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Due to the continuous and rapid evolution of telecommunication equipment, the demand for more efficient and noise-robust detection of dual-tone multi-frequency (DTMF) signals is most significant.

A novel machine learning-based approach to detect DTMF tones affected by noise, frequency and time variations by employing the k-nearest neighbour (KNN) algorithm is proposed. The features required for training the proposed KNN classifier are extracted using Goertzel's algorithm that estimates the absolute discrete Fourier transform (DFT) coefficient values for the fundamental DTMF frequencies with or without considering their second harmonic frequencies. The proposed KNN classifier model is configured in four different manners which differ in being trained with or without augmented data, as well as, with or without the inclusion of second harmonic frequency DFT coefficient values as features.

It is found that the model which is trained using the augmented data set and additionally includes the absolute DFT values of the second harmonic frequency values for the eight fundamental DTMF frequencies as the features, achieved the best performance with a macro classification F1 score of 0.980835, a five-fold stratified cross-validation accuracy of 98.47% and test data set detection accuracy of 98.1053%.

The generated DTMF signal has been classified and detected using the proposed KNN classifier which utilizes the DFT coefficient along with second harmonic frequencies for better classification. Additionally, the proposed KNN classifier has been compared with existing models to ascertain its superiority and proclaim its state-of-the-art performance.

The purpose of this study aims to synthesize a novel donor–acceptor dye based on phenothiazine as a donor (D) and nonconjugated spacer was devised and synthesized by condensing of 2,2'-(1H-indene-1,3(2H)-diylidene) dimalononitrile with aldehyde and the practical synthesis methodology as given in Scheme 1.

The prepared phenothiazine dye was systematically experimentally and theoretically examined and characterized using nuclear magnetic resonance spectroscopy (1H,13C NMR), Fourier-transform infrared spectroscopy (IR) and high-resolution mass spectrometry. Density functional theory (DFT) and time-dependent density functional theory DT-DFT calculations were implemented to determine the electronic properties of the new dye

The UV-Vis absorption and fluorescence spectroscopy of the synthesized dye was investigated in a variety of solvents with varying polarities to demonstrate positive solvatochromism correlated with intramolecular charge transfer (ICT). The probe’s quantum yields (Фf) are experimentally measured in ethanol, and the Stokes shifts are found to be in the 4846–9430 cm⁻¹ range.

The findings depicted that the novel (D-π-A) chromophores may act as a significant factor in the organic optoelectronics.

The paper aims to the preparation of novel disperse dye based on azo salicylaldehyde derivatives TF-A [2-hydroxy-5-((3-(trifluoromethyl)phenyl)diazenyl)benzaldehyde] and full evaluation of their use as disperse dye TF-ASC [bis 2-hydroxy-5-((3-(trifluoromethyl)phenyl)diazenyl)benzaldehyde Schiff base with 4,4'-methylenedianiline] for dyeing polyester fabric at various conditions.

The dispersed dye was synthesized via Schiff base condensation in the presence of ceric ammonium nitrate cerium ammonium nitrate 10 mmole% as an eco-friendly catalyst at room temperature. The chemical structure of the prepared dye was characterized via elemental analysis, Fourier-transform infrared spectroscopy, 1H- and 13 C-NMR spectroscopic analysis tools. This study thoroughly examined the dyeing of disperse dye TF-ASC on polyester at various conditions. The characteristics of dyed polyester fabric were measured by colour measurements, as well as light, washing, crock fastness and finally, colour strength. The discrete fourier transform (DFT) theoretical studies, including E_HOMO, E_LUMO and optimized geometrical structure, were assumed and discussed in detail.

The results showed that the synthesized organic dye TF-ASC was highly functional and appropriate for this kind of dyeing method. The dyeing fabrics obtained from disperse dye TF-ASC, properties possess high colour strength as well as good overall fastness properties. These dyes had a high affinity for polyester fabric, with just a tiny change in dye affinity when the pH was changed, even under alkaline circumstances. The dye levelness and shade depth of the colour results were good, and there were a variety of hues from light brownish yellow to deep brownish yellow. The results obtained from DFT computational studies such as E_HOMO, E_LUMO, optimized structure, diploe moment µ and electrophilicity index deduced that prepared organic dye TF-ASC is more applicable as a dispersed dye.

This research is significant because it provides a new dye for dyeing polyethylene terephthalate fibres with exceptional brightness and levelness; the method of preparation is a useful pathway due to its being known as a green chemistry method.

