jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: H. Zhang | Zheng Li | Lian Tan | Jiapeng Yang | Jiahui Gao
Source: Sensors (article, 2024-10-08)
Field: Chemistry — Analytical Chemistry

Research on the Method of Imperfect Wheat Grain Recognition Utilizing Hyperspectral Imaging Technology As the primary grain crop in China, wheat holds a significant position in the country's agricultural production, circulation, consumption, and various other aspects. However, the presence of imperfect grains has greatly impacted wheat quality and, subsequently, food security. In order to detect perfect wheat grains and six types of imperfect grains, a method for the fast and non-destructive identification of imperfect wheat grains using hyperspectral images was proposed. The main contents and results are as follows: (1) We collected wheat grain hyperspectral data. Seven types of wheat grain samples, each containing 300 grains, were prepared to construct a hyperspectral imaging system for imperfect wheat grains, and visible near-infrared hyperspectral data from 2100 wheat grains were collected. The Savitzky-Golay algorithm was used to analyze the hyperspectral images of wheat grains, selecting 261 dimensional effective hyperspectral datapoints within the range of 420.61-980.43 nm. (2) The Successive Projections Algorithm was used to reduce the dimensions of the 261 dimensional hyperspectral datapoints, selecting 33 dimensional hyperspectral datapoints. Principal Component Analysis was used to extract the optimal spectral wavelengths, specifically selecting hyperspectral images at 647.57 nm, 591.78 nm, and 568.36 nm to establish the dataset. (3) Particle Swarm Optimization was used to optimize the Support Vector Machines model, Convolutional Neural Network model, and MobileNet V2 model, which were established to recognize seven types of wheat grains. The comprehensive recognition rates were 93.71%, 95.14%, and 97.71%, respectively. The results indicate that a larger model with more parameters may not necessarily yield better performance. The research shows that the MobileNet V2 network model exhibits superior recognition efficiency, and the integration of hyperspectral image technology with the classification model can accurately identify imperfect wheat grains. Hyperspectral imaging ; Principal component analysis ; VNIR ; Particle swarm optimization ; Grain quality ; Pattern recognition (psychology) ; Computer science ; Biological system ; Environmental science ; Remote sensing ; Artificial intelligence ; Agronomy ; Biology ; Algorithm ; Geography Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Mengfei Ru | Qingchun Feng | Na Sun | Yajun Li | Jiahui Sun | Jianxun Li | Chunjiang Zhao
Source: Foods (article, 2024-07-09)
Field: Chemistry — Analytical Chemistry

Dynamic Compressive Stress Relaxation Model of Tomato Fruit Based on Long Short-Term Memory Model Tomatoes are prone to mechanical damage due to improper gripping forces during automated harvest and postharvest processes. To reduce this damage, a dynamic viscoelastic model based on long short-term memory (LSTM) is proposed to fit the dynamic compression stress relaxation characteristics of the individual fruit. Furthermore, the classical stress relaxation models involved, the triple-element Maxwell and Caputo fractional derivative models, are compared with the LSTM model to validate its performance. Meanwhile, the LSTM and classical stress relaxation models are used to predict the stress relaxation characteristics of tomato fruit with different fruit sizes and compression positions. The results for the whole test dataset show that the LSTM model achieves a RMSE of 2.829×10-5 Mpa and a MAPE of 0.228%. It significantly outperforms the Caputo fractional derivative model by demonstrating a substantial enhancement with a 37% decrease in RMSE and a 36% reduction in MAPE. Further analysis of individual tomato fruit reveals the LSTM model's performance, with the minimum RMSE recorded at the septum position being 3.438×10-5 Mpa, 31% higher than the maximum RMSE at the locule position. Similarly, the lowest MAPE at the septum stands at 0.375%, outperforming the highest MAPE at the locule position by a significant margin of 90%. Moreover, the LSTM model consistently reports the smallest discrepancies between the predicted and observed values compared to classical stress relaxation models. This accuracy suggests that the LSTM model could effectively supplant classical stress relaxation models for predicting stress relaxation changes in individual tomato fruit. Term (time) ; Biological system ; Stress (linguistics) ; Relaxation (psychology) ; Computer science ; Materials science ; Biology ; Neuroscience ; Physics Spectroscopy and Chemometric Analyses
Authors: Aloïs Castellano | Kévin Alhada–Lahbabi | Jon Ander Arregi | Vojtěch Uhlíř | B. Perrin | C. Gourdon | D. Fournier | Matthieu J. Verstraete | L. Thevenard
Source: Physical Review Materials (article, 2024-08-28)
Field: Materials Science — Materials Chemistry

