jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: José Antonio Martín H. | Freddy Perozo | Manuel López
Source: arXiv (Cornell University) (preprint, 2024-10-09)
Field: Chemistry — Analytical Chemistry

Principal Orthogonal Latent Components Analysis (POLCA Net) Representation learning is a pivotal area in the field of machine learning, focusing on the development of methods to automatically discover the representations or features needed for a given task from raw data. Unlike traditional feature engineering, which requires manual crafting of features, representation learning aims to learn features that are more useful and relevant for tasks such as classification, prediction, and clustering. We introduce Principal Orthogonal Latent Components Analysis Network (POLCA Net), an approach to mimic and extend PCA and LDA capabilities to non-linear domains. POLCA Net combines an autoencoder framework with a set of specialized loss functions to achieve effective dimensionality reduction, orthogonality, variance-based feature sorting, high-fidelity reconstructions, and additionally, when used with classification labels, a latent representation well suited for linear classifiers and low dimensional visualization of class distribution as well. Principal component analysis ; Net (polyhedron) ; Latent variable ; Latent class model ; Mathematics ; Computer science ; Statistics Spectroscopy and Chemometric Analyses ; Industrial Technology and Control Systems ; Text and Document Classification Technologies
Authors: Lin-zhi Lang | Qichen Lu | C. M. Dai | Xingbo Wei | Yanxia Liu | Yunbo Zhang
Source: arXiv (Cornell University) (preprint, 2024-02-26)
Field: Materials Science — Materials Chemistry

Two mass-imbalanced atoms in a hard-wall trap: Deep learning integrability of many-body systems The study of integrable systems has led to significant advancements in our understanding of many-body physics. We design a series of numerical experiments to analyze the integrability of a mass-imbalanced two-body system through energy level statistics and deep learning of wavefunctions. The level spacing distributions are fitted by a Brody distribution and the fitting parameter $ω$ is found to separate the integrable and non-integrable mass ratios by a critical line $ω=0$. The convolutional neural network built from the probability density images could identify the transition points between integrable and non-integrable systems with high accuracy, yet in a much shorter computation time. A brilliant example of the network's ability is to identify a new integrable mass ratio $1/3$ by learning from the known integrable case of equal mass, with a remarkable network confidence of $98.78\%$. The robustness of our neural networks is further enhanced by adversarial learning, where samples are generated by standard and quantum perturbations mixed in the probability density images and the wavefunctions, respectively. Trap (plumbing) ; Physics ; Artificial intelligence ; Computer science ; Statistical physics ; Meteorology Machine Learning in Materials Science ; Quantum, superfluid, helium dynamics
Authors: Theresa Cutler
Source: OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) (article, 2024-05-06)
Field: Chemistry — Physical and Theoretical Chemistry

Polymer Compositions in Critical Experiments: Possibly Not What You Think The Chlorine Worth Study (CWS) Experiment was performed at the National Criticality Experiments Research Center (NCERC) in December 2021. Its goal was to provide a validation benchmark experiment with chlorine and plutonium in the thermal neutron spectrum. This purpose is necessary to reduce the margin of subcriticality in aqueous chloride operations at the Los Alamos National Laboratory Plutonium Facility (PF-4). Reducing the margin of subcriticality will enable higher throughput, required to meet NNSA mission needs. The experiment used layers of plutonium plates, polyethylene (HDPE), aluminum, and polyvinyl chloride (PVC) or chlorinated polyvinyl chloride (CPVC). They were optimized to match plutonium-chloride solutions of 30 g/L, 300 g/L, and 600 g/L. Upon completion of the experiment, a International Criticality Safety Benchmark Evaluation Program (ICSBEP) report was immediately started. The compositions of all materials were assumed pure unless additional information was known (such as for the plutonium plates). During the benchmark analysis, the assumed CPVC composition was questioned. The follow-on work led to lessons learned on compositions in benchmarks. Materials, and specifically polymers, are often much more complex than a basic chemical formula. The CWS experiment is used as an example in this paper to document the lessons learned. Polymer ; Materials science ; Composite material History and advancements in chemistry
Authors: M. N. Shrivastava | Sachin Patel | Hemang Shrivastava
Source: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT (review, 2024-12-23)
Field: Chemistry — Analytical Chemistry

