jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Shulei Ni | Yisheng Qiu | Yunchun Chen | Zihao Song | Hao Chen | Xuejian Jiang | Huaxi Chen
Source: arXiv (Cornell University) (preprint, 2024-03-04)
Field: Chemistry — Analytical Chemistry

PI-AstroDeconv: A Physics-Informed Unsupervised Learning Method for Astronomical Image Deconvolution In the imaging process of an astronomical telescope, the deconvolution of its beam or Point Spread Function (PSF) is a crucial task. However, deconvolution presents a classical and challenging inverse computation problem. In scenarios where the beam or PSF is complex or inaccurately measured, such as in interferometric arrays and certain radio telescopes, the resultant blurry images are often challenging to interpret visually or analyze using traditional physical detection methods. We argue that traditional methods frequently lack specific prior knowledge, thereby leading to suboptimal performance. To address this issue and achieve image deconvolution and reconstruction, we propose an unsupervised network architecture that incorporates prior physical information. The network adopts an encoder-decoder structure while leveraging the telescope's PSF as prior knowledge. During network training, we introduced accelerated Fast Fourier Transform (FFT) convolution to enable efficient processing of high-resolution input images and PSFs. We explored various classic regression networks, including autoencoder (AE) and U-Net, and conducted a comprehensive performance evaluation through comparative analysis. Deconvolution ; Image (mathematics) ; Physics ; Unsupervised learning ; Computer science ; Artificial intelligence ; Optics Spectroscopy and Chemometric Analyses ; Astronomical Observations and Instrumentation ; Astronomy and Astrophysical Research
Authors: Amit Welekar
Source: Journal of Electrical Systems (article, 2024-11-16)
Field: Chemistry — Analytical Chemistry

Enhancing Segmentation Approach: FCM-CNN Based Robust Segmentation Method for Regular and Irregular Shape Fruit Image In today’s era of computer vision, the researcher solved the different challenges. Similar to these aspects, this article defined the FCM (Fuzzy C-Means) based enhanced method for segmentation of irregular shape fruit image captured in natural light is a computer vision technique used to identify artificially ripened fruits. The process involves segmenting the fruit image into its constituent parts using fuzzy logic to capture the ambiguity and variability of the fruit's features. The FCM algorithm utilizes a clustering approach to identify regions in the image that have similar characteristics or properties. This approach is particularly useful when the fruit's shape and color vary widely, making it challenging to segment using traditional image processing methods. The enhanced FCM method takes into account the fact that artificially ripened fruits exhibit distinct color variations compared to naturally ripened fruits. By considering the spectral properties of the fruit, the enhanced FCM method can accurately identify artificially ripened fruits. The segmentation process involves converting the fruit image into a set of pixel values, followed by clustering the pixels into different groups based on their similarity. The final step involves identifying the clusters that correspond to the fruit's color and shape and extracting these regions to obtain a segmented image. The state-of-art that the FCM based enhanced method for segmentation of irregular shape fruit image captured in natural light is a powerful tool for identifying artificially ripened fruits, which are a major issues in the food industry relate to health risks. Artificial intelligence ; Segmentation ; Cluster analysis ; Pixel ; Pattern recognition (psychology) ; Computer science ; Computer vision ; Process (computing) ; Fuzzy logic ; Image segmentation ; Image (mathematics) ; Similarity (geometry) ; Image processing ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Chinnasami Sivaji | M Ramachandran | Vimala Saravanan | Ramya Sharma
Source: Journal on Applied and Chemical Physics (article, 2024-03-01)
Field: Chemistry — Analytical Chemistry