Magnetic phase dependency of the thermal conductivity of FeRh from thermoreflectance experiments and numerical simulations FeRh is well known in its bulk form for a temperature-driven antiferromagnetic (AFM) to ferromagnetic (FM) transition near room temperature. It has aroused renewed interest in its thin-film form, with particular focus on its biaxial AFM magnetic anisotropy which could serve for data encoding, and the possibility to investigate laser-assisted phase transitions, with varying contributions from electrons, phonons, and magnons. In order to estimate the typical temperature increase occurring in these experiments, we performed modulated thermoreflectance microscopy to determine the thermal conductivity $\ensuremath{\kappa}$ of FeRh. As often occurs upon alloying, and despite the good crystallinity of the layer, $\ensuremath{\kappa}$ was found to be lower than the thermal conductivities of its constituting elements. More unexpectedly, given the electrically more conducting nature of the FM phase, it turned out to be three times lower in the FM phase compared to the AFM phase. This trend was confirmed by examining the temporal decay of incoherent phonons generated by a pulsed laser in both phases. To elucidate these results, first- and second-principles simulations were performed to estimate the phonon, magnon, and electron contributions to the thermal conductivity. They were found to be of the same order of magnitude, and to give a quantitative rendering of the experimentally observed ${\ensuremath{\kappa}}_{\mathrm{AFM}}$. In the FM phase, however, simulations overestimate the low experimental values, implying very different (shorter) electron and magnon lifetimes. Materials science ; Condensed matter physics ; Magnon ; Thermal conductivity ; Phonon ; Anisotropy ; Antiferromagnetism ; Ferromagnetism ; Phase (matter) ; Optics ; Physics ; Composite material Thermal properties of materials ; High-pressure geophysics and materials ; Theoretical and Computational Physics
Authors: A Abisha | N. Bharathi
Source: Journal of Advances in Information Technology (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

A Hybrid Feature Extraction and Feature Selection Mechanism to Predict Disease in Plant Leaves The health of the plants is vital to meet the demands of the food cycle.As the symptoms of disease or infection are most commonly seen in plant leaves, selecting features from plant leaves that are highly impacting plant health is crucial.Plant health is a global imperative for food security and ecological balance and must be treated as the top priority.Feature Extraction (FE) and Feature Selection (FS) are significant in Deep Learning (DL) and Machine Learning (ML) models, which are used for classification and prediction.Xception-based feature extraction and random forest classification yield accurate predictions, offering interpretability and adaptability across diverse plant diseases and datasets, benefiting agriculture.In this article, FE is performed using an Xception pre-trained model and the extracted features are sent for FS.Further, six FS methods such as ANOVA, chi-square, Sequential Forward Selection (SFS), Sequential Backward Selection (SBS), Lasso and Ridge, have been deployed and compared with machine learning algorithms such as Logistic Regression (LR), K Nearest Neighbours (KNN), Decision-Trees (DT), Random Forest (RF), Support Vector Machine (SVM), Naive-Bayes (NB) for classification.The article also proposes an Ensemble Feature Selection (EFS)-RF method, which combines feature sets from six feature selection algorithms and classifies based on majority voting.The methodology section details criteria for selecting FE and FS methods, utilizing an ensemble to maximize their respective benefits.The paper contributes to agriculture by employing a hybrid approach, integrating DL (Xception-based FE) and ML (RF-based Classification), utilizing an ensemble of FS methods to identify and assign higher weightage to features prevalent across subsets.The proposed method has outperformed other algorithms for both datasets with 98 % accuracy and 0.02 Mean Squared Error (MSE) for dataset I and 98.125 % accuracy and 0.01875 MSE for dataset II. Feature selection ; Computer science ; Artificial intelligence ; Mechanism (biology) ; Pattern recognition (psychology) ; Selection (genetic algorithm) ; Feature extraction ; Plant disease ; Feature (linguistics) ; Biology ; Biotechnology ; Linguistics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Water Quality Monitoring and Analysis
Authors: Yassine EL Kati | Shulin Wang | Mundher Mohammed Taresh | Talal Ahmed Ali Ali
Source: PeerJ Computer Science (article, 2024-12-23)
Field: Chemistry — Analytical Chemistry