A Comprehensive Review of Research on Disease Prediction in Plant Leaves In agriculture, plant disease detection at an early stage is significant. Plant leaves are an important factor in plant disease detection. Early disease prevention in-line harvest loss benefit is given to plant growers. The odd leaves can be visible after getting infected, and if the software can tell accurately the disease infestation result. Forecasting plant leaf disease works better for an early disease warning. The infected leaf needs to be permanently removed, or it will affect the plant. As leaves are a significant part and produce food from the sun, the infected leaves show different patterns. Many proposing such AI and ML models have forecasted the infected leaves of many plants. This document focuses on forecasting diseases in plant leaves mainly for four types of plants: tomato, potato, apple &amp; grape. Diseases in plant leaves can cause significant damage to plants and can have a detrimental effect on both the quantity and quality of production whether we consider a lower scale kitchen gardening or a high scale agriculture farming. The research begins with an overview of the common plant leaf diseases found in each of these four plants, along with their symptoms and causes. It then discusses the importance of early detection and management of these diseases to ensure healthy growth. This helps a naïve gardener as well as an experienced farmer to prevent a plant or a whole field from getting infected or diseased. Plant disease detection approaches are crucial for prevention and management. Various techniques in plant disease detection using AI- based machine learning and deep learning methods are reviewed comprehensively. Keywords— Agriculture farming, Kitchen Garden, Plant leaf diseases, Forecasting diseases. Plant disease ; Disease ; Biology ; Computational biology ; Biotechnology ; Medicine ; Pathology Spectroscopy and Chemometric Analyses
Authors: Meihong Liu | Long Chai | Min Yang | Jiarui Cheng
Source: Materials (article, 2024-06-20)
Field: Materials Science — Materials Chemistry

Effect of Single Particle High-Speed Impingement on the Electrochemical Step Characteristics of a Stainless-Steel Surface In the process of particle erosion and electrochemical corrosion interaction, the electrolyte flow state change, product film destruction, and matrix structure change caused by particle impact affect the electrochemical corrosion process. Such transient, complex physical and electrochemical changes are difficult to capture because of the short duration of action and the small collision area. The peak, step time, and recovery time in this transient step cycle can indirectly reflect the smoothness and reaction rate of the electrochemical reaction system, and thus characterize the resistance to scouring corrosion coupling damage of metals in liquid-solid two-phase flow. In this study, in order to obtain the electrochemical response at the moment of particle impact, electrochemical monitoring experiments using a specially designed miniature three-electrode system were used to test step-critical values, including step potential, current, and resistance, among others. Meanwhile, an electrochemical step model under particle impact considering boundary layer perturbation was developed. The experimental results reflect the effect law of particle impact velocity and particle size on the peak step and recovery period. Meanwhile, the effect of particle impingement on the electrochemical step of stainless steel in different electrolyte solutions was obtained by comparing the step curves in distilled water and Cl-containing water. The connection between the parameters in the electrochemical step model and in the particle impact, as well as the effect of the variation of these parameters on the surface repassivation process are discussed in this paper. By fitting and modeling the test curves, a new mathematical model of electrochemical step-decay under single-particle impact was obtained, which can be used to characterize the change pattern of electrochemical parameters on the metal surface before and after the impingement. Materials science ; Electrochemistry ; Particle (ecology) ; High-speed steel ; Composite material ; Metallurgy ; Surface (topology) ; Chemistry ; Electrode ; Geometry ; Physical chemistry ; Mathematics Metal Alloys Wear and Properties ; Surface Treatment and Residual Stress ; Hydrogen embrittlement and corrosion behaviors in metals
Authors: Sérgio Paulo Jorge Rodrigues | P. J. S. B. Caridade
Source:  (article, 2024-06-18)
Field: Chemistry — Physical and Theoretical Chemistry

