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To synthesise and characterise homo and copolymer of 4‐nonylphenyl methacrylate (NPMA) and styrene and to determine monomer reactivity ratios by the application of conventional linearisation methods such as Finemann‐Ross (F‐R) and Kelen‐Tudos (K‐T) methods.

New methacrylic monomer, NPMA with a pendant nonylphenyl group was copolymerised with styrene. All monomer and polymers (homo and copolymer) are characterised and subsequently the monomer reactivity ratio was determined.

The monomer reactivity ratios were determined by application of conventional linearisation methods such as F‐R (r₁=0.41±0.05; r₂=3.47±0.31), K‐T (r₁=0.43±0.19; r₂=3.54±0.09) methods. The thermogravimetric analysis (TGA) of the polymer in nitrogen reveals that it posses very good thermal stability in comparison to alkyl acrylates due to presence of pendant nonylphnyl group.

New methacrylic monomer, NPMA was synthesised by reacting nonylphenol dissolved in methyl ethyl ketone (MEK) with methacryloyl chloride in the presence of triethylamine as a base. Copolymers of NPMA with styrene were synthesised in MEK using benzoyl peroxide (BPO) as initiator under nitrogen atmosphere at different feed composition.

The method developed is a simple and easy method of copolymerisation of styrene with methacrylate to obtain copolymer of better properties.

The method developed is a novel method for enhancing the thermal, as well as surface adhesion, properties which has several applications in surface coatings and adhesives.

The purpose of this paper is to synthesise, characterise and study polymerisation kinetics of novel 4‐nonylphenylmethacrylate (NPMA) polymer.

New methacrylic monomer, 4‐NPMA with a pendant nonylphenyl group was synthesised and characterised using various characterisation techniques. The free radical polymerisation kinetics study was done with the help of differential scanning calorimetry data.

The average heat of polymerisation (ΔHp) was found to be 685.43 J/g. Activation energy (E_a) of 95.86 kJ mol⁻¹ and frequency factor of (A) 3.4 × 10⁴ min⁻¹ was obtained using Kissinger method. The thermogravimetric analysis of the polymer in nitrogen reveals that it possesses very good thermal stability in comparison to alkyl methacrylates due to presence of pendant nonylphenyl group.

New methacrylic monomer, 4‐NPMA was synthesised by reacting nonylphenol dissolved in methyl ethyl ketone (MEK) with methacryloyl chloride in the presence of triethylamine as a base. Polymerisation of 4‐NPMA was carried out in MEK using benzoyl peroxide (BPO) as initiator under nitrogen atmosphere. The kinetics study of NPMA monomer with 1.1 wt% BPO was reported for evaluation of kinetic parameters by employing the Kissinger equation.

This is a simple and easy method of modification of methacrylate ester with phenyl groups to obtain a polymer of enhanced properties.

This is a novel method for enhancing the thermal as well as surface adhesion properties of methacrylate polymers which finds several applications in surface coatings and adhesives.

To evaluate the performance of a reactive diluent, nonylphenyl methacrylate (NPM) for toughness improvement of bisphenol‐a‐glycidyl dimethacrylate (BisGMA) and to optimise the results of such a modification.

To achieve desirable rheological and physico‐mechanical properties of BisGMA/NPM network, various compositions were made by incorporating different concentrations of NPM. The effect of concentration on the impact and the adhesive strengths of the unmodified and modified BisGMA networks were characterised.

The modification of BisGMA resin using NPM showed significant enhancement of the impact and the adhesive strengths over the unmodified one. The modification caused a chemical linkage between NPM and BisGMA resin, which led not only to a phase separation but also to establishing the intrinsically strong chemical bonds across the NPM phase/resin matrix interphase, which was responsible for the improvement of the impact and adhesive strength. The optimum results were obtained at 10/h (parts per 100 parts of BisGMA resin) of modifier.

The reactive diluent, used in the present context was synthesised from nonyl phenol, methacrylic acid and benzoyl chloride in methyl ethyl ketone using triethylamine as base.

The method of modification developed provides a simple and practical solution for improving the rheological and physico‐mechanical properties of BisGMA network.

The method developed is a novel one for enhancing the rheological and physico‐mechanical properties of BisGMA network diluted with NPM resin and may find numerous applications in surface coating and adhesive.

To prepare and characterise vinyl ester (Bisphenol‐A‐glycidyldimethacrylate (BisGMA)) prepolymer via a new synthetic route involving the esterification of methacrylic acid and epoxy resin.

BisGMA prepolymer was synthesised from methacrylic acid and diglycidyl ether of bisphenol‐A‐type epoxy resin. The compound synthesised was then purified and characterised.