Enhancing breast cancer diagnosis using deep learning and gradient multi-verse optimizer: a robust biomedical data analysis approach Breast cancer (BC) is one of the most common causes of mortality among women. However, early detection of BC can effectively improve the treatment outcomes. Computer-aided diagnosis (CAD) systems can be utilized clinical specialists for accurate diagnosis of BC in its early stages. Due to their superior classification performance, deep learning (DL) methods have been extensively used in CAD systems. The classification accuracy of a DL model mainly depends on the parameters, such as weights and biases, of the deep neural network (DNN), which are optimized during the training phase. The training of DL models has been carried out by gradient-based techniques, <i>e.g</i>., stochastic gradient descent with momentum (SGDM) and adaptive momentum estimation (ADAM), and metaheuristic techniques, <i>e.g</i>., genetic algorithms (GA) and particle swarm optimization (PSO). However, these techniques suffer from frequent stagnation in local optima due to the huge search space, which can lead to sub-optimal DL performance. This article proposes a hybrid optimization algorithm, based on incorporating a simple gradient search mechanism into a metaheuristic technique, multi-verse optimizer (MVO), to facilitate the search for global optimal solution in the high-dimensional search space of DL models. A DL model for BC diagnosis is developed based on a three-hidden-layer DNN whose parameters are optimized using the proposed hybrid optimizer. Experimental analysis is carried out on the Wisconsin breast cancer dataset (WBCD) and the Wisconsin Diagnosis Breast Cancer (WDBC) dataset, each is divided into 70% for training and 30% for testing. For comparison reasons, similar DL models trained using various optimizers, including gradient-based, metaheuristic, and recently-proposed hybrid optimization algorithms, are also analyzed. The results demonstrate the superior performance of our optimizer in terms of attaining the most accurate DL model in the fastest convergence rate. The proposed model achieves outstanding metrics, including accuracy at 93.5%, precision at 88.06%, specificity at 93.06%, sensitivity at 95.64%, F1 score at 91.67%, and Matthew's correlation coefficient (MCC) at 87.14% on WBCD, and accuracy at 96.73%, precision at 93.38%, specificity at 95.83%, sensitivity at 98.25%, F1 score at 95.75%, and MCC at 93.18% on WDBC, in just six epochs. This research significantly contributes to advancing CAD systems for BC, emphasizing the potential benefits of the proposed optimizer in medical classification domains. Artificial intelligence ; Breast cancer ; Computer science ; Cancer ; Deep learning ; Machine learning ; Pattern recognition (psychology) ; Medicine ; Internal medicine Spectroscopy and Chemometric Analyses ; Brain Tumor Detection and Classification ; Advanced Computing and Algorithms
Authors: Dmitry V. Makhov | Gregory Armstrong | Hsiao‐Han Chuang | Harin Ambalampitiya | Kateryna Lemishko | Sebastian Mohr | Anna Nelson | Jonathan Tennyson | Dmitrii V. Shalashilin
Source: The Journal of Physical Chemistry Letters (article, 2024-03-19)
Field: Chemistry — Spectroscopy

