Numerical Descriptors Research Articles

Enhancing corrosion-resistant alloy design through natural language processing and deep learning.

We propose strategies that couple natural language processing with deep learning to enhance machine capability for corrosion-resistant alloy design. First, accuracy of machine learning models for materials datasets is often limited by their inability to incorporate textual data. Manual extraction of numerical parameters from descriptions of alloy processing or experimental methodology inevitably leads to a reduction in information density. To overcome this, we have developed a fully automated natural language processing approach to transform textual data into a form compatible for feeding into a deep neural network. This approach has resulted in a pitting potential prediction accuracy substantially beyond state of the art. Second, we have implemented a deep learning model with a transformed-input feature space, consisting of a set of elemental physical/chemical property-based numerical descriptors of alloys replacing alloy compositions. This helped identification of those descriptors that are most critical toward enhancing their pitting potential. In particular, configurational entropy, atomic packing efficiency, local electronegativity differences, and atomic radii differences proved to be the most critical.

Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation

Optimal building design in terms of comfort and energy performance means designing and constructing a building that requires the minimum energy demand under the given conditions while also providing a good level of human comfort. This paper focuses on replacing the complex energy and comfort simulation procedure with fast regression model-based processes that encounter the building shape as input. Numerous building shape descriptors were applied as inputs to several regression models. After evaluating the results, it can be stated that, with careful selection of building geometry describing design input variables, complex energy and comfort simulations can be approximated. Six different models with five different building shape descriptors were tested. The worst results were around R2 = 0.75, and the generic results were around R2 = 0.92. The most accurate prediction models, with the highest level of accuracy (R2 > 0.97), were linear regressions using 3rd power and dense neural networks using 1st power of inputs; furthermore, averages of mean absolute percentage errors are 1% in the case of dense neural networks. For the best performance, the building configuration was described by a discrete functional point cloud. The proposed method can effectively aid future building energy and comfort optimization processes.

Evaluating the efficiency of factors influencing valuation of residential buildings: A priority weight-based model

Valuation of buildings is an utmost complication for different socio-economic stakeholders in the building's buying and selling process. The traditional valuation approaches can vary because it depends on the evaluator's opinions. Also, existing approaches have not considered all the parameters and the interrelation between these parameters for their models. The objective of this study is to develop a priority weights-based rating model for the valuation of existing and new buildings. In this paper, the Fuzzy Analytical Hierarchy Process (FAHP) is used to develop a rating system to use in the valuation process of buildings. The complementary novelty of this paper is the inclusion of the primary principles of engineering, sustainability, and adaptability into the valuation process of a building. Further, the subjectivity of this model is eliminated by using numerical descriptors. The opinions of 100 experts were collected and averaged to develop this model, and the consistency ratio of the experts’ opinions was maintained at less than 10 %. Finally, the valuation of a residential building is performed as a case study to show the numerical possibilities of the proposed model. The proposed model can be used to evaluate existing and new residential buildings in property transactions, insurance purposes, taxation, and making investment decisions.

Extractability, selectivity, and comprehensiveness in supercritical fluid extraction of seaweed using ternary mixtures of carbon dioxide, ethanol, and water

It is well-known that an ideal extraction method enabling quantitative analysis should give complete extraction of the target analytes as well as minimal co-extraction of unwanted matrix substances. If the extraction method is part of a nontarget screening protocol, the desired analytes can differ widely in terms of chemical properties. In chromatography, terminologies such as recovery, selectivity, and comprehensiveness are well-established and can easily be determined. However, in extraction, these concepts are much less developed. Hence, the aim of our research is to develop and scrutinize theory in extraction with respect to numerical descriptors for extractability, selectivity, and comprehensiveness. Our approach is based on experiments determining the extractability of target analytes and selected interferences. As a case study, we use a pooled sample of three species of seaweed (Alaria esculenta, Laminaria digitata and Ascophyllum nodosum). Target analytes are β-carotene, fucoxanthin, δ-tocopherol, and phloroglucinol; and selected interferences are carbohydrates, proteins, ash, arsenic, and chlorophyll a. As a “green and clean” extraction technique, supercritical fluid extraction (SFE) using mixtures of CO2, ethanol and water were explored using a design of experiment. The temperature was varied between 40–80°C, and the pressure was held constant at 300 bar. Obtained results clearly demonstrate that highest relative selectivity was achieved with CO2 containing only 5 vol% of ethanol and no water, which primarily enabled high extractability of β-carotene, and yielding an extract free of carbohydrates, proteins, and toxic metals such as arsenic. Best methods for highest extractability of the other target analytes varied quite widely. Analytes requiring the highest water content (fucoxanthin and phloroglucinol), also resulted in the lowest relative selectivity. Maximum relative comprehensiveness was achieved using CO2/ethanol/water (40/55/5, v/v/v) at 70°C and 300 bar. Our study demonstrates the feasibility of using relative quantitative descriptors for extractability, selectivity, and comprehensiveness, in optimization strategies for analytical extractions.

