jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Matthew Andres Moreno | Connor Yang | Emily Dolson | Luis Zaman
Source:  (article, 2024-01-01)
Field: Materials Science — Materials Chemistry

Trackable Agent-based Evolution Models at Wafer Scale Continuing improvements in computing hardware are poised to transform capabilities for in silico modeling of cross-scale phenomena underlying major open questions in evolutionary biology and artificial life, such as transitions in individuality, eco-evolutionary dynamics, and rare evolutionary events. Emerging ML/AI-oriented hardware accelerators, like the 850,000 processor CerebrasWafer Scale Engine (WSE), hold particular promise. However, many practical challenges remain in conducting informative evolution experiments that efficiently utilize these platforms’ large processor counts. Here, we focus on the problem of extracting phylogenetic information from agent-based evolution on the WSE platform. This goal drove significant refinements to decentralized in silico phylogenetic tracking, reported here. These improvements yield order-of-magnitude performance improvements. We also present an asynchronous island-based genetic algorithm (GA) framework forWSE hardware. Emulated and on-hardware GA benchmarks with a simple tracking-enabled agent model clock upwards of 1 million generations a minute for population sizes reaching 16 million agents. This pace enables quadrillions of agent replication events a day. We validate phylogenetic reconstructions from these trials and demonstrate their suitability for inference of underlying evolutionary conditions. In particular, we demonstrate extraction, from wafer-scale simulation, of clear phylometric signals that differentiate runs with adaptive dynamics enabled versus disabled. Together, these benchmark and validation trials reflect strong potential for highly scalable agent-based evolution simulation that is both efficient and observable. Developed capabilities will bring entirely new classes of previously intractable research questions within reach, benefiting further explorations within the evolutionary biology and artificial life communities across a variety of emerging high-performance computing platforms. Computer science ; Scale (ratio) ; Wafer ; Materials science ; Nanotechnology ; Physics Solidification and crystal growth phenomena ; Cultural Industries and Urban Development ; Industrial Vision Systems and Defect Detection
Authors: Yulin Shen | Benoît Mercatoris | Qingzhi Liu | Hongxun Yao | Zongpeng Li | Zhen Chen | Wensheng Wang
Source: Remote Sensing (article, 2024-12-17)
Field: Chemistry — Analytical Chemistry

Use Self-Training Random Forest for Predicting Winter Wheat Yield The effectiveness of supervised ML heavily depends on having a large, accurate, and diverse annotated dataset, which poses a challenge in applying ML for yield prediction. To address this issue, we developed a self-training random forest algorithm capable of automatically expanding the annotated dataset. Specifically, we trained a random forest regressor model using a small amount of annotated data. This model was then utilized to generate new annotations, thereby automatically extending the training dataset through self-training. Our experiments involved collecting data from over 30 winter wheat varieties during the 2019–2020 and 2021–2022 growing seasons. The testing results indicated that our model achieved an R2 of 0.84, RMSE of 627.94 kg/ha, and MAE of 516.94 kg/ha in the test dataset, while the validation dataset yielded an R2 of 0.81, RMSE of 692.96 kg/ha, and MAE of 550.62 kg/ha. In comparison, the standard random forest resulted in an R2 of 0.81, RMSE of 681.02 kg/ha, and MAE of 568.97 kg/ha in the test dataset, with validation results of an R2 of 0.79, RMSE of 736.24 kg/ha, and MAE of 585.85 kg/ha. Overall, these results demonstrate that our self-training random forest algorithm is a practical and effective solution for expanding annotated datasets, thereby enhancing the prediction accuracy of ML models in winter wheat yield forecasting. Training (meteorology) ; Yield (engineering) ; Environmental science ; Forestry ; Meteorology ; Geography ; Materials science Spectroscopy and Chemometric Analyses
Authors: Bo Han | Daqing Cheng | Huizhao Luo | Jutang Li | Jiaoxiang Wu | Xing Jia | Ming Xu | Peng Sun | Cheng Sheng
Source: Scientific Reports (article, 2024-08-13)
Field: Chemistry — Spectroscopy

Peptidomic analysis reveals novel peptide PDLC promotes cell proliferation in hepatocellular carcinoma via Ras/Raf/MEK/ERK pathway Hepatocellular carcinoma (HCC) still presents poor prognosis with low overall survival rates and limited therapeutic options available. Recently, attention has been drawn to peptidomic analysis, an emerging field of proteomics for the exploration of new potential peptide drugs for the treatment of various diseases. However, research on the potential function of HCC peptides is lacking. Here, we analyzed the peptide spectrum in HCC tissues using peptidomic techniques and explored the potentially beneficial peptides involved in HCC. Changes in peptide profiles in HCC were examined using liquid chromatography-mass spectrometry (LC-MS/MS). Analyze the physicochemical properties and function of differently expressed peptides using bioinformatics. The effect of candidate functional peptides on HCC cell growth and migration was evaluated using the CCK-8, colony formation, and transwell assays. Transcriptome sequencing analysis and western blot were employed to delve into the mode of action of potential peptide on HCC. Peptidomic analysis of HCC tissue yielded a total of 8683 peptides, of which 452 exhibited up-regulation and 362 showed down-regulation. The peptides that were differentially expressed, according to bioinformatic analysis, were closely linked to carbon metabolism and the mitochondrial inner membrane. The peptide functional validation identified a novel peptide, PDLC (peptide derived from liver cancer), which was found to dramatically boost HCC cell proliferation through the Ras/Raf/MEK/ERK signaling cascade. Our research defined the peptide's properties and pattern of expression in HCC and identified a novel peptide, PDLC, with a function in encouraging HCC progression, offering an entirely new potential therapeutic target the disease. Peptide ; Biology ; Cancer research ; Cell growth ; Hepatocellular carcinoma ; MAPK/ERK pathway ; Transcriptome ; Proteomics ; Function (biology) ; Computational biology ; Cell biology ; Signal transduction ; Bioinformatics ; Biochemistry ; Gene ; Gene expression Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; RNA and protein synthesis mechanisms
Authors: Jenny Wiklund | Arttu Miettinen | Joni Parkkonen | Lauri Mela | Alp Karakoç | Jouni Paltakari
Source: Materials & Design (article, 2024-08-10)
Field: Materials Science — Biomaterials

A detailed investigation of acetylated cellulose nanofiber films as a substrate for printed electronics The increased interest in printed electronics necessitates the development of suitable sustainable substrates for them. In this study, the suitability of acetylated cellulose nanofiber (ACNF) films as substrates for printed electronics were examined through (I) the ink-substrates interaction, (II) print quality, and (III) electrical and (IV) mechanical properties of the printed pattern on the ACNF substrates. The results have been compared with cellulose nanofiber (CNF) and commercial reference material (CRM) substrates. The wetting of the silver nanoparticle (AgNP) ink on the ACNF substrate was found out to be excellent. The thickness of the printed pattern increased and the hole area fraction decreased with each consecutive layer of ink. The comparative investigations demonstrated that the electrical properties of the printed patterns were almost as good for 4 layers of ink on the ACNF substrate (1.6 Ω resistance) as the ones on the CRM substrate (1.2 Ω resistance). Additionally, the printed pattern on the ACNF substrate endured the adhesion and bending tests significantly better compared to the CRM substrate. Therefore, this study demonstrates that ACNF substrates could be a suitable candidate for printed electronics, which are more sustainable yet with similar functionalities in comparison with the conventional fossil based substrates. Materials science ; Nanofiber ; Cellulose ; Substrate (aquarium) ; Electronics ; Nanotechnology ; Printed electronics ; Flexible electronics ; Stretchable electronics ; Composite material ; Polymer science ; Chemical engineering ; Electrical engineering ; Inkwell ; Engineering Advanced Cellulose Research Studies ; Advanced Sensor and Energy Harvesting Materials ; Electrospun Nanofibers in Biomedical Applications
Authors: Damien Perret | Alexandra Garcin | Carole Soual | François Bergeret
Source: Materials Letters (article, 2024-11-09)
Field: Materials Science — Ceramics and Composites

Subsampling approach and data-driven models to predict silicate glass melt viscosity • Glass melt viscosity remains one of the most challenging property to predict. • An innovative approach of data subsampling was developed. • Viscosity prediction appeared as very accurate over a wide range of silicate glass compositions. This study focused on developing a predictive tool for glass melt viscosity calculation between 900 °C and 1500 °C. Database containing about 16 000 silicate glass compositions was built from both data available in the literature and from a proprietary dataset. The approach combines statistical techniques of design of experiments, machine learning, and subsampling strategies for model training. Prediction accuracy was found to be very promising for the different types of silicate glasses studied. Relative error of viscosity prediction at 1200 °C was around 20 % for simple SBN compositions, and less than one order of magnitude for more complex compositions. Silicate ; Materials science ; Viscosity ; Silicate glass ; Thermodynamics ; Mineralogy ; Composite material ; Chemical engineering ; Geology Glass properties and applications ; Metallurgical Processes and Thermodynamics ; Cultural Heritage Materials Analysis
Authors: Bambang Trisakti | Muhammad Turmuzi | Taslim Taslim | Irvan Irvan
Source: Ecological Engineering & Environmental Technology (article, 2024-08-22)
Field: Chemistry — Analytical Chemistry

Determination of Kinetic Models in Acidogenesis Process of Palm Oil Mill Effluent Palm oil mill effluent (POME) can be transformed into biogas.Acidogenesis, as a fermentation process, involves decomposition of hydrolysis products into simpler organic products in the form of acetate, hydrogen and carbon dioxide.Intermediate products from acidogenesis form of volatile fatty acids (VFA), such as propionate, butyrate, valerate, and their isoforms, which require further metabolic processes to produce biogas.The aim of the research was to determine the suitable kinetics model that describes microbial growth (from volatile suspended solid (VSS) values) and production in the acidogenesis process.The reactor used in this acidogenesis process was a stirred tank reactor with a volume of 6 L and operates in a batch system.This research was conducted with a variable agitation rate variation (200; 250; and 300 rpm) at a temperature of 30 C and 55 C.The pH was varied, including 5; 5.5; and 6.The kinetic models used in this study were Modified Gompertz, Monod, and Logistic.The best effect of pH on VSS was obtained at pH 5.5, agitation rate on VSS was obtained at agitation rate of 250 rpm and operating conditions for acidogenesis process was achieved under thermophilic conditions (55 C).Logistic kinetic model is the best kinetic model that can describe VSS and VFA in this study. Acidogenesis ; Pulp and paper industry ; Palm oil ; Oil mill ; Mill ; Effluent ; Environmental science ; Process (computing) ; Chemistry ; Computer science ; Engineering ; Environmental engineering ; Agroforestry ; Anaerobic digestion Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Biodiesel Production and Applications
Authors: Rodica Voicu | Mihai Gologanu | C. Tibeică | Mercedes Santiago‐Calvo | María Asensio | Esteban Cañibano | Oana Nedelcu | Titus Sandu
Source: Nanomaterials (article, 2024-08-28)
Field: Materials Science — Polymers and Plastics

Prediction of Mechanical Properties of Nano-Clay-Based Biopolymeric Composites An understanding of the mechanical behavior of polymeric materials is crucial for making advancements in the applications and efficiency of nanocomposites, and encompasses their service life, load resistance, and overall reliability. The present study focused on the prediction of the mechanical behavior of biopolymeric nanocomposites with nano-clays as the nanoadditives, using a new modeling and simulation method based on Comsol Multiphysics software 6.1. This modeling considered the complex case of flake-shaped nano-clay additives that could form aggregates along the polymeric matrix, varying the nanoadditive thickness, and consequently affecting the resulting mechanical properties of the polymeric nanocomposite. The polymeric matrix investigated was biopolyamide 11 (BIOPA11). Several BIOPA11 samples reinforced with three different contents of nano-clays (0, 3, and 10 wt%), and with three different nano-clay dispersion grades (employing three different extrusion screw configurations) were obtained by the compounding extrusion process. The mechanical behavior of these samples was studied by the experimental tensile test. The experimental results indicate an enhancement of Young's modulus as the nano-clay content was increased from 0 to 10 wt% for the same dispersion grades. In addition, the Young's modulus value increased when the dispersion rate of the nano-clays was improved, showing the highest increase of around 93% for the nanocomposite with 10 wt% nano-clay. A comparison of the modeled mechanical properties and the experimental measurements values was performed to validate the modeling results. The simulated results fit well with the experimental values of Young's modulus. Materials science ; Nanocomposite ; Composite material ; Extrusion ; Nano- ; Modulus ; Ultimate tensile strength ; Dispersion (optics) ; Compounding ; Dynamic mechanical analysis ; Multiphysics ; Polymer ; Finite element method ; Structural engineering Polymer Nanocomposites and Properties ; biodegradable polymer synthesis and properties ; Bone Tissue Engineering Materials
Authors: T. Abhishek | Dola Sundeep | C. Chandrasekhara Sastry | K. V. Eswaramoorthy | Gagan Chaitanya Kesireddy | B. Veera Siva Reddy | Rakesh Kumar Verma | Sachin Salunkhe | Róbert Čep | Emad Abouel Nasr
Source: Frontiers in Mechanical Engineering (article, 2024-04-16)
Field: Materials Science — Materials Chemistry

Experimental investigation of tungsten–nickel–iron alloy, W95Ni3.5Fe1.5, compared to copper monolithic bullets Introduction: The demand for improved small arms ammunition has led to exploring advanced materials and manufacturing techniques. This research investigates the machining characteristics of CM and WNF alloy bullets, aiming to enhance ballistic performance and durability. Methods: Bullet profile-making trials were conducted to evaluate the impact of machining parameters such as cutting speed and feed. The study also considered variables including surface roughness, cutting temperature, and hardness, alongside a detailed morphological analysis, The evaluation utilized an orthogonal array and MCDM approach, incorporating the TOPSIS method for decision-making processes. Results: The findings reveal that WNF alloy bullets exhibit 3.01% to 27.95% lower machining temperatures, 24.88%-61.85% reduced surface roughness, and 19.45%-34% higher microhardness compared to CM bullets. Moreover, CM bullets demonstrated higher machining temperatures, resulting in 47.53% increased tool flank wear. WNF bullets showed a 24.89% reduction in crater wear and a 38.23% decrease in compressive residual stress in bullet profiles, indicating superior machining performance. Discussion: The superior machining performance of WNF alloy bullets suggests their potential to improve the ballistic performance and durability of small arms ammunition. The reduced tool wear and favorable machining parameters highlight WNF alloy's advantages for military and defense applications. A ballistic impact analysis using a finite element method (FEM) model in Abaqus software further supports the potential of WNF alloy bullets, providing a solid foundation for future advancements in bullet manufacturing technologies. Machining ; Materials science ; Surface roughness ; Alloy ; Superalloy ; Metallurgy ; Mechanical engineering ; Surface finish ; Composite material ; Engineering High-Velocity Impact and Material Behavior ; Advanced materials and composites ; Integrated Circuits and Semiconductor Failure Analysis
Authors: Jimiao Duan | Zhi Kou | Huishu Liu | Keyu Lin | Sichen He | Shiming Chen
Source: Processes (article, 2024-08-23)
Field: Chemistry — Analytical Chemistry

Pour Point Prediction Method for Mixed Crude Oil Based on Ensemble Machine Learning Models Pipelines are the most common way to transport crude oil. The crude oil developed from different fields is mixed first and then transported. The pour point of mixed crude oil is very important for pipeline schemes and ensuring the safe, efficient, and flexible operation of the pipeline. An integrated machine learning model based on XGBoost is identified as optimal to predict the pour point of mixed crude oil by comprehensive comparison among six different types of machine learning models: multiple linear regression, random forest, support vector machine, LightGBM, backpropagation neural network, and XGBoost. A mixed crude oil pour point prediction model with strong engineering adaptability is proposed, focusing on enhancing the flexibility of machine learning model inputs (using density and viscosity instead of component crude oil pour points) and addressing challenges such as data volume and input missing in engineering scenarios. With the inputs of pour point Tg, density ρ, viscosity μ, and ratio Xi in component oils, the mean absolute error of the model prediction estimations after training with 8912 data is 1.12 °C, when the pour point Tg of the component crude oil is missing, the mean absolute error is 1.93 °C and the percentage of the predicted absolute error within 2 °C is 88.0%. This study can provide support for the intelligent control of flow properties of pipeline transport mixed oil. Crude oil ; Ensemble learning ; Point (geometry) ; Computer science ; Machine learning ; Artificial intelligence ; Mathematics ; Petroleum engineering ; Engineering Petroleum Processing and Analysis ; Hydrocarbon exploration and reservoir analysis ; Advanced Data Processing Techniques
Authors: Yanis Zirem | Léa Ledoux | Michel Salzet | Isabelle Fournier
Source: STAR Protocols (article, 2024-09-01)
Field: Chemistry — Spectroscopy

Protocol to analyze 1D and 2D mass spectrometry data from glioblastoma tissues for cancer diagnosis and immune cell identification In context of cancer diagnosis-based mass spectrometry (MS), the classification model created is crucial. Moreover, exploration of immune cell infiltration in tissues can offer insights within the tumor microenvironment. Here, we present a protocol to analyze 1D and 2D MS data from glioblastoma tissues for cancer diagnosis and immune cells identification. We describe steps for training the most optimal model and cross-validating it, for discovering robust biomarkers and obtaining their corresponding boxplots as well as creating an immunoscore based on MS-imaging data. For complete details on the use and execution of this protocol, please refer to Zirem et al.<sup>1</sup>. Glioblastoma ; Protocol (science) ; Computer science ; Context (archaeology) ; Identification (biology) ; Immune system ; Computational biology ; Tumor microenvironment ; Medicine ; Cancer research ; Biology ; Pathology ; Immunology Mass Spectrometry Techniques and Applications ; Single-cell and spatial transcriptomics ; HIV Research and Treatment
Authors: Noor Aida Syakira Ahmad Sabri | Nik Fasha Edora Nik Kamaruzaman | Nurlaila Ismail | Zakiah Mohd Yusoff | Ali Abd Almisreb | Saıful Nizam Tajuddin | Mohd Nasir Taib
Source: International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering (article, 2024-06-04)
Field: Chemistry — Organic Chemistry

Statistical analysis for chemical compound based on several species of aquilaria essential oil The paper examines the characterization of Aquilaria essential oils from different species, namely Aquilaria malaccensis, Aquilaria beccariana, Aquilaria crassna, and Aquilaria subintegra, renowned for agarwood production in Malaysia. Gas chromatography-mass spectrometry (GC-MS) and gas chromatography-flame ionization detector (GC-FID) were employed for extracting essential oil data, facilitating compound identification. Subsequently, a preliminary analysis focused on classifying significant chemical compounds in the samples. The study then utilized boxplot pre-processing for visualizing and interpreting data distribution. The statistical analyses were performed using MATLAB software version R2021b, considering two key parameters which are the peak area (%) of significant chemical compounds and the classification of Aquilaria species (A. beccariana, A. malaccensis, A. crassna, and A. subintegra) based on their chemical composition. The results, presented through boxplot analyses, demonstrated a clear representation of the parameters and their distribution in the data. This method not only confirmed the potential of boxplot analysis in statistical evaluation of significant compounds in Aquilaria essential oil but also suggested its applicability for further classification work. Agarwood ; Essential oil ; Statistical analysis ; Chemistry ; Mathematics ; Chromatography ; Statistics Wood and Agarwood Research ; Identification and Quantification in Food ; Advanced Image and Video Retrieval Techniques