The Applications of Nanotechnologies in Food Science using the ARAS Method Nanotechnologies in Food Science Nanotechnology is an important advanced technology Contribution, growth and sustainability Enable impact in the fields of Food, Medicine, and Agriculture. Nan materials are Healthy, safe, and high-quality functional leading to quality and quantity production of foods, they are biodegradable or semi-degradable in nature. Niño Conventional food processing technologies Better than technologies, food products extend shelf life, prevent contamination, and produce improved food quality. Nanotechnology for functional food development comprehensive review of technologies, in Food Processing, and paid packaging of advanced nonmaterial Current in making, preserving, and storing Describes trends and future perspectives. industry. Food bioavailability, taste, texture, and nana to improve stability Applications of the technologies are particle size possible integration and feeding and surface charge of nonmaterial are achieved by changing food Processing, production, presser, vacation, and products using nanotechnology in pack Refer to created food. Post-harvest Nanotechnology is huge in the food processor and has power. It gets the life of the food character, taste, texture, and consistency Enhances, or unpleasant taste or covers the door, and particle size, size distribution, On Nanotechnologies in Food Science, this detailed review is for future work for Non-food Research and Development and Describes recent trends and future perspectives. Food and beverage, Beverage, Food, Health drink, Food contact material, and Food storage are also reviewed. Nanotechnology is being to detect harmful components in fo Nanosensorssors and Smart Packaging is Very fast and sensitive to food contamination in systems Helping in identification. Nanoencapsulation is very significant in food science Technology, especially biocomposites and flavors. The additive ratio assessment ARAS method for complex decision problems Tries to simplify and for alternatives that can reflect the difference between by the corresponding indicator (degree of use). Selects the "best" alternative. Nutra Leaseanola Active Oil, Nanotea, Tip Top bread, Nanoceuticals Slim Shake, Nano silver cutting board, Fresher Longer TM, and Evaluation Parameters Food and beverage, Beverage, Food, Health drink, Food contact material, Food storage are also reviewed. Result: Fresher Longer TM is showing the highest value of rank whereas Nano tea is showing the lowest value. Nanotechnologies in Food Science using the analysis of Addition Ratio Assessment (ARAS) Fresher Longer TM is showing the highest value of rank whereas Nano tea is showing the lowest value. Materials science ; Nanotechnology ; Environmental science ; Computer science ; Engineering physics ; Engineering Spectroscopy and Chemometric Analyses
Authors: Nils Philipp Walter | Linara Adilova | Jilles Vreeken | Michael Kamp
Source: arXiv (Cornell University) (preprint, 2024-05-27)
Field: Materials Science — Materials Chemistry

The Uncanny Valley: Exploring Adversarial Robustness from a Flatness Perspective Flatness of the loss surface not only correlates positively with generalization, but is also related to adversarial robustness since perturbations of inputs relate non-linearly to perturbations of weights. In this paper, we empirically analyze the relation between adversarial examples and relative flatness with respect to the parameters of one layer. We observe a peculiar property of adversarial examples in the context of relative flatness: during an iterative first-order white-box attack, the flatness of the loss surface measured around the adversarial example first becomes sharper until the label is flipped, but if we keep the attack running, it runs into a flat uncanny valley where the label remains flipped. In extensive experiments, we observe this phenomenon across various model architectures and datasets, even for adversarially trained models. Our results also extend to large language models (LLMs), but due to the discrete nature of the input space and comparatively weak attacks, adversarial examples rarely reach truly flat regions. Most importantly, this phenomenon shows that flatness alone cannot explain adversarial robustness unless we can also guarantee the behavior of the function around the examples. We, therefore theoretically connect relative flatness to adversarial robustness by bounding the third derivative of the loss surface, underlining the need for flatness in combination with a low global Lipschitz constant for a robust model. Adversarial system ; Robustness (evolution) ; Flatness (cosmology) ; Uncanny ; Perspective (graphical) ; Computer science ; Artificial intelligence ; Psychology ; Geology ; Psychoanalysis ; Physics High-Velocity Impact and Material Behavior ; Bacillus and Francisella bacterial research ; Energetic Materials and Combustion
Authors: Stéfano Corrêa | Luiz Carlos Lobato Dos Santos | George Simonelli
Source: Concilium (review, 2024-04-12)
Field: Chemistry — Analytical Chemistry

Prediction of wax disappearance temperature: a review Wax deposition in pipelines is a recurring problem in the oil industry. Therefore, several studies have analyzed the phenomenon and predicted the wax disappearance temperature (WDT). This variable represents the exact solid-liquid equilibrium point. Such information is an effective aid in decision-making regarding pipelines and production facilities. However, the study of paraffin deposition is highly dependent on conducting experiments, which are typically costly and can make this type of analysis impractical. The increasing progress of models based on machine learning techniques has been an alternative to experimental and thermodynamic methods to predict this phenomenon. In the present study, a bibliographic review of the main authors on models for predicting kerosene deposition has been compiled. Wax ; Deposition (geology) ; Pipeline transport ; Kerosene ; Petroleum engineering ; Computer science ; Oil production ; Point (geometry) ; Biochemical engineering ; Process engineering ; Environmental science ; Thermodynamics ; Chemistry ; Engineering ; Mathematics ; Physics ; Geology ; Environmental engineering ; Organic chemistry Petroleum Processing and Analysis ; Oil, Gas, and Environmental Issues
Authors: Yoga A Tandanu | Dionysius J D H Santjojo | Masruroh Masruroh
Source: Indonesian Physical Review (article, 2024-01-29)
Field: Materials Science — Biomaterials