Computational Chemistry in Graduation Courses of Chemistry The experience of the authors on the use of computers on teaching at the various levels of the graduation, in Chemistry and Medicinal Chemistry, ranging from General Chemistry to Drug Design, passing through Computational Chemistry and other courses is presented. The capabilities of computers for revealing the microscopic, unseen, and hidden patterns at all levels of the graduation, and to show how computers are used in research on molecules, materials, and drugs, at higher levels of the graduation, are emphasized. The approach is based on simple ideas and concepts, practical laboratory work, motivational narratives, and clear feedback. New forms of clarifying complex ideas, sometimes presented in a “muddy” way, in particular for Computational Chemistry and Drug Design, are procured. Graduation (instrument) ; Chemistry ; Computer science ; Mathematics education ; Engineering physics ; Engineering ; Mathematics ; Mechanical engineering Various Chemistry Research Topics
Authors: Pavlukhina, O.O. | Buchelnikov, V.D. | Sokolovskiy, V.V. | Zagrebin, M.A. | Zotov, I.S. | Павлухина, О.О. | Бучельников, В.Д. | Соколовский, В.В. | Загребин, М.А. | Зотов, И.С.
Source: Bulletin of the South Ural State University Series Mathematical Modelling Programming and Computer Software (article, 2024-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Magnetic Properties and Electronic Structure of Half-Heusler Alloys FeRhSb1-xZx (Z = P, As, Sn, Si, Ge, Ga, In, Al) The electronic structure and magnetic properties of FeRhSb 1-x Z x (x = 0, 0,25, 0,5, 0,75, 1) alloys with Z = P, As, Sn, Si, Ge, Ga, In, Al are studied by first-principles methods.For all compounds, three cubic phases with different atomic arrangement (, , and ) are considered.It is shown that the -phase is energetically favorable for FeRhSb 1-x P x (x = 0,75, 1), FeRhAs and FeRhSi alloys.For the remaining 29 alloys, the phase is more energetically stable.The values of equilibrium lattice parameters and magnetic moments of stoichiometric ternary alloys are in good agreement with the literature values collected from other theoretical studies.The half-metallic ferromagnetic behavior is predicted for FeRhSb 0,25 Sn 0,75 , FeRhGe, FeRhSn, and FeRhSb 0,5 Al 0,5 .It has been found that the replacement of the Z element with another sp element allows for the creation of new fourcomponent alloys that exhibit 100 % spin polarization. Materials science ; Condensed matter physics ; Crystallography ; Physics ; Chemistry Heusler alloys: electronic and magnetic properties ; Intermetallics and Advanced Alloy Properties ; Magnetic Properties of Alloys
Authors: Jhon Taquila-Velásquez | Alexis Vela-Guerra | Pedro Miguel Portillo Mendoza | Carlos Sotomayor-Beltran
Source: International Journal of Engineering Trends and Technology (article, 2024-01-10)
Field: Chemistry — Analytical Chemistry

Detection of Peruvian Golden Berry Quality using a Convolutional Neural Network In Peru, the National Strategic Export Plan seeks to promote exports of many fruits, among them the Peruvian golden berries. To increase the production speed of golden berries to match foreign competition, the design of a convolutional neural network (CNN) algorithm applied to the selection of this type of berry is proposed. The classification of the golden berry quality has been defined according to its condition, a good one and in poor condition. The developed classification software, at its core, is based on a pre-trained CNN called GoogLeNet, which was implemented using Matlab development software; additionally, the Matlab App designer extension was used to create a graphical user interface. The architecture of the CNN allowed us to obtain the characteristic parameters of the fruit, where the criterion for using the database considered 80% images for training, 10% for validation, and 10% for post-training. For this work, 241 images were used, divided into two blocks, as NO_GOOD (in poor condition and immature) and as GOOD (mature and in good condition). The best-trained CNN, with a validation percentage of 100%, was embedded in the user interface. A total of 6000 iterations and 500 epochs were used for the training of the CNN. The development of the user interface system allowed to select golden berries based on their quality at greater speeds than by doing it manually. Berry ; Convolutional neural network ; Computer science ; Quality (philosophy) ; Artificial intelligence ; Horticulture ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies
Authors: Xiaoxia Qi | Md Gapar Md Johar | Ali Khatibi | Jacquline Tham | Long Cheng
Source: International Journal of Computer Science and Information Technology (article, 2024-11-24)
Field: Materials Science — Electronic, Optical and Magnetic Materials

