Indirect determination of airflow resistance of textiles with reference samples

2021 ◽  
Vol 263 (3) ◽  
pp. 3708-3713
Author(s):  
Juan Carlos Rodríguez Vercher ◽  
Romina del Rey ◽  
Jesús Alba
Keyword(s):  
Reference Materials ◽  
Prediction Models ◽  
Acoustic Parameter ◽  
Airflow Resistance ◽  
Indirect Determination ◽  
Wide Range ◽  
Acoustic Characterization ◽  
Absorbing Material ◽  
Indirect Technique ◽  
The Stability

Airflow resistance is a non-acoustic parameter of great relevance in the acoustic characterization of porous materials. It is used in several sound absorbing material prediction models and it is also a control parameter for acoustic conditioning and insulation in different building solutions. The ISO 9053 standard defines several methods to obtain it, using both direct measurements and indirect techniques. However, both procedures may involve problems related to the placement of the textile samples in the tube or to the stability of the samples during testing. In this work, the use of reference materials to stabilize the measurement of thin materials is proposed. Airflow resistance results obtained for different materials in an impedance tube are presented. The tests have been carried out by following the Ingard & Dear method, as an indirect technique accepted by the standard. Several material compositions with a wide range of airflow resistance values have been analyzed with different reference materials.

Performance of Electronic Structure Methods for the Description of Hückel-Möbius Interconversions in Extended π-Systems

2019 ◽  
Author(s):  
Tatiana Woller ◽  
Ambar Banerjee ◽  
Nitai Sylvetsky ◽  
Xavier Deraet ◽  
Frank De Proft ◽  
...  
Keyword(s):  
Electronic Structure ◽  
Basis Set ◽  
Dft Methods ◽  
Molecular Switching ◽  
Wide Range ◽  
Electronic Structure Methods ◽  
Explicitly Correlated ◽  
Relative Errors ◽  
The Stability ◽  
Basis Set Expansion

<p>Expanded porphyrins provide a versatile route to molecular switching devices due to their ability to shift between several π-conjugation topologies encoding distinct properties. Taking into account its size and huge conformational flexibility, DFT remains the workhorse for modeling such extended macrocycles. Nevertheless, the stability of Hückel and Möbius conformers depends on a complex interplay of different factors, such as hydrogen bonding, p···p stacking, steric effects, ring strain and electron delocalization. As a consequence, the selection of an exchange-correlation functional for describing the energy profile of topological switches is very difficult. For these reasons, we have examined the performance of a variety of wavefunction methods and density functionals for describing the thermochemistry and kinetics of topology interconversions across a wide range of macrocycles. Especially for hexa- and heptaphyrins, the Möbius structures have a pronouncedly stronger degree of static correlation than the Hückel and figure-eight structures, and as a result the relative energies of singly-twisted structures are a challenging test for electronic structure methods. Comparison of limited orbital space full CI calculations with CCSD(T) calculations within the same active spaces shows that post-CCSD(T) correlation contributions to relative energies are very minor. At the same time, relative energies are weakly sensitive to further basis set expansion, as proven by the minor energy differences between MP2/cc-pVDZ and explicitly correlated MP2-F12/cc-pVDZ-F12 calculations. Hence, our CCSD(T) reference values are reasonably well-converged in both 1-particle and n-particle spaces. While conventional MP2 and MP3 yield very poor results, SCS-MP2 and particularly SOS-MP2 and SCS-MP3 agree to better than 1 kcal mol<sup>-1</sup> with the CCSD(T) relative energies. Regarding DFT methods, only M06-2X provides relative errors close to chemical accuracy with a RMSD of 1.2 kcal mol<sup>-1</sup>. While the original DSD-PBEP86 double hybrid performs fairly poorly for these extended p-systems, the errors drop down to 2 kcal mol<sup>-1</sup> for the revised revDSD-PBEP86-NL, again showing that same-spin MP2-like correlation has a detrimental impact on performance like the SOS-MP2 results. </p>

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Insights into Potent Therapeutical Antileukemic Agent L-glutaminase Enzyme Under Solid-state Fermentation: A Review