Intumescent coatings are nowadays a dominant passive system used to protect structural materials in case of fire. Due to their reactive swelling behaviour, intumescent coatings are particularly complex materials to be modelled and predicted, which can be extremely useful especially for performance-based fire safety designs. In addition, many parameters influence their performance, and this challenges the definition and quantification of their material properties. Several approaches and models of various complexities are proposed in the literature, and they are reviewed and analysed in a critical literature review.

Analytical, finite-difference and finite-element methods for modelling intumescent coatings are compared, followed by the definition and quantification of the main physical, thermal, and optical properties of intumescent coatings: swelled thickness, thermal conductivity and resistance, density, specific heat capacity, and emissivity/absorptivity.

The study highlights the scarce consideration of key influencing factors on the material properties, and the tendency to simplify the problem into effective thermo-physical properties, such as effective thermal conductivity. As a conclusion, the literature review underlines the lack of homogenisation of modelling approaches and material properties, as well as the need for a universal modelling method that can generally simulate the performance of intumescent coatings, combine the large amount of published experimental data, and reliably produce fire-safe performance-based designs.

Due to their limited applicability, high complexity and little comparability, the presented literature review does not focus on analysing and comparing different multi-component models, constituted of many model-specific input parameters. On the contrary, the presented literature review compares various approaches, models and thermo-physical properties which primarily focusses on solving the heat transfer problem through swelling intumescent systems.

The presented literature review analyses and discusses the various modelling approaches to describe and predict the behaviour of swelling intumescent coatings as fire protection for structural materials. Due to the vast variety of available commercial products and potential testing conditions, these data are rarely compared and combined to achieve an overall understanding on the response of intumescent coatings as fire protection measure. The study highlights the lack of information and homogenisation of various modelling approaches, and it underlines the research needs about several aspects related to the intumescent coating behaviour modelling, also providing some useful suggestions for future studies.

Corrosion is a major concern for many industries that use metals as structural or functional materials, and the use of corrosion inhibitors is a widely accepted strategy to protect metals from deterioration in corrosive environments. Moreover, the toxic nature, non-biodegradability and price of most conventional corrosion inhibitors have encouraged the application of greener and more sustainable options, with natural and synthetic drugs being major actors. Hence, this paper aims to stress the capability of natural and synthetic drugs as manageable and sustainable, environmentally friendly solutions to the problem of metal corrosion.

In this review, the recent developments in the use of natural and synthetic drugs as corrosion inhibitors are explored in detail to highlight the key advancements and drawbacks towards the advantageous utilization of drugs as corrosion inhibitors.

Corrosion is a critical issue in numerous modern applications, and conventional strategies of corrosion inhibition include the use of toxic and environmentally harmful chemicals. As greener alternatives, natural compounds like plant extracts, essential oils and biopolymers, as well as synthetic drugs, are highlighted in this review. In addition, the advantages and disadvantages of these compounds, as well as their effectiveness in preventing corrosion, are discussed in the review.

This survey stresses on the most recent abilities of natural and synthetic drugs as viable and sustainable, environmentally friendly solutions to the problem of metal corrosion, thus expanding the general knowledge of green corrosion inhibitors.

Nanosensors have a wide range of applications because of their high sensitivity, selectivity and specificity. In the past decade, extensive and pervasive research related to nanosensors has led to significant progress in diverse fields, such as biomedicine, environmental monitoring and industrial process control. This led to better and more efficient detection and monitoring of physical and chemical properties at better resolution, opening new horizons in the development of novel technologies and applications for improved human health, environment protection, enhanced industrial processes, etc.

In this paper, the authors discuss the application of citation network analysis in the field of nanosensor research and development. Cluster analysis was carried out using papers published in the field of nanomaterial-based sensor research, and an in-depth analysis was carried out to identify significant clusters. The purpose of this study is to provide researchers to identify a pathway to the emerging areas in the field of nanosensor research. The authors have illustrated the knowledge base, knowledge domain and knowledge progression of nanosensor research using the citation analysis based on 3,636 Science Citation Index papers published during the period 2011 to 2021.

Among these papers, the bibliographic study identified 809 significant research publications, 11 clusters, 556 research sector keywords, 1,296 main authors, 139 referenced authors, 63 nations, 206 organizations and 42 journals. The authors have identified single quantum dot (QD)-based nanosensor for biological applications, carbon dot-based nanosensors, self-powered triboelectric nanogenerator-based nanosensor and genetically encoded nanosensor as the significant research hotspots that came to the fore in recent years. The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

The future trend in nanosensor research might focus on the development of efficient and cost-effective designs for the detection of numerous environmental pollutants and biological molecules using mesostructured materials and QDs. It is also possible to optimize the detection methods using theoretical models, and generalized gradient approximation has great scope in sensor development.

