jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Aimee Sheehan | Tatsiana Mikulchyk | Catherine S. P. De Castro | Safakath Karuthedath | Wejdan Althobaiti | Metodej Dvoracek | Sabad-e-Gul | Hugh J. Byrne | Frédéric Laquai | Izabela Naydenova | Mikhail A. Filatov
Source: Journal of Materials Chemistry C (article, 2023-01-01)
Field: Materials Science — Materials Chemistry

Diethoxycarbonyl-BODIPYs as heavy-atom-free photosensitizers for holographic recording in cellulose acetate photopolymer Heavy-atom-free diethoxycarbonyl-BODIPY undergo SOCT-ISC and enable photopolymerization of acrylamide monomers inside a cellulose acetate matrix that was applied for holographic recording of diffractive structures with high efficiency. Photopolymer ; Materials science ; Holography ; Cellulose acetate ; Atom (system on chip) ; Photochemistry ; Cellulose ; Optoelectronics ; Optics ; Polymer ; Organic chemistry ; Chemistry ; Composite material ; Monomer ; Physics Luminescence and Fluorescent Materials ; Photorefractive and Nonlinear Optics ; Liquid Crystal Research Advancements
Authors: Qiang Liu | Siyuan Zhuang | Sainan Li | Yuyu Nong | Yutong Zhang | Jiaqi Liang | Yuchi Zhang | Chunming Liu
Source: Phytochemical Analysis (article, 2023-10-05)
Field: Chemistry — Analytical Chemistry

Rapid screening, isolation, and activity evaluation of potential xanthine oxidase inhibitors in <i>Polyporus umbellatus</i> and mechanism of action in the treatment of gout This study provides new ideas for the search for natural XODIs active ingredients, and the study provide valuable support for the further development of functional foods with potential therapeutic benefits. Chemistry ; Xanthine oxidase ; Chromatography ; Biochemistry ; Enzyme Chromatography in Natural Products ; Fungal Biology and Applications ; Medicinal plant effects and applications
Authors: Bernardo Ballotta | Tainah Dorina Marforio | Sergio Rampino | Emilio Martínez‐Núñez | Vincenzo Barone | Mattia Melosso | Andréa Bottoni | Luca Dore
Source: ACS Earth and Space Chemistry (article, 2023-04-12)
Field: Chemistry — Spectroscopy

Toward the Detection of Cyanoketene in the Interstellar Medium: New Hints from Quantum Chemistry and Rotational Spectroscopy A detailed quantum chemical investigation of a new reaction mechanism possibly leading to the formation of cyanoketene (NC–CH═C═O) in the interstellar medium (ISM) was carried out. Different reaction channels have been found by the AutoMeKin program, and the structures and harmonic force fields of the key stationary points have been characterized at the density functional theory level employing last-generation double-hybrid functionals. Finally, single-point computations at those geometries by state-of-the-art composite wave function methods provided accurate energies for the evaluation of thermochemical and kinetic parameters in the framework of an Ab Initio Transition State Theory based Master Equation (AITSTME) strategy. Our results indicate that the barrier-less association reaction of the formyl radical (HCO•) to the cyanocarbene radical (HCCN) can lead to the formation of cyanoketene under the harsh conditions of the ISM. Canonical rate constants computed for temperatures up to 600 K show that the most abundant product is indeed cyanoketene. The formation of other, even more stable, species involves higher activation energies and/or less favorable multi-step processes. Furthermore, to aid the search of cyanoketene, still undetected in the ISM, its rotational spectrum was recorded up to 530 GHz. The refined set of spectroscopic constants obtained in this way allows for spectral predictions from the microwave to the terahertz region, particularly for the bright b-type transitions, which can be targeted for the identification of cyanoketene in spectral line surveys. Despite cyanoketene was already sought without success in a variety of astronomical sources, we suggest to look for it in those sources where HCO or HCCN have already been detected, namely, W3, NGC2024, W51, K3-50, IRC + 2016, and TMC-1. Interstellar medium ; Density functional theory ; Ab initio ; Basis set ; Spectroscopy ; Reaction rate constant ; Rotational spectroscopy ; Physics ; Chemistry ; Computational chemistry ; Microwave ; Atomic physics ; Quantum mechanics ; Kinetics Molecular Spectroscopy and Structure ; Atmospheric Ozone and Climate ; Advanced Chemical Physics Studies
Authors: Zehra Alakoç Burma
Source: International Journal of Nature and Life Sciences (article, 2023-12-26)
Field: Chemistry — Analytical Chemistry

