Current Materials Science - Volume 17, Issue 1, 2024

Knowledge representation and reasoning is a field of ‘Artificial Intelligence’ that encodes knowledge, beliefs, actions, feelings, goals, desires, preferences, and all other mental states in the machine. An ontology is prominently used to represent knowledge and offers the richest machine-interpretable (rather than just machine-processable) and explicit semantics. Ontology does not only provide sharable and reusable knowledge, but it also provides a common understanding of the knowledge; as a result, the interoperability and interconnectedness of the model make it priceless for addressing the issues of querying data. Ontology work with concepts and relations that are very close to the working of the human brain. Ontological engineering provides the methods and methodologies for the development of ontology. Nowadays, ontologies are used in almost every field, and a lot of much research is being done on this topic. The paper aims to elaborate on the need of ontology (from data to knowledge), how does for ontology (from data to knowledge), how semantics come from logic, the ontological engineering field, history from hypertext to linked data, and further possible research directions of the ontology. This paper benefit reader who wishes to embark on ontology-based research and application development.

Background: Feature selection (FS) is a crucial strategy for dimensionality reduction in data preprocessing since microarray data sets typically contain redundant and extraneous features that degrade the performance and complexity of classification models. Objective: The purpose of feature selection is to reduce the number of features from highdimensional cancer datasets and enhance classification accuracy. Methods: This research provides a wrapper-based hybrid model integrating information gain (IG) and Jaya algorithm (JA) for determining the optimum featured genes from high-dimensional microarray datasets. This paper's comprehensive study is divided into two segments: we employed the parameterless JA to identify the featured gene subsets in the first stage without filter methods. Various classifiers evaluate JA's performance, such as SVM, LDA, NB, and DT. In the second section, we introduce a hybrid IG-JA model. The IG is used as a filter to eliminate redundant and noisy features. The reduced feature subset is then given to the JA as a wrapper to improve the hybrid model's performance using the classifiers outlined above. Results: We used 13 benchmark microarray data sets from the public repository for experimental analysis. It is noteworthy to state that the hybrid IG-JA model performs better as compared to its counterparts. Conclusion: Tests and statistics show that the suggested model outperforms the standard feature selection method with JA and other existing models. Our proposed model is unable to provide the best accuracy compared to other existing approaches; however, it is quite steady and good. In the future, this work could be implemented with various filter methods and real-time data sets. A multi-filter approach with the Jaya algorithm will be used to check the efficiency of the proposed one. And it would be better to choose any other hybrid model (chaos-based) with Jaya to enhance the feature selection accuracy with a high dimensional dataset.

Background: The acquisition and exchange of meaningful, integrated, and accurate information are at the forefront of the combat against COVID-19; still, there are many countries whose health systems are disrupted. Moreover, no one is adequately equipped for COVID-19 contingencies. Many organizations have established static information systems to manage the information. This fact presents numerous issues, including delays, inconsistencies, and inaccuracies in COVID-19 information collected for pandemic control and monitoring. Objective: This paper presents a semantic representation of COVID-19 data, a domain ontology to facilitate measurement, clarification, linking, and sharing. We automatically generate a computer- intelligible knowledge base from COVID-19 case information, which contains machineunderstandable information. Furthermore, we have anticipated an ontology population algorithm from tabular data that delivers interoperable, consistent, and accurate content with COVID-19 information. Methods: We utilized the tabula package to extract the tables from PDF files and user NLP libraries to sort and rearrange tables. The proposed algorithm was then applied to all instances to automatically add to the input ontology using the Owlready Python module. Moreover, to evaluate the performance, SPARQL queries were used to retrieve answers to competency questions. Results: When there is an equivalence relationship, the suggested algorithm consistently finds the right alignments and performs at its best or very close to it in terms of precision. Moreover, a demonstration of algorithm performance and a case study on COVID-19 data to information management and visualization of the populated data are also presented. Conclusion: This paper presents an ontology learning/matching tool for ontology and populating instances automatically to ontology by emphasizing the importance of a unit's distinguishing features by unit matching.

In current times, all major discoveries are believed to occur at the intersections of various fields of science. In such a manner, perhaps the main creative region joining material science, biology and medication is the utilization of microwave advances. In such a way, the motivation behind this work was to explain the useful conceivable outcomes of diagnostic, analytical, synthetic and therapeutic microwave advancements. The related exploration and uses of microwave (MW) science are the arising methodologies of modern energy-based green science, on a nuclear and atomic self-aggregating level. As microwaves develop the synthetic response, they have the potential to greatly impact the chemical reaction. This technology has advanced its utilization in the field of research technology, chemical synthesis, diagnostic chemistry, wastewater treatment, and material science. This article cumulates the various available microwave synthesizers in the market with their pros and cons alongside their wide applications.

Objectives: The objective of this study is to review various biomaterials or implants used in ENT and their adverse effects on events that were noticed from the initial days to the present era. Methods: Relevant articles were searched from the databases. PubMed, Scopus, Web of Science, and Google were used to search for the data. Results: Cochlear implant (CI) adequacy depends on biocompatibility, anti-inflammatory treatment, and reduction of fibrosis. Silicone is used in the otologic field, and its allergy is a rare cause of CI extrusion. Nitinol pistons are used in stapedotomy, and polyethylene (Teflon) grafts are used in partial ossicular replacement prosthesis (PORP) or total ossicular replacement prosthesis (TORP). Their complications include graft extrusion and residual perforation. Chronic sphenoid sinusitis is associated with Medpor porous polyethylene implants used for sellar reconstruction in skull-based surgeries. In vocal cord paralysis, injectable collagen preparations form submucosal deposits and dysphonia. Montgomery T-tubes are used in subglottic stenosis, and they produce granulation tissue. Metallic tracheostomy tubes cause the formation of secondary foreign bodies, and biofilms appear in double-lumen tracheostomy tubes. Conclusion: Even though several research studies have been carried out, still a modification of implant design is needed to minimize the complications and to further promote the quality of life of patients.

Background: In this study, the effect of mechanical and physical properties of the metal and temperature is highlighted. The purpose of this study is to analyze the effect of these stresses on multi-materials. This work aimed to conduct a three-dimensional numerical study by the finite element method on the levels and distributions of the stresses in the Al2O3/NI/HAYNES multi-materials. These stresses of thermal and mechanical origin are generally detrimental to the service life of multi-material. Methods: The use of numerical resolution by finite element method is the most suitable for complex mechanical problems. It allows a more in-depth analysis of all points of the structure. In this study, a fundamental tool was constructed to resolve the mechanical behavior of materials subjected to complex solicitations. Therefore, the ABAQUS calculation code version 6.14 was used to analyze the residual stresses. Results: By interpreting the results in terms of stress variation, we identified the areas at risk; in particular, the nature of a joint effect plays a decisive role in the assembly of the right material in its mechanical resistance. This nature is defined in terms of stiffness (Young's modulus) and differential expansion (coefficient of thermal expansion). Conclusion: The presence of strong residual stresses can constitute a risk of damage to several materials. The difference between the coefficients of thermal expansion of the two materials (metal and ceramic) linked together induces, in these two constituents, normal internal stresses. This difference determines the level and distribution of these constraints. Moreover, the sign of this difference determines the state of the normal stresses.