BisGMA prepolymer was synthesised from methacrylic acid and diglycidyl ether of bisphenol‐A‐type epoxy resin using triphenylphosphine as a catalyst and hydroxy toluene as a stabiliser. The synthesised compound was then purified by normal phase liquid chromatography and was analysed by proton NMR and reverse phase HPLC. The cure kinetics of the purified resin was investigated by differential scanning calorimetry (DSC) using NETZSCH thermo kinetics software.

The preparation, purification and kinetics study of the prepolymer described in the present investigation may be useful for preparation, purification and kinetics study of the other vinyl esters by a new synthetic route. Also, the polymer supports based on BisGMA have acceptable mechanical properties, chemical stability and are suitable for dental restoratives, fissure sealants, coatings, adhesives, moulding compounds, structural laminates, electrical applications and military/aerospace applications.

The method for the preparation, purification and kinetics study of the prepolymer is simple and provides a simple and practical solution for some other vinyl esters.

This is a novel method for synthesis and purification of BisGMA and may be useful for the synthesis and purification of other vinyl esters.

This paper seeks to prepare and characterise vinyl ester (bisphenol‐A‐glycidyldimethacrylate (BisGMA)) prepolymer via a new synthetic route by the esterification of methacrylic acid and epoxy resin

BisGMA prepolymer was synthesised from methacrylic acid and diglycidyl ether of bisphenol‐A (BPA) type epoxy resin. The synthesised compound was then purified and characterised.

BisGMA prepolymer was synthesised from methacrylic acid and diglycidyl ether of BPA‐type epoxy resin using triphenylphosphine as a catalyst and hydroxytoluene as a stabiliser. The synthesised compound was then purified by normal phase liquid chromatography and was analysed by proton NMR and reverse phase HPLC. The cure kinetics of the purified resin were investigated by differential scanning calorimetry using NETZSCH thermokinetics software.

The preparation, purification and kinetics study of the prepolymer described in the present investigation may be useful for preparation, purification and kinetics study of the other vinyl esters by a new synthetic route. Also, the polymers supports based on bisphenol‐A‐glycidyldimethacrylate have acceptable mechanical properties, chemical stability and are suitable for dental restoratives, fissure sealants, coatings, adhesives, moulding compounds, structural laminates, electrical applications and military/aerospace applications.

The method for the preparation, purification and kinetics study of the prepolymer is simple and the above method provides a simple and practical solution for some other vinyl esters.

This is a novel method for synthesis and purification of BisGMA and may be useful for the synthesis and purification of other vinyl esters.

The purpose of this paper is to develop an efficient brain tumor detection model using the beneficial concept of hybrid classification using magnetic resonance imaging (MRI) images Brain tumors are the most familiar and destructive disease, resulting to a very short life expectancy in their highest grade. The knowledge and the sudden progression in the area of brain imaging technologies have perpetually ready for an essential role in evaluating and concentrating the novel perceptions of brain anatomy and operations. The system of image processing has prevalent usage in the part of medical science for enhancing the early diagnosis and treatment phases.

The proposed detection model involves five main phases, namely, image pre-processing, tumor segmentation, feature extraction, third-level discrete wavelet transform (DWT) extraction and detection. Initially, the input MRI image is subjected to pre-processing using different steps called image scaling, entropy-based trilateral filtering and skull stripping. Image scaling is used to resize the image, entropy-based trilateral filtering extends to eradicate the noise from the digital image. Moreover, skull stripping is done by Otsu thresholding. Next to the pre-processing, tumor segmentation is performed by the fuzzy centroid-based region growing algorithm. Once the tumor is segmented from the input MRI image, feature extraction is done, which focuses on the first-order and higher-order statistical measures. In the detection side, a hybrid classifier with the merging of neural network (NN) and convolutional neural network (CNN) is adopted. Here, NN takes the first-order and higher-order statistical measures as input, whereas CNN takes the third level DWT image as input. As an improvement, the number of hidden neurons of both NN and CNN is optimized by a novel meta-heuristic algorithm called Crossover Operated Rooster-based Chicken Swarm Optimization (COR-CSO). The AND operation of outcomes obtained from both optimized NN and CNN categorizes the input image into two classes such as normal and abnormal. Finally, a valuable performance evaluation will prove that the performance of the proposed model is quite good over the entire existing model.

From the experimental results, the accuracy of the suggested COR-CSO-NN + CNN was seemed to be 18% superior to support vector machine, 11.3% superior to NN, 22.9% superior to deep belief network, 15.6% superior to CNN and 13.4% superior to NN + CNN, 11.3% superior to particle swarm optimization-NN + CNN, 9.2% superior to grey wolf optimization-NN + CNN, 5.3% superior to whale optimization algorithm-NN + CNN and 3.5% superior to CSO-NN + CNN. Finally, it was concluded that the suggested model is superior in detecting brain tumors effectively using MRI images.