Dissociation of Hydrofluorocarbon Molecules after Electron Impact in Plasma The process of dissociation for two hydrofluorocarbon molecules in low triplet states excited by electron impact in plasma is investigated by <i>ab initio</i> molecular dynamics (AIMD). The interest in the dissociation of hydrofluorocarbons in plasma is motivated by their role in plasma etching in microelectronic technologies. Dissociation of triplet states is very fast, and the reaction products can be predicted. In this work, it was found that higher triplet states relax into the lowest triplet state within a few femtoseconds due to nonadiabatic dynamics, such that the simplest <i>ab initio</i> MD on the lowest triplet state seems to give a reasonable estimate of the reaction channels branching ratios. We provide evidence of the existence of simple rules for the dissociation of hydrofluorocarbon molecules in triplet states. For molecules with a double bond, the bonds adjacent to the double bond dissociate faster than the other bonds. Chemistry ; Dissociation (chemistry) ; Ab initio ; Excited state ; Molecule ; Triplet state ; Ab initio quantum chemistry methods ; Plasma ; Electron ionization ; Atomic physics ; Computational chemistry ; Ion ; Physical chemistry ; Ionization ; Organic chemistry ; Physics Mass Spectrometry Techniques and Applications ; Advanced Chemical Physics Studies ; Atomic and Molecular Physics
Authors: Johannes Stöckelmaier | Chris Oostenbrink
Source: Physical Chemistry Chemical Physics (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Conformational dependence of chemical shifts in the proline rich region of TAU protein Nuclear magnetic resonance (NMR) is an important method for structure elucidation of proteins, as it is an easily accessible and well understood method. To characterize intrinsically disordered proteins (IDPs) using computational models it is often necessary to analyze and integrate calculated observables with measurements derived from solution NMR experiments. In this case study, we investigate whether and which chemical shifts of the proline-rich region of Tau protein (residues 210-240) offer information about the conformational state to distinguish two different microscopic conformers. Using multiple computational methods, the chemical shifts of these two conformationally distinct structures are calculated. The different methods are compared regarding their ability to compute chemical shifts that are sensitive to conformational change. The analysis of the data shows significant differences between the available methods and gives suggestions for an improved pathway for ensemble reweighting. Nevertheless, the variation in the chemical shifts which are predicted for configurations that are commonly considered to belong to the same conformation is such that this obscures a comparison between distinct conformations. Conformational sensitivity is found for up to ∼26% of calculated chemical shifts. It is found to be unrelated to the atom element and has a minor relationship with the change in the corresponding <i>ϕ</i> dihedral angle. Chemical shift ; Chemistry ; Proline ; Chemical physics ; Crystallography ; Computational chemistry ; Physical chemistry ; Amino acid ; Biochemistry Enzyme Structure and Function ; Protein Structure and Dynamics ; Photosynthetic Processes and Mechanisms
Authors: Amirhosein Yazdanbakhsh | Ghodratollah Hashemi Motlagh
Source: e-Polymers (article, 2024-01-01)
Field: Materials Science — Polymers and Plastics

Simultaneous effects of temperature and backbone length on static and dynamic properties of high-density polyethylene-1-butene copolymer melt: Equilibrium molecular dynamics approach Abstract Temperature and chain length play significant roles in determining the physical properties of polymer melts. In the current computational research, a molecular dynamics (MD) approach was implemented to describe the static and dynamic properties of (1) high-density polyethylene-1-butene with 120 beads in backbone (PE120) and (2) entangled high-density polyethylene-1-butene with 600 beads in the backbone (PE600). The transferable potentials for phase equilibria force fields were used for CH 2 beads in a defined initial condition. First, the equilibrium phase of the designed systems was reported with total energy and density convergency at various initial temperatures ( T 0 = 450, 470, and 490 K). Also, gyration radius ( R g ) and end-to-end distance ( R ) were calculated for the static behavior description of the two PEs. Zero-shear viscosity ( η 0 ), mean square displacement, and diffusion coefficient ( D ) were estimated to define the dynamic behavior of PE120 and PE600 systems. MD outputs predicted that 10 ns is sufficient for equilibrium phase detection inside polymeric samples. After equilibrium phase detection, R g converged to 14.97 and 17.35 Å in PE120 and PE600, respectively ( T 0 = 450 K). Furthermore, MD outputs show that temperature variation can considerably affect the time evolution of the system. Numerically, the η 0 of PE120 and PE600 converged to 49 and 168 cp at 450 K. These results of η 0 parameter as a function of temperature are an important output of MD simulations. The results predicted that η 0 decreases to 24 and 44 cp for PE120 and PE600 samples with an increase in temperature from 450 to 490 K. With the creation of the entanglements network, D reached the highest value of 2 × 10 −9 m 2 ·s −1 among the designed polymeric systems. The results are in good consistency with experimental reports. It is expected that the result of this study can be used in designing improved polymeric systems for real applications. Copolymer ; 1-Butene ; Materials science ; Molecular dynamics ; Polyethylene ; Melting temperature ; Dynamics (music) ; Thermodynamics ; High-density polyethylene ; Polymer chemistry ; Composite material ; Polymer ; Chemistry ; Computational chemistry ; Organic chemistry ; Physics ; Catalysis Polymer crystallization and properties ; Rheology and Fluid Dynamics Studies ; Material Dynamics and Properties
Authors: Anna Santin | Flavio Collura | G.V. Singh | Maria Silvia Morlino | Edoardo Bizzotto | Alessandra Bellan | Ameya Pankaj Gupte | Lorenzo Favaro | Stefano Campanaro | Laura Treu | Tomas Morosinotto
Source: Biotechnology for Biofuels and Bioproducts (article, 2024-07-16)
Field: Materials Science — Biomaterials

