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This paper aims to discuss how acrylic resin influences the smoke generation of intumescent flame retardant coatings.

Thermal decomposition kinetics is used in this study to simulate the burning process. The thermal decomposition of acrylic resin can be identified in the intumescent coatings through the multi-peak fitting of derivative thermogravimetric (DTG) curves. The dormant influence of acrylic resin, combined with the smoke density, is calculated.

Multiple peaks fitting method of DTG curves helps estimate the decomposition process of acrylic resin in flame retardant coating. Combining DTG data with the smoking curve, smoking generation of acrylic resin during the combustion could be evaluated. The decomposition conversion rate of acrylic resin is 21.13 per cent. Acrylic resin generates 34.64 per cent of the total amount of smoke produced during the combustion of intumescent flame retardant coatings.

All the other intumescent flame retardant coating systems could be studied using the same approach as that used in this work to achieve an improved understanding of the smoke generation process during combustion.

The method developed here provided a simple and practical solution to analyse the decomposition and smoking generation of acrylic resin in the coating mixtures. It also can be used to analyse any thermal decomposition process of any mixed compounds.

The analysis method to evaluate resin’s smoking generation of coating’s total generation is novel, and it could be applied in all kinds of coatings and mixtures to estimate the smoking generation of one composition.

Bacterial exopolysaccharides (eps) have fascinating chemical compositions, properties and structures which could be used in the modification of natural fibres. Bacterial eps have therefore been used to modify plant cellulose fibre surface and impart desired properties. The purpose of this paper is therefore to investigate the influence of gin trash cultured bacteria eps on the physical and structural properties of cotton fibres.

Gin trash soil sample was collected from a ginnery in Kenya, and physiochemical and microbial characterization was done. The soil sample was then fermented for 24 h before being used to treat raw cotton fibres at varied conditions of temperature, pH and treatment time periods. Physical and structural properties of the treated fibres were then determined using USTER HVI-1000 M700, Fourier transform infrared, scanning electron microscope (SEM) and X-ray diffraction (XRD) and compared with those of the raw fibres.

The bacteria broth treated fibres were found to have increased in strength, spinning consistency index, elongation and fineness by 25.44, 24.30, 11.70 and 3.60%, respectively. The variations were attributed to interactions of bacterial eps with cotton cellulose through hydrogen bonding. SEM and XRD analysis revealed an increase in fibre surface roughness and crystallinity, respectively.

Bacterial eps have been used to modify plant cellulose fibre surface and impart desired properties. Eps producing bacteria have been isolated from different habitats such as saline water, soil samples, food wastes and petroleum-contaminated soil. To the best of the authors’ knowledge, bacterial eps cultured from gin trash soil sample for modification of cotton fibres have however not been previously done, hence the originality of the current study.

The purpose of the study reported in this paper is to synthesis curcumin, which was reported to possess broad pharmacological effects and excellent dyeing properties, via Claisen-Schmidt condensation reaction catalysed by an efficient and green solid base catalyst, KF/Al₂O₃.

A series of catalysts with different KF loadings and varying calcination temperatures had been prepared, characterised by Brunauer–Emmett–Taller surface area, temperature-programmed desorption with CO₂ using infrared spectroscopy, X-ray powder diffraction and X-ray photoelectron spectroscopy, and tested in the Claisen–Schmidt condensation reaction. The effects of different reaction parameters such as temperature, the amount of catalyst, reactant ratio and time on the synthesis of curcumin were examined. The possibility of recycling the catalyst was also investigated in detail. Moreover, the reaction mechanism and the role of KF/Al₂O₃ in the synthetic process were discussed.

The study provides an efficient and green solid base catalyst, KF/Al₂O₃, and 64.3 per cent yield of curcumin was obtained under the optimum reaction conditions. Experimental results indicate that the third reused catalyst could retain part of the catalytic activity, and the regenerated one could be reused with reasonable catalytic activity. Besides, K₃AlF₆ was proposed as the active site of the catalyst for the reaction by the analysis of the characterization results of KF/Al₂O₃.

KF/Al₂O₃ was found to be an efficient catalyst for catalytic synthesis of curcumin and could be easily recycled several times. This information may be useful for further research and practical applications of curcumin.

This study aimed to Simultaneous matching of color and antimicrobial properties of silk fabric treated with silver nanoparticle. The antimicrobial finishing using silver nanoparticles (AgNPs) is one of the most important finishing processes in the textile industry. Color matching is widely applied in the textile industry, but there has been a need for the prediction of AgNPs concentration for the matching of dyed silver-treated samples.

In this research, the silk fabrics were dyed with various concentrations of C.I. Acid Red 359 dye at 0.5, 1, 1.5 and 2 per cent (w/w). The dyed fabrics were then coated with AgNPs in several concentrations at 0.015, 0.030, 0.050, 0.100 and 0.250 ml/l. The prediction of dye and AgNPs concentrations were evaluated using single constant color matching and artificial neural network techniques.

