QSPR-Modeling for the Second Virial Cross-Coefficients of Binary Organic Mixtures

2016 ◽  
Vol 1 (2) ◽  
pp. 72-84 ◽  
Author(s):  
Elena Mokshyna ◽  
Pavel Polishchuk ◽  
Vadim Nedostup ◽  
Victor Kuz'min
Keyword(s):  
Intermolecular Interactions ◽  
High Performance ◽  
Pair Potential ◽  
Statistical Characteristics ◽  
Prediction Errors ◽  
Organic Mixtures ◽  
Different Temperatures ◽  
Qspr Modeling ◽  
Predicted Values ◽  
Heterogeneous Interactions

The second virial cross-coefficient is an important characteristic of the pair intermolecular interactions that describes solely the heterogeneous interactions. In the current study, the authors made the first attempt to develop rigorous QSPR models for analysis and prediction of the second virial cross-coefficient. Novel descriptors to describe pair intermolecular interactions were implemented. Statistical characteristics of the obtained models showed high performance. Prediction errors are comparable to the errors of data. Theoretically predicted values of the second virial cross-coefficient may be used to derive PVT-properties of mixtures at the different temperatures as well as to calculate intermolecular pair potential.

The second virial coefficient of a non-associating gas with anisotropic interactions

1980 ◽  
Vol 45 (9) ◽  
pp. 2375-2383 ◽  
Author(s):  
Miloš Ševčík ◽  
Tomáš Boublík
Keyword(s):  
Experimental Data ◽  
Perturbation Theory ◽  
Intermolecular Interactions ◽  
Virial Coefficient ◽  
Second Virial Coefficient ◽  
Pair Potential ◽  
Lennard Jones ◽  
Different Temperatures ◽  
Anisotropic Interactions ◽  
Experimental Values

The second virial coefficient in systems with permanent and induced multipole interactions was studied by using a statistical-thermodynamics correlation based on the perturbation theory of fluids. Several pair potential combinations of the Lennard-Jones function with different, subsequently more complex anisotropic contributions, were considered; the improvement in the description of intermolecular interactions due to these non-central contributions brought about an improvement in the interpretation of experimental data. The characteristic dependence of the parameters ε/k on σ at different temperatures was obtained for all of the three systems studied (Ar, CH4 and CH3F). It was found that if experimental values of the second virial coefficient of methyl fluoride are correlated by a relation derived from the Stockmayer potential, two sets of the ε/k and σ can be employed.

Microstructure of sputter deposited Ni-Sb ohmic contacts on GaAs

1989 ◽  
Vol 47 ◽  
pp. 450-451
Author(s):  
S. Yegnasubramanian ◽  
V.C. Kannan ◽  
R. Dutto ◽  
P.J. Sakach
Keyword(s):  
High Performance ◽  
Ohmic Contacts ◽  
Surface Defects ◽  
Pure Metal ◽  
Device Fabrication ◽  
Native Oxide ◽  
Metal Thin Films ◽  
Recent Developments ◽  
Different Temperatures ◽  
Sputter Deposited

Recent developments in the fabrication of high performance GaAs devices impose crucial requirements of low resistance ohmic contacts with excellent contact properties such as, thermal stability, contact resistivity, contact depth, Schottky barrier height etc. The nature of the interface plays an important role in the stability of the contacts due to problems associated with interdiffusion and compound formation at the interface during device fabrication. Contacts of pure metal thin films on GaAs are not desirable due to the presence of the native oxide and surface defects at the interface. Nickel has been used as a contact metal on GaAs and has been found to be reactive at low temperatures. Formation Of Ni2 GaAs at 200 - 350C is reported and is found to grow epitaxially on (001) and on (111) GaAs, but is shown to be unstable at 450C. This paper reports the investigations carried out to understand the microstructure, nature of the interface and composition of sputter deposited and annealed (at different temperatures) Ni-Sb ohmic contacts on GaAs by TEM. Attempts were made to correlate the electrical properties of the films such as the sheet resistance and contact resistance, with the microstructure. The observations are corroborated by Scanning Auger Microprobe (SAM) investigations.