GENERALIZED REDEFINED ZAGREB INDEX ON SOME NANOSTRUCTURES

Chemical graph theory has become a significant part of the research. Topological indices are numerical descriptors used to determine physico-chemical properties of chemical compounds. In this work, the generalized redefined Zagreb index is defined. This index is applied on several topological indices viz., redefined Zagreb indices (ReZG1(G), ReZG2(G), ReZG3(G)), sum-connectivity Zagreb index (SCI(G)), hyper Zagreb index (HM(G)), harmonic index (H(G)), arithmetic-geometric index (AGI(G)), SK index (SK(G)) and SK1 index (SK1(G)). The above said indices are calculated for aromatic compounds such as boron triangular sheet, borophene chain and melem chain nanostructures. The obtained results are useful in understanding the behaviour of compounds under the study.

Eccentric Connectivity Index of Nanosheets and Nanotube of SiO2

Chemical graph theory is a field related to Chemistry which is used to predict the behavior of chemical structures. The current trend in Chemical graph theory is evaluating topological indices. They are the numerical descriptors of the molecular structures derived from their corresponding molecular graph. These indices are studied and used in drugs, pharmaceutical research, and other fields. In this paper, the eccentric connectivity index is computed for nanosheets and nanotubes of SiO2.

Hosoya, Schultz, and Gutman Polynomials of Generalized Petersen Graphs P n , 1 and P n , 2

The graph theory has wide important applications in various other types of sciences. In chemical graph theory, we have many topological polynomials for a graph G through which we can compute many topological indices. Topological indices are numerical values and descriptors which are used to quantify the physiochemical properties and bioactivities of the chemical graph. In this paper, we compute Hosoya polynomial, hyper-Wiener index, Tratch–Stankevitch–Zefirov index, Harary index, Schultz polynomial, Gutman polynomial, Schultz index, and Gutman index of generalized Petersen graphs P n , 1 and P n , 2 .

On the Hyper Zagreb Index of Trees with a Specified Degree of Vertices

Topological indices are the numerical descriptors that correspond to some certain physicochemical properties of a chemical compound such as the boiling point, acentric factor, enthalpy of vaporisation, heat of fusion, etc. Among these topological indices, the Hyper Zagreb index, is the most effectively used topological index to predict the acentric factor of some octane isomers. In the current work, we investigate the extremal values of the Hyper Zagreb index for some classes of trees.

Comparative study of multilayered graphene using numerical descriptors through M-polynomial

Molecular descriptors play a powerful role in encoding the information of a chemical compound using its molecular structure based on a defined algorithm. Regardless of the evolution in drug design, the main tool used in discovering the lead molecules of a drug refers to the use of topological descriptors. This article focusses on M-polynomial of multilayered graphene, viz., monolayer, bilayer and trilayer. Using the M-polynomial, eleven degree-based topological indices are derived for the said three structures. 3D-plot of the polynomials shows variation among the three types of graphene. The numerical and graphical comparison of the indices for varying values of n are tabulated for better understanding.

