jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Costas Demetzos
Source: The AAPS Journal (review, 2024-07-02)
Field: Materials Science — Materials Chemistry

Nanosimilars: A Scientific or A Regulatory Debate? The paper highlights the necessity for a robust regulatory framework for assessing nanomedicines and their off-patent counterparts, termed as nanosimilar, which could be considered as 'similar' to the prototype nanomedicine,based on essential criteria describing the 'similarity'. The term 'similarity' should be focused on criteria that describe nanocarriers, encompassing their physicochemical, thermodynamic, morphological, and biological properties, including surface interactions and pharmacokinetics. Nanocarriers can be regarded as advanced self-assembled excipients (ASAEs) due to their complexity and chaotic behavior and should be evaluated by using essential criteria in order for off-patent nanomedicines be termed as nanosimilars, from a regulatory perspective. Collaboration between the pharmaceutical industry, regulatory bodies, and artificial intelligence (AI) startups is pivotal for the precise characterization and approval processes for nanomedicines and nanosimilars and embracing innovative tools and terminology facilitates the development of a sustainable regulatory framework, ensuring safety and efficacy. This crucial shift toward precision R&D practices addresses the complexity inherent in nanocarriers, paving the way for therapeutic advancements with economic benefits. Nanocarriers ; Terminology ; Risk analysis (engineering) ; Nanomedicine ; Computer science ; Nanotechnology ; Management science ; Biochemical engineering ; Engineering ; Business ; Drug delivery ; Materials science Nanoparticles: synthesis and applications ; Nanoparticle-Based Drug Delivery ; 3D Printing in Biomedical Research
Authors: Christopher J. McDevitt | Xian-Zhu Tang
Source: Physics of Plasmas (preprint, 2024-06-01)
Field: Materials Science — Materials Chemistry

A physics-informed deep learning description of Knudsen layer reactivity reduction A physics-informed neural network (PINN) is used to evaluate the fast ion distribution in the hot spot of an inertial confinement fusion target. The use of tailored input and output layers to the neural network is shown to enable a PINN to learn the parametric solution to the Vlasov–Fokker–Planck equation in the absence of any synthetic or experimental data. As an explicit demonstration of the approach, the specific problem of Knudsen layer fusion yield reduction is treated. Here, the predictions from the Vlasov–Fokker–Planck PINN are used to provide a non-perturbative solution of the fast ion tail in the vicinity of the hot spot, thus allowing the spatial profile of the fusion reactivity to be evaluated for a range of collisionalities and hot spot conditions. Excellent agreement is found between the predictions of the Vlasov–Fokker–Planck PINN and the results from traditional numerical solvers with respect to both the energy and spatial distribution of fast ions and the fusion reactivity profile, demonstrating that the Vlasov–Fokker–Planck PINN provides an accurate and efficient means of determining the impact of Knudsen layer yield reduction across a broad range of plasma conditions. Knudsen number ; Reduction (mathematics) ; Reactivity (psychology) ; Layer (electronics) ; Statistical physics ; Theoretical physics ; Epistemology ; Physics ; Mathematics education ; Psychology ; Nanotechnology ; Materials science ; Mathematics ; Philosophy ; Mechanics ; Medicine ; Geometry Machine Learning in Materials Science ; Catalytic Processes in Materials Science ; Semiconductor materials and devices
Authors: Qing Wang | Md Gapar Md Johar | Mohd Shukri Yajid Jacquline Tham
Source:  (article, 2024-05-22)
Field: Materials Science — Materials Chemistry

SF6 gas concentration prediction model based on PSO-BP and PSO-RBF algorithms As a new generation of electrical insulation medium, SF<sub>6</sub> gas is widely used in electrical insulation and arc extinguishing in high-voltage power equipment-Therefore, the accuracy of its concentration measurement is very important, which has an important impact on improving the performance and safety of power equipment. The paper elaborates particle swarm optimization (PSO) algorithm combining the application effect of radial basis function (RBF) network and backpropagation (BP) network in predicting sulfur hexafluoride (SF<sub>6</sub>) gas concentration prediction. In order to avoid the factors of insufficient data and obvious data characteristics, a large number of data sets with different trends are randomly generated for model verification. The performance of the two methods is verified experimentally, and the results show that the PSO-RBF method performs better in predicting SF<sub>6</sub> gas concentration, by which the changes of the gas concentration can be predicted more accurately, and shows robustness for prediction under different conditions. In addition, the PSO-RBF method converges faster in the training of temperature compensation model, which improves the efficiency of the prediction model. It has practical application value for the prediction and monitoring of power equipment, and also provides new solutions for other similar gas prediction problems. Particle swarm optimization ; Sulfur hexafluoride ; Backpropagation ; Radial basis function ; Artificial neural network ; Robustness (evolution) ; Algorithm ; Computer science ; Dissolved gas analysis ; Voltage ; Engineering ; Machine learning ; Transformer oil ; Chemistry High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Water Systems and Optimization
Authors: Long Wang | Lijun Yang | Liuying Wang | Xiujian Tang | Gu Liu
Source: Scientific Reports (article, 2024-06-11)
Field: Materials Science — Materials Chemistry