2020 ◽  
Vol 21 (3) ◽  
pp. 211-220 ◽  
Author(s):  
Chandrasai Potla Durthi ◽  
Madhuri Pola ◽  
Satish Babu Rajulapati ◽  
Anand Kishore Kola
Keyword(s):  
Solid State ◽  
Solid State Fermentation ◽  
Food Industry ◽  
Energy Requirement ◽  
Production Studies ◽  
Wide Range ◽  
Hybridoma Technology ◽  
Bacteria And Fungi ◽  
The Stability ◽  
Glutaminase Activity

Aim & objective: To review the applications and production studies of reported antileukemic drug L-glutaminase under Solid-state Fermentation (SSF). Overview: An amidohydrolase that gained economic importance because of its wide range of applications in the pharmaceutical industry, as well as the food industry, is L-glutaminase. The medical applications utilized it as an anti-tumor agent as well as an antiretroviral agent. L-glutaminase is employed in the food industry as an acrylamide degradation agent, as a flavor enhancer and for the synthesis of theanine. Another application includes its use in hybridoma technology as a biosensing agent. Because of its diverse applications, scientists are now focusing on enhancing the production and optimization of L-glutaminase from various sources by both Solid-state Fermentation (SSF) and submerged fermentation studies. Of both types of fermentation processes, SSF has gained importance because of its minimal cost and energy requirement. L-glutaminase can be produced by SSF from both bacteria and fungi. Single-factor studies, as well as multi-level optimization studies, were employed to enhance L-glutaminase production. It was concluded that L-glutaminase activity achieved by SSF was 1690 U/g using wheat bran and Bengal gram husk by applying feed-forward artificial neural network and genetic algorithm. The highest L-glutaminase activity achieved under SSF was 3300 U/gds from Bacillus sp., by mixture design. Purification and kinetics studies were also reported to find the molecular weight as well as the stability of L-glutaminase. Conclusion: The current review is focused on the production of L-glutaminase by SSF from both bacteria and fungi. It was concluded from reported literature that optimization studies enhanced L-glutaminase production. Researchers have also confirmed antileukemic and anti-tumor properties of the purified L-glutaminase on various cell lines.

Intelligent Techniques Analysis for Glycosylation Site Prediction

2021 ◽  
Vol 15 ◽  
Author(s):  
Alhassan Alkuhlani ◽  
Walaa Gad ◽  
Mohamed Roushdy ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Cell Interaction ◽  
Glycosylation Site ◽  
Machine Learning Classification ◽  
Site Prediction ◽  
Glycosylation Sites ◽  
Wide Range ◽  
Feature Extraction And Selection ◽  
Computational Intelligent

Background: Glycosylation is one of the most common post-translation modifications (PTMs) in organism cells. It plays important roles in several biological processes including cell-cell interaction, protein folding, antigen’s recognition, and immune response. In addition, glycosylation is associated with many human diseases such as cancer, diabetes and coronaviruses. The experimental techniques for identifying glycosylation sites are time-consuming, extensive laboratory work, and expensive. Therefore, computational intelligence techniques are becoming very important for glycosylation site prediction. Objective: This paper is a theoretical discussion of the technical aspects of the biotechnological (e.g., using artificial intelligence and machine learning) to digital bioinformatics research and intelligent biocomputing. The computational intelligent techniques have shown efficient results for predicting N-linked, O-linked and C-linked glycosylation sites. In the last two decades, many studies have been conducted for glycosylation site prediction using these techniques. In this paper, we analyze and compare a wide range of intelligent techniques of these studies from multiple aspects. The current challenges and difficulties facing the software developers and knowledge engineers for predicting glycosylation sites are also included. Method: The comparison between these different studies is introduced including many criteria such as databases, feature extraction and selection, machine learning classification methods, evaluation measures and the performance results. Results and conclusions: Many challenges and problems are presented. Consequently, more efforts are needed to get more accurate prediction models for the three basic types of glycosylation sites.

scholarly journals Portable near Infrared Spectroscopy as a Tool for Fresh Tomato Quality Control Analysis in the Field

2021 ◽  
Vol 11 (7) ◽  
pp. 3209
Author(s):  
Karla R. Borba ◽  
Didem P. Aykas ◽  
Maria I. Milani ◽  
Luiz A. Colnago ◽  
Marcos D. Ferreira ◽  
...  
Keyword(s):  
Citric Acid ◽  
Near Infrared ◽  
Prediction Models ◽  
Quality Attributes ◽  
Internal Quality ◽  
Control Analysis ◽  
Tomato Quality ◽  
Wide Range ◽  
Different Types ◽  
Fresh Tomato