Reverse-degree-based topological indices of fullerene cage networks

Totally made of carbon atoms, fullerene is a spherical molecule with a three-dimensional pattern like a soccer ball. The most prevalent kind of fullerene is C60, which has 60 carbon atoms organised in a pattern like that of a soccer ball (12 pentagons and 20 hexagons). The carbon atoms are connected by a web of covalent connections to form what is known as a truncated icosahedron. The fullerene molecule's spherical form makes it beneficial in many applications, including medication delivery, lubricants, and even as a possible component of nanotechnology. Topological indices or numerical descriptors have been used for decades to connect essential physicochemical parameters with significant molecular structural features including cyclicity, boiling and melting point, and enthalpy for a range of two- along with three-dimensional in form chemical arrangement networking or graphs. The valency of atom-based numerical descriptors in this study are computed using the valency of atom in a chemical network or structure of the molecular investigation. In chemoinformatics, the numerical descriptors based on the degree of defined by reverse-degree concept technique is a more newly developed methodology for evaluating chemical systems and geometries. It draws attention to molecular traits as numerical descriptors based on the degree of defined by reverse-degree concept and presents numerical descriptors in algebraic form. In this work, we describe several fullerene cages as numerical descriptors based on the degree of defined by reverse-degree concept and compute a number of numerical descriptors based on the degree of defined by reverse-degree concept, such as the Randić index, atom-bond-connectivity, geometric arithmetic, forgotten, hyper, first and second Zagreb indices are computed.

An Efficient Mobile Application for Identification of Immunity Boosting Medicinal Plants using Shape Descriptor Algorithm.

In the Covid-19 pandemic situation, the world is looking for immunity-boosting techniques for fighting against coronavirus. Every plant is medicine in one or another way, but Ayurveda explains the uses of plant-based medicines and immunity boosters for specific requirements of the human body. To help Ayurveda, botanists are trying to identify more species of medicinal immunity-boosting plants by evaluating the characteristics of the leaf. For a normal person, detecting immunity-boosting plants is a difficult task. Deep learning networks provide highly accurate results in image processing. In the medicinal plant analysis, many leaves are like each other. So, the direct analysis of leaf images using the deep learning network causes many issues for medicinal plant identification. Hence, keeping the requirement of a method at large to help all human beings, the proposed leaf shape descriptor with the deep learning-based mobile application is developed for the identification of immunity-boosting medicinal plants using a smartphone. SDAMPI algorithm explained numerical descriptor generation for closed shapes. This mobile application achieved 96%accuracy for the 64 × 64 sized images.

Machine Learning-Based Framework for Predicting Creep Rupture Life of Modified 9Cr-1Mo Steel

Efficient and accurate predictions of creep rupture life are essential for ensuring the integrity of high-temperature components. In this work, a machine learning-based framework is developed for the quick screening of crucial features and accurate prediction of the creep rupture life of modified 9Cr-1Mo steels. A feature screening protocol based on correlation filtering and sequential feature selection techniques is established for identifying critical features that significantly affect the prediction performance from a set of numerous descriptors. Moreover, several machine learning algorithms are employed for model training to examine their ability to map the complex nonlinear interactions between multivariate features and creep life. The results show that the test stress, test temperature, tempering time, and the contents of S and Cr are identified as the crucial features that greatly influence the life prediction performance of modified 9Cr-1Mo steels. Moreover, the Gaussian process regression (GPR) model with these five selected crucial features exhibits the highest prediction accuracy among various machine learning strategies. Finally, an additional dataset out of model training and testing is used to further validate the efficacy of the constructed GPR model. The validated results demonstrate that most creep data are distributed inside the two-factor band lines. Results from this work show that the developed machine learning framework can offer high accuracy and excellent adaptability in predicting the creep life of modified 9Cr-1Mo steels under various environmental conditions.

Sierpinski Graflarının Tepe-Ayrıt Temelli Derece Özellikleri Üzerine

Network science and graph theory are two important branches of mathematics and computer science. Many problems in engineering and physics are modeled with networks and graphs. Topological analysis of networks enable researchers to analyse networks in relation some physical and engineering properties without conducting expensive experimental studies. Topological indices are numerical descriptors which defined by using degree, distance and eigen-value notions in any graph. Most of the topological indices are defined as by using classical degree concept in graph theory, network and computer science. Recently two novel degree parameters have been defined in graph theory: Vertex-edge degree and Edge-vertex degree. Vertex-edge degree and edge-vertex degree based topological indices have been defined as parallel to their corresponding classical degree counterparts. Generalized Sierpinski networks have an important place of applications in view of engineering science especially in computer science. Classical degree based topological properties of generalized Sierpinski graphs have been investigated by many studies. In this article, vertex-edge degree based topological indices values of generalized Sierpinski graphs have been computed.

