jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Nele Mandre | Tiia Plamus | Angelika Linder | Toivo Varjas | Jüri Majak | Andres Krumme
Source: Materials Science (article, 2023-06-08)
Field: Materials Science — Polymers and Plastics

Design of Performance Characteristics on Laser Treated Denim Fabric The current paper covers an experimental study, mathematical modelling, and design optimization of the laser treatment process of denim fabrics. Laser fading is used in the finishing phase of garment production, unfortunately, it decreases fabric durability. Denim fabric is known as a cotton fabric but nowadays it is blended with other fibres to improve fabric properties. For this reason, the main purpose of this study was to test multicomponent fabric longevity after laser treatment. The knowledge from this study is used by garment production company. The tear force, abrasion resistance, and colour difference are considered as performance characteristics of laser treated denim fabrics subjected to maximization. To achieve the goals, novel, mathematical models were developed. In the preliminary tests, the design space was determined. The full factorial design of experiments was utilized in the experimental study and the values of the performance characteristics are measured. Based on the results of the experimental study the artificial neural network and Haar wavelet based mathematical models were developed to predict the behaviour of the tear force, abrasion resistance, and colour difference. The artificial intelligence-based multicriteria design optimization procedure is developed. Denim ; Factorial experiment ; Design of experiments ; Fractional factorial design ; Durability ; Mechanical engineering ; Computer science ; Materials science ; Composite material ; Engineering ; Mathematics ; Machine learning Textile materials and evaluations ; Dyeing and Modifying Textile Fibers
Authors: Mohammad Akbar Faqeerzada | Tanjima Akter | Umuhoza Aline | Muhammad Fahri Reza Pahlawan | Byoung–Kwan Cho
Source: BIO Web of Conferences (article, 2023-01-01)
Field: Chemistry — Analytical Chemistry

Application of Hyperspectral Imaging for Rapid and Nondestructive Detection of Paraffine-Contaminated Rice The emergence of paraffin-coated rice in China, aimed at enhancing its market appeal and achieving a translucent appearance, has given rise to a significant global food safety concern. This situation poses substantial health risks to consumers. Hyperspectral analysis, recognized as a powerful and nondestructive technique for assessing food quality and safety, offers a potential solution. This study conducted a comprehensive investigation using Visible-Near Infrared (VIS-NIR) hyperspectral imaging systems operating within the 400-1000 nm range to identify paraffin-contaminated rice. Various rice varieties from diverse regions were obtained and intentionally tainted with varying levels of paraffin. Imaged samples were further preprocessed for spectral data extraction from individual rice seeds’ regions of interest (ROI). The dataset encompassed 3000 spectral records obtained from both non-contaminated and contaminated samples. The obtained spectral data were employed to develop partial least squares discriminant analysis (PLS-DA) and principal component linear discriminant analysis. The primary goal was to discriminate between contaminated and non-contaminated rice samples effectively. Notably, the results indicated that PLS-DA consistently achieved an accuracy exceeding 94% across various preprocessing techniques. Overall, this study showcased the potential of combining hyperspectral imaging with chemometrics to detect paraffin-contaminated rice seeds, providing a valuable contribution to food safety assessment in the industry. Hyperspectral imaging ; Chemometrics ; Contamination ; Linear discriminant analysis ; Principal component analysis ; Preprocessor ; Environmental science ; Partial least squares regression ; Computer science ; Remote sensing ; Pattern recognition (psychology) ; Artificial intelligence ; Geography ; Machine learning Spectroscopy and Chemometric Analyses ; Spectroscopy Techniques in Biomedical and Chemical Research ; Advanced Chemical Sensor Technologies
Authors: Dapeng Jiang | Yizhuo Zhang | Jinhao Chen
Source: BioResources (article, 2023-04-05)
Field: Chemistry — Analytical Chemistry