ANALYSIS OF HYDROXYAPATITE/GELATINE COMPOSITION ON THE FILAMENT FORMATION USING PISTON EXTRUSION METHOD HA/Gelatine bio composite is the main material in making scaffolds that have the advantage of biocompatibility and high biodegradability. One method of making scaffolds is using piston extrusion 3D printing technology, which is compatible with several types of materials, especially HA/gelatine biocomposites. This study aims to determine the effect of HA/gelatine bio composite composition on the filament formation process which is influenced by the rheological properties of the material. The filament extrusion process is influenced by rheological properties in the form of viscosity. The synthesized HA material was then dissolved with gelatin in the ratio of 1:2, 1:3, 1:4, 1:5, and 1:6 homogeneously. After that, viscosity measurements were made on each variation of HA/gelatine composition with a viscometer. The biocomposite solution that has been mixed homogeneously is then extracted until it comes out of the nozzle. Meanwhile, the viscosity of the HA/gelatine bio composite solution when given piston pressure can be known through the calculation process. The viscosity test results show that there is a change in the viscosity of the solution. This is caused by the shear-thickening phenomenon due to the application of pressure on the fluid. Based on the experimental results, the extrusion results still do not form filaments, which indicates that the rheological properties of the HA/gelatine bio composite solution are still too liquid so other material modifications are needed. The extrusion speed of 0.42 mm/s used in this study is too fast for the HA/gelatin material solution, so it has not formed optimal filaments. Extrusion ; Piston (optics) ; Protein filament ; Composition (language) ; Materials science ; Composite material ; Chemical engineering ; Engineering ; Art ; Physics ; Optics biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Bone Tissue Engineering Materials
Authors: Sevda Türk | Didem Akkaya | Burak Kırılmaz | Ezgi Merve ÖNAL | Burak Barut
Source: Journal of Research in Pharmacy (article, 2024-01-01)
Field: Chemistry — Organic Chemistry

Synthesis, characterization and biological activity potential of some novel thiourea derivatives Within the scope of this study, a new series of thiourea derivatives were synthesized by refluxing different anthranilic acid derivatives with various isothiocyanates in dry acetone medium. The synthesized compounds were purified by crystallization and their purity was determined by TLC method. The structures of the obtained compounds were elucidated by using different spectroscopic methods such as IR, 1H-NMR, 13C-NMR, besides elemental analysis. The inhibitory effects of the compounds against DPPH radical scavenging, α-glucosidase and acetylcholinesterase were investigated by using spectrophotometric method. The results showed that the compounds had moderate radical scavenging activity according to gallic acid (92.25 ± 0.14% at 100 µM) which was used as a reference compound. Compound 5 in the presence of trifluoromethyl group demonstrated the highest α-glucosidase inhibitory effect with 52.26 ± 2.35% at 100 µM. On the other hand, the compounds demonstrated low AChE inhibitory effects compared to galantamine (80.33 ± 0.77% at 100 µM) which was used as a reference compound. Thiourea ; Characterization (materials science) ; Chemistry ; Combinatorial chemistry ; Nanotechnology ; Organic chemistry ; Materials science Synthesis and biological activity ; Synthesis and Characterization of Heterocyclic Compounds ; Synthesis and Reactivity of Sulfur-Containing Compounds
Authors: Zhiqiang Pang | Hong Wang | Qi Xie | Deyu Meng | Zongben Xu
Source: arXiv (Cornell University) (preprint, 2024-03-15)
Field: Materials Science — Surfaces, Coatings and Films

TRG-Net: An Interpretable and Controllable Rain Generator Exploring and modeling rain generation mechanism is critical for augmenting paired data to ease training of rainy image processing models. Against this task, this study proposes a novel deep learning based rain generator, which fully takes the physical generation mechanism underlying rains into consideration and well encodes the learning of the fundamental rain factors (i.e., shape, orientation, length, width and sparsity) explicitly into the deep network. Its significance lies in that the generator not only elaborately design essential elements of the rain to simulate expected rains, like conventional artificial strategies, but also finely adapt to complicated and diverse practical rainy images, like deep learning methods. By rationally adopting filter parameterization technique, we first time achieve a deep network that is finely controllable with respect to rain factors and able to learn the distribution of these factors purely from data. Our unpaired generation experiments demonstrate that the rain generated by the proposed rain generator is not only of higher quality, but also more effective for deraining and downstream tasks compared to current state-of-the-art rain generation methods. Besides, the paired data augmentation experiments, including both in-distribution and out-of-distribution (OOD), further validate the diversity of samples generated by our model for in-distribution deraining and OOD generalization tasks. Net (polyhedron) ; Generator (circuit theory) ; Environmental science ; Computer science ; Mathematics ; Physics ; Geometry ; Power (physics) Surface Modification and Superhydrophobicity
Source: Letters in Applied NanoBioScience (article, 2024-06-30)
Field: Chemistry — Analytical Chemistry