Digital Transformation in Beekeeping to Carrying Beehives into the Future Today, dynamic elements such as producer/consumer supply, demand, presentation, marketing and transportation transformed by technology; It has initiated a rapid change and transformation process in beekeeping, food production and agriculture sectors. Beekeeping activities; Today, it faces challenges such as climate change, disease threats, loss of habitat and bee colonies, and pesticides. It has become necessary to switch to digital transformation for the future, sustainability, efficiency, competitiveness of beekeeping and the general health of the agricultural sector. The digital transformation movement in beekeeping is a movement to bring together traditional beekeeping with modern technologies. This article explores the importance and impact of digital transformation in the beekeeping industry from the perspective of the management information systems (MIS) field. It is aimed to discuss how the transformation can be supported in terms of management information systems and what kind of road map should be followed for future studies. Digital beekeeping; It improves the productivity of bees, monitors the health of bee colonies and optimizes decision-making processes. It also creates positive effects on plant diversity in the agricultural sector and world ecosystem health. The basis of this transformation is industry 4.0, such as IoT, sensors, big data, data management, data analysis, data mining, data security, artificial intelligence and cloud computing. These technologies can be used for disease monitoring, early diagnosis, pollination and plant flowering monitoring. It is thought that this transformation will have positive effects on both beekeepers and environmental factors and will make significant contributions to progress in this field and a more sustainable future. Beekeeping ; Business ; Sustainability ; Digital transformation ; Agriculture ; Environmental planning ; Environmental resource management ; Computer science ; Geography ; Ecology ; World Wide Web ; Economics ; Biology Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; IoT and Edge/Fog Computing
Authors: Miguel Macías Macías | Héctor Sánchez | Carlos J. García–Orellana | Horacio M. González–Velasco | Ramón Gallardo–Caballero | Antonio García‐Manso
Source: Multimedia Tools and Applications (article, 2023-02-18)
Field: Chemistry — Analytical Chemistry

Mask R-CNN for quality control of table olives Abstract In this paper we propose an object detector based on deep learning for scanning samples of table olives. For the construction of the system we have used a Mask R-CNN neural network. This network is able to segment the image providing a mask for each of the olives in the sample from which we can obtain the calibre of the object. In addition, the system is able to measure the degree of ripeness of the olives classifying them as green, semi-ripe and ripe, and identifying those fruits that are defective due to disease or damage caused by the harvesting process. The proposed system achieves success rates of 99.8% in the detection of olive fruits in photograms, 93.5% in the classification of fruit by ripeness and close to 80% in the detection of defects. Ripeness ; Computer science ; Table (database) ; Artificial intelligence ; Object detection ; Sample (material) ; Object (grammar) ; Detector ; Computer vision ; Pattern recognition (psychology) ; Data mining ; Horticulture ; Ripening ; Telecommunications Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Qi Liu | Tingting Sun | Xiaojie Wen | Minghao Zeng | Jing Chen
Source: Plants (article, 2023-07-29)
Field: Chemistry — Analytical Chemistry