YOLOv5-DCN: An Effective Improvement Based on YOLOv5 Detector Urine sediment detection is of great significance for the diagnosis and monitoring of kidney diseases, urinary tract infections, stones, etc. This study aims to propose fast, high-precision and lightweight Urinary particles detection model based on YOLOv5, deformable convolution, and evaluate its performance in Urinary particles detection tasks. Computer science ; Detector ; Telecommunications Ga2O3 and related materials ; CCD and CMOS Imaging Sensors ; GaN-based semiconductor devices and materials
Authors: Preeti Jaidka | Prashant Upadhyay | Aman Kumar | Anuj Kumar | Satya Prakash Yadav
Source: Evergreen (article, 2024-12-01)
Field: Chemistry — Inorganic Chemistry

Transforming Coconut Farming with Deep Learning Disease Detection Early detection of plant diseases is crucial for effective prevention and control of food insecurity in agriculture. Coconut trees are vulnerable to various pathogens such as fungi, bacteria, viruses, and nematodes, which can cause significant quantitative and qualitative losses. In this study, we suggested a strategy that applies the resulting hybrid model (NNSVCLD), which is created by modifying SVM and CNN, to extract deep features from images and classify out of 5 diseases to which class it belongs. Comprehensive testing is carried out on Kaggle dataset, and performance measures like accuracy, precision, recall, specificity, and F1-rating are assessed and examined. According to the experimental results, the introduced version outperformed the other cutting-edge hybrid learning models in terms of accuracy, precision, recall, and F1 rating for different folds. The proposed method shows accuracy with 98.9% for three, 99.3% for five folds, and 99.4% for 10 folds respectively. The efficacy of the model is further supported by the precision, recall, and F1-scores for each category, which range from 98.7 to 99.4%. Agriculture ; Agroforestry ; Biotechnology ; Artificial intelligence ; Computer science ; Biology ; Ecology Coconut Research and Applications ; Smart Agriculture and AI
Authors: Zakarya Benayad | Guillaume Stirnemann
Source: Biophysical Journal (article, 2024-02-01)
Field: Chemistry — Spectroscopy

Enhanced conformational space exploration for biomolecules using denoising diffusion probabilistic models Biomolecule ; Probabilistic logic ; Diffusion ; Space (punctuation) ; Statistical physics ; Biological system ; Computer science ; Nanotechnology ; Materials science ; Physics ; Artificial intelligence ; Biology ; Thermodynamics Analytical Chemistry and Chromatography ; Computational Drug Discovery Methods ; Protein Structure and Dynamics
Authors: Joanna Li | Jonathan J. Wang | Dvira Segal
Source: arXiv (Cornell University) (preprint, 2024-02-09)
Field: Chemistry — Organic Chemistry

Thermal transport in fullerene-based molecular junctions: Molecular dynamics simulations We investigate phonon thermal transport of fullerene-based single-molecule junctions by employing classical molecular dynamics simulations. The thermal conductances of fullerene monomers, dimers, and trimers are computed through three distinct molecular dynamics methods, by following the equilibration dynamics in one method, and using two other nonequilibrium simulation methods. We discuss technical aspects of the simulation techniques, and show that their predictions for the thermal conductance agree. Our simulations reveal that the thermal conductance of fullerene monomer and dimer junctions are similar, yet the thermal conductance of trimer junctions is significantly reduced. This study could assist in the design of high-performing thermoelectric junctions, where low thermal conductance is desired. Molecular dynamics ; Fullerene ; Chemical physics ; Thermal ; Dynamics (music) ; Materials science ; Nanotechnology ; Chemistry ; Physics ; Computational chemistry ; Thermodynamics ; Organic chemistry Fullerene Chemistry and Applications ; Molecular Junctions and Nanostructures ; Carbon Nanotubes in Composites
Authors: Ananda Suci Annisa | Trisna Kumala Sari | Indang Dewata | Miftahul Khair | Okta Suryani
Source: Jurnal Periodic Jurusan Kimia UNP (article, 2024-04-01)
Field: Chemistry — Physical and Theoretical Chemistry