Portable spectrometers are promising tools that can be an alternative way, for various purposes, of analyzing food quality, such as monitoring in a few seconds the internal quality during fruit ripening in the field. A portable/handheld (palm-sized) near-infrared (NIR) spectrometer (Neospectra, Si-ware) with spectral range of 1295–2611 nm, equipped with a micro-electro-mechanical system (MEMs), was used to develop prediction models to evaluate tomato quality attributes non-destructively. Soluble solid content (SSC), fructose, glucose, titratable acidity (TA), ascorbic, and citric acid contents of different types of fresh tomatoes were analyzed with standard methods, and those values were correlated to spectral data by partial least squares regression (PLSR). Fresh tomato samples were obtained in 2018 and 2019 crops in commercial production, and four fruit types were evaluated: Roma, round, grape, and cherry tomatoes. The large variation in tomato types and having the fruits from distinct years resulted in a wide range in quality parameters enabling robust PLSR models. Results showed accurate prediction and good correlation (Rpred) for SSC = 0.87, glucose = 0.83, fructose = 0.87, ascorbic acid = 0.81, and citric acid = 0.86. Our results support the assertion that a handheld NIR spectrometer has a high potential to simultaneously determine several quality attributes of different types of tomatoes in a practical and fast way.

scholarly journals Polymer-Drug Conjugates as Nanotheranostic Agents

2021 ◽  
Vol 2 (1) ◽  
pp. 63-81
Author(s):  
Sajana Manandhar ◽  
Erica Sjöholm ◽  
Johan Bobacka ◽  
Jessica M. Rosenholm ◽  
Kuldeep K. Bansal
Keyword(s):  
Drug Delivery ◽  
Drug Release ◽  
Treatment Options ◽  
The Body ◽  
Drug Conjugates ◽  
Imaging Characteristics ◽  
Wide Range ◽  
Systemic Side Effects ◽  
Theranostic Agents ◽  
The Stability

Since the last decade, the polymer-drug conjugate (PDC) approach has emerged as one of the most promising drug-delivery technologies owing to several benefits like circumventing premature drug release, offering controlled and targeted drug delivery, improving the stability, safety, and kinetics of conjugated drugs, and so forth. In recent years, PDC technology has advanced with the objective to further enhance the treatment outcomes by integrating nanotechnology and multifunctional characteristics into these systems. One such development is the ability of PDCs to act as theranostic agents, permitting simultaneous diagnosis and treatment options. Theranostic nanocarriers offer the opportunity to track the distribution of PDCs within the body and help to localize the diseased site. This characteristic is of particular interest, especially among those therapeutic approaches where external stimuli are supposed to be applied for abrupt drug release at the target site for localized delivery to avoid systemic side effects (e.g., Visudyne®). Thus, with the help of this review article, we are presenting the most recent updates in the domain of PDCs as nanotheranostic agents. Different methodologies utilized to design PDCs along with imaging characteristics and their applicability in a wide range of diseases, have been summarized in this article.

scholarly journals Prediction and Stability Assessment of Soft Foundation Settlement of the Fishbone-Shaped Dike Near the Estuary of the Yangtze River Using Machine Learning Methods

2021 ◽  
Vol 13 (7) ◽  
pp. 3744
Author(s):  
Mingcheng Zhu ◽  
Shouqian Li ◽  
Xianglong Wei ◽  
Peng Wang
Keyword(s):  
Extreme Learning Machine ◽  
Prediction Models ◽  
Good Prediction ◽  
Foundation Settlement ◽  
Machine Method ◽  
Settlement Prediction ◽  
Soft Foundation ◽  
Simulation Results ◽  
Learning Machine ◽  
The Stability