A study of two‐dimensional coronene fractal structures with M‐polynomials

AbstractThe intriguing properties of polycyclic structure known as benzenoid hydrocarbons, which are unsaturated and totally conjugated molecules with hexagonal rings, continue to entice researchers to investigate their chemical and physical molecular structure with reference to aromaticity. To correlate important molecular structural attributes such as enthalpy, melting, boiling point, and cyclicity, numerical descriptors or topological indices which have been used for decades, for variety of two and three dimensional chemical structures networks or graphs with vital physicochemical properties. To determine the degree‐based topological indices, in this study, the degrees of the chemical structure or graph of the molecule under examination are utilized. In molecular graph theory, M‐polynomial is a relatively recent approach for investigating chemical networks and structures. It shows numerical descriptors in algebraic form and highlights molecular properties as a polynomial function. In this study, we propose a polynomials presentation of two‐dimensional coronene fractal structures and compute numerous M‐polynomials, including the M‐polynomials of the first and second Zagreb indexes, as well as the Randic index.

Topological indices of line graph of transition metal tetra cyano benzene organic network

The molecular graph theory is one of the essential tools for developing and constructing molecular structure. In particular, using topological invariant, which is a numerical descriptor of molecular graph, has become widespread in predicting physical properties. Topological indices provide knowledge about the overall form of molecular graphs, particularly various degree-based topological indices. This research paper focuses on the first and second Zagreb index and their coindices for the line graph of a transition metal-organic network. The network, denoted by , comprises the transition metals Ti, V, Cr, Fe, Co, Ni, Cu or Zn, and the TCNB ligand. The line graph of a network is a graph that represents the connections between the edges of the original network. The Zagreb indices are widely used to describe the molecular graph's shape and structure. The paper highlights the importance of these indices in modelling the chemical properties of the transition metal-organic network. The study results show that the Zagreb indices and their coindices strongly correlate with several physical characteristics, for instance the boiling points, melting points, and density of the network.

A combinatorial machine-learning-driven approach for predicting glass transition temperature based on numerous molecular descriptors

ABSTRACT Glass transition temperature (Tg ) is one of the most significant thermophysical property which is hard to measure experimentally. With the development of machine learning, many molecular presentation methods and prediction algorithms have been proposed for predicting Tg . However, most descriptors of these algorithms are linear, while the values of molecular descriptors obtained from experiments or simulation software may have nonlinear relationships. General nonlinear machine learning methods are difficult to apply for the super high dimensional characteristics of molecular descriptors. In this paper, molecular descriptors are defined in manifold space and manifold learning is used for dimension reduction. Different from other linear algorithms for selecting molecular descriptors, Locally Linear Embedding (LLE) is utilised to reduce dimensionalities by extracting new features from numerous molecular descriptors. The features with dimensionality reduction are fed to the eXtreme Gradient Boosting (XGBoost) model. This ensemble algorithm is suitable for predicting glass transition temperature due to its high adaptability and nonlinear approximation ability. Genetic algorithm (GA) is adopted for optimising the parameters of XGBoost. The combinatorial approach consists of dimensionality reduction, prediction and optimisation. Experimental results show that the proposed machine learning approach LLE-XGBoost-GA has higher accuracy in Tg prediction of polymers.

On Molecular Structural Characterization of Cyclen Cored Dendrimers

Macromolecules are gaining much attention in various fields today. Dendrimers are artificially synthesized macromolecules by convergent or divergent approach. They are compact regular structures with spherical dimension and has a vast number of application in disparate fields such as drug delivery, material science, and biology, magnetic resonance imaging, an organic light-emitting device, etc. Determining the pharmacological, chemical, and biological characteristics of a substance necessitates a significant amount of effort. From the chemical graph of the dendrimer structure, we can infer those characteristics with the help of numerical descriptors known as the topological index. The Wiener and Szeged indices are two important distance-based topological indices applicable in nanoscience. The degree-based topological indices also have great importance and huge applications in structural chemistry. These indices together with graph entropy are found to be more effective and have found application in different sciences. In this work, the Wiener index, Szeged indices, Mostar indices, and Padmakar Ivan index for cyclen cored dendrimers are evaluated by converting the original graph into quotient graphs using Θ * - classes. This technique is applied since the regular cut method is a lengthy process while moving on to higher generations and also due to the presence of odd cycles in the structure. The degree-based indices and the degree-based graph entropies for the cyclen cored dendrimers are further studied. The comparison graphs with respect to the topological indices as well as graph entropies are also presented.