scholarly journals An IoT-Focused Intrusion Detection System Approach Based on Preprocessing Characterization for Cybersecurity Datasets

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 656
Author(s):  
Xavier Larriva-Novo ◽  
Víctor A. Villagrá ◽  
Mario Vega-Barbas ◽  
Diego Rivera ◽  
Mario Sanz Rodrigo
Keyword(s):  
Machine Learning ◽  
Intrusion Detection ◽  
High Performance ◽  
Learning Algorithm ◽  
Detection System ◽  
Machine Learning Algorithms ◽  
Statistical Characteristics ◽  
Detection Techniques ◽  
Traffic Characteristics ◽  
Benchmark Datasets

Security in IoT networks is currently mandatory, due to the high amount of data that has to be handled. These systems are vulnerable to several cybersecurity attacks, which are increasing in number and sophistication. Due to this reason, new intrusion detection techniques have to be developed, being as accurate as possible for these scenarios. Intrusion detection systems based on machine learning algorithms have already shown a high performance in terms of accuracy. This research proposes the study and evaluation of several preprocessing techniques based on traffic categorization for a machine learning neural network algorithm. This research uses for its evaluation two benchmark datasets, namely UGR16 and the UNSW-NB15, and one of the most used datasets, KDD99. The preprocessing techniques were evaluated in accordance with scalar and normalization functions. All of these preprocessing models were applied through different sets of characteristics based on a categorization composed by four groups of features: basic connection features, content characteristics, statistical characteristics and finally, a group which is composed by traffic-based features and connection direction-based traffic characteristics. The objective of this research is to evaluate this categorization by using various data preprocessing techniques to obtain the most accurate model. Our proposal shows that, by applying the categorization of network traffic and several preprocessing techniques, the accuracy can be enhanced by up to 45%. The preprocessing of a specific group of characteristics allows for greater accuracy, allowing the machine learning algorithm to correctly classify these parameters related to possible attacks.

Effects of additives on the cure kinetics of vinyl ester pultrusion resins

2021 ◽  
pp. 002199832110015
Author(s):  
Alexander Vedernikov ◽  
Yaroslav Nasonov ◽  
Roman Korotkov ◽  
Sergey Gusev ◽  
Iskander Akhatov ◽  
...  
Keyword(s):  
Vinyl Ester ◽  
Mean Squared Error ◽  
Cure Kinetics ◽  
Zinc Stearate ◽  
Heating Rates ◽  
Scanning Calorimetry ◽  
Different Temperatures ◽  
Predicted Values ◽  
Kinetics Of ◽  
Final Degree

Pultrusion is a highly efficient composite manufacturing process. To accurately describe pultrusion, an appropriate model of resin cure kinetics is required. In this study, we investigated cure kinetics modeling of a vinyl ester pultrusion resin (Atlac 430) in the presence of aluminum hydroxide (Al(OH)3) and zinc stearate (Zn(C18H35O2)2) as processing additives. Herein, four different resin compositions were studied: neat resin composition, composition with Al(OH)3, composition comprising Zn(C18H35O2)2, and composition containing both Al(OH)3 and Zn(C18H35O2)2. To analyze each composition, we performed differential scanning calorimetry at the heating rates of 5, 7.5, and 10 K/min. To characterize the cure kinetics of Atlac 430, 16 kinetic models were tested, and their performances were compared. The model based on the [Formula: see text]th-order autocatalytic reaction demonstrated the best results, with a 4.5% mean squared error (MSE) between the experimental and predicted data. This study proposes a method to reduce the MSE resulting from the simultaneous melting of Zn(C18H35O2)2. We were able to reduce the MSE by approximately 34%. Numerical simulations conducted at different temperatures and pulling speeds demonstrated a significant influence of resin composition on the pultrusion of a flat laminate profile. Simulation results obtained for the 600 mm long die block at different die temperatures (115, 120, 125, and 130 °C) showed that for a resin with a final degree of cure exceeding 95% at the die exit, the maximum difference between the predicted values of pulling speed for a specified set of compositions may exceed 1.7 times.

