jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Danish ul Khairi | Kamran Ahsan | Gran Badshah | Syed Zeeshan Ali | Syed Akhter Raza | Omar Alqahtani | Muhammad Shiraz
Source: Research Square (preprint, 2023-06-08)
Field: Chemistry — Analytical Chemistry

Comparison Analysis of Machine Learning Classification on Apple Fruit Quality Abstract This study presents a comparative analysis of classical and deep learning approaches for the classification of apple fruit quality, within the broader context of machine vision applications. Emphasizing the importance of the fruit's physical appearance in meeting market standards, the research explores the performance of classical methods such as Support Vector Machine (SVM), K-nearest neighbour (KNN), and Decision Tree (DT), in comparison to deep learning methods like Mobile-Net and Convolutional Neural Network (CNN) with self-design. A self-created dataset comprising 150 apple fruit images categorized into fresh, mid, and rotten classes are utilized, with an 80:20 training-test split. The evaluation of the approaches reveals promising results, with SVM achieving 86% accuracy, DT achieving 93%, KNN achieving 96.6%, and Mobile-Net and CNN achieving 97% accuracy. Notably, the study demonstrates the efficiency of these methods in classifying different quality classes of apple fruit. This research contributes to the existing knowledge in agricultural quality control and provides insights for researchers seeking suitable algorithms in the field of machine vision for fruit quality assessment, ultimately advancing the application of machine vision techniques in real-world scenarios. Machine learning ; Artificial intelligence ; Support vector machine ; Computer science ; Decision tree ; Convolutional neural network ; Context (archaeology) ; Quality (philosophy) ; Field (mathematics) ; Artificial neural network ; Deep learning ; Mathematics ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Date Palm Research Studies
Authors: Xuanyu Zhu | Marc Riera | Ethan F. Bull-Vulpe | Francesco Paesani
Source: ChemRxiv (preprint, 2023-03-21)
Field: Materials Science — Materials Chemistry

MB-pol(2023): Sub-chemical accuracy for water simulations from the gas to the liquid phase We use the MB-pol theoretical/computational framework to introduce a new family of data- driven many-body potential energy functions (PEFs) for water, named MB-pol(2023). By employing larger 2-body and 3-body training sets, including an explicit machine-learned representation of 4-body energies, and adopting more sophisticated machine-learned representations of 2-body and 3-body energies, we demonstrate that the MB-pol(2023) PEFs achieve sub-chemical accuracy in modeling the energetics of the hexamer isomers, outperforming both the original MB-pol and q-AQUA PEFs, which currently provide the most accurate description of water clusters in the gas phase. Importantly, the MB-pol(2023) PEFs provide remarkable agreement with the experimental results for various properties of liquid water, improving upon the original MB-pol PEF and effectively closing the gap with experimental measurements. Random hexamer ; Gas phase ; Water body ; Liquid phase ; Liquid water ; Physics ; Chemistry ; Thermodynamics ; Environmental science ; Crystallography Machine Learning in Materials Science ; Protein Structure and Dynamics ; Spectroscopy and Quantum Chemical Studies
Authors: Hilmi Eriklioglu | Esmanur İlhan | М. А. Ходасевич | Darya Korolko | Marena Manley | Rosario del P. Castillo | Mecit Halil Öztop
Source: Journal of Food Science (article, 2023-06-22)
Field: Chemistry — Analytical Chemistry

