jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nurettin Şenyer | Recai OKTAŞ | Mehmet Serhat Odabaş | Dursun Kurt | Eren KARABOĞA
Source: Türkiye Tarımsal Araştırmalar Dergisi (article, 2023-07-23)
Field: Chemistry — Analytical Chemistry

A Hybrid Mobile Application for Quality Grade of Tobacco (Nicotiana tabacum L.) Using Correlated Color Temperature Concerning tobacco (Nicotiana tabacum L.) producers the size and color of the leaf are important factors in understanding the quality grade of tobacco leaves in the market. The color of tobacco leaves serves as an indicator of quality and is referred to as the maturity index when determining the optimal time for harvesting. In this study, a hybrid mobile application was developed to help determine the harvest time. CoLab was preferred as the backend. Python Imaging Library (Pillow) was used for image processing on the server side. Color correction was performed on the images taken with the help of X-rite. The correlated color temperature (CCT) value of the repaired images was calculated. The CCT values were calculated using the Ohno method. Quality grade (QG) was calculated using the mean CCT value. The data of the images obtained depending on the time were used in the application as a graphic. We present quality grade of tobacco automatically identifying the plant leaves in a given image with the help of the mobile application. Nicotiana tabacum ; Tobacco leaf ; Mathematics ; Python (programming language) ; Computer science ; Horticulture ; Artificial intelligence ; Computer vision ; Biology ; Engineering ; Agricultural engineering Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Leaf Properties and Growth Measurement
Authors: David N. Palacio | Alejandro Velasco | Daniel Rodríguez-Cárdenas | Kevin Moran | Denys Poshyvanyk
Source: arXiv (Cornell University) (preprint, 2023-08-07)
Field: Materials Science — Materials Chemistry

Evaluating and Explaining Large Language Models for Code Using Syntactic Structures Large Language Models (LLMs) for code are a family of high-parameter, transformer-based neural networks pre-trained on massive datasets of both natural and programming languages. These models are rapidly being employed in commercial AI-based developer tools, such as GitHub CoPilot. However, measuring and explaining their effectiveness on programming tasks is a challenging proposition, given their size and complexity. The methods for evaluating and explaining LLMs for code are inextricably linked. That is, in order to explain a model's predictions, they must be reliably mapped to fine-grained, understandable concepts. Once this mapping is achieved, new methods for detailed model evaluations are possible. However, most current explainability techniques and evaluation benchmarks focus on model robustness or individual task performance, as opposed to interpreting model predictions. To this end, this paper introduces ASTxplainer, an explainability method specific to LLMs for code that enables both new methods for LLM evaluation and visualizations of LLM predictions that aid end-users in understanding model predictions. At its core, ASTxplainer provides an automated method for aligning token predictions with AST nodes, by extracting and aggregating normalized model logits within AST structures. To demonstrate the practical benefit of ASTxplainer, we illustrate the insights that our framework can provide by performing an empirical evaluation on 12 popular LLMs for code using a curated dataset of the most popular GitHub projects. Additionally, we perform a user study examining the usefulness of an ASTxplainer-derived visualization of model predictions aimed at enabling model users to explain predictions. The results of these studies illustrate the potential for ASTxplainer to provide insights into LLM effectiveness, and aid end-users in understanding predictions. Computer science ; Robustness (evolution) ; Visualization ; Code (set theory) ; Transformer ; Security token ; Granularity ; Data science ; Artificial intelligence ; Machine learning ; Programming language ; Computer security Machine Learning in Materials Science ; Software Engineering Research ; Ferroelectric and Negative Capacitance Devices
Authors: Yufei Song | Xi Meng | Yan Li | Zhiguo Liu | Huitao Zhang
Source: Traitement du signal (article, 2023-06-28)
Field: Chemistry — Analytical Chemistry