Thermodynamic Properties of Argan as an Environmentally Friendly Adsorbent of Methylene Blue from Wastewaters This paper aims explicitly to eliminate the cationic dye methylene blue (MB) in aqueous solutions using crushed Argan shells. Raw materials were first characterized by various techniques (FTIR, thermogravimetric analysis, elemental analysis, biochemical analysis, SEM, mercury porosimetry, and xerometer). Within this work, the experimental parameters vary with the concentration of MB. We studied the adsorption kinetics in the solution, i.e., pseudo-second-order (P2edO) followed by first-order (P1stO). P2edO best defines the adsorption kinetics of empirical materials with a correlation coefficient of R2 = 0.9999. Survey equilibrium data were analyzed using the linearized forms of ‘Langmuir isotherm’ and ‘Freundlich isotherm’. The ‘Langmuir isotherm’ provides the best ‘theoretical correlation’ between the facts investigated. Thermodynamic parameters were calculated using equilibrium constants obtained at different temperatures. Adsorption thermodynamics indicated an endothermic and spontaneous process. We found that MB adsorption by the biosorbents increased with increasing temperature. To better understand the underlying reaction mechanism, mathematical models were utilized to analyze the obtained results. Consequently, the adsorption rate constants are identified using simple, empirical models tailored to the adsorption of organic compounds on different adsorbents; data is presented on the rate constants for both the P1stO and P2edO models. Freundlich equation ; Adsorption ; Endothermic process ; Langmuir ; Thermogravimetric analysis ; Langmuir adsorption model ; Methylene blue ; Thermodynamics ; Chemistry ; Aqueous solution ; Equilibrium constant ; Materials science ; Organic chemistry ; Physical chemistry Dye analysis and toxicity
Authors: Patric Müller | Achim Sack | Jens Dümler | Michael Heckel | Tim Wenzel | Teresa Siegert | Sonja Schuldt-Lieb | Henning Gieseler | Thorsten Pöschel
Source: arXiv (Cornell University) (preprint, 2024-04-18)
Field: Materials Science — Materials Chemistry

Automated tomographic assessment of structural defects of freeze-dried pharmaceuticals The topology and surface characteristics of lyophilisates significantly impact the stability and reconstitutability of freeze-dried pharmaceuticals. Consequently, visual quality control of the product is imperative. However, this procedure is not only time-consuming and labor-intensive but also expensive and prone to errors. In this paper, we present an approach for fully automated, non-destructive inspection of freeze-dried pharmaceuticals, leveraging robotics, computed tomography, and machine learning. Computed tomographic ; Biomedical engineering ; Computer science ; Computed tomography ; Medicine ; Radiology Crystallization and Solubility Studies
Authors: Solikhun | Tri Yunita
Source: Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) (article, 2024-08-07)
Field: Chemistry — Analytical Chemistry

A Quantum Perceptron: A New Approach for Predicting Rice Prices at the Indonesian Wholesale Trade Level The wholesale rice trade in Indonesia encounters various challenges in forecasting prices. These challenges are influenced by factors such as weather, government policies, global market conditions, and other economic variables. Accurate price predictions are crucial for informing government policy in a timely manner. This research introduces a new approach that utilizes the Quantum Perceptron algorithm to forecast rice prices. The algorithm, an innovative method in quantum computing, is expected to enhance the efficiency and effectiveness of price predictions. Although the research is still in the analytical stage, the use of Quantum Perceptron shows promise in dynamically addressing the complexity of market data and the variability of factors affecting rice prices. The method focuses on developing models that can leverage quantum computing to process information more effectively than classical methods. By harnessing the unique properties of quantum mechanics, such as superposition and entanglement, Quantum Perceptron can identify complex patterns and optimize predictions of future rice prices. The research describes the implementation of quantum algorithms in the context of the Indonesian rice wholesale market, including the technical challenges encountered and future development prospects. The research utilizes quantum computing along with the perceptron algorithm. The researchers focused on analyzing the quantum perceptron algorithm because of the limited availability of quantum computing devices. The findings of this research are confined to analysis. In order to advance this research, the author recommends that future studies employ quantum devices to achieve more accurate predictions Indonesian ; Multilayer perceptron ; Quantum ; Agricultural economics ; Economics ; Business ; Computer science ; Artificial neural network ; Artificial intelligence ; Physics Spectroscopy and Chemometric Analyses ; Market Dynamics and Volatility ; Stock Market Forecasting Methods
Authors: Arafater Rahman | Mohammad Abu Hasan Khondoker
Source: Discover Applied Sciences (article, 2024-11-14)
Field: Materials Science — Polymers and Plastics

Structural analysis and fatigue prediction of harrow tines used in Canadian prairies The online version contains supplementary material available at 10.1007/s42452-024-06310-5. Harrow ; Geography ; Archaeology Textile materials and evaluations ; Wood Treatment and Properties ; Tree Root and Stability Studies