Non-destructive testing of mechanical properties of solid wood panel based on partial least squares structural equation modeling transfer method Calibration transfer between near infrared (NIR) spectrometers is a subtle issue in the chemometrics and process industry. Similar instruments may generate strongly different spectral responses, and regression models developed on a first NIR system can rarely be used with spectra collected by a second apparatus. In this work, two novel methods based on Structural Equation Modeling (SEM), called Enhanced Feature Extraction Approaches for factor analysis (EFEA-FA) and Enhanced Feature Extraction Approaches for spectral space transformation (EFEA-SST), were proposed to perform calibration transfer between NIR spectrometers. They were applied to a NIR nondestructive testing model for solid wood panels mechanical properties. Four different standardization algorithms were evaluated for transferring solid wood panels quality databases between a portable NIRS (InGaAs)-array spectrometer (NIRquest512) and a HSI Camera (SPECIM FX17). The results showed that EFEA-SST yielded the best model evaluation metrics (R2 and Root Mean Square Error of Prediction (RMSEP)) values for tensile strength (RMSEP=11.309, R2=0.865) parameters, while EFEA-FA gave the best fit for flexural strength (RMSEP=10.653, R2=0. 912). These results suggest the potential of two novel quality parameters prediction methods based on spectral databases transferred between diverse NIRS spectrometers. Partial least squares regression ; Calibration ; Spectrometer ; Chemometrics ; Mean squared error ; Materials science ; Biological system ; Nondestructive testing ; Near-infrared spectroscopy ; Computer science ; Analytical Chemistry (journal) ; Mathematics ; Statistics ; Optics ; Machine learning ; Chemistry ; Chromatography ; Physics Spectroscopy and Chemometric Analyses ; Industrial Vision Systems and Defect Detection
Authors: Myriam M. Chaumeil | Caroline Guglielmetti | Kai Qiao | Brice Tiret | Mustafa Ozen | Karen Krukowski | Amber Nolan | Maria Serena Paladini | Carlos Lopez | Susanna Rosi
Source: Research Square (preprint, 2023-08-14)
Field: Chemistry — Spectroscopy

Hyperpolarized 13C metabolic imaging detects long-lasting metabolic alterations following mild repetitive traumatic brain injury Traumatic brain injury ; Neuroimaging ; Medicine ; Neuroscience ; Psychology ; Psychiatry Advanced NMR Techniques and Applications ; Advanced MRI Techniques and Applications ; Atomic and Subatomic Physics Research
Authors: Xin Zhao | Shuo Liu | Haotian Que | Min Huang | Qibing Zhu
Source: Sensors (article, 2023-09-27)
Field: Chemistry — Analytical Chemistry

ADFSNet: An Adaptive Domain Feature Separation Network for the Classification of Wheat Seed Using Hyperspectral Images Wheat seed classification is a critical task for ensuring crop quality and yield. However, the characteristics of wheat seeds can vary due to variations in climate, soil, and other environmental factors across different years. Consequently, the present classification model is no longer adequate for accurately classifying novel samples. To tackle this issue, this paper proposes an adaptive domain feature separation (ADFS) network that utilizes hyperspectral imaging techniques for cross-year classification of wheat seed varieties. The primary objective is to improve the generalization ability of the model at a minimum cost. ADFS leverages deep learning techniques to acquire domain-irrelevant features from hyperspectral data, thus effectively addressing the issue of domain shifts across datasets. The feature spaces are divided into three parts using different modules. One shared module aligns feature distributions between the source and target datasets from different years, thereby enhancing the model's generalization and robustness. Additionally, two private modules extract class-specific features and domain-specific features. The transfer mechanism does not learn domain-specific features to reduce negative transfer and improve classification accuracy. Extensive experiments conducted on a two-year dataset comprising four wheat seed varieties demonstrate the effectiveness of ADFS in wheat seed classification. Compared with three typical transfer learning networks, ADFS can achieve the best accuracy of wheat seed classification with small batch samples updated, thereby addressing new seasonal variability. Hyperspectral imaging ; Robustness (evolution) ; Computer science ; Artificial intelligence ; Transfer of learning ; Feature (linguistics) ; Pattern recognition (psychology) ; Generalization ; Machine learning ; Domain (mathematical analysis) ; Data mining ; Mathematics Spectroscopy and Chemometric Analyses ; Smart Agriculture and AI ; Advanced Chemical Sensor Technologies
Authors: Konstantinos Kalogeropoulos | Aleksander M. Haack | Elizabeta Madzharova | Antea Di Lorenzo | Rawad Hanna | Erwin M. Schoof | Ulrich auf dem Keller
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2023-12-01)
Field: Chemistry — Spectroscopy