An Integrative Approach for Mineral Nutrient Quantification in Dioscorea Leaves: Uniting Image Processing and Machine Learning Dioscorea, lauded for its environmental sustainability and versatility, is pivotal in various industries, ranging from food to medicine.The significant yields it offers, coupled with low cultivation inputs and substantial nutritional benefits -including high protein content and minimal sugar levels, necessitate precision in yield predictions and growth management.This, in turn, fosters the evolution of mechanized agriculture and automation.To address this need, a novel methodology amalgamating digital image processing and machine learning algorithms has been established to accurately determine the mineral nutrient content in Dioscorea leaves.This methodology initiates with the separation of the foreground and background in leaf images, achieved through the implementation of the H-component OTSU algorithm.Subsequently, the computation of 54 color features is carried out, and machine learning techniques are harnessed to form models that delineate image features in correlation with a SPAD value exceeding 0.9.The aspiration of this model development lies in the prediction of chlorophyll, nitrogen, phosphorus, and potassium content in Dioscorea leaves.It has been determined that the Multilayer Perceptron (MLP), post 100 iterations, constructs the most accurate model for predicting SPAD content in Dioscorea nipponica.In terms of nitrogen content prediction, a regression model exploiting the SL characteristic has been discovered to be optimal, demonstrating an R2 of 0.850.For phosphorus, a model incorporating the NRI characteristic has yielded an R2 of 0.819, affirming its efficacy.Meanwhile, potassium content prediction has been found to be most precise with a model centered on the Sb characteristic, as evidenced by an achieved R2 of 0.865.This cuttingedge methodology can significantly advance the agricultural sector, particularly in the realm of mechanized agriculture and automation. Dioscorea ; Image processing ; Mineral ; Artificial intelligence ; Agricultural engineering ; Computer science ; Image (mathematics) ; Machine learning ; Environmental science ; Engineering ; Biology ; Ecology ; Medicine Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI
Authors: Simon León Krug | O. Anatole von Lilienfeld
Source: arXiv (Cornell University) (preprint, 2023-12-07)
Field: Chemistry — Physical and Theoretical Chemistry

The Alchemical Integral Transform revisited We recently introduced the Alchemical Integral Transform (AIT) enabling the prediction of energy differences, and guessed an Ansatz to parametrize space $\pmb{r}$ in some alchemical change $λ$. Here, we present a rigorous derivation of AIT's kernel $\mathcal{K}$ and discuss the parametrization $\pmb{r}(λ)$ in $n$ dimensions, i.e. necessary conditions, mathematical freedoms and additional constraints when obtaining it. Analytical expressions for changes in energy spectra and densities are given for a number of systems. Examples include homogeneous potentials like the quantum harmonic oscillator, Hydrogen-like atom, and Dirac well, both for one- and multiparticle cases, and a multiparticle system beyond coordinate scaling for harmonic potentials. Atom (system on chip) ; Electron ; Alchemy ; Physics ; Energy (signal processing) ; Atomic physics ; Quantum mechanics ; Computer science ; Art ; Literature History and advancements in chemistry ; Various Chemistry Research Topics
Authors: Nilüfer Ertekin
Source: Ömer Halisdemir Üniversitesi Mühendislik Bilimleri Dergisi (article, 2023-10-18)
Field: Materials Science — Materials Chemistry

Investigating the Width Effect on Energy Gaps of Electrodes in Boron Arsenide based Nano-Transistors, A DFT Study This study deals with electronic properties of Boron Arsenide nanoribbons using Density function theory (DFT) calculations. Under specific conditions, nanoribbons can be used as Nano-transistors. It is feasible to modify nanoribbons' electronic properties, such as bandgap and conductivity, by varying their width and edge shape. Source, drain, and gate are basic parts of a conventional field-effect transistor (FET). The channel's conductivity can be changed by applying a voltage to the gate electrode. In current research, boron Arsenide nanoribbons has been investigated as lead electrode of a transistor and stability of the sheet has been confirmed by positive phonon vibrational modes. Utilizing the band structure spectrum, band gap as an electronic property is measured and reported. Different width values for electrodes have been considered and it has represented that the band gap is size dependent and increasing in ribbon’s size to bulk structure, results in decrements in band gap energy value. Band gap ; Materials science ; Condensed matter physics ; Field-effect transistor ; Electrode ; Conductivity ; Ribbon ; Transistor ; Optoelectronics ; Phonon ; Voltage ; Electrical engineering ; Chemistry ; Physics ; Composite material Boron and Carbon Nanomaterials Research ; Thermal properties of materials ; Semiconductor materials and interfaces
Authors: Venugopal Naidu Manubolu | Diwakar Reddy Vanimireddy
Source: International Journal of experimental research and review (article, 2023-12-30)
Field: Materials Science — Polymers and Plastics

