jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nima Zoghipour | Yusuf Kaynak
Source: International Journal of Advances in Engineering and Pure Sciences (article, 2023-03-29)
Field: Materials Science — Materials Chemistry

Prediction of the Production Quality During Flat Bottom Drilling of Low Lead Brass Alloy using Fuzzy Logic and Regression models The approved restrictive rules on the containment of Lead element in the chemical composition of the brass alloys which are utilized in drinking water and pumping systems have resulted in developing of new material generations. Neglection or limitation of this element has faced the industry with some serious problems such as lower machinability as compared to the conventional ones. Furthermore, since the most application of the manufactured components from these alloys corresponds to the fluids transfer, the permeability property between the parts and their surface becomes prominent. In other words, any burrs or extra material which are left on the surfaces of manufactured components will make assembly troublesome, causing seals to tear, and permeability problems in the user's hands. In this study, the quality of the machined blind holes with flat bottom drills with various geometries including radial, axial rake angle as well as the cutting edge-radius have been investigated for machining of low-lead brass alloy. Moreover, it has attempted to develop fuzzy logic and regression models in order to predict the machined holes burr height and surface quality. The model predictions have been compared with the experimental data. The obtained results have demonstrated that the developed models are able in predicting of the product quality. Brass ; Machinability ; Machining ; Fuzzy logic ; Rake angle ; Drilling ; Mechanical engineering ; Materials science ; Rake ; Alloy ; Metallurgy ; Computer science ; Engineering ; Artificial intelligence Metallurgy and Material Science ; Aluminum Alloy Microstructure Properties ; Materials Engineering and Processing
Authors: Sukanta Das | Suvasis Swain | Krishnendu Gope | Vishvesh Tadsare | Vaibhav S. Prabhudesai
Source: arXiv (Cornell University) (preprint, 2023-06-29)
Field: Chemistry — Spectroscopy

Effect of Background Signal on Momentum Imaging The velocity Slice Imaging technique has revolutionised electron molecule interaction studies. Multiple electrostatic lens assemblies are often used in spectrometers for resolving low kinetic energy fragments. However, in a crossed-beam experiment with an effusive molecular beam, the extended source of ion generation due to the presence of the background gas creates artefacts on the momentum images as we try to magnify them beyond a certain size. Here, we present a systematic study of this effect on momentum imaging and the solutions to address this issue by background subtraction with suitable magnification. Additionally, we demonstrated that a supersonic molecular beam target helps minimise these artefacts in the image magnification by reducing the background signal. These systematic findings may bring valuable insight into the investigation of low kinetic energy release processes involving electron impact, ion impact, and merge beam experiments with large interaction volumes where high magnification is needed. Magnification ; Kinetic energy ; Background subtraction ; Physics ; Electrostatic lens ; Momentum (technical analysis) ; Optics ; Atomic physics ; Beam (structure) ; Computational physics ; Nuclear physics ; Classical mechanics ; Pixel Mass Spectrometry Techniques and Applications ; Nuclear Physics and Applications ; Ion-surface interactions and analysis
Authors: Pelayo Álvarez Penanes | Vladimir Gorshkov | Mark V. Ivanov | Mikhail V. Gorshkov | Frank Kjeldsen
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-03-06)
Field: Chemistry — Spectroscopy