Detecting the Minimum Limit on Wheat Stripe Rust in the Latent Period Using Proximal Remote Sensing Coupled with Duplex Real-Time PCR and Machine Learning Wheat stripe rust (WSR) is an airborne disease that causes severe damage to wheat. The rapid and early detection of WSR is essential for the prevention and control of this disease. The minimum detection limit (MDL) is one of the most important characteristics of quantitative methods that can be used to determine the scope and applicability of a measurement technique. Three wheat cultivars were inoculated with <i>Puccinia striiformis</i> f.sp. <i>tritici</i> (<i>Pst</i>), and a spectrometer was used to collect the canopy hyperspectral data, and the <i>Pst</i> content was obtained via a duplex real-time polymerase chain reaction (PCR) during the latent period, respectively. The disease index (DI) and molecular disease index (MDI) were calculated. The regression tree algorithm was used to determine the MDL of the <i>Pst</i> based on hyperspectral feature parameters. The logistic, IBK, and random committee algorithms were used to construct the classification model based on the MDL. The results showed that when the MDL was 0.7, IBK had the best recognition accuracy. The optimal model, which used the spectral feature R_2nd.dv ((the second derivative of the original hyperspectral value)) and the modeling ratio 2:1, had an accuracy of 91.67% on the testing set and 90.67% on the 10-fold cross-validation. Thus, during the latent period, the MDL of <i>Pst</i> was determined using hyperspectral technology as 0.7. Hyperspectral imaging ; Mathematics ; Duplex (building) ; Pattern recognition (psychology) ; Detection limit ; Computer science ; Statistics ; Artificial intelligence ; Biological system ; Biology Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Mycotoxins in Agriculture and Food
Authors: Yanlei Li | Zihao Yang | Wenxiu Wang | Xiangwu Wang | Chengqi Zhang | Jun Dong | Mengyu Bai | Hui Teng
Source: Molecules (article, 2023-12-06)
Field: Chemistry — Analytical Chemistry

Research Progress of Rapid Non-Destructive Detection Technology in the Field of Apple Mold Heart Disease Apples are rich in vitamins and dietary fiber and are one of the essential fruits in people's daily diet. China has always been a big apple consumer, and with the improvement of people's life quality, nutrition, and health requirements, the demand for high-quality apples has increased year by year. Apple mold heart disease is one of the main diseases affecting apple quality. However, this disease cannot be easily detected from the surface, so it is difficult to detect mold heart disease. Therefore, this paper focuses on the analysis of seven non-destructive detection technologies, including near infrared spectroscopy technology, hyperspectral technology, Raman spectroscopy technology, electronic nose technology, acoustic technology, electrical technology, and magnetic technology, summarizes their application status in the detection of apple mold heart disease, and then analyzes their advantages and disadvantages. Combined with the current rapid development of artificial intelligence (AI) technology, this paper proposes the future development trends of using non-destructive technologies to detect apple mold heart disease. It is expected to provide basic theory and application references for the intelligent detection of apple mold heart disease. Mold ; Computer science ; Heart disease ; Quality (philosophy) ; Medicine ; Materials science ; Pathology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Binbin Lin | Setareh Medghalchi | Sandra Korte‐Kerzel | Bai‐Xiang Xu
Source: PAMM (article, 2023-03-01)
Field: Materials Science — Metals and Alloys

A Machine Learning Enabled Image‐data‐driven End‐to‐end Mechanical Field Predictor For Dual‐Phase Steel Abstract This contribution presents convolutional neural nets (CNN) based surrogate models for prediction of von Mises stress and equivalent plastic strain fields of commonly used Dual‐Phase (DP) steels in automotive applications. The models predict field quantities in an end‐to‐end manner, driven by segmented phase images from real experimental scanning electron micrographs as inputs and FEM calculations as outputs. Hereby, we train CNN models with the U‐net neural network structure based on around 900 elastoplastic FEM simulations of various DP steel microstructure samples under tensile test. The trained CNN models are validated and tested on 250 and 50 samples, respectively. Thereby CNN models are employed sequentially for different tasks , from the real micrographs to segmented phase maps, then from segmented phase maps to stress, strain field predictions, in an end‐to‐end manner. The field predictor model results show good agreement with the test data and convincing performance on unseen microstructural dataset. This work demonstrates the large potential of a Machine Learning model to make accumulatively use of the physics‐based simulation data of large number of boundary value problems with varying microstructure. It recaptures not only the physics, implied in each simulation training data obtained from the partial different governing equations of mechanics, but also the overarching correlation between the microstructure and the stress and strain field responses. Convolutional neural network ; Finite element method ; von Mises yield criterion ; Artificial intelligence ; Field (mathematics) ; Phase (matter) ; Artificial neural network ; Stress (linguistics) ; Test data ; Computer science ; Microstructure ; Materials science ; Structural engineering ; Engineering ; Mathematics ; Physics ; Composite material Hydrogen embrittlement and corrosion behaviors in metals ; Microstructure and Mechanical Properties of Steels ; Metallurgy and Material Forming
Authors: Mukesh Kumar Tripathi | Praveen Kumar Reddy | M. Neelakantappa | Chetan Vikram Andhare | Shivendra Shivendra
Source: Indonesian Journal of Electrical Engineering and Computer Science (article, 2023-07-30)
Field: Chemistry — Analytical Chemistry

