jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Mohsen Sabzi-Nojadeh | Gniewko Niedbała | Mehdi Younessi‐Hamzekhanlu | S Aharizad | Mohammad Esmaeilpour | Moslem Abdipour | Sebastian Kujawa | Mohsen Niazian
Source: Agriculture (article, 2021-11-26)
Field: Chemistry — Analytical Chemistry

Modeling the Essential Oil and Trans-Anethole Yield of Fennel (Foeniculum vulgare Mill. var. vulgare) by Application Artificial Neural Network and Multiple Linear Regression Methods Foeniculum vulgare Mill. (commonly known as fennel) is used in the pharmaceutical, cosmetic, and food industries. Fennel widely used as a digestive, carminative, galactagogue and diuretic and in treating gastrointestinal and respiratory disorders. Improving low heritability traits such as essential oil yield (EOY%) and trans-anethole yield (TAY%) of fennel by direct selection does not result in rapid gains of EOY% and TAY%. Identification of high-heritable traits and using efficient modeling methods can be a beneficial approach to overcome this limitation and help breeders select the most advantageous traits in medicinal plant breeding programs. The present study aims to compare the performance of the artificial neural network (ANN) and multilinear regression (MLR) to predict the EOY% and TAY% of fennel populations. Stepwise regression (SWR) was used to assess the effect of various input variables. Based on SWR, nine traits—number of days to 50% flowering (NDF50%), number of days to maturity (NDM), final plant height (FPH), number of internodes (NI), number of umbels (NU), seed yield per square meter (SY/m2), number of seeds per plant (NS/P), number of seeds per umbel (NS/U) and 1000-seed weight (TSW)—were chosen as input variables. The network with Sigmoid Axon transfer function and two hidden layers was selected as the final ANN model for the prediction of EOY%, and the TanhAxon function with one hidden layer was used for the prediction of TAY%. The results revealed that the ANN method could predict the EOY% and TAY% with more accuracy and efficiency (R2 of EOY% = 0.929, R2 of TAY% = 0.777, RMSE of EOY% = 0.544, RMSE of TAY% = 0.264, MAE of EOY% = 0.385 and MAE of TAY% = 0.352) compared with the MLR model (R2 of EOY% = 0.553, R2 of TAY% = 0.467, RMSE of EOY% = 0.819, RMSE of TAY% = 0.448, MAE of EOY% = 0.624 and MAE of TAY% = 0.452). Based on the sensitivity analysis, SY/m2, NDF50% and NS/P were the most important traits to predict EOY% as well as SY/m2, NS/U and NDM to predict of TAY%. The results demonstrate the potential of ANNs as a promising tool to predict the EOY% and TAY% of fennel, and they can be used in future fennel breeding programs. Foeniculum ; Umbel ; Essential oil ; Biology ; Mathematics ; Botany ; Anethole ; Heritability ; Horticulture ; Cultivar Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Essential Oils and Antimicrobial Activity
Authors: Qingqing Cao | Yash Kumar Lal | Harsh Trivedi | Aruna Balasubramanian | Niranjan Balasubramanian
Source:  (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

IrEne: Interpretable Energy Prediction for Transformers Qingqing Cao, Yash Kumar Lal, Harsh Trivedi, Aruna Balasubramanian, Niranjan Balasubramanian. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021. Computer science ; Transformer ; Natural language processing ; Artificial intelligence ; Engineering ; Electrical engineering Machine Learning in Materials Science ; Green IT and Sustainability ; Traffic Prediction and Management Techniques
Authors: Paola Cucuzza | Silvia Serranti | Giuseppe Bonifazi | Giuseppe Capobianco
Source: Journal of Imaging (article, 2021-09-08)
Field: Chemistry — Analytical Chemistry

