jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Kittipon Aparatana | Daitaro Ishikawa | Kanvisit Maraphum | Khwantri Saengprachatanarug | Hitoshi Agarie | Takeshi Shikanai | Munehiro Tanaka | Eizo Taira
Source: Engineering in Agriculture Environment and Food (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Non-destructive Laboratory Analysis of the Detection of Unhealthy Sugarcane Stalks Using a Portable Vis-NIR Spectrometer The production of sugar is adversely affected by unhealthy sugarcane, which decreases the yield and quality and is difficult to detect through non-destructive tests. This study aims to accurately differentiate between healthy and unhealthy sugarcane in a laboratory environment using a portable visible near-infrared spectrometer with multivariate analyses. The spectra of 100 each of healthy and unhealthy sugarcane parts are analyzed in this study. The classification rates of the partial least-squares discriminant analysis and support vector machine classification of healthy models are 100 % and 91.9 %, respectively, while the classification rates of unhealthy models are 65.2 % and 78.3 %, respectively. The overall results demonstrate that NIR spectroscopy and multivariate analyses are effective at classifying healthy and unhealthy sugarcane. Multivariate statistics ; Linear discriminant analysis ; Multivariate analysis ; Spectrometer ; Partial least squares regression ; Sugar ; Near-infrared spectroscopy ; Mathematics ; Environmental science ; Artificial intelligence ; Statistics ; Computer science ; Food science ; Chemistry ; Biology ; Physics Spectroscopy and Chemometric Analyses ; Sugarcane Cultivation and Processing ; Natural Products and Biological Research
Authors: Minrui Jia | Zhenkai Wan | Xiaoyuan Pei | Jianmin Guo | Weichen Bao | Gong Liying | Yan Liu | Jun Zhao
Source: Materials (article, 2022-11-30)
Field: Materials Science — Polymers and Plastics

Network Optimization of CNT Yarn Sensor Based on NNIA Algorithm in Damage Monitoring of 3D Braided Composites In order to solve the optimization problem of carbon nanotube (CNT) yarn sensor network embedded in three-dimensional (3D) braided composite materials and realize the structural health monitoring of internal damage of aerospace parts, the multi-objective optimization of the number and location of sensors was studied using non-dominated neighborhood immune algorithm (NNIA). Through the research of 3D six-direction braiding process, stress sensitivity of single CNT yarn sensor, and damage location of 3D braided composites, the number, position, and coverage constraint functions based on NNIA algorithm are constructed. In addition, the number and position of three-dimensional braided composite embedded CNT yarn sensors with different sizes are solved. Through the stress experiment and data analysis of damaged parts, it is proved that the optimized configuration result of CNT yarn sensor obtained by NNIA algorithm is suitable for the damage monitoring of 3D braided composites. The damage location error is less than 1 mm. This study lays a foundation for the establishment of damage source localization model of 3D braided composites. Yarn ; Materials science ; Carbon nanotube ; Composite material ; Composite number ; Position (finance) ; Aerospace ; Structural health monitoring ; Wireless sensor network ; Stress (linguistics) ; Algorithm ; Computer science ; Engineering Textile materials and evaluations ; Industrial Vision Systems and Defect Detection ; AI and Multimedia in Education
Authors: Jhan-Rong Shih
Source: Security and Communication Networks (article, 2022-08-17)
Field: Materials Science — Polymers and Plastics

Application of Digital Image Processing Technology in Textile and Garment Field In order to study the shortcomings of traditional manual inspection of clothing, improve the quality of clothing. A method of applying digital image processing technology in the field of textile and clothing is proposed. This method combines computer digital image technology to propose an image processing and measurement process technology based on the Labview platform. Through the effective combination of neural networks and support vector machines, methods such as deep learning can better realize the recognition and classification of clothing images. According to a random interview survey of 80 people, 78 people think that the quality of clothing is the most important, and 2 people think that beauty is the most important. The experimental results show that the digital book processing technology platform can further improve the accuracy of clothing detection. Clothing ; Computer science ; Field (mathematics) ; Process (computing) ; Image processing ; Artificial intelligence ; Textile ; Digital image processing ; Quality (philosophy) ; Computer vision ; Digital image ; Image (mathematics) Textile materials and evaluations ; Industrial Vision Systems and Defect Detection
Authors: Gülsün Akdemir Evrendilek | Nurullah Bulut | Sibel Uzuner
Source: CLEAN - Soil Air Water (article, 2022-08-10)
Field: Materials Science — Biomaterials

Assessing Edible Composite Film Polymer from Potato Industry Effluent under High Hydrostatic Pressure and Its Antimicrobial Properties Abstract Development of edible film from potato industry effluent having antimicrobial properties against Salmonella enteritidis and Escherichia coli O157:H7 by addition of Citrus sinensis volatile oil (VO), and changes of its textural properties under high hydrostatic pressure (HHP) are investigated. The optimum operational conditions are determined as 500 MPa pressure, 36.97 µL VO, and 15 min processing time with the minimum force value of 372.33 × g . Textural properties are also modeled through empirical modeling, best fit Box‐Behnken design, and artificial neuron network. Inhibition zones for Salmonella enteritidis and E. coli O157:H7 at the optimum HHP conditions are 1.50 ± 0.11 and 2.18 ± 0.07 cm, respectively. Textural properties of force and elongation at break of the HHP‐processed films range from 2.27 ± 0.52 to 5.23 ± 0.38 N, and from 8.57 ± 1.31 to 13.36 ± 1.36 mm, respectively. Thermal transition of the edible film is observed at 87.42 °C for 7.36 min. Addition of C. sinensis VO improves the antimicrobial properties, whereas HHP improves the textural properties of the film. It is suggested that the developed film has potential to be used as an edible food packaging material. Hydrostatic pressure ; Antimicrobial ; Food science ; Salmonella enteritidis ; Materials science ; Effluent ; Composite number ; Food industry ; Composite material ; Salmonella ; Chemistry ; Bacteria ; Biology ; Environmental engineering ; Environmental science Nanocomposite Films for Food Packaging ; Microplastics and Plastic Pollution ; Nanoparticles: synthesis and applications
Authors: Agnes Beenfeldt Petersen | Idd A. Christensen | Mette E. Rønne | Emil G. P. Stender | David Tezé | Birte Svensson | Finn L. Aachmann
Source: Biomolecular NMR Assignments (article, 2022-08-30)
Field: Materials Science — Materials Chemistry

1H, 13C, 15N resonance assignment of the enzyme KdgF from Bacteroides eggerthii To fully utilize carbohydrates from seaweed biomass, the degradation of the family of polysaccharides known as alginates must be understood. A step in the degradation of alginate is the conversion of 4,5-unsaturated monouronates to 4-deoxy-L-erythro-5-hexoseulose catalysed by the enzyme KdgF. In this study BeKdgF from Bacteroides eggerthii from the human gut microbiota has been produced isotopically labelled in Escherichia coli. Here the <sup>1</sup>H, <sup>13</sup>C, and <sup>15</sup>N NMR chemical shift assignment for BeKdgF is reported. Bacteroides ; Resonance (particle physics) ; Enzyme ; Chemistry ; Nuclear magnetic resonance ; Physics ; Biology ; Biochemistry ; Genetics ; Bacteria ; Atomic physics Enzyme Structure and Function ; Metabolomics and Mass Spectrometry Studies ; Amino Acid Enzymes and Metabolism
Authors: Qiang Pan | Tian He | Liu Wen-dong | Xiaofeng Liu | Haibing Chen
Source: Sensors (article, 2022-02-24)
Field: Materials Science — Materials Chemistry

Study on a Fault Mitigation Scheme for Rub-Impact of an Aero-Engine Based on NiTi Wires The aim of this study was to solve the frequently occurring rotor-stator rub-impact fault in aero-engines without causing a significant reduction in efficiency. We proposed a fault mitigation scheme, using shape memory alloy (SMA) wire, whereby the tip clearance between the rotor and the stator is adjusted. In this scheme, an acoustic emission (AE) sensor is utilized to monitor the rub-impact fault. An active control actuator is designed with pre-strained two-way SMA wires, driven by an electric current via an Arduino control board, to mitigate the rub-impact fault once it occurs. In order to investigate the feasibility of the proposed scheme, a series of tests on the material properties of NiTi wires, including heating response rate, ultimate strain, free recovery rate, and restoring force, were carried out. A prototype of the actuator was designed, manufactured, and tested under various conditions. The experimental result verifies that the proposed scheme has the potential to mitigate or eliminate the rotor-stator rub-impact fault in aero-engines. Stator ; Actuator ; Rotor (electric) ; Fault (geology) ; Shape-memory alloy ; SMA* ; Automotive engineering ; Reduction (mathematics) ; Nickel titanium ; Engineering ; Control theory (sociology) ; Computer science ; Structural engineering ; Materials science ; Mechanical engineering ; Control (management) ; Electrical engineering ; Composite material Shape Memory Alloy Transformations ; Magnetic Bearings and Levitation Dynamics ; Laser and Thermal Forming Techniques
Authors: Cristiano André da Costa | Yu Zhang | Kirk Howatt | Billy G. Ram | John Stenger | John Nowatzki | Sreekala G. Bajwa | Xin Sun
Source: Journal of the ASABE (article, 2022-01-01)
Field: Chemistry — Analytical Chemistry

Palmer Amaranth (<i>Amaranthus palmeri</i> S. Watson) and Soybean (<i>Glycine max </i>L.) Classification in Greenhouse Using Hyperspectral Imaging and Chemometrics Methods Highlights Hyperspectral image processing was used to classify Palmer amaranth and soybean species. Chemometrics methods (PCA, PLS-DA, and SIMCA) were used to extract features and establish classification models. Abstract . Herbicide-resistant weed species are one of the largest threats to modern agriculture, as ineffective weed control results in significant yield losses or increased costs through alternatives such as mechanical methods. Palmer amaranth (Amaranthus palmeri S. Watson) has been one of the most troublesome weeds. Its identification through the adoption of site-specific weed management systems will help farmers select more appropriate control options to reduce costs and improve efficacy, resulting in increased farm revenue. In this study, a pixel-wised method was evaluated for the classification of Palmer amaranth and soybean (Glycine max L.). A pushbroom hyperspectral imagery acquisition system was used to collect imagery from 224 spectral bands ranging from 400 to 1000 nm. Greenhouse experiments were conducted in three different runs. Greenhouse-grown plants were evaluated to generate predictive models from paired samples generated with 56 replications in each run. Data collection occurred weekly when Palmer amaranth plants were between approximately 2.5 and 12.7 cm tall. Partial least squares discriminant analysis (PLS-DA) and soft independent modeling of class analogy (SIMCA) were tested to classify Palmer amaranth and soybean. Half of the dataset (Palmer amaranth = 42, soybean = 42) was used to train the models, and the other half (Palmer amaranth = 42, soybean = 42) was used to test model performance. Preliminary results showed that the PLS-DA model with PLS factors as input had a cumulative variation (R2Y(cum)) of 60% and predictive ability (Q2Y(cum)) of 60%. The SIMCA model showed a cumulative variation of 85% and a predictive ability of 82%. Overall, this study illustrated the capability of hyperspectral imagery to classify Palmer amaranth and soybean, which will increase the efficiency of weed control in modern agriculture. Keywords: Chemometrics methods, Hyperspectral imaging, Palmer amaranth classification. Amaranth ; Hyperspectral imaging ; Chemometrics ; Mathematics ; Weed ; Partial least squares regression ; Weed control ; Horticulture ; Artificial intelligence ; Agronomy ; Computer science ; Machine learning ; Biology ; Statistics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Identification and Quantification in Food
Authors: M. Saifizi | N.A.M. Amin | ‘Aqilah Che Sulaiman | Mohd Rizal Manan | Siti Marhainis Othman | Wan Azani Mustafa | Norfariza Ab Wahab
Source: Journal of Advanced Research in Fluid Mechanics and Thermal Sciences (article, 2022-08-13)
Field: Materials Science — Materials Chemistry

The ARX and ARMAX Models for Thermoelectric Cooling on Glass Windows: A Comparative Study Thermoelectric cooling (TEC), in particular, can be combined with a heat sink for local cooling, but they can also be integrated into electronic chips for point-to-point cooling. The study aims to develop a dynamic model of a cooling system integrated with TEC for glass window. The main target of this study is to develop a dynamic model of a cooling system integrated with TEC. The black box modelling approach in producing a mathematical model was selected based on the ARMAX and ARX model that corresponds to the actual dynamic state of the cooling system. The best model was finalized based on the best match on curve patterns when comparing the real and estimated models using the system identification tools in MATLAB, and also having the least error. The accuracy of the models was compared and analysed. The results showed that the 4th order of the ARMAX model produced a higher best fitting and standard deviation values of 80.23% and 0.027592 compared to the 4th order of the ARX model of 78.14% and 0.030769 respectively. This system accuracy is almost within the acceptable range for most error calculations in the validation method. Yet, this cooling system integrated with TEC is found more suitable for the 4th order of the ARMAX model when compared to the ARX model due to the noise parameter in the ARMAX model. Nevertheless, the noise order in this system is not dominant, therefore, whenever the noise order of the system in the ARMAX model is high than the second structure (nb), the number of errors is also high. In addition, the ARMAX model is found incapable of achieving the highest fitting due to the losses from the dynamic environment and losses from the TEC itself. Still, the use of this black box model used in this study is a significant variation where system parameters can be identified even offline. TEC ; Thermoelectric cooling ; Noise (video) ; MATLAB ; System identification ; Standard deviation ; Water cooling ; Computer science ; Control theory (sociology) ; Mathematics ; Thermoelectric effect ; Engineering ; Statistics ; Data modeling ; Physics ; Thermodynamics ; Mechanical engineering Advanced Thermoelectric Materials and Devices
Authors: Ashley M. Frankenfield | Jiawei Ni | Mustafa Ahmed | Ling Hao
Source: bioRxiv (Cold Spring Harbor Laboratory) (preprint, 2022-04-28)
Field: Chemistry — Spectroscopy

Protein Contaminants Matter: Building Universal Protein Contaminant Libraries for DDA and DIA Proteomics ABSTRACT Mass spectrometry-based proteomics is constantly challenged by the presence of contaminant background signals. In particular, protein contaminants from reagents and sample handling are often abundant and almost impossible to avoid. For data-dependent acquisition (DDA) proteomics, exclusion list can be used to reduce the influence of protein contaminants. However, protein contamination has not been evaluated and is rarely addressed in data-independent acquisition (DIA). How protein contaminants influence proteomics data is also unclear. In this study, we established protein contaminant FASTA and spectral libraries that are applicable to all proteomic workflows and evaluated the impact of protein contaminants on both DDA and DIA proteomics. We demonstrated that including our contaminant libraries can reduce false discoveries and increase protein identifications, without influencing the quantification accuracy in various proteomic software platforms. With the pressing need to standardize proteomic workflow in the research community, we highly recommend including our contaminant FASTA and spectral libraries in all bottom-up proteomics workflow. Our contaminant libraries and a step-by-step tutorial to incorporate these libraries in different DDA and DIA data analysis platforms can be valuable resources for proteomics researchers, which are freely accessible at https://github.com/HaoGroup-ProtContLib . Proteomics ; Workflow ; Contamination ; Computer science ; Quantitative proteomics ; Computational biology ; Data science ; Chemistry ; Biology ; Database ; Ecology Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies
Authors: Д. А. Иванов
Source: Power engineering research equipment technology (article, 2022-05-24)
Field: Materials Science — Materials Chemistry

Features of monitoring extra large partial discharge in high voltage insulators THE PURPOSE of the described study is to study the effect of partial discharges on the physical state of previously detected defects in high-voltage insulators. The article describes the results obtained by a complex of non-contact methods for remote diagnostics of high-voltage insulators under operating voltage during operation using a two-channel remote diagnostic device. METHOD is based on the registration of partial discharges by electromagnetic and acoustic sensors. With the help of electromagnetic and acoustic sensors, the characteristics of partial discharges in high-voltage polymer and porcelain insulators were studied without contact in laboratory and field conditions. The developed monitoring system is largely based on the discovered effect of extra large PDs, which create an accumulation of induced electric charges on the dielectric surfaces of defects, which form electric fields with a strength exceeding the strength of the applied fields to high-voltage insulators. THE RESULTS of experimental studies confirm the possibility of industrial applicability of the proposed method for non-contact remote diagnostics of the state of high-voltage insulators under operating voltage. The use of acoustic registration of partial discharges in the control of defects makes it possible to more accurately determine the total number of PDs and their phase distribution. Partial discharge ; Voltage ; High voltage ; Materials science ; Electric field ; Electrical engineering ; Acoustics ; Dielectric ; Electrical equipment ; Optoelectronics ; Engineering ; Physics High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrical Fault Detection and Protection
Authors: Dylan Jayatilaka | Amir Karton
Source: Australian Journal of Chemistry (article, 2022-03-28)
Field: Materials Science — Materials Chemistry

The duhka of DFT: a noble path to better functionals via a point electron approximation for the exchange–correlation hole†,‡ Density functional theory (DFT) is currently experiencing a golden age. The past two decades witnessed remarkable advances in the general applicability of density functionals in the top rungs of Jacob’s Ladder. Nevertheless, Jacob’s Ladder may have reached its highest rung in terms of dependencies on occupied (rung four) and unoccupied orbitals (rung five). Moreover, the fifth rung is associated with a computational cost far greater than the lower rungs. Another limitation is that each rung includes dozens of different functionals, and at present, there is no clear pathway for systematic improvements within each rung of the ladder. This highlight provides an overview of the exchange–correlation (XC) hole and how it could be used in developing new density functionals. We begin with a brief overview of the current status and challenges in developing better density functionals, followed by the intimate relationship between the XC functional and hole. We present a conceptually simple and computationally economical method for calculating the XC hole and how this method could offer new directions in developing better exchange–correlation functionals. Density functional theory ; Simple (philosophy) ; Atomic orbital ; Theoretical physics ; Computer science ; Computational chemistry ; Physics ; Statistical physics ; Quantum mechanics ; Chemistry ; Epistemology ; Philosophy Machine Learning in Materials Science ; Catalysis and Oxidation Reactions ; Computational Drug Discovery Methods
Authors: Daryl Wilding‐McBride | Andrew I. Webb
Source: PLoS ONE (article, 2022-11-30)
Field: Chemistry — Spectroscopy

A de novo MS1 feature detector for the Bruker timsTOF Pro Identification of peptides by analysis of data acquired by the two established methods for bottom-up proteomics, DDA and DIA, relies heavily on the fragment spectra. In DDA, peptide features detected in mass spectrometry data are identified by matching their fragment spectra with a peptide database. In DIA, a peptide's fragment spectra are targeted for extraction and matched with observed spectra. Although fragment ion matching is a central aspect in most peptide identification strategies, the precursor ion in the MS1 data reveals important characteristics as well, including charge state, intensity, monoisotopic m/z, and apex in retention time. Most importantly, the precursor's mass is essential in determining the potential chemical modification state of the underlying peptide sequence. In the timsTOF, with its additional dimension of collisional cross-section, the data representing the precursor ion also reveals the peptide's peak in ion mobility. However, the availability of tools to survey precursor ions with a wide range of abundance in timsTOF data across the full mass range is very limited. Here we present a de novo feature detector called three-dimensional intensity descent (3DID). 3DID can detect and extract peptide features down to a configurable intensity level, and finds many more features than several existing tools. 3DID is written in Python and is freely available with an open-source MIT license to facilitate experimentation and further improvement (DOI 10.5281/zenodo.6513126). The dataset used for validation of the algorithm is publicly available (ProteomeXchange identifier PXD030706). Monoisotopic mass ; Peptide ; Bottom-up proteomics ; Chemistry ; Mass spectrometry ; Physics ; Mass spectrum ; Computer science ; Tandem mass spectrometry ; Protein mass spectrometry ; Nuclear magnetic resonance ; Chromatography Advanced Proteomics Techniques and Applications ; Mass Spectrometry Techniques and Applications ; Metabolomics and Mass Spectrometry Studies