Classification and quantification of sucrose from sugar beet and sugarcane using optical spectroscopy and chemometrics Sucrose, obtained from either sugar beet or sugarcane, is one of the main ingredients used in the food industry. Due to the same molecular structure, chemical methods cannot distinguish sucrose from both sources. More practical and affordable methods would be valuable. Sucrose samples (cane and beet) were collected from nine countries, 25% (w/w) aqueous solutions were prepared and their absorbances recorded from 200 to 1380 nm. Spectral differences were observable in the ultraviolet-visible (UV-Vis) region from 200 to 600 nm due to impurities in sugar. Linear discriminant analysis (LDA), classification and regression trees, and soft independent modeling of class analogy were tested for the UV-Vis region. All methods showed high performance accuracies. LDA, after selection of five wavelengths, gave 100% correct classification with a simple interpretation. In addition, binary mixtures of the sugar samples were prepared for quantitative analysis by means of partial least squares regression and multiple linear regression (MLR). MLR with first derivative Savitzky-Golay were most acceptable with root mean square error of cross-validation, prediction, and the ratio of (standard error of) prediction to (standard) deviation values of 3.92%, 3.28%, and 9.46, respectively. Using UV-Vis spectra and chemometrics, the results show promise to distinguish between the two different sources of sucrose. An affordable and quick analysis method to differentiate between sugars, produced from either sugar beet or sugarcane, is suggested. This method does not involve complex chemical analysis or high-level experts and can be used in research or by industry to detect the source of the sugar which is important for some countries' agricultural policies. Chemometrics ; Sugar beet ; Partial least squares regression ; Linear discriminant analysis ; Sucrose ; Sugar ; Chemistry ; Mathematics ; Linear regression ; Biological system ; Analytical Chemistry (journal) ; Statistics ; Chromatography ; Food science ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Shunli Peng | Yunxin Wu | Tao Zhang | Qiumin Xie | Zhongyu Yuan | Lan Yin
Source: Materials (article, 2023-03-24)
Field: Materials Science — Biomaterials

Dynamic Constitutive Relationship of Mg–Gd–Y–Zr–Ag Alloy during High Temperature Deformation Process The thermal deformation behavior of the Mg-Gd-Y-Zr-Ag alloy was studied by isothermal hot compression tests at high temperatures. The flow stress increased with increased strain rates and decreased temperatures, first increasing and finally remaining stable with increased strain. A hot processing map was built. Using the processing map and microstructural analysis, the temperature should remain at 673-773 K for this alloy to ensure the deformation quality. The primary softening mechanism is discontinuous dynamic recrystallization (DDRX). Rising temperatures and declining strain rates facilitated the emergence and growth of Dynamic recrystallization (DRX) grains. An original JC (O-JC) model and a modified JC (M-JC) model were established. The M-JC model indicated a better prediction than the O-JC model. Still, it was deficient in predicting flow stresses with insufficient coupling effects. Hence, based on the M-JC model, a newly modified JC (NM-JC) model, which further enhances the interaction between strain and strain rate as well as strain and temperature, is proposed. Its projected values can better align with the tested values. Dynamic recrystallization ; Materials science ; Isothermal process ; Flow stress ; Strain rate ; Softening ; Deformation (meteorology) ; Alloy ; Constitutive equation ; Hot working ; Metallurgy ; Recrystallization (geology) ; Composite material ; Thermodynamics ; Finite element method Magnesium Alloys: Properties and Applications ; Metallurgy and Material Forming ; Metal and Thin Film Mechanics
Authors: Jong Ho Kim | Byantara Darsan Purusatama | Alvin Muhammad Savero | Denni Prasetia | Go-Un Yang | Song-Yi Han | Seung Hwan Lee | Nam–Hun Kim
Source: Forests (article, 2023-06-15)
Field: Chemistry — Organic Chemistry

Performance Influencing Factors of Convolutional Neural Network Models for Classifying Certain Softwood Species This study aims to verify the wood classification performance of convolutional neural networks (CNNs), such as VGG16, ResNet50, GoogLeNet, and basic CNN architectures, and to investigate the factors affecting classification performance. A dataset from 10 softwood species consisted of 200 cross-sectional micrographs each from the total part, earlywood, and latewood of each species. We used 80% and 20% of each dataset for training and testing, respectively. To improve the performance of the architectures, the dataset was augmented, and the differences in classification performance before and after augmentation were compared. The four architectures showed a high classification accuracy of over 90% between species, and the accuracy increased with increasing epochs. However, the starting points of the accuracy, loss, and training speed increments differed according to the architecture. The latewood dataset showed the highest accuracy. The epochs and augmented datasets also positively affected accuracy, whereas the total part and non-augmented datasets had a negative effect on accuracy. Additionally, the augmented dataset tended to derive stable results and reached a convergence point earlier. In the present study, an augmented latewood dataset was the most important factor affecting classification performance and should be used for training CNNs. Convolutional neural network ; Softwood ; Computer science ; Convergence (economics) ; Artificial intelligence ; Pattern recognition (psychology) ; Artificial neural network ; Machine learning ; Biology ; Botany Wood and Agarwood Research ; Industrial Vision Systems and Defect Detection ; Remote Sensing and LiDAR Applications
Authors: Andrés Alejandro Garcés Cadena | Oswaldo Menéndez | Edgar Patricio Córdova | Alvaro Javier Prado Romo
Source: Ingeniare. Revista chilena de ingeniería (article, 2023-08-01)
Field: Chemistry — Analytical Chemistry