Effective Recycling Solutions for the Production of High-Quality PET Flakes Based on Hyperspectral Imaging and Variable Selection In this study, effective solutions for polyethylene terephthalate (PET) recycling based on hyperspectral imaging (HSI) coupled with variable selection method, were developed and optimized. Hyperspectral images of post-consumer plastic flakes, composed by PET and small quantities of other polymers, considered as contaminants, were acquired in the short-wave infrared range (SWIR: 1000-2500 nm). Different combinations of preprocessing sets coupled with a variable selection method, called competitive adaptive reweighted sampling (CARS), were applied to reduce the number of spectral bands useful to detect the contaminants in the PET flow stream. Prediction models based on partial least squares-discriminant analysis (PLS-DA) for each preprocessing set, combined with CARS, were built and compared to evaluate their efficiency results. The best performance result was obtained by a PLS-DA model using multiplicative scatter correction + derivative + mean center preprocessing set and selecting only 14 wavelengths out of 240. Sensitivity and specificity values in calibration, cross-validation and prediction phases ranged from 0.986 to 0.998. HSI combined with CARS method can represent a valid tool for identification of plastic contaminants in a PET flakes stream increasing the processing speed as requested by sensor-based sorting devices working at industrial level. Hyperspectral imaging ; Preprocessor ; Computer science ; Polyethylene terephthalate ; Pattern recognition (psychology) ; Partial least squares regression ; Variable elimination ; Artificial intelligence ; Sorting ; Feature selection ; Calibration ; Biological system ; Materials science ; Mathematics ; Algorithm ; Statistics ; Machine learning Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Water Quality Monitoring and Analysis
Authors: Johnatan M. Rodríguez‐Serna | Ricardo Albarracín
Source: Polymers (article, 2021-01-20)
Field: Materials Science — Materials Chemistry

A Study on the Life Estimation and Cavity Surface Degradation Due to Partial Discharges in Spherical Cavities within Solid Polymeric Dielectrics Using a Simulation Based Approach Partial Discharges (PD) in cavities are responsible for the greatest ageing rate in polymeric solid dielectrics due to chemical and physical deterioration mechanisms activated by the charge carriers, Ultra Violet (UV) radiation and local temperature rising during PDs activity. From the above, it is necessary to develop prognosis tools based on PDs measurements as diagnostic quantities in order to infer the time-to-breakdown, life, of solid dielectrics for improving the reliability of electrical assets, especially in current applications where they are subject to great electrical stresses in voltage frequency and magnitude. In this paper, the degradation in polymeric materials induced by PDs in cavities is briefly discussed from a phenomenological point of view, and then it is quantitatively evaluated using a simulation-based approach and a new proposed damage function. The time-to-breakdown calculated from simulations exhibits good agreement when compared with experimental measurements. Additionally, an analysis on the effect of the magnitude and frequency of the applied voltage on the degradation rate is also presented and the effectiveness of a degradation indicator, proposed by other authors, is evaluated under different stress conditions. Degradation (telecommunications) ; Materials science ; Dielectric ; Reliability (semiconductor) ; Stress (linguistics) ; Voltage ; Partial discharge ; Characterization (materials science) ; Composite material ; High voltage ; Optoelectronics ; Electronic engineering ; Nanotechnology ; Electrical engineering ; Power (physics) ; Thermodynamics ; Physics High voltage insulation and dielectric phenomena ; Dielectric materials and actuators ; Power Transformer Diagnostics and Insulation
Authors: Daniel Schletz | Johannes Schultz | Pavel Potapov | Anja Maria Steiner | Jonas Krehl | Tobias A. F. König | Martín Mayer | Axel Lubk | Andreas Fery
Source: Advanced Optical Materials (article, 2021-02-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Exploiting Combinatorics to Investigate Plasmonic Properties in Heterogeneous AgAu Nanosphere Chain Assemblies Abstract Chains of coupled metallic nanoparticles are of special interest for plasmonic applications because they can sustain highly dispersive plasmon bands, allowing strong ballistic plasmon wave transport. Whereas early studies focused on homogeneous particle chains exhibiting only one dominant band, heterogeneous assemblies consisting of different nanoparticle species came into the spotlight recently. Their increased configuration space principally allows engineering multiple bands, bandgaps, or topological states. Simultaneously, the challenge of the precise arrangement of nanoparticles, including their distances and geometric patterns, as well as the precise characterization of the plasmonics in these systems, persists. Here, the surface plasmon resonances in heterogeneous AgAu nanoparticle chains are reported. Wrinkled templates are used for directed self‐assembly of monodisperse gold and silver nanospheres as chains, which allows assembling statistical combinations of more than 10 9 particles. To reveal the spatial and spectral distribution of the plasmonic response, state‐of‐the‐art scanning transmission electron microscopy coupled with electron energy loss spectroscopy accompanied by boundary element simulations is used. A variety of modes in the heterogeneous chains are found, ranging from localized surface plasmon modes occurring in single gold or silver spheres, respectively, to modes that result from the hybridization of the single particles. This approach opens a novel avenue toward combinatorial studies of plasmonic properties in heterosystems. Plasmon ; Materials science ; Dispersity ; Nanoparticle ; Characterization (materials science) ; Localized surface plasmon ; Nanotechnology ; Electron energy loss spectroscopy ; Surface plasmon ; Particle (ecology) ; Plasmonic nanoparticles ; Chemical physics ; Transmission electron microscopy ; Optoelectronics ; Physics ; Polymer chemistry Gold and Silver Nanoparticles Synthesis and Applications ; Plasmonic and Surface Plasmon Research ; Photonic Crystals and Applications
Authors: Ala’ Salem | Anna Takácsi‐Nagy | Sándor Nagy | Alexandra Hagymási | Fruzsina Gősi | Barbara Vörös-Horváth | Tomislav Balić | Szilárd Pál | Aleksandar Széchenyi
Source: Pharmaceutics (article, 2021-02-19)
Field: Chemistry — Physical and Theoretical Chemistry