This is a novel bibliometric analysis in the area of “nanomaterial based sensor,” which is carried out in CiteSpace software.

The purpose of this article is to contribute to the digital development and utilization of China’s intangible cultural heritage resources, research on the theft of intangible cultural heritage resources and knowledge integration based on linked data is proposed to promote the standardized description of intangible cultural heritage knowledge and realize the digital dissemination and development of intangible cultural heritage.

In this study, firstly, the knowledge organization theory and semantic Web technology are used to describe the intangible cultural heritage digital resource objects in metadata specifications. Secondly, the ontology theory and technical methods are used to build a conceptual model of the intangible cultural resources field and determine the concept sets and hierarchical relationships in this field. Finally, the semantic Web technology is used to establish semantic associations between intangible cultural heritage resource knowledge.

The study findings indicate that the knowledge organization of intangible cultural heritage resources constructed in this study provides a solution for the digital development of intangible cultural heritage in China. It also provides semantic retrieval with better knowledge granularity and helps to visualize the knowledge content of intangible cultural heritage.

This study summarizes and provides significant theoretical and practical value for the digital development of intangible cultural heritage and the resource description and knowledge fusion of intangible cultural heritage can help to discover the semantic relationship of intangible cultural heritage in multiple dimensions and levels.

This study aims to synthesize two different benzoxazines (Bz) monomers using bio-based and petroleum-based primary amines, respectively, and they have been compared to study their thermal and mechanical performances.

A bio-based bisphenol, Divanillin (DiVa), was formed by reacting two moles of vanillin with one mole of ethylenediamine (EDA) which was then reacted firstly with paraformaldehyde and EDA to form the benzoxazine DiVa-EDA-Bz, and secondly with paraformaldehyde and furfuryl amine (FFA) to form the benzoxazine DiVa-FFA-Bz. The molecular structure and thermal properties of the benzoxazines were characterized by fourier transform infrared spectroscopy and nuclear magnetic resonance (1H,13C) spectroscopies, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA), respectively. The benzoxazines were further coated on mild steel panels to evaluate their mechanical properties and chemical resistance.

The DSC results of DiVa-FFA-Bz showed two exothermic peaks related to crosslinking compared to the one in DiVa-EDA-Bz. The DiVa-FFA-Bz also showed a higher heat of polymerization than DiVa-EDA-Bz. The TGA results showed that DiVa-FFA-Bz exhibited higher thermal stability with a residual char of 54.10% than 43.24% for DiVa-EDA-Bz. The chemical resistance test results showed that DiVa-FFA-Bz showed better chemical resistance and mechanical properties due to its higher crosslinking density.

This study shows the use of bio-based materials, vanillin and FFA, for synthesizing a benzoxazine resin and its application at high temperatures.

This study aims to describe the design and synthesis of two novel azo and imine chromophores-based dyes derived from two different aldehydes with intramolecular colour matching that are pH sensitive.

The visible absorption wavelength (λ_max) was extended when azo chromophore was included in imine-based systems. The dyed patterns created sophisticated colour-changing paper packaging sensors with pH-sensitive chromophores using alum as a mediator or mordant. Due to the tight adhesive bonding, the dyes on paper’s cellulose fibres could not be removed by ordinary water even at extremely high or low pH, which was confirmed by scanning electron microscopy analysis. The dyed patterns demonstrated an evident, sensitive and fast colour-changing mechanism with varying pH, from pale yellow to red for Dye-I and from pale yellow to brown-violet for Dye-II.

The λ_max for colour changing was recorded from 400 to 490 nm for Dye-I, whereas from 400 to 520 for Dye-II. The freshness judgement of food was checked using actual experiments with cooked crab spoilage, where the cooked crab was incubated at 37 ^oC for 6 h to see the noticeable colour change from yellow to brown-violet with Dye-II. The colour-changing mechanism was studied with Fourier transform infrared (FTIR) spectra at different pH, and thin layer chromatography, nuclear magnetic resonance and FTIR spectroscopy studied the desired structure formation of the dyes. Potential uses for smart packaging sensors include quickly detecting food freshness during transportation or right before consumption.

1. Two novel azo-imine dyes have been synthesized with a pH-responsive effect. 2. The pH-responsive mechanism was studied. 3. The study was supported by computational chemistry using density functional theory. 4. The obtained dyes were used to make pH-responsive sensors for seafood packaging to judge the freshness.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.