Influence mechanism of grinding surface quality of 20CrMnTi steel on contact failure To reveal the influence mechanism of the grinding surface quality of 20CrMnTi steel components on the tribological characteristics and contact fatigue performance, accelerated tests for sliding friction wear and fatigue damage were carried out. Tribological characteristics and contact fatigue performance get worse with increasing surface roughness while getting better with increasing surface microhardness. Residual compressive stress is conducive to inhibiting the initiation and propagation of cracks and promoting contact fatigue performance. Additionally, mechanical friction, abrasive wear, adhesive wear and fatigue damage coexist and form a competing failure mechanism under the synergistic effect of frictional wear and contact fatigue failure. The damage process mainly manifests as wear, stress concentration induced fatigue, microcracks, pitting, and spalling in the shallow layer. This study is more beneficial to promote the 20CrMnTi steel transmission parts manufacturing products for high precision, low damage, and long life. Spall ; Materials science ; Abrasive ; Tribology ; Surface roughness ; Grinding ; Indentation hardness ; Residual stress ; Composite material ; Mechanism (biology) ; Surface integrity ; Contact mechanics ; Surface finish ; Metallurgy ; Structural engineering ; Microstructure Metal Alloys Wear and Properties ; Tribology and Wear Analysis ; Mechanical stress and fatigue analysis
Authors: Jiannan Sun | Ke Yan | Pan Zhang | Aizhao Pan | Xuehang Chen | Xinyi Shi
Source: Lubricants (article, 2024-03-11)
Field: Materials Science — Materials Chemistry

Green-Emitting Carbon Quantum Dots: Highly Sensitive Temperature Sensing Probe in Nanocomposite and Lubrication System Carbon quantum dots (CQDs) have already demonstrated their utility as lubricant additives, and non-contact temperature sensing based on CQDs offers considerable potential for condition monitoring in mechanical, electrical, and other fields, as well as lubrication-temperature multifunctional applications in lubricants. In this paper, we have successfully synthesized and designed high-brightness carbon quantum dots/polyvinyl alcohol (PVA) temperature sensor thin film and dispersions of CQDs in a liquid paraffin lubrication system. Based on fluorescence intensity and the fluorescence intensity ratio, the carbon quantum dot/PVA film exhibited exponential temperature-dependent properties with a wide applicability range, a high goodness of fit (R2 &gt; 0.99), and high relative thermal sensitivity (relative sensitivities of 1.74% K−1 and 1.39% K−1 for fluorescence intensity and fluorescence intensity ratio, respectively). In addition, based on the fluorescence intensity, the CQDs exhibited a wide temperature range (20–90 °C), a high goodness of fit (R2 &gt; 0.99), and higher sensitivity (2.84% K−1) in a liquid paraffin lubrication system, which reflects the temperature responsive properties of carbon quantum dots as additives in lubrication systems. These findings provide convenient and effective possibilities for the sensing and monitoring of carbon quantum dots and their multifunctional applications under lubrication systems. Quantum dot ; Nanocomposite ; Lubrication ; Materials science ; Carbon quantum dots ; Nanotechnology ; Carbon fibers ; Optoelectronics ; Chemical engineering ; Composite material ; Composite number Carbon and Quantum Dots Applications ; Gas Sensing Nanomaterials and Sensors ; Electrochemical sensors and biosensors
Authors: Yuanwei Xu | T. Mamie Lih | Angelo M. De Marzo | Qing Kay Li | Hui Zhang
Source: Clinical Proteomics (article, 2024-10-24)
Field: Chemistry — Spectroscopy