Experimental Study on Mechanical Behaviour of Arhar-Reinforced Composites using GRA with PCA The fiber composite plays a vital role in various applications in the current scenario. Natural fibers provide the desired advantages over synthetic fibers. Fiber composites deliver the exploration of durable, degradable, and lightweight materials. In several industrial sectors, transportation of materials from one place to another place is required. Materials like thermocoal boxes and iron boxes are inefficient in strength, weight, and cost. In order to overcome these, natural fiber-based composites, which give both strength and durability with low weight and cost, are chosen. This work explains the sample preparation using Arhar powder with an addition of reinforcements using the hand lay-up method by obeying the ASTM standards. To analyze the performance of the sample specimen, properties evaluated like Compression, Flexural, and other tests were conducted. GRA (Grey-Relational Analysis) with PCA (Principal-Component Analysis) was employed to carry out the most significant parameters, the most significant factor, and the weights of optimal parameters. Based on the output responses, it is experiential that the most effective parameter and best combination factor at Acrylonitrile butadiene styrene (ABS-4%), Guar Gum (8%), and Carboxy methyl cellulose (CMC-8%) with the values of 83.12 MPa compression strength, 61.70 MPa tensile strength and 0.249 joules of Impact strength. Materials science ; Composite material ; Ultimate tensile strength ; Fiber ; Flexural strength ; Natural fiber ; Composite number ; Compressive strength ; Durability ; Grey relational analysis ; Compression (physics) ; Mathematics Natural Fiber Reinforced Composites ; Mechanical Engineering and Vibrations Research
Authors: Cecilia Herrero | Ludovic Berthier
Source: arXiv (Cornell University) (preprint, 2023-10-25)
Field: Materials Science — Materials Chemistry

Direct numerical analysis of dynamic facilitation in glass-forming liquids We propose a computational strategy to quantify the temperature evolution of the timescales and lengthscales over which dynamic facilitation affects the relaxation dynamics of glass-forming liquids at low temperatures, that requires no assumption about the nature of the dynamics. In two glass models, we find that dynamic facilitation depends strongly on temperature, leading to a subdiffusive spreading of relaxation events which we characterize using a temperature-dependent dynamic exponent. We also establish that this temperature evolution represents a major contribution to the increase of the structural relaxation time. Facilitation ; Relaxation (psychology) ; Dynamic relaxation ; Exponent ; Dynamics (music) ; Materials science ; Statistical physics ; Chemical physics ; Mechanics ; Physics ; Psychology Material Dynamics and Properties ; Glass properties and applications ; Theoretical and Computational Physics
Authors: Isaac Kante
Source: arXiv (Cornell University) (preprint, 2023-11-13)
Field: Materials Science — Surfaces, Coatings and Films

Machine Learning For Beamline Steering Beam steering is the process involving the calibration of the angle and position at which a particle accelerator's electron beam is incident upon the x-ray target with respect to the rotation axis of the collimator. Beam Steering is an essential task for light sources. In the case under study, the LINAC To Undulator (LTU) section of the beamline is difficult to aim. Each use of the accelerator requires re-calibration of the magnets in this section. This involves a substantial amount of time and effort from human operators, while reducing scientific throughput of the light source. We investigate the use of deep neural networks to assist in this task. The deep learning models are trained on archival data and then validated on simulation data. The performance of the deep learning model is contrasted against that of trained human operators. Beamline ; Computer science ; Calibration ; Collimator ; Undulator ; Beam (structure) ; Task (project management) ; Artificial neural network ; Optics ; Deep learning ; Artificial intelligence ; Simulation ; Physics ; Engineering ; Systems engineering Electron and X-Ray Spectroscopy Techniques ; Particle Accelerators and Free-Electron Lasers ; Medical Imaging Techniques and Applications
Authors: Satish Pujari | Alekya Pilla | Sivarao Subramonian | Daniel Mandrumaka
Source: Scientia Iranica (article, 2023-06-20)
Field: Materials Science — Polymers and Plastics

Python programming predictions of Thermal Behavioral Aspects of Orange Peel and Coconut-Coir Reinforced Epoxy Composites Using a hand lay-up approach, both orange peel and coconut coir fibres are used in particulate form with an epoxy matrix to create partly green biodegradable composites. The findings indicate great opportunities for employing these natural fibres. The thermal conductivity of orange peel and coconut-coir epoxy composites was measured experimentally for various volume fractions of particulate fibres. The experimental findings show that as fibre concentration increases, thermal conductivity decreases. Experimental data are compared to theoretical models to determine the change in thermal conductivity with fibre amount fraction.There was a clear correlation between the hypotheses and the actual results. Regression analysis using Python programming is also done for the prediction of the thermal properties of particulate orange peel and coconut-coir fibre composites. It is observed that coir fibre composites outperformed the orange peel, indicating that the coir fibre composite is a proper thermal insulator that can be used in many industries, like the automotive industry, buildings, and steam pipes, to reduce heat transfer and thereby save a lot of energy. Coir ; Composite material ; Materials science ; Epoxy ; Thermal conductivity ; Homogenization (climate) Natural Fiber Reinforced Composites
Authors: Shih-Yu Chen | Yung-Ming Kuo | Ruihong Zhang | Tsai-Yi Cheng | Po-Yu Chu | Li‐Wei Kang | Chinsu Lin
Source: Preprints.org (preprint, 2023-06-02)
Field: Chemistry — Analytical Chemistry

The Development of Peanut Defect Detection Technique Using Hyperspectral Images In Taiwan, peanuts are classified by hand. The kernel quality is judged based on the appearance of peanuts. The identification takes considerable time and labor. Fatigue can induce misrecogni-tion or grade cognition instability due. Peanut defects are generally divided into health, underde-velopment, insect bite, and rupture. This study employed two imaging methods push-boom FX10 and Snapshot. Deep learning was used to detect peanut defects. A push-broom instrument was used for testing. There were 1,560 peanuts, including 240 good and 240 bad ones. There were a total of 1,080 good peanuts and bad peanuts in the test set data, the band selection was used, and a CNN model was built. Based on the results from three methods, 3DCNN could classify with 97%accuracy. The Snapshot was used to achieve the real-time design of a lightweight CNN model. Finally, five bands were selected using PCA, and the screening could be accurate and efficient. The maximum overall accuracy was about 98.5%. Kappa was 97.3% with real-time recognition. This will be a major advantage in the future practical application and commercialization processes. This technique can reduce labor costs, achieve intelligent detection and intelligent grading, and bring breakthroughs to smart agriculture for other crops as well. Hyperspectral imaging ; Computer science ; Artificial intelligence ; Snapshot (computer storage) ; Pattern recognition (psychology) Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Julien Martinelli | Jeremy Grignard | Sylvain Soliman | Annabelle Ballesta | François Fages
Source: HAL (Le Centre pour la Communication Scientifique Directe) (preprint, 2023-01-21)
Field: Chemistry — Physical and Theoretical Chemistry

Reactmine: a statistical search algorithm for inferring chemical reactions from time series data Inferring chemical reaction networks (CRN) from concentration time series is a challenge encouragedby the growing availability of quantitative temporal data at the cellular level. This motivates thedesign of algorithms to infer the preponderant reactions between the molecular species observed ina given biochemical process, and build CRN structure and kinetics models. Existing ODE-basedinference methods such as SINDy resort to least square regression combined with sparsity-enforcingpenalization, such as Lasso. However, we observe that these methods fail to learn sparse modelswhen the input time series are only available in wild type conditions, i.e. without the possibility toplay with combinations of zeroes in the initial conditions. We present a CRN inference algorithmwhich enforces sparsity by inferring reactions in a sequential fashion within a search tree of boundeddepth, ranking the inferred reaction candidates according to the variance of their kinetics on theirsupporting transitions, and re-optimizing the kinetic parameters of the CRN candidates on the wholetrace in a final pass. We show that Reactmine succeeds both on simulation data by retrievinghidden CRNs where SINDy fails, and on two real datasets, one of fluorescence videomicroscopyof cell cycle and circadian clock markers, the other one of biomedical measurements of systemiccircadian biomarkers possibly acting on clock gene expression in peripheral organs, by inferringpreponderant regulations in agreement with previous model-based analyses. The code is available athttps://gitlab.inria.fr/julmarti/crninf/ together with introductory notebooks. Series (stratigraphy) ; Computer science ; Algorithm ; Time series ; Machine learning ; Geology History and advancements in chemistry ; Advanced Chemical Sensor Technologies
Authors: Daohong Chen | Leixi Wang | Yawei Lv | Lei Liao | Kenli Li | Changzhong Jiang
Source: Journal of Physics Condensed Matter (article, 2023-04-11)
Field: Materials Science — Materials Chemistry

