jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Fatima Omeis | Zahia Boubegtiten-Fezoua | Ana Filipa Santos Seiça | Romain Bernard | Muhammad Haseeb Iqbal | Nicolas Javahiraly | Robrecht M. A. Vergauwe | Hicham Majjad | Fouzia Boulmedais | David A. Moss | Petra Hellwig
Source: Molecules (article, 2021-12-23)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Plasmonic Resonant Nanoantennas Induce Changes in the Shape and the Intensity of Infrared Spectra of Phospholipids Surface enhanced infrared absorption spectroscopic studies (SEIRAS) as a technique to study biological molecules in extremely low concentrations is greatly evolving. In order to use the technique for identification of the structure and interactions of such biological molecules, it is necessary to identify the effects of the plasmonic electric-field enhancement on the spectral signature. In this study the spectral properties of 1,2-Dipalmitoyl-sn-glycero-3 phosphothioethanol (DPPTE) phospholipid immobilized on gold nanoantennas, specifically designed to enhance the vibrational fingerprints of lipid molecules were studied. An AFM study demonstrates an organization of the DPPTE phospholipid in bilayers on the nanoantenna structure. The spectral data were compared to SEIRAS active gold surfaces based on nanoparticles, plain gold and plain substrate (Si) for different temperatures. The shape of the infrared signals, the peak positions and their relative intensities were found to be sensitive to the type of surface and the presence of an enhancement. The strongest shifts in position and intensity were seen for the nanoantennas, and a smaller effect was seen for the DPPTE immobilized on gold nanoparticles. This information is crucial for interpretation of data obtained for biological molecules measured on such structures, for future application in nanodevices for biologically or medically relevant samples. Plasmon ; Infrared ; Infrared spectroscopy ; Molecule ; Nanoparticle ; Colloidal gold ; Materials science ; Absorption (acoustics) ; Spectral signature ; Spectroscopy ; Chemistry ; Nanotechnology ; Analytical Chemistry (journal) ; Chemical physics ; Optoelectronics ; Optics ; Organic chemistry ; Physics Gold and Silver Nanoparticles Synthesis and Applications ; Lipid Membrane Structure and Behavior ; Plasmonic and Surface Plasmon Research
Authors: Julio Nogales‐Bueno | Francisco J. Rodríguez‐Pulido | Berta Baca‐Bocanegra | Dolores Pérez‐Marín | Francisco J. Heredia | Ana Garrido‐Varo | José Miguel Hernández‐Hierro
Source: Foods (article, 2021-01-23)
Field: Chemistry — Analytical Chemistry

Reduction of the Number of Samples for Cost-Effective Hyperspectral Grape Quality Predictive Models Developing chemometric models from near-infrared (NIR) spectra requires the use of a representative calibration set of the entire population. Therefore, generally, the calibration procedure requires a large number of resources. For that reason, there is a great interest in identifying the most spectrally representative samples within a large population set. In this study, principal component and hierarchical clustering analyses have been compared for their ability to provide different representative calibration sets. The calibration sets generated have been used to control the technological maturity of grapes and total phenolic compounds of grape skins in red and white cultivars. Finally, the accuracy and precision of the models obtained with these calibration sets resulted from the application of the selection algorithms studied have been compared with each other and with the whole set of samples using an external validation set. Most of the standard errors of prediction (SEP) in external validation obtained from the reduced data sets were not significantly different from those obtained using the whole data set. Moreover, sample subsets resulting from hierarchical clustering analysis appear to produce slightly better results. Principal component analysis ; Calibration ; Hyperspectral imaging ; Cluster analysis ; Set (abstract data type) ; Data set ; Hierarchical clustering ; Mathematics ; Population ; Selection (genetic algorithm) ; Statistics ; Computer science ; Pattern recognition (psychology) ; Data mining ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Horticultural and Viticultural Research ; Fermentation and Sensory Analysis
Authors: Shibang Ma