SPOT: spatial proteomics through on-site tissue-protein-labeling SPOT is a robust and versatile technique that allows comprehensive profiling of spatially-resolved proteomics across diverse types of tissue slides to advance our understanding of intricate molecular landscapes. Proteomics ; Proteome ; H&E stain ; Staining ; Pathology ; Brain tissue ; Quantitative proteomics ; Biology ; Molecular biology ; Computational biology ; Chemistry ; Bioinformatics ; Biochemistry ; Neuroscience ; Medicine Advanced Proteomics Techniques and Applications ; Biotin and Related Studies ; Advanced biosensing and bioanalysis techniques
Authors: Şenol Çelik | Halit Tutar | Erdal Gönülal | Hasan Er
Source: Scientific Reports (article, 2024-09-13)
Field: Chemistry — Analytical Chemistry

Prediction of fresh herbage yield using data mining techniques with limited plant quality parameters The purpose of this study was to ascertain the fresh herbage yield, fertilizer dosage, and plant characteristics of the Sorghum-Sudangrass hybrid grown in arid and semi-arid regions, as well as their interrelationships. For this reason, data from the Sorghum-Sudangrass hybrid were used to assess the predictive performance of several data mining techniques, including CHAID, CART, MARS, and Bagging MARS. Plant traits were measured in Konya and Sanliurfa during 2021 and 2022. The descriptive statistical values were calculated as follows: plant height 306.27 cm, stem diameter 9.47 mm, fresh herbage yield 10852.51 kg/da, crude protein ratio 9.66%, acid detergent fiber 33.39%, neutral detergent fiber 51.85%, acid detergent lignin 9.76%, dry matter digestibility 62.88%, dry matter intake 2.34%, and relative feed value 114.68 (average values). The predictive capacities of the fitted models were assessed using model fit statistics such as the coefficient of determination (R²), adjusted R², root mean square error (RMSE), mean absolute percentage error (MAPE), standard deviation ratio (SD ratio), and Akaike Information Criterion (AIC). With the lowest values for RMSE, MAPE, SD ratio, and AIC (246, 1.926, 0.085, and 845, respectively), and the highest R² value (0.993) and adjusted R² value (0.989), the MARS algorithm was determined to be the best model for characterizing fresh herbage yield. As a solid alternative to other data mining techniques, the MARS algorithm was shown to be the most appropriate model for forecasting fresh herbage production. Yield (engineering) ; Quality (philosophy) ; Computer science ; Data mining ; Materials science Spectroscopy and Chemometric Analyses ; Leaf Properties and Growth Measurement ; Smart Agriculture and AI
Authors: Xinyang Jiang | Jinfu Ding | Chengwu Wang | E Shiju | Hong Ling | Weifeng Yao | Huadong Wang | Chongqiu Zhou | Wei Yu
Source: Scientific Reports (article, 2024-09-11)
Field: Materials Science — Materials Chemistry

Parameter identification of Johnson–Cook constitutive model based on genetic algorithm and simulation analysis for 304 stainless steel Addressing the significant discrepancy between actual experimental cutting force and its predicted values derived from traditional constitutive model parameter identification methods, a reverse identification research of the Johnson-Cook (J-C) constitutive model for 304 stainless steel was conducted via genetic algorithm. Considering actual cutting zone and the influence of feed motion on the rake (flank) angle, an unequal division shear zone model was established to implement the theoretical calculation for shear zone stress. Through cutting experiments, the spindle speed was negatively correlated with the cutting force at first, and then became positively correlated; The empirical formula (EXP model) for turning force was corrected, revealing that the EXP model was unable to provide optimal predicted values for cutting force. The influence of the J-C constitutive parameter C on the cutting morphology was firstly investigated through simulation analysis, and determined an appropriate value for C, then obtained the precise values for the other four constitutive parameters by genetic algorithm. Moreover, the simulated values of cutting force in JC1 model (obtained from the Split Hopkinson Pressure Bar test) and JCM model (the improved model using genetic algorithm) were obtained by three-dimensional (3-D) simulation via FEM software. The results indicated that, the maximum error between actual experimental cutting force and its simulated values (by JCM model) was 14.8%, with an average error of 6.38%. These results outperformed the JC1 and EXP models, suggesting that the JCM model identified via genetic algorithm was more reliable. Identification (biology) ; Constitutive equation ; Computer science ; Genetic algorithm ; Algorithm ; Computational biology ; Engineering ; Biology ; Structural engineering ; Finite element method ; Machine learning ; Ecology High-Velocity Impact and Material Behavior ; Advanced machining processes and optimization ; Advanced Surface Polishing Techniques
Authors: Adjie Bagaskara | Rachmawati Rachmawati | Suwarno Suwarno
Source: Emerging Science Journal (article, 2024-02-01)
Field: Materials Science — Materials Chemistry