Clasificación de calidad de manzana para monitoreo de cosechabilidad utilizando visión por computador y algoritmos de aprendizaje profundo La industria agrícola comprende una actividad de marcada influencia sobre el crecimiento económico y calidad de vida de las personas. Dada la necesidad de cubrir la demanda de alimentos debido al crecimiento poblacional, actualmente se requieren de sistemas capaces de optimizar el rendimiento del cultivo. Este trabajo contribuye con una herramienta práctica para asistir al agricultor en tareas de reconocimiento de calidad de fruta, permitiéndole mejorar el proceso de cuantificación de manzana y monitoreo del estado cosechable de la fruta mediante el uso de visión por computador y algoritmos de aprendizaje profundo. El sistema propuesto presenta: i) la detección del tipo de manzanas para el conteo y ii) la clasificación de su calidad para la inspección y validación de la fruta por categoría. Se utiliza el modelo de red de detección SSD-MobileNet y para la segmentación de instancias de calidad a nivel de píxel se emplea una red neuronal convolucional rápida FCN-ResNet 18. El sistema fue entrenado, validado y probado en varios ensayos experimentales de laboratorio y campo, empleando dos bases de datos de imágenes construidas en ambientes controlados y en entornos agrícolas reales. Los resultados muestran una precisión de entre 86,7% y 92,6% para la detección y de 94,7 ± 2,5% para la segmentación, superando en ambos casos los resultados presentados en trabajos relacionados. Humanities ; Physics ; Political science ; Art Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Smart Agriculture and AI
Authors: Yihuang Xiong | Milena Mathew | Sinéad M. Griffin | Alp Sipahigil | Geoffroy Hautier
Source: arXiv (Cornell University) (preprint, 2023-02-21)
Field: Materials Science — Materials Chemistry

Midgap state requirements for optically active quantum defects Optically active quantum defects play an important role in quantum sensing, computing, and communication. The electronic structure and the single-particle energy levels of these quantum defects in the semiconducting host have been used to understand their opto-electronic properties. Optical excitations that are central for their initialization and readout are linked to transitions between occupied and unoccupied single-particle states. It is commonly assumed that only quantum defects introducing levels well within the band gap and far from the band edges are of interest for quantum technologies as they mimic an isolated atom embedded in the host. In this perspective, we contradict this common assumption and show that optically active defects with energy levels close to the band edges can display similar properties. We highlight quantum defects that are excited through transitions to or from a band-like level (bound exciton), such as the T center and Se$\rm _{Si}^+$ in silicon. We also present how defects such as the silicon divacancy in diamond can involve transitions between localized levels that are above the conduction band or below the valence band. Loosening the commonly assumed requirement on the electronic structure of quantum defects offers opportunities in quantum defects design and discovery, especially in smaller band gap hosts such as silicon. We discuss the challenges in terms of operating temperature for photoluminescence or radiative lifetime in this regime. We also highlight how these alternative type of defects bring their own needs in terms of theoretical developments and fundamental understanding. This perspective clarifies the electronic structure requirement for quantum defects and will facilitate the identification and design of new color centers for quantum applications especially driven by first principles computations. Physics ; Excited state ; Band gap ; Quantum ; Condensed matter physics ; Quantum dot ; Electronic structure ; Exciton ; Optoelectronics ; Atomic physics ; Quantum mechanics Diamond and Carbon-based Materials Research ; Integrated Circuits and Semiconductor Failure Analysis ; Advanced Surface Polishing Techniques
Authors: Xihui Bian | Zitong Shi | Yingjie Shao | Yuanyuan Chu | Xiaoyao Tan
Source: Preprints.org (preprint, 2023-07-17)
Field: Chemistry — Analytical Chemistry