CLIPPER 2.0: Peptide level annotation and data analysis for positional proteomics Abstract Positional proteomics methodologies have transformed protease research, and have brought mass spectrometry (MS)-based degradomics studies to the forefront of protease characterization and system-wide interrogation of protease signaling. Considerable advancements in sensitivity and throughput of liquid chromatography (LC)-MS/MS instrumentation enable generation of enormous positional proteomics datasets of natural and protein termini and neo-termini of cleaved protease substrates. However, such progress has not been observed to the same extent in data analysis and post-processing steps, which arguably constitute the largest bottleneck in positional proteomics workflows. Here, we present a computational tool, CLIPPER 2.0, that builds on prior algorithms developed for MS-based protein termini analysis, facilitating peptide level annotation and data analysis. CLIPPER 2.0 can be used with several sample preparation workflows and proteomics search algorithms, and enables fast and automated database information retrieval, statistical and network analysis, as well as visualization of terminomic datasets. We demonstrate our tool by analyzing GluC and MMP9 cleavages in HeLa lysates. CLIPPER 2.0 is available at https://github.com/UadKLab/CLIPPER-2.0 . Proteomics ; Clipper (electronics) ; Workflow ; Computer science ; Bottleneck ; Phosphoproteomics ; Computational biology ; Annotation ; Artificial intelligence ; Database ; Biology ; Engineering ; Embedded system ; Biochemistry Advanced Proteomics Techniques and Applications ; Machine Learning in Bioinformatics ; Mass Spectrometry Techniques and Applications
Authors: Sukhwinder Singh | Hanan Shakeel | R. P. Sharma
Source: Egyptian Journal of Forensic Sciences (article, 2023-12-13)
Field: Chemistry — Analytical Chemistry

Overview of chemometrics in forensic toxicology Abstract Background The beginning of chemometrics within pattern recognition of the 1960s and 1970s is defined. This article shows a comprehensive deliberation on application of the chemometric techniques to chemical data analysis. Main body of the abstract Many review papers along with the usage of chemometrics in forensic chemistry have been available. The present article has been distributed into several parts which comprise chemometrics, its history, its function and chemometrics methods. Conclusion It is advised that these new chemometrics methods should be applied in forensic chemistry to get accurate and fast results. Chemometrics ; Forensic toxicology ; Computer science ; Chemistry ; Chromatography ; Machine learning Spectroscopy and Chemometric Analyses ; Metabolomics and Mass Spectrometry Studies ; Advanced Chemical Sensor Technologies
Authors: Matthew R. Hennefarth | Daniel S. King | Laura Gagliardi
Source: ChemRxiv (preprint, 2023-08-07)
Field: Materials Science — Materials Chemistry

Linearized Pair-Density Functional Theory for Vertical Excitation Energies Multiconfiguration pair-density functional theory (MC-PDFT) is a computationally efficient method that computes the energies of electronic states in a state specific or state average framework via an on-top functional. However, MC-PDFT does not include state-interaction among these statets, since the final energies do not come from the diagonalization of an effective model-space Hamiltonian. Recently, multi-state extensions such as linearized (L-) PDFT have been developed to accurately model the potentials near conical intersections and avoided crossings; however, there has not been any systematic study evaluating their performance for predicting vertical excitations at the equilibrium geometry of a molecule, when the excited states are generally well separated. In this letter, we report the performance of L-PDFT on the extensive QUESTDB data set of vertical excitations using a database of automatically selected active spaces. We show that L-PDFT performs well on all these excitations and successfully reproduces the performance of MC-PDFT. These results further demonstrate the potential of L-PDFT, as its scaling is constant with the number of states included in the state-average manifold, whereas MC-PDFT scales linearly in this regard. Hamiltonian (control theory) ; Excited state ; Density functional theory ; Statistical physics ; Physics ; Scaling ; Quantum mechanics ; Mathematics ; Geometry ; Mathematical optimization Machine Learning in Materials Science ; Spectroscopy and Quantum Chemical Studies ; Advanced Chemical Physics Studies
Authors: Frysz, M. | Nethander, M. Nethander | Ebsim, R. | Cootes, T. | Lindner, C. | Saunders, F. | Gregory, J. | Aspden, R. | Harvey, N. | Faber, Benjamin G. | Tobias, Jonathan H | Ohlsson, Claes
Source: JBMR Plus (article, 2023-04-01)
Field: Chemistry — Inorganic Chemistry