Environmental Effects on Parameters of Leakage Current Equivalent Circuits of Outdoor Insulators The performance of outdoor insulators in transmission lines may deteriorate due to aging and can even be enhanced by the presence of pollutants. Leakage current (LC) measurement is one of the most effective methods to diagnose the insulator condition, utilizing LC parameters such as magnitude and total harmonic distortion (THD). However, research on the interpretation of these parameters is still limited. This paper discusses the diagnostic method by simulation, employing the LC equivalent circuits of different types of insulators and examining the influence of environmental factors, such as humidity and pollutant levels. LC waveforms are first obtained through experiments on various insulator types, including traditional ceramic and glass insulators, advanced composite insulators, or the hybrid type of RTV silicone rubber-coated and conducting glazed insulators. Subsequently, simulations on the LC circuits of Suwarno’s and Kizilcay’s models are performed to obtain similar LC waveforms and properties. The values of the equivalent circuit parameters are then used to diagnose each insulator’s characteristics and environmental effects. The results indicate that composite insulators of epoxy resin or SiR have a larger intrinsic resistance of ~40 GΩ and nonlinear resistance (a few MΩ to tens of GΩ), representing high surface resistance of the insulators against water and pollutants. A comparison of these parameters is expected to indicate the severity levels of insulator condition. Doi: 10.28991/ESJ-2024-08-01-022 Full Text: PDF Electronic circuit ; Leakage (economics) ; Current (fluid) ; Environmental science ; Electrical engineering ; Materials science ; Electronic engineering ; Engineering ; Economics High voltage insulation and dielectric phenomena
Authors: Xin Chen | Qingle Lan | Yaolin Zhu
Source: Autex Research Journal (article, 2024-01-01)
Field: Chemistry — Analytical Chemistry

Non-destructive identification of wool and cashmere fibers based on improved LDA using NIR spectroscopy Abstract With the advancement of near-infrared (NIR) spectroscopy and chemometrics technology, non-destructive qualitative testing has been widely applied in many fields. Both wool and cashmere are keratin protein fibers with many similarities in tissue structure, making it very difficult to distinguish between them. In order to achieve rapid and non-destructive identification of wool and cashmere, an improved linear discriminant analysis (ILDA) algorithm combined with NIR spectroscopy technology is proposed. The proposed method can also be used for the classification of extremely similar fibers and substances, with better classification performance. First, the spectral data of wool and cashmere are collected using an NIR spectrometer so as to reduce the influence of noise in the spectra; data preprocessing methods are used to correct the collected fiber spectra. Then, principal component analysis (PCA), linear discriminant analysis (LDA), and ILDA are used to extract the characteristic variables from the spectral data. Finally, the extracted characteristic variables are input into the machine learning algorithm K-nearest neighbor (K-NN) classifier. In the experimental stage, three dimensionality reduction methods (PCA, LDA, and ILDA) are evaluated using the K-NN classification model. The fiber classification accuracy can reach 97% when using the ILDA method for dimensionality reduction. The results show that the proposed method is effective for the qualitative detection of different types of wool and cashmere fibers. Linear discriminant analysis ; Principal component analysis ; Pattern recognition (psychology) ; Chemometrics ; Dimensionality reduction ; Artificial intelligence ; Wool ; Near-infrared spectroscopy ; Spectroscopy ; Materials science ; Computer science ; Mathematics ; Biological system ; Machine learning ; Optics ; Physics Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Bee Products Chemical Analysis
Authors: İsabel Revilla | Miriam Hernández-Jiménez | Iván Martínez-Martín | Patrícia Valderrama | Marta Rodríguez‐Fernández | Ana María Vivar Quintana
Source: Foods (article, 2024-01-31)
Field: Chemistry — Analytical Chemistry