Source: Journal of Engineering Science and Technology Review (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Nondestructive Determination of Kiwifruit SSC using Visible/Near-Infrared Spectroscopy with Genetic Algorithm Soluble solid content (SSC) is one of the most important internal quality attributes that affect the quality and price of fresh fruit. Spectral technology has been widely used to detect the attributes of agricultural products. However, the accuracy and stability of a prediction model are the main factors that affect its practical application. Visible (Vis)-nearinfrared (NIR) spectroscopy combined with genetic algorithm (GA) was used to realize the rapid and nondestructive detection of kiwifruit SSC and improve the accuracy and stability of prediction model. The laboratory Vis-NIR spectroscopy system was built to collect the reflectance spectra of 120 kiwifruit samples at 400-2200 nm. Reflectance spectra were obtained after different pretreatments, namely, multiplicative scatter correction (MSC) and its combination with Savitzky-Golay smoothing (SG) method. The prediction models of multiple linear regression (MLR), partial least squares regression (PLSR), and least squares support vector machine (LS-SVM) were constructed for the prediction of kiwifruit SSC using the full-spectrum and effective wavelengths selected by GA. Results demonstrate that MSC+SG is the best pretreatment, the performances of the models established using the effective wavelengths selected by GA are better than those of the full-spectrum models, and the best performance is achieved by the GA-LS-SVM model, which is able to predict kiwifruit SSC with a correlation coefficient of prediction of 0.973 and a standard error of prediction of 0.362. The prediction accuracy is improved. This study demonstrates that the GA-LS-SVM model built using Vis-NIR spectroscopy with GA can rapidly and nondestructively determine kiwifruit SSC. The proposed algorithm provides evidence for the further development of a device for the nondestructive and rapid determination of kiwifruit SSC. Spectroscopy ; Infrared ; Algorithm ; Infrared spectroscopy ; Nondestructive testing ; Materials science ; Computer science ; Optics ; Physics ; Astronomy Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Zhen Peng | Charles A. Ross | Qi Jian Lim | Gabriele Gradoni
Source:  (preprint, 2021-05-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Engineering Reflective Metasurfaces with Ising Hamiltonian and Quantum Annealing &lt;div&gt;&lt;div&gt;&lt;div&gt;&lt;p&gt;We present a novel and flexible method to optimize the phase response of reflective metasurfaces towards a desired scattering profile. The scattering power is expressed as a spin-chain Hamiltonian using the radar cross section formalism. For metasurfaces reflecting an oblique plane wave, an Ising Hamiltonian is obtained. Thereby, the problem of achieving the scattering profile is recast into finding the ground-state solution of the associated Ising Hamiltonian. To rapidly explore the configuration states, we encode the Ising coefficients with quantum annealing algorithms, taking advantage of the fact that the adiabatic evolution efficiently performs energy minimization in the Ising model. Finally, the optimization problem is solved on the D-Wave 2048-qubit quantum adiabatic optimizer machine for binary phase as well as quadriphase reflective metasurfaces. Even though the work is focused on the phase modulation of metasurfaces, we believe this approach paves the way to fast optimization of reconfigurable intelligent surfaces that are mod- ulated in both amplitude and phase for multi-beam generation in and beyond 5G/6G mobile networks.&lt;/p&gt;&lt;/div&gt;&lt;/div&gt;&lt;/div&gt; Ising model ; Hamiltonian (control theory) ; Adiabatic process ; Physics ; Adiabatic quantum computation ; Scattering ; Quantum ; Qubit ; Geometric phase ; Quantum mechanics ; Quantum computer ; Mathematics ; Mathematical optimization Metamaterials and Metasurfaces Applications ; Advanced Wireless Communication Technologies ; Satellite Communication Systems
Authors: Yuhao Zhang | Xingyu Xiong | Xin Wu | Zhonghui Song | Zhenzhong Xue
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Optimization of SOFC stack gas distribution structure based on BP Neural network and CFD The flow field distribution of solid oxide fuel cells significantly affects the performance of the stack. The flow uniformity can be improved and the power generation efficiency can be improved by optimizing the gas distribution structure of the stack. Based on the simplified 6kW stack model, the stack gas distribution structure with two-stage buffer cavity was designed, and the stack model was numerically simulated by ANSYS Fluent software. The BP neural network model, which can predict the uniformity of the outlet of the integrated stack, is established successfully. The parameters of the gas distribution structure are analyzed and optimized by using the orthogonal test and BP neural network. The results show that at the same time considering pile distribution structure under the condition of surface area and uniformity, when the first stage inlet buffer chamber depth is 40 mm, the