scholarly journals Can artificially selected phenotypes influence a component of field fitness? Thermal selection and fly performance under thermal extremes

2006 ◽  
Vol 274 (1611) ◽  
pp. 771-778 ◽  
Author(s):  
Torsten Nygaard Kristensen ◽  
Volker Loeschcke ◽  
Ary A Hoffmann
Keyword(s):  
High Performance ◽  
Quantitative Traits ◽  
Genetic Basis ◽  
Selected Lines ◽  
Resistant Lines ◽  
Correlated Patterns ◽  
Different Temperatures ◽  
Evolutionary Trajectories ◽  
Low Performance ◽  
Selected Traits

Artificially selected lines are widely used to investigate the genetic basis of quantitative traits and make inferences about evolutionary trajectories. Yet, the relevance of selected traits to field fitness is rarely tested. Here, we assess the relevance of thermal stress resistance artificially selected in the laboratory to one component of field fitness by investigating the likelihood of adult Drosophila melanogaster reaching food bait under different temperatures. Lines resistant to heat reached the bait more often than controls under hot and cold conditions, but less often at intermediate temperatures, suggesting a fitness cost of increased heat resistance but not at temperature extremes. Cold-resistant lines were more common at baits than controls under cold as well as hot field conditions, and there was no cost at intermediate temperatures. One of the replicate heat-resistant lines was caught less often than the others under hot conditions. Direct and correlated patterns of responses in laboratory tests did not fully predict the low performance of the heat selected lines at intermediate temperatures, nor the high performance of the cold selected lines under hot conditions. Therefore, lines selected artificially not only behaved partly as expected based on laboratory assays but also evolved patterns only evident in the field releases.

scholarly journals Determination of Oleanolic and Ursolic Acids inHedyotis diffusaUsing Hyphenated Ultrasound-Assisted Supercritical Carbon Dioxide Extraction and Chromatography

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ming-Chi Wei ◽  
Yu-Chiao Yang ◽  
Show-Jen Hong
Keyword(s):  
Carbon Dioxide ◽  
Supercritical Carbon Dioxide ◽  
High Performance ◽  
Extraction Yield ◽  
Extraction Time ◽  
Different Temperatures ◽  
Ultrasound Assisted ◽  
High Concentrations ◽  
Static Extraction ◽  
Supercritical Carbon

Oleanolic acid (OA) and ursolic acid (UA) were extracted fromHedyotis diffusausing a hyphenated procedure of ultrasound-assisted and supercritical carbon dioxide (HSC–CO2) extraction at different temperatures, pressures, cosolvent percentages, and SC–CO2flow rates. The results indicated that these parameters significantly affected the extraction yield. The maximal yields of OA (0.917 mg/g of dry plant) and UA (3.540 mg/g of dry plant) were obtained at a dynamic extraction time of 110 min, a static extraction time of 15 min, 28.2 MPa, and 56°C with a 12.5% (v/v) cosolvent (ethanol/water = 82/18, v/v) and SC–CO2flowing at 2.3 mL/min (STP). The extracted yields were then analyzed by high performance liquid chromatography (HPLC) to quantify the OA and UA. The present findings revealed thatH. diffusais a potential source of OA and UA. In addition, using the hyphenated procedure for extraction is a promising and alternative process for recovering OA and UA fromH. diffusaat high concentrations.

Determination of Recrystallization Kinetics Model of 30Cr2Ni4MoV Steel Based on Dislocation Density

2017 ◽  
Vol 263 ◽  
pp. 59-66
Author(s):  
Peng Zhou ◽  
Qing Xian Ma
Keyword(s):  
Dislocation Density ◽  
Model Comparison ◽  
Kinetics Model ◽  
Structure Evolution ◽  
Metallographic Analysis ◽  
Recrystallization Kinetics ◽  
Proposed Model ◽  
Different Temperatures ◽  
Predicted Values ◽  
Experimental Values

A new model to predict the structure evolution of 30Cr2Ni4MoV steel is proposed based on the dislocation density in this research. Hot compression of 30Cr2Ni4MoV steel is carried out on Gleeble 1500 at different temperatures from 1233 K to 1473 K with a strain rate of 0.01 s-1 and the deformed samples are immediately quenched by water to frozen the austenite structure. The recrystallization kinetics model of 30Cr2Ni4MoV steel is successfully established by inverse analysis of the flow curve based on the relation between flow stress and dislocation density. In order to validate the proposed model, comparison between the predicted values and experimental values obtained by metallographic analysis is implemented. It is shown that the predicted results agree with the experimental results well.