This paper adopts the latest optimization algorithm called COR-CSO to detect brain tumors using NN and CNN. This is the first study that uses COR-CSO-based optimization for accurate brain tumor detection.

With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.

This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.

Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.

At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

This paper aims to focus on the development and study properties of bisphenol-A glycidyldimethacrylate (BisGMA) and ethylene–propylene–diene monomer (EPDM) blend-based nanocomposites containing amine-functionalised multi-walled carbon nanotubes (MWCNT-NH2) as a compatibiliser.

First, BisGMA was synthesised from epoxy and methacrylic acid followed by the amine functionalisation of MWCNTs. A novel two-roll milling technique was then conducted to prepare nanocomposite specimens with MWCNT-NH2 as compatibiliser. Effect of MWCNT-NH2 content on the mechanical, thermal, electrical, corrosive and water absorption properties of the nanocomposites was investigated and results have been reported.

The results of the present work reveal that MWCNT-NH2 acts as a potential compatibiliser and nanofiller in BisGMA/EPDM blend-based nanocomposites. The authors report here that the nanocomposites exhibit improved mechanical, thermal and electrical properties with increased addition of MWCNT-NH2. Moreover, desirable results are obtained at 5 phr of nanofiller loading beyond which the properties deteriorate due to particle agglomeration. The nanocomposites display negligible corrosion and water absorption characteristics. Thus, the above fabricated nanocomposites with optimum compatibiliser content can serve as low-cost structural, thermal and electrical materials which can also be utilised in corrosive and moist environments.

The present investigation has come up with the successful and cost-effective fabrication of BisGMA/EPDM blend-based nanocomposites with optimum nanofiller/compatibiliser (MWCNT-NH2) content that can be used for a wide range of structural, thermal and electrical projects, as it is corrosion and moisture resistant. It is also the most durable from the mechanical point of view.

The above nanocomposites have never been designed before.

The infill design approach for heritage settings is a challenging task, and it draws the attention of design professionals and residents. The extant literature has advocated for a contextual design approach for new buildings in heritage sites. However, the degree of contextualism for a new building in heritage sites is subjective, and it varies between exact replication and contrast scales. This study aims to evaluate an appropriate design approach for historic precincts of Odisha, an eastern state of India.

Two prime eastern heritage sites (Puri and Ekamra Kshetra) are selected as cases in this study. This research methodology involves identifying key architectural elements from both sites and a questionnaire (prepared by design experts) based on interviewing 400 residents and 36 design professionals on their aesthetic preferences for the different architectural styles and elements. The questionnaire was prepared by the design experts based on the identified architectural styles and elements of both sites. Descriptive statistics and correlation analysis are used to measure the significance of design approaches and elements.

This study's outcome confirms that most of the respondents (design professionals and residents) prefer the replication design approach with traditional architectural elements of Odisha. Also, documentation of the chronological development of architectural styles and elements of heritage sites of Odisha is done in this research.

This study has a few limitations: first, the land use characters (mixed, residential, commercial, etc.) of buildings in the heritage precinct are not considered in this research; second, this research has not included the financial aspect of infill design and last, the impact of respondents' socioeconomic factors on their aesthetic perceptions is not considered in this research.

The development authorities can use the outcomes of this research to implement a design strategy for infill buildings in the historical sites of Odisha.

This research article has documented traditional architectural elements of two prime heritage sites of India.

To date, no quantitative research has been done on infill design approaches in any Indian heritage precincts. This is the first quantitative research on the perception of stakeholders and users on the infill design in historical settings of Eastern India. This research has identified key architectural styles, elements and materials of the heritage sites.

The main purpose is to construct the mapping relationship between garment flat and pattern. Particle swarm optimization–least-squares support vector machine (PSO-LSSVM), the data-driven model, is proposed for predicting the pattern design dimensions based on small sample sizes by digitizing the experience of the patternmakers.

For this purpose, the sleeve components were automatically localized and segmented from the garment flat by the Mask R-CNN. The sleeve flat measurements were extracted by the Douglas–Peucker algorithm. Then, the PSO algorithm was used to optimize the LSSVM parameters. PSO-LSSVM was trained by utilizing the experience of patternmakers.

The experimental results demonstrated that the PSO-LSSVM model can effectively improve the generation ability and prediction accuracy in pattern design dimensions, even with small sample sizes. The mean square error could reach 1.057 ± 0.06. The fluctuation range of absolute error was smaller than the others such as pure LSSVM, backpropagation and radial basis function prediction models.

By constructing the mapping relationship between sleeve flat and pattern, the problems of the garment flat objective recognition and pattern design dimensions accurate prediction were solved. Meanwhile, the proposed method overcomes the problem that the parameters are determined by PSO rather than empirically. This framework could be extended to other garment components.