Potential of Negative Ion Mode Proteomics: MS1-Only Approach ABSTRACT Current proteomics approaches rely almost exclusively on using positive ionization mode, which results in inefficient ionization of many acidic peptides. With an equal quantity of acidic and basic proteins and, correspondingly, the similar number for their derived peptides in case of the human proteome, this inefficient ionization poses both a substantial challenge and a potential. In this work, we study the efficiency of protein identification in the bottom-up proteomic analysis performed in negative ionization mode, using the recently introduced MS1-only ultra-fast data acquisition method DirectMS1. This method is based on accurate peptide mass measurements and predicted retention times. Our method achieves the highest rate of protein identifications in negative ion mode to date, with over 1,000 proteins identified in a human cell line at a 1% false discovery rate using a single-shot 10-min separation gradient, which is comparable with hours-long MS/MS-based analyses. Evaluating the proteins as a function of pI indicated preferable identification of the acidic part of the proteome. Optimization of separation and mass spectrometric experimental conditions facilitated the performance of the method with the best results in terms of spray stability and signal abundance obtained using mobile buffers at 2.5 mM imidazole and 3% isopropanol. The work also highlighted the complementarity of data acquired in positive and negative modes: Combining the results for all replicates for both polarities, the number of identified proteins increased up to 1,774. Finally, we performed analysis of the method’s efficiency when different proteases are used for protein digestion. Among the four studied proteases (LysC, GluC, AspN, and trypsin), we found that trypsin and LysC performed best in terms of protein identification yield. Thus, digestion procedures used for positive mode proteomics can be efficiently utilized for analysis in negative ion mode. Proteome ; Proteomics ; Chemistry ; Ionization ; Chromatography ; Mass spectrometry ; Electrospray ionization ; Analytical Chemistry (journal) ; Bottom-up proteomics ; Human proteome project ; Ion ; Transferability ; Computational biology ; Biological system ; Biochemistry ; Protein mass spectrometry ; Computer science ; Biology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Daniel Ungureanu | Brînduşa Tiperciuc | Cristina Nastasă | Ioana Ionuț | Gabriel Marc | Ilioara Oniga | Ovidiu Oniga
Source: Preprints.org (preprint, 2023-12-05)
Field: Chemistry — Organic Chemistry

An Overview on the Structure-Activity Relationship in Novel Antimicrobial Thiazoles Clubbed with Various Heterocycles (2017-2022) Antimicrobial resistance is an increasing problem for the global public health. One of the strategies to combat this issue is the synthesis of novel antimicrobials through rational drug design based on extensive structure-activity relationship studies. The thiazole nucleus is a prominent feature in the structure of many authorized antimicrobials, being clubbed with different heterocycles. The purpose of this review is to study the structure-activity relationship in antimicrobial thiazoles clubbed with various heterocycles reported in the literature between 2017 and 2022, in order to offer an overview of the last years in terms of antimicrobial research and provide a helpful instrument for future research in the field. Antimicrobial ; Thiazole ; Antimicrobial drug ; Biochemical engineering ; Risk analysis (engineering) ; Computer science ; Business ; Chemistry ; Biology ; Engineering ; Microbiology ; Stereochemistry Synthesis and biological activity ; Click Chemistry and Applications ; Synthesis and Biological Evaluation
Authors: Makoto Otsuka | Yoshihiro Hayashi | Kohei Miyazaki | Masami Mizu | Masahiro Okuno | Tetsuo Sasaki
Source: SSRN Electronic Journal (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Quality Evaluation of White Sugar Crystals Using the Friability Test and Their Non-Destructive Prediction Using Near-Infrared Spectroscopy Friability ; Sugar ; Quality (philosophy) ; White (mutation) ; Test (biology) ; Spectroscopy ; Near infrared reflectance spectroscopy ; Materials science ; Econometrics ; Near-infrared spectroscopy ; Analytical Chemistry (journal) ; Mathematics ; Food science ; Chemistry ; Composite material ; Botany ; Chromatography ; Optics ; Physics ; Biochemistry ; Biology Spectroscopy and Chemometric Analyses
Authors: Annina Z. Lieberherr | Paola Gori‐Giorgi | Klaas J. H. Giesbertz
Source: arXiv (Cornell University) (preprint, 2023-08-17)
Field: Materials Science — Materials Chemistry

Optimal transport distances to characterise electronic excitations Understanding the character of electronic excitations is important in computational and reaction mechanistic studies, but their classification from simulations remains an open problem. Distances based on optimal transport have proven very useful in a plethora of classification problems and seem therefore a natural tool to try to tackle this challenge. We propose and investigate a new diagnostic $Θ$ based on the Sinkhorn divergence from optimal transport. We evaluate a $k$-NN classification algorithm on $Θ$, the popular $Λ$ diagnostic as well as their combination, and assess their performance in labelling excitations, finding that (i) The combination only slightly improves the classification, (ii) Rydberg excitations are not separated well in any setting, and (iii) $Θ$ breaks down for charge transfer in small molecules. We then define a length scale-normalised version of $Θ$ and show that the result correlates closely with $Λ$ for results obtained with Gaussian basis functions. Finally, we discuss the orbital-dependence of our approach and explore an orbital-independent version. Using an optimised combination of the optimal transport and overlap diagnostics together with a different metric is in our opinion the most promising for future classification studies. Metric (unit) ; Rydberg formula ; Computer science ; Gaussian ; Charge (physics) ; Lambda ; Physics ; Divergence (linguistics) ; Statistical physics ; Artificial intelligence ; Algorithm ; Quantum mechanics ; Ionization ; Engineering Machine Learning in Materials Science ; Advanced Chemical Physics Studies ; Spectroscopy and Quantum Chemical Studies
Authors: Claudia Gonzalez Viejo | Natalie Harris | Sigfredo Fuentes
Source: BIO Web of Conferences (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Near-infrared spectroscopy analysis of wines through bottles to assess quality traits and provenance Due to increased fraud rates through counterfeiting and adulteration of quality wines, it is important to develop novel non-destructive techniques to assess wine quality and provenance. Therefore, our research group developed a novel method using near-infrared (NIR) spectroscopy (1596-2396 nm) coupled with machine learning (ML) modeling to assess wine vintages and quality traits based on the intensity of sensory descriptors through the bottle. These were developed using samples from an Australian vineyard for Shirazwines. Models resulted in high accuracy 97% for classification (vintages) and R =0.95 regression (sensory quality traits). The proposed method will allow to assess authenticity and sensory quality traits of any wines in the market without the need to open the bottles, which is rapid, accurate, effective, and convenient. Furthermore, currently, there are low-cost NIR devices available in the market with the required spectral range and sensitivity, which can be affordable for winemakers and retailers that can be used with the ML models proposed here. Wine ; Vineyard ; Quality (philosophy) ; Bottle ; Sensory analysis ; Computer science ; Mathematics ; Statistics ; Food science ; Materials science ; Chemistry ; Horticulture Spectroscopy and Chemometric Analyses ; Meat and Animal Product Quality ; Advanced Chemical Sensor Technologies
Authors: Bünyamin ÜZÜMCÜ | Burak Sarı | Emrah Temel
Source: TEKSTİL VE KONFEKSİYON (article, 2023-06-16)
Field: Materials Science — Polymers and Plastics

Effect of UV Exposure on the Mechanical Properties of Polyurethane-Coated Fabrics Polyurethane materials can be used industrially in different ways, some of which can be used as textile materials or as auxiliary materials applied to textile materials. Polyurethane stands out as a widely used polymer for coating textile products used in outdoor applications, because of high stability at low temperature, flexibility, no or very little volatile organic component content, high water resistance, pH stability, excellent solvent resistance, weather resistance, and many other chemical and mechanical properties. In the study, cotton, polyester, and viscose fabrics were coated with polyurethane and aged under UV light to investigate the causes and behaviors of the mechanical degradation effects of UV on the coating material and fiber. The results indicate that the PU coating process improves the mechanical properties of textile materials while being exposed to UV rays deteriorates the fabric structure. The deterioration in the structure of raw and coated fabrics with the effect of UV increased the air permeability. According to the results of DSC analysis, the increase in the time of UV exposure did not create significant differences in terms of thermal degradation temperatures in both cotton and viscose fabrics. The glass transition temperatures (Tg) increase with more exposure to UV rays, and the UV exposure time had a negative effect on the melting temperature (Tm) and enthalpy (ΔH) of coated polyester fabrics. Polyurethane ; Materials science ; Viscose ; Polyester ; Coating ; Composite material ; Air permeability specific surface ; Polymer ; Textile ; Glass transition ; Thermal stability ; Chemical resistance ; Degradation (telecommunications) ; Chemical engineering ; Layer (electronics) Textile materials and evaluations ; Dyeing and Modifying Textile Fibers ; Color perception and design
Authors: Luis Fernando Cofas‐Vargas | Rodrigo A. Moreira | Simón Poblete | Mateusz Chwastyk | Adolfo B. Poma
Source: arXiv (Cornell University) (preprint, 2023-11-14)
Field: Materials Science — Materials Chemistry

The GōMartini approach: Revisiting the concept of contact maps and the modelling of protein complexes We present a review of a series of contact maps for the determination of native interactions in proteins and nucleic acids based on a distance-threshold. Such contact maps are mostly based on physical and chemical construction, and yet they are sensitive to some parameters (e.g. distances or atomic radii) and can neglect some key interactions. Furthermore, we also comment on a new class of contact maps that only requires geometric arguments. The contact map is a necessary ingredient to build a robust GōMartini model for proteins and their complexes in the Martini 3 force field. We present the extension of a popular structure-based Gō-like approach for the study of protein-sugar complexes, and also limitations of this approach are discussed. The GōMartini approach was first introduced by Poma et al. J. Chem. Theory Comput. 2017, 13(3), 1366-1374 in Martini 2 force field and recently, it has gained the status of gold-standard for protein simulation undergoing conformational changes in Martini 3 force field. We discuss several studies that have provided support to this approach in the context of the biophysical community. Force field (fiction) ; Context (archaeology) ; Field (mathematics) ; Series (stratigraphy) ; Computer science ; Nucleic acid ; Extension (predicate logic) ; Chemistry ; Artificial intelligence ; Mathematics ; Pure mathematics ; Biology ; Geography ; Archaeology Enzyme Structure and Function ; Protein Structure and Dynamics ; Microbial Metabolic Engineering and Bioproduction
Authors: Nilesh Kumar Singh | Shraddha Rawat | Shweta Gautam
Source: International Journal of Environment and Climate Change (article, 2023-09-06)
Field: Chemistry — Analytical Chemistry

Weather-Based Rice Crop Yield Forecasting using Different Regression Techniques &amp; Neural Network Approach for Prayagraj Region Rice crop yield data and weather data were considered in this study, covering the past twenty-nine years (1991-2019) in Prayagraj District, Uttar Pradesh. The data was sourced from DACNET and the College of Forestry, SHUATS Prayagraj. The analysis comprised a calibration period of 26 years (90% of the dataset) and a validation period using the remaining data (10%). In this study, 75.9% of the data were utilized for training the Artificial Neural Network (ANN) model, while the remaining 24.1% were allocated for testing and validation, ensuring comprehensive model assessment. The primary evaluation metric employed for model efficiency was the Normalized Root Mean Squared Error (nRMSE), with a focus on achieving the lowest values. Both a Stepwise Linear Regression technique and a Neural Network were employed for rice yield prediction. Notably, the regression-based model exhibited superior performance compared to the ANN model, as indicated by the nRMSE values. This conclusion was drawn from the observation that the regression-based model yielded the best-fitting results. The study's findings highlight the significance of Bright Sunshine Hours in relation to nRMSE and the coefficient of determination, which were recorded at 0.00025 and 0.94, respectively. This underlines the importance of this meteorological factor in accurately predicting rice crop yield. Artificial neural network ; Regression analysis ; Linear regression ; Regression ; Metric (unit) ; Statistics ; Stepwise regression ; Yield (engineering) ; Mean squared error ; Crop ; Crop yield ; Coefficient of determination ; Mathematics ; Computer science ; Agronomy ; Machine learning ; Biology ; Engineering Spectroscopy and Chemometric Analyses ; Agricultural Economics and Practices ; Smart Agriculture and AI
Authors: Fabian Frommelt | Andrea Fossati | Federico Uliana | Fabian Wendt | Peng Xue | Moritz Heusel | Bernd Wollscheid | Ruedi Aebersold | Rodolfo Ciuffa | Matthias Gstaiger
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-03-24)
Field: Chemistry — Spectroscopy

DIP-MS: A novel ultra-deep interaction proteomics for the deconvolution of protein complexes Abstract Most, if not all, proteins are organized in macromolecular assemblies, which represent key functional units regulating and catalyzing the majority of cellular processes in health and disease. Ever-advancing analytical capabilities promise to pinpoint lesions in proteome modularity driving disease phenotypes. Affinity purification of the protein of interest combined with LC-MS/MS (AP-MS) represents the method of choice to identify interacting proteins. The composition of complex isoforms concurrently present in the AP sample can however not be resolved from a single AP-MS experiment but requires computational inference from multiple time-and resource-intensive reciprocal AP-MS experiments. In this study we introduce Deep Interactome Profiling by Mass Spectrometry (DIP-MS) which combines affinity enrichment with BN-PAGE separation, DIA mass spectrometry and deep-learning-based signal processing to resolve complex isoforms sharing the same bait protein in a single experiment. We applied DIP-MS to probe the organisation of the human prefoldin (PFD) family of complexes, resolving distinct PFD holo- and sub-complex variants, complex-complex interactions and complex isoforms with new subunits that were experimentally validated. Our results demonstrate that DIP-MS can reveal proteome modularity at unprecedented depth and resolution and thus represents a critical steppingstone to relate a proteome state to phenotype in both healthy and diseased conditions. Interactome ; Proteome ; Computational biology ; Proteomics ; Mass spectrometry ; Modularity (biology) ; Chemistry ; Phenotype ; Gene isoform ; Proteogenomics ; Biology ; Chromatography ; Biochemistry ; Genome ; Genetics ; Genomics Advanced Proteomics Techniques and Applications ; Genetic Neurodegenerative Diseases ; Protein Structure and Dynamics
Authors: Milan K Biju | Shivam Trivedi | Rajat Hebbar | J. Brema
Source: Research Square (preprint, 2023-09-08)
Field: Chemistry — Inorganic Chemistry