Cervical Cancer Diagnosis Based on Multi-Domain Features Using Deep Learning Enhanced by Handcrafted Descriptors

Cervical cancer, among the most frequent adverse cancers in women, could be avoided through routine checks. The Pap smear check is a widespread screening methodology for the timely identification of cervical cancer, but it is susceptible to human mistakes. Artificial Intelligence-reliant computer-aided diagnostic (CAD) methods have been extensively explored to identify cervical cancer in order to enhance the conventional testing procedure. In order to attain remarkable classification results, most current CAD systems require pre-segmentation steps for the extraction of cervical cells from a pap smear slide, which is a complicated task. Furthermore, some CAD models use only hand-crafted feature extraction methods which cannot guarantee the sufficiency of classification phases. In addition, if there are few data samples, such as in cervical cell datasets, the use of deep learning (DL) alone is not the perfect choice. In addition, most existing CAD systems obtain attributes from one domain, but the integration of features from multiple domains usually increases performance. Hence, this article presents a CAD model based on extracting features from multiple domains not only one domain. It does not require a pre-segmentation process thus it is less complex than existing methods. It employs three compact DL models to obtain high-level spatial deep features rather than utilizing an individual DL model with large number of parameters and layers as used in current CADs. Moreover, it retrieves several statistical and textural descriptors from multiple domains including spatial and time–frequency domains instead of employing features from a single domain to demonstrate a clearer representation of cervical cancer features, which is not the case in most existing CADs. It examines the influence of each set of handcrafted attributes on diagnostic accuracy independently and hybrid. It then examines the consequences of combining each DL feature set obtained from each CNN with the combined handcrafted features. Finally, it uses principal component analysis to merge the entire DL features with the combined handcrafted features to investigate the effect of merging numerous DL features with various handcrafted features on classification results. With only 35 principal components, the accuracy achieved by the quatric SVM of the proposed CAD reached 100%. The performance of the described CAD proves that combining several DL features with numerous handcrafted descriptors from multiple domains is able to boost diagnostic accuracy. Additionally, the comparative performance analysis, along with other present studies, shows the competing capacity of the proposed CAD.

Topological aspects of metal–organic frameworks: Zinc silicate and oxide networks

Metal–organic frameworks (MOFs) are intriguing porous materials that are formed by combining organic materials with metals. MOFs have a vast range of utilizations in distinct medical fields with great efficiency. Recently, Zinc-related MOFs have been investigated and are in demand due to their efficient utilization in medical fields such as biosensing, cancer therapy and drug delivery. To mathematically characterize the chemical structures using numerical graph descriptors is the present-day line of research. A numerical graph descriptor or a topological index (TI) is a numerical quantity that is linked with graphs and assists in correlating the topology of a chemical compound. Various distance-based descriptors can be found in the literature. Connection-based TIs, instead of degree-based TIs are considered to be more potent in measuring the chemical aspects of molecular compounds. In this paper, we compute the connection-based TIs of zinc-related MOFs such as zinc oxide and zinc silicate. Further, to examine superiority, we numerically and graphically compare these zinc-related MOFS with each other on the basis of their computed results.

Association between Risk Communication Format and Perceived Risk of Adverse Events after COVID-19 Vaccination among US Adults.

The format used to communicate probability-verbal versus numerical descriptors-can impact risk perceptions and behaviors. This issue is salient for the Coronavirus disease 2019 (COVID-19), where concerns about vaccine-related risks may reduce uptake and verbal descriptors have been widely used by public health, news organizations and on social media, to convey risk. Because the effect of risk-communication format on perceived COVID-19 vaccine-related risks remains unknown, we conducted an online randomized survey among 939 US adults. Participants were given risk information, using verbal or numerical descriptors and were asked to report their perceived risk of experiencing headache, fever, fatigue or myocarditis from COVID-19 vaccine. Associations between risk communication format and perceived risk were assessed using multivariable regression. Compared to numerical estimates, verbal descriptors were associated with higher perceived risk of headache (β = 5.0 percentage points, 95% CI = 2.0-8.1), fever (β = 27 percentage points, 95% CI = 23-30), fatigue (β = 4.9 percentage points, 95% = CI 1.8-8.0) and myocarditis (β = 4.6 percentage points, 95% CI = 2.1-7.2), as well as greater variability in risk perceptions. Social media influence was associated with differences in risk perceptions for myocarditis, but not side effects. Verbal descriptors may lead to greater, more inaccurate and variable vaccine-related risk perceptions compared to numerical descriptors.