Fishbone-shaped dikes are always built on the soft soil submerged in the water, and the soft foundation settlement plays a key role in the stability of these dikes. In this paper, a novel and simple approach was proposed to predict the soft foundation settlement of fishbone dikes by using the extreme learning machine. The extreme learning machine is a single-hidden-layer feedforward network with high regression and classification prediction accuracy. The data-driven settlement prediction models were built based on a small training sample size with a fast learning speed. The simulation results showed that the proposed methods had good prediction performances by facilitating comparisons of the measured data and the predicted data. Furthermore, the final settlement of the dike was predicted by using the models, and the stability of the soft foundation of the fishbone-shaped dikes was assessed based on the simulation results of the proposed model. The findings in this paper suggested that the extreme learning machine method could be an effective tool for the soft foundation settlement prediction and assessment of the fishbone-shaped dikes.

scholarly journals No-z Model for Magnetic Fields of Different Astrophysical Objects and Stability of the Solutions

Data ◽  
2021 ◽  
Vol 6 (1) ◽  
pp. 4
Author(s):  
Evgeny Mikhailov ◽  
Daniela Boneva ◽  
Maria Pashentseva
Keyword(s):  
Magnetic Field ◽  
Magnetic Fields ◽  
Large Scale ◽  
Point Of View ◽  
Accretion Discs ◽  
Wide Range ◽  
Astrophysical Objects ◽  
Alpha Effect ◽  
The Stability ◽  
The Magnetic Field

A wide range of astrophysical objects, such as the Sun, galaxies, stars, planets, accretion discs etc., have large-scale magnetic fields. Their generation is often based on the dynamo mechanism, which is connected with joint action of the alpha-effect and differential rotation. They compete with the turbulent diffusion. If the dynamo is intensive enough, the magnetic field grows, else it decays. The magnetic field evolution is described by Steenbeck—Krause—Raedler equations, which are quite difficult to be solved. So, for different objects, specific two-dimensional models are used. As for thin discs (this shape corresponds to galaxies and accretion discs), usually, no-z approximation is used. Some of the partial derivatives are changed by the algebraic expressions, and the solenoidality condition is taken into account as well. The field generation is restricted by the equipartition value and saturates if the field becomes comparable with it. From the point of view of mathematical physics, they can be characterized as stable points of the equations. The field can come to these values monotonously or have oscillations. It depends on the type of the stability of these points, whether it is a node or focus. Here, we study the stability of such points and give examples for astrophysical applications.

scholarly journals Risk Factors and Prediction Models for Venous Thromboembolism in Ambulatory Patients with Lung Cancer

Healthcare ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 778
Author(s):  
Ann-Rong Yan ◽  
Indira Samarawickrema ◽  
Mark Naunton ◽  
Gregory M. Peterson ◽  
Desmond Yip ◽  
...  
Keyword(s):  
Risk Factors ◽  
Lung Cancer ◽  
Venous Thromboembolism ◽  
High Risk ◽  
Prediction Models ◽  
Poor Performance ◽  
Risk Models ◽  
Related Risk ◽  
Wide Range ◽  
Ambulatory Patients

Venous thromboembolism (VTE) is a significant cause of mortality in patients with lung cancer. Despite the availability of a wide range of anticoagulants to help prevent thrombosis, thromboprophylaxis in ambulatory patients is a challenge due to its associated risk of haemorrhage. As a result, anticoagulation is only recommended in patients with a relatively high risk of VTE. Efforts have been made to develop predictive models for VTE risk assessment in cancer patients, but the availability of a reliable predictive model for ambulate patients with lung cancer is unclear. We have analysed the latest information on this topic, with a focus on the lung cancer-related risk factors for VTE, and risk prediction models developed and validated in this group of patients. The existing risk models, such as the Khorana score, the PROTECHT score and the CONKO score, have shown poor performance in external validations, failing to identify many high-risk individuals. Some of the newly developed and updated models may be promising, but their further validation is needed.

Rapid boron isotope and concentration measurements of silicate geological reference materials dissolved through sodium peroxide sintering

2021 ◽  
Author(s):  
Yue Cai ◽  
Troy E Rasbury ◽  
Kathleen M Wooton ◽  
Xin Jiang ◽  
Di Wang
Keyword(s):  
Solid Earth ◽  
Reference Materials ◽  
Earth Science ◽  
Boron Isotope ◽  
Earth System ◽  
Sodium Peroxide ◽  
Wide Range ◽  
Concentration Measurements

Understanding the movement of fluids in the solid Earth system is crucial for answering a wide range of important questions in Earth Science. Boron (B) is a perfect tracer for...

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