Synthesis and Characterization of Nano-Sized 4-Aminosalicylic Acid–Sulfamethazine Cocrystals Drug-drug cocrystals are formulated to produce combined medication, not just to modulate active pharmaceutical ingredient (API) properties. Nano-crystals adjust the pharmacokinetic properties and enhance the dissolution of APIs. Nano-cocrystals seem to enhance API properties by combining the benefits of both technologies. Despite the promising opportunities of nano-sized cocrystals, the research at the interface of nano-technology and cocrystals has, however, been described to be in its infancy. In this study, high-pressure homogenization (HPH) and high-power ultrasound were used to prepare nano-sized cocrystals of 4-aminosalysilic acid and sulfamethazine in order to establish differences between the two methods in terms of cocrystal size, morphology, polymorphic form, and dissolution rate enhancement. It was found that both methods resulted in the formation of form I cocrystals with a high degree of crystallinity. HPH yielded nano-sized cocrystals, while those prepared by high-power ultrasound were in the micro-size range. Furthermore, HPH produced smaller-size cocrystals with a narrow size distribution when a higher pressure was used. Cocrystals appeared to be needle-like when prepared by HPH compared to those prepared by high-power ultrasound, which had a different morphology. The highest dissolution enhancement was observed in cocrystals prepared by HPH; however, both micro- and nano-sized cocrystals enhanced the dissolution of sulfamethazine. Cocrystal ; Crystallinity ; Dissolution ; Materials science ; Active ingredient ; Nanoparticle ; Nano- ; Chemical engineering ; Chemistry ; Nanotechnology ; Organic chemistry ; Crystallography ; Molecule ; Pharmacology Crystallography and molecular interactions ; Crystallization and Solubility Studies ; Drug Solubulity and Delivery Systems
Authors: Waluyo Waluyo | Dini Fauziah | Ismail Muhammad Khaidir
Source: IEEE Access (article, 2021-01-01)
Field: Materials Science — Materials Chemistry

The Evaluation of Daily Comparative Leakage Currents on Porcelain and Silicone Rubber Insulators Under Natural Environmental Conditions In this research, the leakage current (LC) and applied voltage waveforms on samples of installed outdoor porcelain and silicone rubber (SIR) insulators were measured by using a storage digital oscilloscope concurrent with humidity, temperature, and illuminance measurements. Essentially, the measurements were conducted daily, every three hours, for thirty days, and the data were analyzed using exponential regressions, box plots, and correlation coefficients. The porcelain and SIR insulator leakage currents varied from 23.7 μA to 28.8 μA and 8.4 μA to 10.35 μA for median values, respectively, while for outlier excluded values, they varied from 20.3 μA to 40.0 μA and 5.1 μA to 17.1 μA, respectively. Both leakage currents slightly increased as the exposure duration of the insulator rose, while the ratio decreased very slightly. Additionally, the leakage currents decreased moderately and slightly as the temperature and illuminance increased, respectively. However, the parabolic leakage currents increased as the humidity rose, especially at high humidity. Therefore, the porcelain insulator was more susceptible than the daily SIR, while the SIR insulator was more highly influenced by aging of the exposure duration. The rainy condition contributed to the highest leakage currents on both insulators, whereas the drizzle condition contributed to the highest leakage current on the SIR insulator. The total harmonic distortion (THD) and phase angle of the porcelain insulator leakage currents experienced differences of 5.8% and 7.8 degrees in the minimum and maximum humidity, respectively, but both quantities were less for the SIR insulator. Insulator (electricity) ; Silicone rubber ; Relative humidity ; Leakage (economics) ; Humidity ; Materials science ; Composite material ; Illuminance ; Electrical engineering ; Optics ; Meteorology ; Physics ; Engineering High voltage insulation and dielectric phenomena ; Lightning and Electromagnetic Phenomena ; Power Transformer Diagnostics and Insulation
Authors: Yvonne Gunning | Fouad Taous | Tibari El Ghali | James D. Gibbon | Emma Wilson | Rachel M. Brignall | E. Kate Kemsley
Source: Food Chemistry (article, 2021-10-05)
Field: Chemistry — Analytical Chemistry