Variational Mode Decomposition for Raman Spectral Denoising As a fast and nondestructive spectroscopic analysis technique, Raman spectroscopy has been widely applied in chemistry. However, noise is usually unavoidable in Raman spectra. Hence, denoising is an important step before Raman spectral analysis. A novel spectral denoising method based on variational mode decomposition (VMD) was introduced to solve the above problem. The spectrum is decomposed into a series of modes (uk) by VMD. Then, the high frequency noise modes are removed and the remaining modes are reconstructed to obtain the denoised spectrum. The proposed method was verified by two artificial noised signals and two actual Raman spectra. As comparison, empirical mode decomposition (EMD), Savitzky-Golay (SG) smoothing and discrete wavelet transformation (DWT) are also investigated. At the same time, signal-to-noise ratio (SNR) was introduced as evaluation indicators to verify the performance of the proposed method. The results show that compared with EMD, VMD can significantly improve mode mixing and endpoint effect. Some information of the small sharp peak is lost after VMD denoising. However, VMD lost fewer information than that of EMD, SG smoothing and DWT. Moreover, the Raman spectrum by VMD denoising is more excellent than that of EMD, SG smoothing and DWT in terms of visualization and SNR. Therefore, VMD provides superior denoising capabilities for Raman spectra. Raman spectroscopy ; Noise reduction ; Smoothing ; Hilbert–Huang transform ; Noise (video) ; Wavelet ; Algorithm ; Signal-to-noise ratio (imaging) ; Artificial intelligence ; Computer science ; Pattern recognition (psychology) ; Mathematics ; Materials science ; Optics ; Physics ; White noise ; Computer vision ; Image (mathematics) ; Telecommunications Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Fault Detection and Control Systems
Authors: S. Ajith | Manoj Kanti Debnath | Deb Sankar Gupta | Pradip Basak
Source: International Journal of Statistics and Applied Mathematics (article, 2023-05-01)
Field: Chemistry — Analytical Chemistry

Application of statistical and machine learning models in combination with stepwise regression for predicting rapeseed-mustard yield in Northern districts of West Bengal Rapeseed-mustard crop is an important oilseed crop in India. District-wise yield prediction is essential for various location specific decision making. The performance of two machine learning models namely Support Vector Regression (SVR) and Artificial Neural Network (ANN) were compared with basic linear regression model for district-wise yield prediction of rapeseed-mustard crop. The study area for the present investigation were Cooch Behar, Malda, Jalpaiguri and Uttar Dinajpur districts of West Bengal. Yearly unweighted and weighted weather indices were calculated from weekly weather parameters. The indices that significantly affecting yield were selected using stepwise regression for fitting the models. The ANN model was fitted using backpropagation algorithm. The optimum number of neurons in hidden layer for ANN were ranging between two to four. The Tangent hyperbolic function was found to be suitable hidden layer activation function. The nonlinear Radial Basis Function kernel was the best kernel for Support Vector Regression. While evaluating the performance of fitted models in both calibration and validation stages, the ANN model was the best fitted model for Cooch Behar and Malda and SVR was the best fitted model for Jalpaiguri and Uttar Dinajpur districts. It was concluded that the machine learning models outperformed multiple linear regression model for district-wise yield prediction of rapeseed-mustard crop. Rapeseed ; Support vector machine ; Mathematics ; Stepwise regression ; Regression analysis ; Statistics ; Linear regression ; Kernel (algebra) ; Regression ; Machine learning ; Yield (engineering) ; Artificial intelligence ; Computer science ; Agronomy ; Biology Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis
Authors: Samuele Pellacani | Caterina Durante | Silvia Celli | Manuel Mariani | Andrea Marchetti | Marina Cocchi | Lorenzo Strani
Source: Chemometrics and Intelligent Laboratory Systems (article, 2023-08-25)
Field: Chemistry — Analytical Chemistry