Insights into electronic properties of strained two-dimensional semiconductors by out-of-plane bending Strain engineering is an important strategy to modulate the electronic and optical properties of two-dimensional (2D) semiconductors. In experiments, an effective and feasible method to induce strains on 2D semiconductors is the out-of-plane bending. However, in contrast to the in-plane methods, it will generate a combined strain effect on 2D semiconductors, which deserves further explorations. In this work, we theoretically investigate the carrier transport-related electronic properties of arsenene, antimonene, phosphorene, and MoS<sub>2</sub>under the out-of-plane bending. The bending effect can be disassembled into the in-plane and out-of-plane rolling strains. We find that the rolling always degrades the transport performance, while the in-plane strain could boost carrier mobilities by restraining the intervalley scattering. In other words, pursuing the maximum in-plane strain at the expense of minimum rolling should be the primary strategy to promote transports in 2D semiconductors through bending. Electrons in 2D semiconductors usually suffer from the serious intervalley scattering caused by optical phonons. The in-plane strain can break the crystal symmetry and separate nonequivalent energy valleys at band edges energetically, confining carrier transports at the Brillouin zone Γ point and eliminating the intervalley scattering. Investigation results show that the arsenene and antimonene are suitable for the bending technology, because of their small layer thicknesses which can relieve the rolling burden. Their electron and hole mobilities can be doubled simultaneously, compared with their unstrained 2D structures. From this study, the rules for the out-of-plane bending technology towards promoting transport abilities in 2D semiconductors are obtained. Semiconductor ; Band bending ; Bending ; Condensed matter physics ; Phosphorene ; Materials science ; Brillouin zone ; Plane (geometry) ; Scattering ; Electron ; Electron mobility ; Band gap ; Optoelectronics ; Optics ; Physics ; Composite material ; Geometry 2D Materials and Applications ; MXene and MAX Phase Materials ; Ga2O3 and related materials
Authors: Seongmin Lee | Kyoung-Chul Kim | Kangjin Lee | Jinhwan Ryu | Youngki Hong | Byeong-Hyo Cho
Source: Korean Journal of Agricultural Science (article, 2023-09-01)
Field: Chemistry — Analytical Chemistry

Potential of multispectral imaging for maturity classification and recognition of oriental melon In this study, we aimed to apply multispectral imaging (713 -920 nm, 10 bands) for maturity classification and recognition of oriental melons grown in hydroponic greenhouses.A total of 20 oriental melons were selected, and time series multispectral imaging of oriental melons was 7 -9 times for each sample from April 21, 2023, to May 12, 2023.We used several approaches, such as Savitzky-Golay (SG), standard normal variate (SNV), and Combination of SG and SNV (SG + SNV), for pre-processing the multispectral data.As a result, 713 -759 nm bands were preprocessed with SG for the maturity classification of oriental melons.Additionally, a Light Gradient Boosting Machine (LightGBM) was used to train the recognition model for oriental melon.R 2 of recognition model were 0.92, 0.91 for the training and validation sets, respectively, and the F-scores were 96.6 and 79.4% for the training and testing sets, respectively.Therefore, multispectral imaging in the range of 713 -920 nm can be used to classify oriental melons maturity and recognize their fruits. Multispectral image ; Melon ; Maturity (psychological) ; Artificial intelligence ; Computer science ; Geography ; Pattern recognition (psychology) ; Remote sensing ; Cartography ; Biology ; Horticulture ; Psychology Spectroscopy and Chemometric Analyses ; Advances in Cucurbitaceae Research ; Seed and Plant Biochemistry