Mitigating instrument effects in 60 MHz 1H NMR spectroscopy for authenticity screening of edible oils Low field (60 MHz) <sup>1</sup>H NMR spectroscopy was used to analyse a large (n = 410) collection of edible oils, including olive and argan, in an authenticity screening scenario. Experimental work was carried out on multiple spectrometers at two different laboratories, aiming to explore multivariate model stability and transfer between instruments. Three modelling methods were employed: Partial Least Squares Discriminant Analysis, Random Forests, and a One Class Classification approach. Clear inter-instrument differences were observed between replicated data collections, sufficient to compromise effective transfer of models based on raw data between instruments. As mitigations to this issue, various data pre-treatments were investigated: Piecewise Direct Standardisation, Standard Normal Variates, and Rank Transformation. Datasets comprised both phase corrected and magnitude spectra, and it was found that that the latter spectral form may offer some advantages in the context of pattern recognition and classification modelling, particularly when used in combination with the Rank Transformation pre-treatment. Context (archaeology) ; Multivariate statistics ; Linear discriminant analysis ; Partial least squares regression ; Chemometrics ; Transformation (genetics) ; Computer science ; Piecewise ; Artificial intelligence ; Mathematics ; Pattern recognition (psychology) ; Raw data ; Biological system ; Data mining ; Statistics ; Machine learning ; Chemistry ; Geography Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Huidong Wei
Source: Polymers (article, 2021-02-22)
Field: Materials Science — Biomaterials

Optimisation on Thermoforming of Biodegradable Poly (Lactic Acid) (PLA) by Numerical Modelling Poly (lactic acid) (PLA) has a broad perspective for manufacturing green thermoplastic products by thermoforming for its biodegradable properties. The mechanical behaviour of PLA has been demonstrated by its strong dependence on temperature and strain rate at biaxial deformation. A nonlinear viscoelastic model by the previous study was employed in a thermoforming process used for food packaging. An optimisation approach was developed by achieving the optimal temperature profile of specimens by defining multiple heating zones based on numerical modelling with finite element analysis (FEA). The forming process of a PLA product was illustrated by modelling results on shape evolution and biaxial strain history. The optimal temperature profile was suggested in scalloped zones to achieve more even thickness distribution. The sensitivity of the optimal results was addressed by checking the robustness under perturbation. Thermoforming ; Materials science ; Finite element method ; Composite material ; Thermoplastic ; Lactic acid ; Viscoelasticity ; Biodegradable polymer ; Polymer ; Structural engineering ; Engineering biodegradable polymer synthesis and properties ; Additive Manufacturing and 3D Printing Technologies ; Natural Fiber Reinforced Composites
Authors: Yuzhen Lu | Renfu Lu
Source: Foods (article, 2021-05-14)
Field: Chemistry — Analytical Chemistry