Optimization of an analytical method based on SPME-Arrow and chemometrics for the characterization of the aroma profile of commercial bread A SPME-Arrow GC-MS approach, coupled with chemometrics, was used to thoroughly investigate the impact of different types of yeast (sourdough, bear's yeast and a mixture of both) and their respective leaving time (one, three and five hours) on VOCs of commercial bread samples. This aspect is of paramount importance for the baking industry to adjust recipe modifications and production parameters, as well as to meet consumer needs in formulating new products. A deep learning approach, PARADISe (PARAFAC2-based deconvolution and identification system), was used to analyse the obtained chromatograms in an untargeted manner. In particular, PARADISe, was able to perform a fast deconvolution of the chromatographic peaks directly from raw chromatographic data to allow a putatively identification of 66 volatile organic compounds, including alcohols, esters, carboxylic acids, ketones, aldehydes. Finally, Principal Component Analysis, applied on the areas of the resolved compounds, showed that bread samples differentiate according to their recipe and highlighted the most relevant volatile compounds responsible for the observed differences. Chemometrics ; Recipe ; Aroma ; Chemistry ; Chromatography ; Deconvolution ; Principal component analysis ; Raw material ; Biochemical engineering ; Food science ; Computer science ; Organic chemistry ; Artificial intelligence Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Fermentation and Sensory Analysis
Authors: Previndran Guinda Rajoo | Mohamed Thariq Hameed Sultan | Andrzej Łukaszewicz | Adi Azriff Basri | Ain Umaira Md Shah | Suhas Yeshwant Nayak | Rafał Grzejda | Andriy Holovatyy
Source: Preprints.org (preprint, 2023-07-10)
Field: Materials Science — Polymers and Plastics

The Development of UAV Airframes Made of Natural Fibers:A Review Natural fibers have been widely used as a reinforcing material for the construction of drones. The main goal of this review is to identify a biodegradable natural fiber material that can be built for Unmanned Aerial Vehicles (UAVs). The review shows that natural fibers such as kenaf and PALF can be used as an alternative composite material, particularly for multifunctional applications where their smaller weight and less expensive characteristics make them incredibly realistic. This review also discusses the mechanical properties, advantages and disadvantages, and fabrication of the composites. Kenaf ; Natural (archaeology) ; Airframe ; Natural fiber ; Natural materials ; Fiber ; Materials science ; Computer science ; Construction engineering ; Engineering ; Architectural engineering ; Biochemical engineering ; Composite material ; Geology ; Polymer science Natural Fiber Reinforced Composites ; Belt Conveyor Systems Engineering
Authors: John Kendrick | Andrew D. Burnett
Source: Crystal Growth & Design (article, 2023-06-29)
Field: Chemistry — Physical and Theoretical Chemistry

Exploring the Stability and Disorder in the Polymorphs of <scp>L</scp> -Cysteine through Density Functional Theory and Vibrational Spectroscopy Static and dynamic density functional calculations are reported for the four known polymorphs of l-cysteine. Static calculations are used to explore the relative free energies (within the harmonic approximation) of the polymorphs as a function of pressure. An important feature of the structural differences between the polymorphs is shown to be the dihedral angle of the C-C-S-H bond. It is shown that, by varying this angle, it is possible to move between hydrogen bonding motifs S-H<b>···</b>S and S-H<b>···</b>O in all four polymorphs. The energetics for dihedral angle rotation are explored, and the barriers for rotation between the hydrogen bonding motifs have been calculated for each polymorph. Two possible models for the experimental disorder observed in Form I at room temperature are explored using both static and dynamic methods; a domain disorder model, where the disorder is localized, and a dispersed disorder model, where the disorder is randomly distributed throughout the crystal. Molecular dynamics calculations show transitions between the two hydrogen bonding motifs occurring in the dispersed disorder model at 300 and 350 K. In addition, molecular dynamics calculations of Form IV also showed the onset of hydrogen bond disorder at 300 K. Calculations of the predicted infrared and terahertz absorption are performed for both the static and dynamic simulations, and the results are compared with experimental results to understand the influence of disorder on the observed spectra. Density functional theory ; Spectroscopy ; Cysteine ; Chemistry ; Infrared spectroscopy ; Crystallography ; Computational chemistry ; Chemical physics ; Stereochemistry ; Organic chemistry ; Physics ; Enzyme Crystallography and molecular interactions ; Nonlinear Optical Materials Research ; Solid-state spectroscopy and crystallography