Deciphering the genetic landscape of enhanced poly-3-hydroxybutyrate production in Synechocystis sp. B12 Our findings shed light on the molecular mechanisms of PHB biosynthesis, providing valuable insights for optimising the use of Synechocystis in economically viable and sustainable PHB production. In addition, this work supplies crucial knowledge about the metabolic processes involved in production and accumulation of these molecules, which can be seminal for the application to other microorganisms as well. Synechocystis ; Chemistry ; Gene ; Biochemistry biodegradable polymer synthesis and properties ; Microplastics and Plastic Pollution ; Microbial Metabolic Engineering and Bioproduction
Authors: SHETTAHALLI MANTAIAH VINU KUMAR | R. Jeyakumar | Manikandaprabu Nallasivam | CHANDRASEKARAN SASIKUMAR
Source: Cellulose Chemistry and Technology (article, 2024-09-25)
Field: Materials Science — Polymers and Plastics

EXPERIMENTAL INVESTIGATION ON FREE VIBRATIONAL DAMPING AND DRILLING BEHAVIOR OF FLAX REINFORCED EPOXY COMPOSITES USING ADAPTIVE NEURO FUZZY INFERENCE SYSTEM Flax reinforced epoxy (F-Ep) composites were prepared by the compression moulding technique, varying the fiber content (0, 25, 35 and 45 wt%). The free vibration test was performed on the neat epoxy and F-Ep composites to understand their dynamic characteristics, and results showed that natural frequency and damping factor of the F-Ep composites increased with an increase in the fiber content. The F-Ep composite (45F-Ep) that exhibited better damping was selected for performing the drilling operation. Factors such as spindle speed (rpm), feed rate (mm/min) and drill point angle (degree) were chosen as input parameters and the tabulated set of experiments were in accordance with Taguchi’s design of experiment. The response measured was thrust force and the obtained values were found in the range of 19.66 to 50.75 N. The minimum value of thrust force was achieved when the F-Ep composite was drilled at high spindle speed (3000 rpm), with a feed rate of 75 mm/min using the drill point angle of 118°. ANOVA analysis showed that the developed regression model was significant and thrust force was mainly influenced by the spindle speed. Mathematical models were developed for drilling F-Ep composite using response surface methodology (RSM) and adaptive neuro fuzzy inference system (ANFIS) and compared for their efficacy. Epoxy ; Adaptive neuro fuzzy inference system ; Composite material ; Inference system ; Fuzzy inference system ; Drilling ; Chemistry ; Fuzzy inference ; Fuzzy logic ; Materials science ; Fuzzy control system ; Mechanical engineering ; Computer science ; Artificial intelligence ; Engineering Natural Fiber Reinforced Composites ; Tribology and Wear Analysis ; Mechanical Engineering and Vibrations Research
Authors: Arti Yadav | Sweety Sharma | L. K. Gupta | Vaibhav Singh | Rajinder Singh
Source: Journal of Wildlife Science. (review, 2024-08-06)
Field: Chemistry — Organic Chemistry

Current Perspectives in the Forensic Analysis of Timbers using Vibrational Spectroscopy: A Review Wildlife crime has increased significantly with respect to timbers such as illegal timber trading, logging, harvesting, and counterfeiting. It has tremendously drained the economy of different countries since timber trafficking at the global level has an average annual net worth of US$ 50-150 billion. Moreover, timbers can act as important forensic evidence as they can be found at the crime scenes revealing the relationships between the crime scene and corpus delicti. Since ancient times, various traditional techniques have been used for timber identification such as anatomical investigation by visual method at the macroscopic and microscopic levels. However, morphological and anatomical techniques have some advantages, such as cost effectiveness, and limitations as they require experienced personnel. Vibrational spectroscopic tools such as infrared and Raman spectroscopy help in discriminating various species of timber as different timber species have unique phytochemicals. By examining the concentrations of cellulose, lignin, and hemicelluloses, the chemical composition can also be estimated. Herein, this review is carried out using vibrational spectroscopic methods for timber identification to combat criminal activities related to timbers for the dissemination of justice. Recent advancements and prospects are also emphasized in this review paper. Identification (biology) ; Logging ; Crime scene ; Lignin ; Species identification ; Forensic engineering ; Computer science ; Business ; Agroforestry ; Engineering ; Environmental science ; Geography ; Ecology ; Archaeology ; Forestry ; Biology ; Botany ; Evolutionary biology Wood and Agarwood Research ; Spectroscopy and Chemometric Analyses ; Identification and Quantification in Food