Abstracts of the 50th ECTS Congress featuring BRS Annual Meeting, 15‐18 April 2023, Liverpool Background: Individuals with diabetes have increased risk of fractures.The observational and potential causal effects of glycaemic control on skeletal outcomes have not been explored in a large population-based setting. Purpose:We examined the observational and causal associations between glycaemic control on estimated bone mineral density (eBMD) and fracture risk in individuals from the UK Biobank.Methods: This study comprises 452,131 individuals from the UK biobank.At baseline 20,444 individuals with had type 2 diabetes (T2D) and 4,078 had type 1 (T1D).Individuals with diabetes were stratified by glycated haemoglobin levels (HbA1C) into two groups: Adequately Controlled Diabetes (ACD;n=14,651; HbA1c<7.0%) and Inadequately Controlled Diabetes (ICD;n =9,867; HbA1c 7.0%).Fractures were assessed using ICD-10 codes from hospital inpatient records.Linear and cox regression models, controlling for covariates, were used to estimate associations with eBMD/fractures across groups.HbA1c was also assessed in T1D and T2D continuously.One-sample Mendelian randomisation (MR) was used to obtain causal inferences on the relationship between HbA1c levels and fractures risk.Results: Fracture risk hazard ratios were increased for T2D ACD (1.25, 95%CI[1.16,1.35]),ICD (1.31, 95%CI[1.18,1.45])groups and T1D ACD (2.26, 95%CI[1.91,2.69])and ICD (2.84, 95%CI[2.53,3.19])groups, in comparison to non-diabetics.Additionally, eBMD was higher in T2D ACD (beta=0.07SD,95%CI[0.05,0.08])and ICD (beta=0.12SD,95%CI [0.10,0.14]),but lower in the T1D ACD (beta=-0.04SD,95%CI[-0.08,0.01])and ICD (beta=-0.07SD,95%CI[-0.10,-0.02])groups, in comparison to non-diabetics.In individuals with T1D a 1% unit increase in HbA1c levels was associated with a 11% increase in fracture risk, but not in T2D (HR: 1.03, 95%CI [0.98-1.09]).Genetically predicted HbA1c levels were not significantly associated with increased fracture risk (RR: 1.01, 95%CI [0.84,1.22]). Conclusion:We infer no evidence for a causal relationship between increasing HbA1c levels and fracture risk.Aiming to reduce HbA1c levels will effectively reduce fracture risk in patients with T1D, but not T2D. Citation ; Library science ; Political science ; Computer science Inorganic Chemistry and Materials ; Hydrogen Storage and Materials ; Inorganic Fluorides and Related Compounds
Authors: Haresh Kumar | Kimmo Kauhaniemi | Mohammed Elmusrati | Muhammad Shafiq
Source:  (article, 2023-11-06)
Field: Materials Science — Materials Chemistry

Emerging Technologies based Use Case Development for Condition Monitoring and Predictive Maintenance of MV Cables Condition monitoring (CM) and predictive maintenance (PM) techniques can provide a system to achieve a high quality of service with minimal maintenance costs. Using CM -based data, repairing assets is possible based on predictions of the occurrence of faults in assets. Underground medium voltage (MV) cables are vulnerable to deterioration due to stress factors and degradation of the insulating material that lead to cable failure. The gradual deterioration produces incipient faults that lead to MV cable breakdown if not diagnosed at early stages. Efficient diagnosis avoids unscheduled outages and partial discharge (PD) analysis is the most effective method for CM and diagnostics of MV cables. The health of the MV cable can be monitored by collecting and processing a huge amount of data. A use case is developed that shows how data is transferred using emerging technologies to the supervisory control and data acquisition (SCADA) system for diagnostic purposes. SCADA ; Predictive maintenance ; Reliability engineering ; Partial discharge ; Condition monitoring ; Maintenance engineering ; Voltage ; Degradation (telecommunications) ; Computer science ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Shiyuan Li | Yang Liu | Xiyue Niu | Yurong Tang | Haipeng Lan | Yong Zeng
Source: Agronomy (article, 2023-06-21)
Field: Chemistry — Analytical Chemistry