“Demarcation and Mapping of a Tree Species Using VHRS Data and Deep learning methods” Abstract Coconut holds significant importance in India as a vital source of oil for consumption, particularly due to the high demand for coconut oil. As a result, there has been a rapid expansion of oil coconut tree plantations. Coconut cultivation is widespread, spanning across 90 plus countries, making it one of the majorly grown plantations globally. India, being one of the top producers of coconuts, annually yields 13 billion nuts, utilizing approximately 1.78 million hectares of land for coconut plantations. The primary aim of this study is to assess the accuracy of detecting coconut trees using advanced deep learning techniques applied to high-resolution remote sensing images. The process of tree counting serves two essential purposes. Firstly, it provides an estimate of the number of trees within the plantation, enabling farmers to plan irrigation and fertilization processes more effectively. Secondly, this information is crucial for evaluating the estimated production and determining the value of the field. Deep learning serves as a fundamental framework for accurately detecting trees in high-resolution remote sensing images. Cutting-edge software packages like ArcGIS Pro incorporate deep learning tools that utilize pattern recognition concepts to identify objects in such images. The study reveals the detection of 11,325 coconut trees in the village using deep learning approaches, achieving an accuracy of 87.4%. The findings demonstrate that deep learning techniques offer improved object interpretation. Additionally, the assessment of plantation density in the village suggests that moderately dense coconut plantations occupy a larger area (82.74 hectares) compared to other types of plantations. Deep learning ; Tree (set theory) ; Hectare ; Computer science ; Artificial intelligence ; Agroforestry ; Agricultural engineering ; Geography ; Mathematics ; Environmental science ; Engineering ; Archaeology ; Agriculture Coconut Research and Applications ; Date Palm Research Studies ; Identification and Quantification in Food
Authors: Mete Erdoğan | Nuri Berke Baytekin | Serhat Emre Coban | Alper Demir
Source: arXiv (Cornell University) (preprint, 2023-06-01)
Field: Chemistry — Spectroscopy

Machine Learning and Kalman Filtering for Nanomechanical Mass Spectrometry Nanomechanical resonant sensors are used in mass spectrometry via detection of resonance frequency jumps. There is a fundamental trade-off between detection speed and accuracy. Temporal and size resolution are limited by the resonator characteristics and noise. A Kalman filtering technique, augmented with maximum-likelihood estimation, was recently proposed as a Pareto optimal solution. We present enhancements and robust realizations for this technique, including a confidence boosted thresholding approach as well as machine learning for event detection. We describe learning techniques that are based on neural networks and boosted decision trees for temporal location and event size estimation. In the pure learning based approach that discards the Kalman filter, the raw data from the sensor are used in training a model for both location and size prediction. In the alternative approach that augments a Kalman filter, the event likelihood history is used in a binary classifier for event occurrence. Locations and sizes are predicted using maximum-likelihood, followed by a Kalman filter that continually improves the size estimate. We present detailed comparisons of the learning based schemes and the confidence boosted thresholding approach, and demonstrate robust performance for a practical realization. Kalman filter ; Thresholding ; Computer science ; Artificial intelligence ; Machine learning ; Extended Kalman filter ; Pattern recognition (psychology) Mass Spectrometry Techniques and Applications ; Analytical Chemistry and Chromatography ; Advanced Chemical Sensor Technologies