channel width is 40 mm, the secondary inlet buffer chamber depth is 80 mm, can effectively reduce the electric pile distribution structure, surface area, to reduce heat loss, at the same time guarantee the integrated electric reactor outlet flow uniformity of more than 96%, greatly improves the efficiency of power generation. Stack (abstract data type) ; Inlet ; Fluent ; Computational fluid dynamics ; Power (physics) ; Artificial neural network ; Flow (mathematics) ; Airflow ; Materials science ; Nuclear engineering ; Mechanics ; Computer simulation ; Engineering ; Mechanical engineering ; Simulation ; Computer science ; Physics ; Thermodynamics Advancements in Solid Oxide Fuel Cells ; Fuel Cells and Related Materials ; Catalytic Processes in Materials Science
Authors: Víctor Rodrigo Cerda Mejía | Octavio Edelberto Guijarro-Rubio | Isnel Benítez Cortés | Galo Cerda-Mejía | Estela Guardado Yordi | Bernabé Ortega-Tenezaca | Erenio Gónzalez Suárez | Amaury ̈Pérez Martínez
Source: Lámpsakos (article, 2021-07-12)
Field: Chemistry — Analytical Chemistry

Remote monitoring of operational parameters for the cane honey production process A local data acquisition, processing and storage system were implemented by means of an open source micro-controlled development card. To continuously measure the variables of the cane honey production process, it was performed using five temperature sensors, pH sensor and a level sensor. Each sensor was associated with a component set with its respective upper and lower reference values, during the processing of a batch of product. The main objective was to determine the effectiveness of using sensors and wireless communication technology to monitor operational parameters, in real time. In general, the recorded temperature, pH and level data obtained from sensors corresponded closely to the changes that occurred in the process, and the wireless communication nodes developed successfully measured and monitored the temperature, pH and level readings in real time. The study also found that the temperature, pH and level readings obtained by the sensors began to standardize more closely within the upper and lower limits. Furthermore, operational parameters could be reasonably predicted by applying a statistical model to measure temperature, pH and level. The present study also found that the operational parameters analyzed showed variability with its consequent effect on the quality of the final product. The findings of this study should serve as the first step towards any future research and development that may take place in the field of agro-industrial process design Process (computing) ; Process engineering ; Wireless sensor network ; Measure (data warehouse) ; Computer science ; Real-time computing ; Data acquisition ; Production (economics) ; Environmental science ; Wireless ; Engineering ; Data mining ; Telecommunications Spectroscopy and Chemometric Analyses ; Natural Products and Biological Research ; Fault Detection and Control Systems
Authors: O. A. Kurasov | Petr V. Burkov
Source: E3S Web of Conferences (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

Substantiation of methods of improving safety of pipeline gas transportation Ensuring the operational efficiency of hazardous production facilities throughout their entire service life becomes a formidable challenge associated with coordinating thorough monitoring of such facilities, assessing their exposure, mitigation and sustainability, and taking measures to minimize and prevent negative outcomes that can lead to negative consequences, including human fatalities, injuries and economic losses. At the same time, the optimal choice of the ratio between levels of risk and costs of identification, regulation and increase in the resource will ensure both a reduction to a minimum of the risk of failures in gas pipeline systems and increase the efficiency of resource use to maximize technogenic safety. The paper proposes a methodology for the calculation of risk indicators and the probability of emergencies on gas networks and gas distribution facilities during their operation in normal and emergency situations. Risk analysis (engineering) ; Hazardous waste ; Environmental economics ; Business ; Resource (disambiguation) ; Sustainability ; Production (economics) ; Pipeline (software) ; Service (business) ; Identification (biology) ; Computer science ; Engineering ; Waste management ; Economics Material Properties and Failure Mechanisms ; Engineering Diagnostics and Reliability ; Industrial Engineering and Technologies
Authors: Isabell Bludau | Charlotte Nicod | Claudia Martelli | Peng Xue | Moritz Heusel | Andrea Fossati | Federico Uliana | Fabian Frommelt | Ruedi Aebersold | Ben C. Collins
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2021-12-20)
Field: Chemistry — Spectroscopy

Rapid profiling of protein complex re-organization in perturbed systems Abstract Protein complexes constitute the primary functional modules of cellular activity. To respond to perturbations, complexes undergo changes in their abundance, subunit composition or state of modification. Understanding the function of biological systems requires global strategies to capture this contextual state information on protein complexes and interaction networks. Methods based on co-fractionation paired with mass spectrometry have demonstrated the capability for deep biological insight but the scope of studies using this approach has been limited by the large measurement time per biological sample and challenges with data analysis. As such, there has been little uptake of this strategy beyond a few expert labs into the broader life science community despite rich biological information content. We present a rapid integrated experimental and computational workflow to assess the re-organization of protein complexes across multiple cellular states. It enables complex experimental designs requiring increased sample/condition numbers. The workflow combines short gradient chromatography and DIA/SWATH mass spectrometry with a data analysis toolset to quantify changes in complex organization. We applied the workflow to study the global protein complex rearrangements of THP-1 cells undergoing monocyte to macrophage differentiation and a subsequent stimulation of macrophage cells with lipopolysaccharide. We observed massive proteome organization in functions related to signaling, cell adhesion, and extracellular matrix during differentiation, and less pronounced changes in processes related to innate immune response induced by the macrophage stimulation. We therefore establish our integrated differential pipeline for rapid and state-specific profiling of protein complex organization with broad utility in complex experimental designs. Proteome ; Computational biology ; Workflow ; Profiling (computer programming) ; Systems biology ; Proteomics ; Computer science ; Biology ; Chemistry ; Cell biology ; Bioinformatics ; Biochemistry ; Database Advanced Proteomics Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Bioinformatics and Genomic Networks
Authors: Lucas S. Palacios | S. Tchoumakov | Maria Guix | Ignasio Pagonabarraga | Samuel Sánchez | Adolfo G. Grushin
Source: Nature Communications (preprint, 2021-08-03)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Guided accumulation of active particles by topological design of a second-order skin effect Collective guidance of out-of-equilibrium systems without using external fields is a challenge of paramount importance in active matter, ranging from bacterial colonies to swarms of self-propelled particles. Designing strategies to guide active matter and exploiting enhanced diffusion associated to its motion will provide insights for application from sensing, drug delivery to water remediation. However, achieving directed motion without breaking detailed balance, for example by asymmetric topographical patterning, is challenging. Here we engineer a two-dimensional periodic topographical design with detailed balance in its unit cell where we observe spontaneous particle edge guidance and corner accumulation of self-propelled particles. This emergent behaviour is guaranteed by a second-order non-Hermitian skin effect, a topologically robust non-equilibrium phenomenon, that we use to dynamically break detailed balance. Our stochastic circuit model predicts, without fitting parameters, how guidance and accumulation can be controlled and enhanced by design: a device guides particles more efficiently if the topological invariant characterizing it is non-zero. Our work establishes a fruitful bridge between active and topological matter, and our design principles offer a blueprint to design devices that display spontaneous, robust and predictable guided motion and accumulation, guaranteed by out-of-equilibrium topology. Active matter ; Topology (electrical circuits) ; Detailed balance ; Computer science ; Balance (ability) ; Physics ; Statistical physics ; Mathematics Metamaterials and Metasurfaces Applications ; Micro and Nano Robotics ; Topological Materials and Phenomena
Authors: Marius M. Kostelic | Ciara K. Zak | Yang Liu | Victor Chen | Zhuchun Wu | Jared Sivinski | Eli Chapman | Michael T. Marty
Source:  (preprint, 2021-07-26)
Field: Chemistry — Spectroscopy