Identification of mango variety using near infrared spectroscopy &lt;p&gt;The structure of the proposed framework is separated into three stages: i) foundation deduction, ii) component extraction, and iii) preparing and characterization. At first, K-implies grouping methods were carried out for foundation de- duction. The second step applies color, texture, and shape-based feature extraction methods. Finally, a “merging” fusion feature is analyzed with a C4.5, support vector machine (SVM), and K-nearest neighbors (KNN). Overall, the recognition system produces an adequate performance accuracy with 97.89, 94.60, and 90.25 percent values by utilizing C4.5, SVM, and KNN, respectively. The experimentation points out that the proposed fusion scheme can significantly support accurately recognizing various fruits and vegetables.&lt;/p&gt; Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Feature extraction ; Fusion ; Texture (cosmology) ; Identification (biology) ; Feature (linguistics) ; k-nearest neighbors algorithm ; Computer science ; Mathematics ; Image (mathematics) ; Botany ; Biology Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Cheng Giuseppe Chen | Mauro Giustini | Marco D’Abramo | Andrea Amadei
Source: Journal of Chemical Theory and Computation (article, 2023-06-17)
Field: Chemistry — Physical and Theoretical Chemistry

Unveiling the Excited State Dynamics of Indole in Solution In this paper, we reconstruct in detail the dynamics of the emitting electronic excited state of aqueous indole, investigating its relaxation mechanism and kinetics to be related to the time-dependent fluorescence signal. Taking advantage of the results shown in a very recent paper, we were able to model the relaxation process in solution in terms of the transitions between two gas-phase singlet electronic states (<sup>1</sup>L<sub>a</sub> and <sup>1</sup>L<sub>b</sub>), subsequently irreversibly relaxing to the gas-phase singlet dark state (<sup>1</sup>πσ*). A comparison of the results with the available experimental data shows that the relaxation mechanism we obtain by our theoretical-computational model is reliable, reproducing rather accurately all the experimental observables. Relaxation (psychology) ; Observable ; Excited state ; Singlet state ; Chemical physics ; Indole test ; Computer science ; Dynamics (music) ; Phase (matter) ; Mechanism (biology) ; Kinetics ; Gas phase ; Biological system ; Statistical physics ; Chemistry ; Physics ; Atomic physics ; Physical chemistry ; Quantum mechanics ; Neuroscience Photochemistry and Electron Transfer Studies ; Spectroscopy and Quantum Chemical Studies ; Molecular Junctions and Nanostructures
Authors: Yihan Wang | Shunxiang Li | Kun Qian
Source: Nanoscale Advances (review, 2023-01-01)
Field: Chemistry — Spectroscopy

Nanoparticle-based applications by atmospheric pressure matrix assisted desorption/ionization mass spectrometry Recently, the development of atmospheric pressure matrix assisted desorption/ionization mass spectrometry (AP MALDI MS) has made contributions not only to biomolecule analysis but also to spatial distribution. This has positioned AP MALDI as a powerful tool in multiple domains, thanks to its comprehensive advantages compared to conventional MALDI MS. These developments have addressed challenges associated with previous AP MALDI analysis systems, such as optimization of apparatus settings, synthesis of novel matrices, preconcentration and isolation strategies before analysis. Herein, applications in different fields using AP MALDI MS were described, including peptide and protein analysis, metabolite analysis, pharmaceutical analysis, and mass spectrometry imaging. Mass spectrometry ; Desorption electrospray ionization ; Desorption ; Atmospheric pressure ; Field desorption ; Ionization ; Soft laser desorption ; Nanoparticle ; Matrix (chemical analysis) ; Chemistry ; Analytical Chemistry (journal) ; Materials science ; Nanotechnology ; Matrix-assisted laser desorption/ionization ; Environmental chemistry ; Chromatography ; Thermal ionization mass spectrometry ; Physics ; Physical chemistry ; Organic chemistry ; Ion Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Proteomics Techniques and Applications
Authors: Noman Mujeeb Khan | Abbas Ahmed | Syed Kamran Haider | Muhammad Hamza Zafar | Majad Mansoor | Naureen Akhtar
Source: Electronics (article, 2023-04-03)
Field: Materials Science — Materials Chemistry