Pengaruh Supporting Electrolyte pada Penentuan Formaldehida secara Voltametri Siklik Menggunakan Au/MWCNT/GCE Formaldehyde was detected electrochemically using a modified glassy carbon electrode with a multi-walled carbon nanotube (MWCNT) and a thin layer of gold (Au). Modification of MWCNT was carried out by drop coating, while modification of the thin layer of gold was carried out by sweeping-potential electrodeposition using the cyclic voltammetry method. The purpose of this study was to develop a formaldehyde sensor using Au/MWCNT/GCE and to determine the optimum conditions of the supporting electrolyte for formaldehyde measurement. The results showed that Au/MWCNT/GCE provided better catalytic activity than GCE, MWCNT/GCE and Au/GCE electrodes in the determination of formaldehyde. The optimum supporting electrolyte obtained was KOH with a test analyte concentration of 10 mM. The oxidation peak of formaldehyde I is at a potential of 0.0264 V and peak of oxidation of formaldehyde II is at a potential of 0.075 V. Electrolyte ; Materials science ; Chemical engineering ; Chemistry ; Physical chemistry ; Engineering ; Electrode Advanced Physical and Chemical Molecular Interactions ; Engineering and Technology Innovations ; Chemical and Physical Properties of Materials
Authors: CH Sakhinamma | Yogesh Chandra | K Sri Lakshmi Samyuktha
Source: International Journal of Pharmacy and Pharmaceutical Science (article, 2024-01-01)
Field: Chemistry — Spectroscopy

Recent innovations in HPLC column technology: Advancements, applications and future trends in analytical separations High-Performance Liquid Chromatography (HPLC) has long been a fundamental analytical technique used across a variety of industries, thanks to its high resolution, sensitivity, and versatility in separating complex mixtures. The performance of HPLC is primarily determined by the technology used in its columns. Over the years, significant innovations in column design, materials, and stationary phases have dramatically improved separation efficiency, resolution, speed, and reproducibility. These advancements have enabled new applications in diverse fields such as pharmaceuticals, food safety, environmental monitoring, and biotechnology. This review focuses on recent innovations in HPLC column technology and explores their implications for modern analytical practices. Key advancements in small particle size technology, core-shell design, monolithic columns, and new stationary phase materials are discussed, alongside their applications across various industries. The integration of these innovations into HPLC systems continues to push the boundaries of what can be achieved in terms of separation performance and analysis speed. Column (typography) ; Chromatography ; Computer science ; Nanotechnology ; Data science ; Materials science ; Chemistry ; Telecommunications Analytical Chemistry and Chromatography ; Water Quality Monitoring and Analysis ; Analytical chemistry methods development
Authors: Jie Yan | Jing Liu | Yi-Zi Ning | Zhongyuan Zhang
Source: arXiv (Cornell University) (preprint, 2024-02-20)
Field: Materials Science — Materials Chemistry

CCFC++: Enhancing Federated Clustering through Feature Decorrelation In federated clustering, multiple data-holding clients collaboratively group data without exchanging raw data. This field has seen notable advancements through its marriage with contrastive learning, exemplified by Cluster-Contrastive Federated Clustering (CCFC). However, CCFC suffers from heterogeneous data across clients, leading to poor and unrobust performance. Our study conducts both empirical and theoretical analyses to understand the impact of heterogeneous data on CCFC. Findings indicate that increased data heterogeneity exacerbates dimensional collapse in CCFC, evidenced by increased correlations across multiple dimensions of the learned representations. To address this, we introduce a decorrelation regularizer to CCFC. Benefiting from the regularizer, the improved method effectively mitigates the detrimental effects of data heterogeneity, and achieves superior performance, as evidenced by a marked increase in NMI scores, with the gain reaching as high as 0.32 in the most pronounced case. Decorrelation ; Cluster analysis ; Feature (linguistics) ; Computer science ; Data mining ; Artificial intelligence ; Computer vision ; Linguistics Nanocluster Synthesis and Applications ; Privacy-Preserving Technologies in Data ; Spam and Phishing Detection
Authors: Robert V. Stanton | Liang Xue | Shivani Tiwary | Mykola Bordyuh | Pierre M. Jean Beltran
Source: Research Square (preprint, 2024-02-05)
Field: Chemistry — Spectroscopy

CoSpred: Machine learning workflow to predict tandem mass spectrum in proteomics Workflow ; Computer science ; Proteomics ; Artificial intelligence ; Machine learning ; Chemistry ; Database Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies