jablonkagroup/rise_ai_scientists huggingface dataset:
Authors: Hadiza Lawal Abdulrahman | Adamu Uzairu | Sani Uba
Source: Journal of Bioenergetics and Biomembranes (article, 2020-11-27)
Field: Chemistry — Organic Chemistry

QSAR, molecular docking, design, and pharmacokinetic analysis of 2-(4-fluorophenyl) imidazol-5-ones as anti-breast cancer drug compounds against MCF-7 cell line The anti-proliferative activities of Novel series of 2-(4-fluorophenyl) imidazol-5-ones against MCF-7 breast cancer cell line were explored via in-slico studies which includes Quantitative structure-activity relationship QSAR, molecular docking studies, designing new compounds, and analyzing the pharmacokinetics properties of the designed compounds. From the QSAR analysis, model number one emerged the best as seen from the arithmetic assessments of (R<sup>2</sup>) = 0.6981, (R<sup>2</sup><sub>adj</sub>) = 0.6433, (Q<sup>2</sup>) = 0.5460 and (R<sup>2</sup><sub>pred</sub>) of 0.5357. Model number one was used in designing new derivative compounds, with higher effectiveness against estrogen positive breast cancer (MCF-7 cell line). The Molecular docking studies between the derivatives and Polo-like kinases (Plk1) receptor proved that the derivatives of 2-(4-fluorophenyl) imidazol-5-ones bind tightly to the receptor, thou ligand 24 and 27 had the highest binding affinities of -8.8 and - 9.1 kcal/mol, which was found to be higher than Doxorubicin with a docking score of -8.0 kcal/mol. These new derivatives of 2-(4-fluorophenyl) imidazol-5-ones shall be excellent inhibitors against (plk1). The pharmacokinetics analysis performed on the new structures revealed that all the structures passed the test and also the Lipinski rule of five, and they could further proceed to pre-clinical tests. They both revealed a revolution in medicine for developing novel anti-breast cancer drugs against MCF-7 cell line. MCF-7 ; Quantitative structure–activity relationship ; Docking (animal) ; Lipinski's rule of five ; Stereochemistry ; Chemistry ; Estrogen receptor ; Breast cancer ; Pharmacokinetics ; Ligand (biochemistry) ; Pharmacology ; Combinatorial chemistry ; Receptor ; In silico ; Biochemistry ; Cancer ; Biology ; Human breast ; Medicine ; Internal medicine Synthesis and biological activity ; Computational Drug Discovery Methods ; Synthesis and Characterization of Heterocyclic Compounds
Authors: Roni Choudhury | Muratahan Aykol | Samuel Gratzl | Joseph H. Montoya | Jens S. Hummelshøj
Source: The Journal of Open Source Software (article, 2020-03-09)
Field: Materials Science — Materials Chemistry

MaterialNet: A web-based graph explorer for materials science data allowing a more data-driven approach to carrying out materials research. Recent studies have demonstrated that representing these databases as material networks can enable extraction of new materials knowledge ( Computer science ; Graph ; World Wide Web ; Web application ; Information retrieval ; Data science ; Theoretical computer science Machine Learning in Materials Science ; X-ray Diffraction in Crystallography ; Advanced Materials Characterization Techniques
Authors: Yen‐Ju Wu | Yann­‐Wen Lan | Shih‐Chieh Hsu | Chen‐Hao Yeh | Y. H. Ku | Jyh‐Chiang Jiang | Yibin Xu
Source: Advanced Materials Interfaces (article, 2020-05-27)
Field: Materials Science — Materials Chemistry

Tuning Interfacial Thermal and Electrical Conductance across a Metal/MoS<sub>2</sub> Monolayer through <i>N</i>‐Methyl‐2‐pyrrolidone Wet Cleaning Abstract Extensive effort is dedicated to developing 2D materials as an alternative to Si‐based semiconductor technology. As the size decreases, heat dissipation at various interfaces becomes increasingly important in controlling device performance. On the other hand, the high interfacial thermal resistances can be applied for thermoelectric devices or thermal insulators by achieving ultralow thermal conductivity via nanostructuring. Here, it is found that a) the thermal and electrical conductance of the Au/MoS 2 monolayer interface can be tuned by changing the interfacial chemical properties through N ‐methyl‐2‐pyrrolidone (NMP) wet cleaning while preserving the MoS 2 structure; b) the effectiveness of the NMP cleaning process, which removes surface adsorbates, exhibits a temperature dependence; c) experimental results demonstrate that adequate oxygen adsorbates at the Au/MoS 2 interface significantly improve the thermal and electrical conductance, in agreement with the simulation results. The interfacial thermal conductance increases by 339.87% when oxygen adsorbates are partially removed and decreases by 74.37% as a sharp interface exists (oxygen adsorbates are removed) compared to the as‐deposited interfaces. The electrical conductance shows up to 2 order increase after NMP cleaning. This finding can both enhance the heat dissipation in functional devices and provide new options for the interface design of thermal insulating thin films. Materials science ; Monolayer ; Conductance ; Thermal conductivity ; Thermal ; Oxygen ; Chemical engineering ; Electrical resistance and conductance ; Thermoelectric effect ; Electrical resistivity and conductivity ; Wet cleaning ; Semiconductor ; Nanotechnology ; Optoelectronics ; Composite material ; Thermodynamics ; Condensed matter physics ; Organic chemistry ; Electrical engineering ; Chemistry Thermal properties of materials ; 2D Materials and Applications ; Advanced Thermoelectric Materials and Devices
Authors: Christian Gianoglio | Edoardo Ragusa | Andrea Bruzzone | Paolo Gastaldo | Rodolfo Zunino | F. Guastavino
Source: Energies (article, 2020-03-02)
Field: Materials Science — Materials Chemistry

Unsupervised Monitoring System for Predictive Maintenance of High Voltage Apparatus The online monitoring of a high voltage apparatus is a crucial aspect for a predictive maintenance program. Partial discharges (PDs) phenomena affect the insulation system of an electrical machine and—in the long term—can lead to a breakdown, with a consequent, significant economic loss; wind turbines provide an excellent example. Embedded solutions are therefore required to monitor the insulation status. The paper presents an online system that adopts unsupervised methodologies for assessing the condition of the monitored machine in real time. The monitoring process does not rely on any prior knowledge about the apparatus; nonetheless, the method can identify the relevant drifts in the machine status. In addition, the system is specifically designed to run on low-cost embedded devices. Predictive maintenance ; Reliability engineering ; High voltage ; Process (computing) ; Computer science ; Voltage ; Condition monitoring ; Partial discharge ; Term (time) ; Engineering ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Electrostatic Discharge in Electronics
Authors: Roberta Palmeri | Tommaso Isernia
Source: EPJ Applied Metamaterials (article, 2020-01-01)
Field: Materials Science — Electronic, Optical and Magnetic Materials

Inverse design of EBG waveguides through scattering matrices Inverse design procedures aim at determining optimal parameters for a given device in order to satisfy assigned specifications. In this contribution, the design of optimal EBG waveguides through inverse problems tools is addressed. In particular, an inversion tool based on the so called ‘scattering matrices’ is proposed and assessed to optimize the guiding effect for straight and bent waveguides. Bent molecular geometry ; Inverse ; Inverse scattering problem ; Scattering ; Inverse problem ; Inversion (geology) ; Quantum inverse scattering method ; Computer science ; Mathematics ; Optics ; Physics ; Engineering ; Mathematical analysis ; Geometry ; Inverse scattering transform ; Structural engineering Metamaterials and Metasurfaces Applications ; Photonic and Optical Devices ; Photonic Crystals and Applications
Authors: Jing Huang | Chongwen Yu
Source: BioResources (article, 2020-03-13)
Field: Chemistry — Analytical Chemistry

Fiber content determination of linen/viscose blends using NIR spectroscopy Linen/viscose blended yarns provide unique properties, but the quality and cost of the fabric composed of the blended yarns are affected by the amount of linen fibers. The existing methods of detecting blending ratio are microscopy or specific component dissolution, which is time-consuming and inconvenient. This study considers the possibility of rapid and simple determination of linen content by the near infrared (NIR) method. A set of linen/viscose powdered blends with 11 different ratios was fabricated. For each sample, 10 sets of spectra were collected by Fourier transform (FT)-NIR spectrometer. A total of 110 spectra sets were generated, in which 60 were used for calibration and 50 for validation. There were verified differences in NIR peaks assigning to representative chemical bonds in cellulose. With the chemometric analysis, a partial least squares (PLS) model was established to predict the linen content in a blended sample. With a combination of smoothing, baseline offset, and multiplicative scatter correction processing of the spectral data, the established PLS model was further improved to achieve a standard validation error of only 1.182% and SD value of the predicted linen content less than 0.2, which indicated the accuracy of the developed method. Viscose ; Materials science ; Partial least squares regression ; Analytical Chemistry (journal) ; Fourier transform infrared spectroscopy ; Near-infrared spectroscopy ; Fiber ; Cellulose ; Composite material ; Optics ; Mathematics ; Chromatography ; Chemical engineering ; Chemistry Spectroscopy and Chemometric Analyses ; Textile materials and evaluations ; Dyeing and Modifying Textile Fibers
Authors: Mohd Ali, Maimunah | Hashim, Norhashila | Abd Aziz, Samsuzana | Lasekan, Ola
Source: Journal of Agricultural and Food Engineering (article, 2020-05-01)
Field: Chemistry — Analytical Chemistry

An overview of non-destructive approaches for quality determination in pineapples Pineapple is one of the healthful and popular tropical fruits in the world. The quality determination of pineapples was mostly evaluated by human inspection which is inconsistent and subjective. The increasing demand for pineapples creates more opportunities for the advancement of rapid and non-destructive approaches to seek quality evaluation of the fruit. This review gives an overview of the non-destructive approaches on the quality determination of pineapples including computer vision, imaging-based approaches, spectroscopy-based approaches, acoustic impulse, and electronic nose. The advance of non-destructive techniques to evaluate the quality of pineapple can produce better yield and improve postharvest handling. This paper also highlighted the recent works on the quality determination of pineapple fruit using non-destructive approaches along with the abundant information that can be explored for real-time purposes. This information is expected to be useful not only for pineapples growers/industries but also for other agro-food commodities. Quality (philosophy) ; Postharvest ; Electronic nose ; Computer science ; Agricultural engineering ; Engineering ; Artificial intelligence ; Horticulture Spectroscopy and Chemometric Analyses ; Water Quality Monitoring and Analysis ; Spectroscopy Techniques in Biomedical and Chemical Research
Authors: Hiroaki Matsumori | Toshihisa Shimizu | Takashi Kosaka | Nobuyuki Matsui
Source: IEEJ Journal of Industry Applications (article, 2020-09-30)
Field: Materials Science — Electronic, Optical and Magnetic Materials

High-speed Core Loss Base Data Collection for Core Loss Calculation Under Power Electronics Converter Excitation The calculation of core loss of power electronics converters requires a large number of core loss data, including the excitation frequency, flux density ripple, and DC magnetic field bias characteristic under rectangular voltage excitation. However, in the case of the conventional method (that employs a B-H analyzer with a chopper circuit), a very long time is required to collect this core loss base data, because circuit parameter tuning is needed for each measurement point. Therefore, we propose a high-speed core loss base data collection method for core loss calculation under power electronics converter excitation. The core loss base data collection time was substantially reduced upon usage of a pulse width modulated (PWM) inverter and a neural network because various core loss characteristics were expressed upon excitation of the PWM inverter, and these core losses were learned by the neural network. The core loss base data, as measured via the proposed and conventional methods, were compared to validate the measurement accuracy of the proposed method, and the difference was found to be lesser than 12%. Thus, the proposed method achieves high-speed core loss base data collection. Ripple ; Pulse-width modulation ; Core (optical fiber) ; Excitation ; Computer science ; Data loss ; Chopper ; Electrical engineering ; Electronic engineering ; Voltage ; Engineering ; Telecommunications Magnetic Properties and Applications ; Multilevel Inverters and Converters ; Advanced DC-DC Converters
Authors: Haresh Kumar | Muhammad Shafiq | Ghulam Amjad Hussain | Lauri Kumpulainen | Kimmo Kauhaniemi
Source:  (article, 2020-10-26)
Field: Materials Science — Materials Chemistry

Classification of PD Faults Using Features Extraction and K-Means Clustering Techniques Partial discharge (PD) diagnostic is a crucial tool for condition monitoring of power system equipment (e.g. switchgear, cable) in the medium voltage (MV) network, which is degraded by the gradual deterioration of insulation elements, ageing, and various operational and environmental stresses. In the MV network, different types of PD faults are generated from different sources and to know the impact of an individual PD fault on the health of MV equipment, classification plays an important role. This paper aims to provide suitable techniques for classifying PD faults. The data is collected from an experimental investigation of three different types of PD faults from MV switchgear and classified using features extraction, dimensionality reduction and clustering techniques. To identify the best classification technique, dimensionality reduction techniques (principal component analysis and t-distributed stochastic neighbour embedding) are used, and their results are compared using the confusion matrix after applying k-means clustering technique. Switchgear ; Cluster analysis ; Principal component analysis ; Dimensionality reduction ; Computer science ; Data mining ; Reliability engineering ; Pattern recognition (psychology) ; Partial discharge ; Fault (geology) ; Curse of dimensionality ; Artificial intelligence ; Engineering ; Voltage ; Electrical engineering High voltage insulation and dielectric phenomena ; Power Transformer Diagnostics and Insulation ; Power Systems Fault Detection
Authors: Wojciech Rządkowski | Nicolò Defenu | Silvia Chiacchiera | Andrea Trombettoni | Giacomo Bighin
Source: Repository for Publications and Research Data (ETH Zurich) (article, 2020-09-01)
Field: Materials Science — Materials Chemistry

Detecting composite orders in layered models via machine learning Determining the phase diagram of systems consisting of smaller subsystems 'connected' via a tunable coupling is a challenging task relevant for a variety of physical settings. A general question is whether new phases, not present in the uncoupled limit, may arise. We use machine learning and a suitable quasidistance between different points of the phase diagram to study layered spin models, in which the spin variables constituting each of the uncoupled systems (to which we refer as layers) are coupled to each other via an interlayer coupling. In such systems, in general, composite order parameters involving spins of different layers may emerge as a consequence of the interlayer coupling. We focus on the layered Ising and Ashkin–Teller models as a paradigmatic case study, determining their phase diagram via the application of a machine learning algorithm to the Monte Carlo data. Remarkably our technique is able to correctly characterize all the system phases also in the case of hidden order parameters, i.e. order parameters whose expression in terms of the microscopic configurations would require additional preprocessing of the data fed to the algorithm. We correctly retrieve the three known phases of the Ashkin–Teller model with ferromagnetic couplings, including the phase described by a composite order parameter. For the bilayer and trilayer Ising models the phases we find are only the ferromagnetic and the paramagnetic ones. Within the approach we introduce, owing to the construction of convolutional neural networks, naturally suitable for layered image-like data with arbitrary number of layers, no preprocessing of the Monte Carlo data is needed, also with regard to its spatial structure. The physical meaning of our results is discussed and compared with analytical data, where available. Yet, the method can be used without any a priori knowledge of the phases one seeks to find and can be applied to other models and structures. Physics ; Composite number ; Machine learning ; Artificial intelligence ; Medicine ; Composite material Machine Learning in Materials Science ; Neural Networks and Applications ; Quasicrystal Structures and Properties
Authors: Anna Adamczak-Bugno | Aleksandra Krampikowska
Source: Materials (article, 2020-07-02)
Field: Materials Science — Polymers and Plastics

The Acoustic Emission Method Implementation Proposition to Confirm the Presence and Assessment of Reinforcement Quality and Strength of Fiber–Cement Composites This article proposes to use the acoustic emission (AE) method to evaluate the degree of change in the mechanical parameters of fiber-cement boards. The research was undertaken after a literature review, due to the lack of a methodology that would allow nondestructive assessment of the strength of cement-fiber elements. The tests covered the components cut out from a popular type of board available on the construction market. The samples were subjected to environmental (soaking in water, cyclic freezing-thawing) and exceptional (burning with fire and exposure to high temperature) factors, and then to three-point bending strength tests. The adopted conditions correspond to the actual working environment of the boards. When applying the external load, AE signals were generated, which were then grouped into classes, and initially assigned to specific processes occurring in the material. The frequencies occurring over time for the tested samples were also analysed, and microscopic observations were made to confirm the suppositions based on the first part of the tests. Comparing the results obtained from a group of samples subjected to environmental and exceptional actions, significant differences were noted between them, which included the types of recorded signal class, the frequency of events, and the construction of the microstructure. The degradation of the structure, associated with damage to the fibers or their complete destruction, results in the generation under load of AE signals that indicate the uncontrolled development of scratches, and a decrease in the frequency of these events. According to the authors, the methodology used allows the control of cement-fiber boards in use. The registration and analysis of active processes under the effect of payloads makes it possible to distinguish mechanisms occurring inside the structure of the elements, and to formulate a quick response to the situation when the signals indicate a decrease in the strength of the boards. Reinforcement ; Acoustic emission ; Composite material ; Materials science ; Cement ; Fiber ; Quality (philosophy) Natural Fiber Reinforced Composites ; Innovative concrete reinforcement materials ; Fire effects on concrete materials
Authors: Dedik Romahadi | Fajar Anggara | Andi Firdaus Sudarma | Hui Xiong
Source: SINERGI (article, 2020-11-11)
Field: Chemistry — Analytical Chemistry

THE IMPLEMENTATION OF ARTIFICIAL NEURAL NETWORKS IN DESIGNING INTELLIGENT DIAGNOSIS SYSTEMS FOR CENTRIFUGAL MACHINES USING VIBRATION SIGNAL It is important to maintain every machine affecting the process of making sugar to ensure excellent product quality with minimal losses and to accelerate productivity and profitability targets. The centrifuges are widely used in industry today with some being very difficult and critical for surgery, and the collapse of the engine has the ability to cause expensive damage. One of these is the centrifugal machines, and they are expected to be efficient to produce high-quality sugar. Meanwhile, an efficient diagnostic tool to predict the correct time for centrifugal repair is vibration signal analysis namely by attaching the accelerometer sensor to the location of the centrifugal bearing to produce vibration data that is ready to be analyzed. Still, the process requires sufficient insight and experience. The manual method usually used is complicated and requires a lot of time to obtain results of a centrifugal diagnosis. Therefore, this study was conducted to design an intelligent system to diagnose centrifugal vibrations using Artificial Neural Networks (ANN). The situation is involved in applying and training the concept of vibration analysis from spectrum data to ANN to produce diagnostic results according to the spectrum diagnosis reference. The results obtained were quite good with the largest cross-entropy value of 10.67 having 0% error value with the largest Mean Square Error value being 0.0023 while the smallest regression was 0.993. The test conducted on nine new spectrums produced eight true predictions and one false. The system can provide fairly accurate results in a short time. Classification quality improvement can be made by adding training data. Vibration ; Artificial neural network ; Computer science ; Process (computing) ; Entropy (arrow of time) ; Accelerometer ; Artificial intelligence ; Profitability index ; SIGNAL (programming language) ; Engineering ; Acoustics Spectroscopy and Chemometric Analyses ; Advanced Measurement and Detection Methods ; Machine Fault Diagnosis Techniques
Authors: Rébecca Dowek | Céline Cagna | J. Noirot | I. Zacharie-Aubrun | Thierry Blay | K. Hanifi | Philippe Bienvenu | I. Roure | Martiane Cabié | M. Dumont
Source: Journal of Nuclear Materials (article, 2020-10-23)
Field: Materials Science — Materials Chemistry

Determination of the pressure in micrometric bubbles in irradiated nuclear fuels Bubble ; Materials science ; Nuclear fuel ; Thermal conductivity ; Fission ; Nuclear engineering ; Analytical Chemistry (journal) ; Neutron ; Nuclear physics ; Composite material ; Mechanics ; Chemistry Nuclear Materials and Properties ; Radioactive element chemistry and processing ; Nuclear Physics and Applications
Authors: H. Paradis | C. Bobin | J. Bobin | J. Bouchard | Valérie Lourenço | C. Thiam | Rémi André | L. Ferreux | Anne de Vismes Ott | Mathieu Thévenin
Source: Applied Radiation and Isotopes (article, 2020-02-01)
Field: Chemistry — Analytical Chemistry

Spectral unmixing applied to fast identification of γ-emitting radionuclides using NaI(Tl) detectors Detector ; Monte Carlo method ; Radiological weapon ; Radionuclide ; Identification (biology) ; Semiconductor detector ; Multiplicative function ; Common emitter ; Nuclear material ; Environmental science ; Physics ; Computer science ; Nuclear medicine ; Nuclear engineering ; Nuclear physics ; Radiochemistry ; Chemistry ; Statistics ; Optics ; Engineering ; Mathematics ; Optoelectronics Spectroscopy and Chemometric Analyses ; Advanced Chemical Sensor Technologies ; Isotope Analysis in Ecology