UniDecCD: Deconvolution of Charge Detection-Mass Spectrometry Data Native mass spectrometry (MS) has become a versatile tool for characterizing high-mass complexes and measuring biomolecular interactions. Native MS usually requires resolution of different charge states produced by electrospray ionization to measure the mass, which is difficult for highly heterogeneous samples that have overlapping and unresolvable charge states. Charge detection-mass spectrometry (CD-MS) seeks to address this challenge by simultaneously measuring the charge and m/z for isolated ions. However, CD-MS often shows uncertainty in the charge measurement that limits the resolution. To overcome this charge state uncertainty, we developed UniDecCD (UCD) software for computational de-convolution of CD-MS data, which significantly improves the resolution of CD-MS data. Here, we describe the UCD algorithm and demonstrate its ability to improve CD-MS resolution of proteins, megadalton viral capsids, and heterogeneous nanodiscs made from natural lipid extracts. UCD provides a user-friendly interface that will increase the accessibility of CD-MS technology and provide a valuable new computational tool for CD-MS data analysis. Mass spectrometry ; Deconvolution ; Chemistry ; Charge (physics) ; Electrospray ionization ; Resolution (logic) ; Analytical Chemistry (journal) ; Computer science ; Physics ; Chromatography ; Algorithm Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies ; Advanced Proteomics Techniques and Applications
Authors: Austin Craig Hayes | T. David Reed
Source: Journal of Spectral Imaging (article, 2021-09-28)
Field: Chemistry — Analytical Chemistry

Hyperspectral reflectance for non-invasive early detection of black shank disease in flue-cured tobacco Flue-cured tobacco (Nicotiana tabacum L.) is a high value-per-acre crop that is intensively managed to optimise the yield of high-quality cured leaf. A 15-day study assessed the potential of hyperspectral reflectance data for detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Hyperspectral reflectance data were taken from a commercial flue-cured tobacco field with a progressing black shank infestation. The effort encompassed two key objectives. First, develop hyperspectral indices and/or machine learning classification models capable of detecting Phytophthora nicotianae (black shank) incidence in flue-cured tobacco. Second, evaluate the model’s ability to separate pre-symptomatic plants from healthy plants. Two hyperspectral indices were developed to detect black shank incidence based on differences in the spectral profiles of asymptomatic flue-cured tobacco plants compared to those with black shank symptoms. While one of the indices is a broad-band index and the other uses narrow wavelength values, the statistical difference between the two indices was not significant and both provided an accurate classification of symptomatic plants. Further analysis of the indices showed significant differences between the index values of healthy and symptomatic plants (α = 0.05). In addition, the indices were able to detect black shank symptoms pre-symptomatically (α = 0.09). Subspace linear discriminant analysis, a machine learning classification, was also used for prediction of black shank incidence with up to 85.7% classification accuracy. The implications of using either spectral indices or machine learning for classification for future black shank research are discussed. Hyperspectral imaging ; Linear discriminant analysis ; Reflectivity ; Curing of tobacco ; Phytophthora nicotianae ; Spectral signature ; Principal component analysis ; Discriminative model ; Mathematics ; Statistics ; Remote sensing ; Environmental science ; Biology ; Horticulture ; Artificial intelligence ; Phytophthora ; Computer science ; Geography ; Physics ; Optics Spectroscopy and Chemometric Analyses ; Remote Sensing in Agriculture ; Plant Pathogens and Resistance
Authors: P. Poornima
Source: Bioscience Biotechnology Research Communications (article, 2021-09-25)
Field: Chemistry — Analytical Chemistry

Liver Cancer Detection Using Image Processing Image processing is a processing technique with the help of mathematical operations. It uses any of the form of signal processing. Here the input is an image or video and the output is also an image or a set of images. This technique is also used in medical applications for various detection and treatment. In this project, it has been used to detect cancer cell of the liver. Here Otsu's method is used for enhancing the MRI image and Marker controlled watershed segmentation method is used to segment the cancer cell from the image. Active contour model was added to identify all the unique segments in the input MRI. Dice similarity score (Dice coefficient) were calculated for both segmented image and image after applying contour model and are compared. Cancer ; Image processing ; Liver cancer ; Computer science ; Artificial intelligence ; Image (mathematics) ; Computer vision ; Medicine ; Internal medicine Spectroscopy and Chemometric Analyses ; Brain Tumor Detection and Classification