Detection of Chilling Injury in Pickling Cucumbers Using Dual-Band Chlorophyll Fluorescence Imaging Pickling cucumbers are susceptible to chilling injury (CI) during postharvest refrigerated storage, which would result in quality degradation and economic loss. It is, thus, desirable to remove the defective fruit before they are marketed as fresh products or processed into pickled products. Chlorophyll fluorescence is sensitive to CI in green fruits, because exposure to chilling temperatures can induce detectable alterations in chlorophylls of tissues. This study evaluated the feasibility of using a dual-band chlorophyll fluorescence imaging (CFI) technique for detecting CI-affected pickling cucumbers. Chlorophyll fluorescence images at 675 nm and 750 nm were acquired from pickling cucumbers under the excitation of ultraviolet-blue light. The raw images were processed for vignetting corrections through bi-dimensional empirical mode decomposition and subsequent image reconstruction. The fluorescence images were effective for ascertaining CI-affected tissues, which appeared as dark areas in the images. Support vector machine models were developed for classifying pickling cucumbers into two or three classes using the features extracted from the fluorescence images. Fusing the features of fluorescence images at 675 nm and 750 nm resulted in overall accuracies of 96.9% and 91.2% for two-class (normal and injured) and three-class (normal, mildly and severely injured) classification, respectively, which are statistically significantly better than those obtained using the features at a single wavelength, especially for the three-class classification. Furthermore, a subset of features, selected based on the neighborhood component feature selection technique, achieved the highest accuracies of 97.4% and 91.3% for the two-class and three-class classification, respectively. This study demonstrated that dual-band CFI is an effective modality for CI detection in pickling cucumbers. Pickling ; Postharvest ; Fluorescence ; Chlorophyll fluorescence ; Chlorophyll ; Horticulture ; Chemistry ; Botany ; Materials science ; Food science ; Biology ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Listeria monocytogenes in Food Safety
Authors: Tran Thi Kim Nga | Tuan Viet Pham | Dang Minh Tâm | Insoo Koo | Vladimir Y. Mariano | Tuan Do-Hong
Source: IEEE Access (article, 2021-01-01)
Field: Chemistry — Analytical Chemistry

Combining Binary Particle Swarm Optimization With Support Vector Machine for Enhancing Rice Varieties Classification Accuracy The objective of this study is to classify the rice grains of seventeen different varieties popularly planted in Vietnam. Image processing is used to extract color, morphological, and texture features of the rice grains. Five feature subsets are formed, namely, morphological, basic color, clustering color, statistical, and gray level co-occurrence matrix (GLCM). These subsets and combined sets are evaluated for classification ability with a support vector machine (SVM). A dataset of 248 features, including a total of color, morphological, and texture features classified with the SVM gives an overall accuracy of 88.29%. To decrease the number of used features and to improve the classification accuracy, the proposed method combining binary particle swarm optimization (BPSO) and the SVM, called BPSO+SVM, is applied to the dataset. In the results, classification accuracy from BPSO+SVM reaches 93.94% using only 96 selected features. The obtained result shows the proposed method achieves higher classification accuracy than the SVM alone, and the required number of features is only 39% of the total dataset. This result can be applied for developing an automatic classification and identification system of rice varieties. Support vector machine ; Pattern recognition (psychology) ; Artificial intelligence ; Particle swarm optimization ; Cluster analysis ; Computer science ; Local binary patterns ; Gray level ; Feature (linguistics) ; Binary number ; Mathematics ; Pixel ; Image (mathematics) ; Machine learning ; Histogram Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Remote Sensing and Land Use
Authors: Luke Szymanski | Gurkan Gok | Anthony Grbic
Source: IEEE Transactions on Antennas and Propagation (article, 2021-12-28)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse Design of Multi-Input Multi-Output 2-D Metastructured Devices In this work, an optimization-based inverse-design method is provided for multi-input&#x2013;multioutput (MIMO) metastructured devices. Typically, optimization-based methods use a full-wave solver in conjunction with an optimization routine to design devices. Due to the computational cost, this approach is not practical for designing electrically large aperiodic metastructured devices. To address this issue, a 2-D circuit network solver using reduced-order models of the metastructure&#x2019;s unit cells is introduced. The circuit network solver is used in conjunction with a gradient-based optimization routine that uses the adjoint variable method to solve large-scale optimization problems such as those posed by metastructured devices. To validate the inverse-design method, a planar beamformer and an analog signal processor for aperture field reconstruction are designed and validated with full-wave simulations. Solver ; Computer science ; Aperiodic graph ; MIMO ; Inverse ; Inverse problem ; Electronic engineering ; Mathematical optimization ; Computational science ; Mathematics ; Beamforming ; Telecommunications ; Engineering Metamaterials and Metasurfaces Applications ; Microwave Engineering and Waveguides ; Antenna Design and Optimization