Comparison of Prediction Models for Determining the Degree of Damage to Korla Fragrant Pears For a fast and accurate evaluation of the values of damaged fragrant pears, a prediction method of the damage degree of Korla fragrant pears was proposed. To study variation laws of damages of fragrant pears under different volumes of squeezing deformation, the partial least squares regression (PLSR), the generalised regression neural network (GRNN) and the adaptive neural fuzzy inference system (ANFIS) were chosen to predict the damage degree of fragrant pears and establish the optimal prediction model. The results demonstrated that with the increase of ripeness or deformation value, the damage degree of fragrant pears increases gradually. For performance comparison of prediction models based on PLSR, GRNN and ANFIS, it was found that the trained PLSR, GRNN and ANFIS can all predict the damage degree of Korla fragrant pears. The ANFIS, which inputs the membership function of dsigmf (R2 = 0.9979, RMSE = 46.6) and psigmf (R2 = 0.9979, RMSE = 46.6), achieves the best performance. Research results can provide theoretical references to the evaluation of the commodity value of damaged fragrant pears, quality grading of fragrant pears and design of the picking machine. Adaptive neuro fuzzy inference system ; Partial least squares regression ; Ripeness ; Mean squared error ; Statistics ; Regression ; Mathematics ; Artificial neural network ; Degree (music) ; Artificial intelligence ; Computer science ; Biological system ; Fuzzy logic ; Food science ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Postharvest Quality and Shelf Life Management ; Sensory Analysis and Statistical Methods
Authors: Sukarjati | Pungky Slamet Wisnu Kusuma | Asti Rahayu
Source: Journal of Scientific Health (article, 2023-02-09)
Field: Chemistry — Inorganic Chemistry

COMBINATION OF N-BUTANOL GOTU KOLA EXTRACT, N-BUTANOL LERAK EXTRACT, AND NEEM SEED OIL IN A NANOSTRUCTURED LIPID CARRIER HERBAL ANTIMICROBIAL SPERMICIDE Introduction. The spermicidal effect of the nonoxynol-9 compound paralyzes sperm by rupturing the cell membrane. Nonoxynol-9 compounds, however, can irritate and itch the vagina. Using a Nanostructured Lipid Carrier (NLC) delivery system, alternative herbal spermicides were created in this research using gotu kola, lerak, and neem. To optimize and formulate of NLC formulation herbal spermicides made from a combination of lipids on Glyceryl Monostearate (GMS) and liquid lipids of olive oil using a response surface full factorial design model (22), and analyzing particle size tests, spreadability tests, pH, viscosity, and potential zeta tests. Method. A full factorial design model (22) was employed in this research to optimize and formulate NLC herbal spermicides using a combination of lipids in Glyceryl Monostearate (GMS) and liquid lipid olive oil. Results and Analysis. NLC herbal spermicide particle sizes range from 103.8 ± 0.3 to 178.0 ± 1.2 nm. The spreading power of NLC Herbal Spermicide ranges from 5.8 ± 0.05 to 6.3 ± 0.05 cm. The Zeta Potential of NLC Herbal Spermicide has a range of -17.0 ± 0.8 to -24.8 ± 3.3 mV. Resulting pH range is 5.7 ± 0.2 to 6.5 ± 0.1, resulting viscosity is 564 ± 0.3 to 661 ± 0.5 cps. Discussion. The equation model for the power distribution response has a p-value smaller than 0.05 which means it is significant so that the equation model can be used to predict the power response. Different things occur in the zeta potential response and spreadability which have a p-value greater than 0.05, so it can be concluded that the ratio of solid lipids and liquid lipids has no effect on the particle size and zeta potential parameters. Spermicide ; Zeta potential ; Factorial experiment ; Chromatography ; Particle size ; Traditional medicine ; Materials science ; Chemistry ; Medicine ; Population ; Nanoparticle ; Nanotechnology ; Mathematics Coconut Research and Applications ; Natural Products and Applications