: arXiv (Cornell University) (preprint, 2024-09-25)
Field: Materials Science — Materials Chemistry

Improper flexoelectricity in hexagonal rare-earth ferrites Flexoelectricity is a universal effect that generates electric polarization due to broken inversion symmetry caused by local strain gradient. The large strain gradient at nanoscale makes flexo-electric effects, especially in nanoscopic ferroelectric materials, promising in sensors, actuator, energy harvesting, and memory applications. In this work, we studied flexoelectricity in hexagonal ferrites h-YbFeO3, an improper ferroelectric expected to have weak piezoelectricity and low sensitivity to depolarization field, which are advantageous for studying flexoelectric effects. We show that in h-YbFeO3 epitaxial thin films, strain gradient on the order of 10^6 m-1 occurs near grain boundaries and edge dislocation, which has a significant impact on the non-polar K3 structural distortion that induces spontaneous polarization. The phenomenological model based on the Landau theory of improper ferroelectricity suggests an indirect flexoelectric effect on the order of 10 nC/m in h-YbFeO3, which is substantially larger than the expectation from Kogan mechanism. These results reveal a novel microscopic mechanism of coupling between strain gradient and polarization mediated by structural distortion, which we call improper flexoelectricity. Rare earth ; Flexoelectricity ; Hexagonal crystal system ; Materials science ; Chemistry ; Crystallography ; Composite material ; Metallurgy ; Piezoelectricity Nonlocal and gradient elasticity in micro/nano structures ; Electromagnetic Effects on Materials ; Magnetic Properties and Applications