Hybrid General Regression NN Model for Efficient Operation of Centralized TEG System under Non-Uniform Thermal Gradients The global energy demand, along with the proportionate share of renewable energy, is increasing rapidly. Renewables such as thermoelectric generators (TEG) systems have lower power ratings but a highly durable and cost-effective renewable energy technology that can deal with waste heat energy. The main issues associated with TEG systems are related to their vigorous operating conditions. The dynamic temperature gradient across TEG surfaces induces non-uniform temperature distribution (NUTD) that significantly impacts the available output electrical energy. The mismatching current impact may lower the energy yield by up to 70% of extractable thermal energy. As a solution, a hybrid general regression neural network (GRNN) orca predation algorithm (OPA) is proposed; backpropagation limitations are minimized by utilizing the stochastic optimization algorithm named OPA. The conclusions are evaluated and contrasted with highly improved versions of the conventional particle swarm optimization (PSO), grey wolf optimizer (GWO), and Harris hawk optimization (HHO). A detailed analytical and statistical analysis is carried out through five distinct case studies, including field stochastic data study, NUTD, varying temperature, and load studies. Along with statistical matrix errors such as MAE, RMSE, and RE, the results are assessed in terms of efficiency, tracking, and settling time. The results show that superior performance is achieved by the proposed GRNN-OPA based MPPT by 35% faster tracking, and up to 90–110% quicker settling time which, in turn, enables the 4–8% higher energy accumulation over a longer period of operation. A low-cost experimental setup is devised to further validate the practicality of the proposed techniques. From such comprehensive analysis, it can be safely concluded that the proposed GRNN-OPA successfully undertakes the drawbacks of existing classical MPPT methods with higher efficiency. Renewable energy ; Particle swarm optimization ; Settling time ; Computer science ; Artificial neural network ; Mathematical optimization ; Photovoltaic system ; Process engineering ; Algorithm ; Mathematics ; Engineering ; Artificial intelligence ; Control engineering ; Electrical engineering Advanced Thermoelectric Materials and Devices ; Solar Radiation and Photovoltaics ; Machine Learning and ELM
Authors: Luis A. Aldama | Kevin M. Dalton | Doeke R. Hekstra
Source: Acta Crystallographica Section D Structural Biology (article, 2023-07-20)
Field: Materials Science — Materials Chemistry

Correcting systematic errors in diffraction data with modern scaling algorithms X-ray diffraction enables the routine determination of the atomic structure of materials. Key to its success are data-processing algorithms that allow experimenters to determine the electron density of a sample from its diffraction pattern. Scaling, the estimation and correction of systematic errors in diffraction intensities, is an essential step in this process. These errors arise from sample heterogeneity, radiation damage, instrument limitations and other aspects of the experiment. New X-ray sources and sample-delivery methods, along with new experiments focused on changes in structure as a function of perturbations, have led to new demands on scaling algorithms. Classically, scaling algorithms use least-squares optimization to fit a model of common error sources to the observed diffraction intensities to force these intensities onto the same empirical scale. Recently, an alternative approach has been demonstrated which uses a Bayesian optimization method, variational inference, to simultaneously infer merged data along with corrections, or scale factors, for the systematic errors. Owing to its flexibility, this approach proves to be advantageous in certain scenarios. This perspective briefly reviews the history of scaling algorithms and contrasts them with variational inference. Finally, appropriate use cases are identified for the first such algorithm, Careless, guidance is offered on its use and some speculations are made about future variational scaling methods. Algorithm ; Scaling ; Computer science ; Non-sampling error ; Mathematics ; Geometry X-ray Diffraction in Crystallography ; Enzyme Structure and Function ; Advanced X-ray Imaging Techniques