Study of Intermolecular Interactions of CTAB with Amino Acids at Different Temperatures: A Multi Technique Approach

2019 ◽  
Vol 233 (2) ◽  
pp. 167-182 ◽  
Author(s):  
Anwar Ali ◽  
Nizamul Haque Ansari ◽  
Ummer Farooq ◽  
Shadma Tasneem ◽  
Firdosa Nabi
Keyword(s):  
Molar Volume ◽  
Intermolecular Interactions ◽  
Cetyltrimethylammonium Bromide ◽  
Complete Characterization ◽  
Viscosity Data ◽  
Viscosity Coefficients ◽  
Free Energy Of Activation ◽  
Different Temperatures ◽  
Amino Acid Solutions

Abstract The densities, ρ, viscosities, η and specific conductivities κ, of (0.0002, 0.0004, 0.0006 and 0.0008 m) CTAB in 0.1 m aqueous valine, leucine and isoleucine were measured at different temperatures. The measured data were used to calculate various useful thermodynamic parameters. A complete characterization of any mixture can be performed by means of these thermodynamic properties. The apparent molar volume, ϕv, partial molar volume, $\phi _v^0$ and partial molar isobaric expansibilities, $\phi _E^0,$ were calculated using density data. The viscosity data were analyzed using Jones–Dole equation to obtain viscosity coefficients, A- and B-, free energy of activation per mole of solvent, Δμ1°∗, and solute, Δμ2°∗, enthalpy, ΔH∗ and entropy, ΔS∗ of activation of viscous flow. Measuring the changes in these properties has been found to be an excellent qualitative and quantitative way to obtain information regarding the molecular structure and intermolecular interactions occurring in these mixtures. Various structure-making/breaking ability of solute (cetyltrimethylammonium bromide) in presence of aqueous amino acid solutions were discussed. In addition, fluorescence study using pyrene as a photophysical probe has been carried out, the results of which support the conclusions obtained from other techniques.

An Interpretable Machine Learning Model Enhanced Integrated CPU-GPU DVFS Governor

2021 ◽  
Vol 20 (6) ◽  
pp. 1-28
Author(s):  
Jurn-Gyu Park ◽  
Nikil Dutt ◽  
Sung-Soo Lim
Keyword(s):  
Machine Learning ◽  
Energy Efficiency ◽  
High Performance ◽  
Linear Models ◽  
Piecewise Linear ◽  
Prediction Errors ◽  
Linear Regression Models ◽  
Mobile Games ◽  
Interpretable Machine Learning ◽  
Mathematical Formulas

Modern heterogeneous CPU-GPU-based mobile architectures, which execute intensive mobile gaming/graphics applications, use software governors to achieve high performance with energy-efficiency. However, existing governors typically utilize simple statistical or heuristic models, assuming linear relationships using a small unbalanced dataset of mobile games; and the limitations result in high prediction errors for dynamic and diverse gaming workloads on heterogeneous platforms. To overcome these limitations, we propose an interpretable machine learning (ML) model enhanced integrated CPU-GPU governor: (1) It builds tree-based piecewise linear models (i.e., model trees) offline considering both high accuracy (low error) and interpretable ML models based on mathematical formulas using a simulatability operation counts quantitative metric. And then (2) it deploys the selected models for online estimation into an integrated CPU-GPU Dynamic Voltage Frequency Scaling governor. Our experiments on a test set of 20 mobile games exhibiting diverse characteristics show that our governor achieved significant energy efficiency gains of over 10% (up to 38%) improvements on average in energy-per-frame with a surprising-but-modest 3% improvement in Frames-per-Second performance, compared to a typical state-of-the-art governor that employs simple linear regression models.

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