The Potential Use of Near Infrared Spectroscopy (NIRS) to Determine the Heavy Metals and the Percentage of Blends in Tea The following study analyzed the potential of Near Infrared Spectroscopy (NIRS) to predict the metal composition (Al, Pb, As, Hg and Cu) of tea and for establishing discriminant models for pure teas (green, red, and black) and their different blends. A total of 322 samples of pure black, red, and green teas and binary blends were analyzed. The results showed that pure red teas had the highest content of As and Pb, green teas were the only ones containing Hg, and black teas showed higher levels of Cu. NIRS allowed to predict the content of Al, Pb, As, Hg, and Cu with ratio performance deviation values > 3 for all of them. Additionally, it was possible to discriminate pure samples from their respective blends with an accuracy of 98.3% in calibration and 92.3% in validation. However, when the samples were discriminated according to the percentage of blending (>95%, 95-85%, 85-75%, or 75-50% of pure tea) 100% of the samples of 10 out of 12 groups were correctly classified in calibration, but only the groups with a level of pure tea of >95% showed 100% of the samples as being correctly classified as to validation. Black tea ; Green tea ; Chemistry ; Analytical Chemistry (journal) ; Chromatography ; Food science Heavy Metals in Plants ; Geochemistry and Geologic Mapping ; Spectroscopy and Chemometric Analyses
Authors: Hugo B.C. Kleikamp | Ramon van der Zwaan | Ramon van Valderen | Jitske M. van Ede | Mario Pronk | Pim Schaasberg | Maximilienne T. Allaart | Mark C.M. van Loosdrecht | Martin Pabst
Source: ISME Communications (article, 2024-01-01)
Field: Chemistry — Spectroscopy

NovoLign: metaproteomics by sequence alignment Tremendous advances in mass spectrometric and bioinformatic approaches have expanded proteomics into the field of microbial ecology. The commonly used spectral annotation method for metaproteomics data relies on database searching, which requires sample-specific databases obtained from whole metagenome sequencing experiments. However, creating these databases is complex, time-consuming, and prone to errors, potentially biasing experimental outcomes and conclusions. This asks for alternative approaches that can provide rapid and orthogonal insights into metaproteomics data. Here, we present NovoLign, a <i>de novo</i> metaproteomics pipeline that performs sequence alignment of <i>de novo</i> sequences from complete metaproteomics experiments. The pipeline enables rapid taxonomic profiling of complex communities and evaluates the taxonomic coverage of metaproteomics outcomes obtained from database searches. Furthermore, the NovoLign pipeline supports the creation of reference sequence databases for database searching to ensure comprehensive coverage. We assessed the NovoLign pipeline for taxonomic coverage and false positive annotations using a wide range of <i>in silico</i> and experimental data, including pure reference strains, laboratory enrichment cultures, synthetic communities, and environmental microbial communities. In summary, we present NovoLign, a <i>de novo</i> metaproteomics pipeline that employs large-scale sequence alignment to enable rapid taxonomic profiling, evaluation of database searching outcomes, and the creation of reference sequence databases. The NovoLign pipeline is publicly available via: https://github.com/hbckleikamp/NovoLign. Metaproteomics ; Sequence (biology) ; Computational biology ; Computer science ; Biology ; Metagenomics ; Genetics Advanced Proteomics Techniques and Applications ; Genomics and Phylogenetic Studies ; Microbial Community Ecology and Physiology
Authors: Cristiana Maduro Dias | Hélder Nunes | Alfredo Borba
Source: Analytica—A Journal of Analytical Chemistry and Chemical Analysis (article, 2024-10-02)
Field: Chemistry — Analytical Chemistry

Near-Infrared Spectroscopy in Animal Nutrition: Historical Insights, Technical Principles, and Practical Applications Optimizing forage quality is vital for enhancing animal performance and supporting the global animal production industry. Near-infrared (NIR) spectroscopy offers a rapid, non-destructive alternative to traditional, time-intensive laboratory analyses, enabling the on-site assessment of forage properties with significant advantages in cost, speed, and environmental impact. This review traces the development of NIR spectroscopy, outlines its core principles, and highlights its applications in animal nutrition. Additionally, it discusses the current technological state, challenges, and future prospects, emphasizing NIR’s growing role in promoting more sustainable and efficient animal production systems. Infrared ; Infrared spectroscopy ; Spectroscopy ; Nanotechnology ; Computer science ; Engineering ethics ; Engineering ; Materials science ; Chemistry ; Physics ; Optics ; Astronomy Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Meat and Animal Product Quality