The obtained results indicate that the accuracy of dye concentration prediction, as well as AgNPs concentration prediction, was improved by using a neural network method. Also, the correlation between actual and predicted dye and AgNPs concentrations in the best neural networks is more than the single constant color matching method.

Simultaneous antibacterial and color matching of nanosilver-treated fabric is novel. This method achieved acceptable accuracy for antibacterial and color matching.

This paper aims to develop an efficient surface‐plane intersection (SPI) algorithm for direct slicing of free‐form surfaces to be produced by layered manufacturing.

A semi‐analytical method for direct slicing has been formulated and tested on Bezier and B‐spline surfaces commonly used in CAD modeling. This method solves for the intersection points by a “root” finding procedure and establishes their connectivity, unlike the conventional “marching” procedures.

The proposed algorithm solves intersection contours between free form surfaces and planes. The solution procedure is efficient with respect to computational time and accuracy (feature detection) over some of the conventional SPI strategies. The method involves a global solution procedure in contention with the traditional methodologies which are generally spatially distinctive in approach.

Use of higher order terms in the representation of parametric surfaces makes the algorithm computationally intensive and time‐expensive.

This algorithm would be of practical use in the direct slicing of free form surfaces used in CAD modeling. Direct slicing methods solve for the actual intersection of surface and plane without resorting to “tessellation.” Reducing the computation time and detection of features within a given resolution is of primary importance for developing commercial rapid prototyping software, which is achieved in the present paper.

A novel method has been developed for SPI for use in direct slicing of CAD models. While a major proportion of the direct slicing strategies employ the “marching” procedure involving determination of “critical points,” the proposed method utilizes the evaluation of “roots” of a surface in a global manner to determine the intersection points with proper connectivity. Hence, it is effective in reducing the computation time and is simple but generic in approach. Although Bezier and B‐spline surfaces are used as the representative cases, the algorithm can be extended for any parametric surface for direct slicing.

Several national- and state-level studies have been predicting the course of the COVID-19 pandemic using supervised machine learning algorithms. However, the comparison of such models has not been discussed before. This is the first-ever research wherein the two leading models, susceptible-infected-recovered (SIR) and logistic are compared. The purpose of this study is to observe their utility, at both the National and Municipal Corporation level in India.

The modified SIR and the logistic were deployed for analysis of the COVID-19 patients’ database of India and three Municipal Corporations, namely, Akola, Kalyan-Dombivli and Mira-Bhayander. The data for the study was collected from the official notifications for COVID-19 released by respective government websites.

This study provides evidence to show the superiority of the modified SIR over the logistic model. The models give accurate predictions for a period up to 14 days. The prediction accuracy of the models is limited due to change in government policies. This can be observed by the drastic increase in the COVID-19 cases after Unlock 1.0 in India. The models have proven that they can effectively predict for both National and Municipal Corporation level database.

The modified SIR model can be used to signify the practicality and effectiveness of the decisions taken by the authorities to contain the spread of coronavirus.

This study modifies the SIR model and also identifies and fulfills the need to find a more accurate prediction algorithm to help curb the pandemic.

Drawing on configuration theory and adopting a holistic perspective, the authors aim to explore strategic supply chain elements' interaction with internal and external contextual conditions. Specifically, they evaluate multivariate co-alignment of supply chain's capabilities (agility, adaptability, alignment), adhocracy organizational structure and environmental uncertainty (dynamism, munificence and complexity) that lead to superior performance outcomes (supply chain cycle time, financial performance, market performance).

A survey research design is adopted to collect primary and secondary data from 182 international firms. Cluster analysis and profile deviation techniques are employed first to derive a taxonomy of strategic supply chains and then examine multivariate co-alignment.

The analysis reveals that four groups of triple-A supply chains could emerge. It is found that strategic supply chains exhibit a flexible/organic/fluid organizational structure and a high level of triple-As in their processes. These supply chains perform superior to a higher level of environmental complexity, munificence and dynamism.

This study's results provide insights for practitioners by informing their investment decisions for developing strategic supply chains. Specifically, this research highlights the contextuality of triple-A supply chains and offers best practices to capitalize on strategic supply chains.

Executives in organizations require a holistic understanding of various elements to ensure that the organizational system as a whole performs effectively. This research offers insights into the interdependencies of triple-A supply chains with other organizational and external environmental elements. Noteworthy implications for scholarship in operations/supply chain management and supply chain practitioners are presented.

This study aims to solve the problem of temperature cross sensitivity of fiber Bragg grating in structural health monitoring, proposing a novel acceleration sensor based on strain chirp effect which is insensitive to temperature.

A kind of M-shaped double cantilever beam structure is developed. The fiber grating is pasted in the gradient strain region of the beam, and the chirp effect is produced under the action of non-uniform stress, and then the change of acceleration is converted into the change of reflection bandwidth to demodulate and eliminate the temperature interference. Through theoretical analysis, simulation and experimental verification with rectangular beam sensor.

The results show that the sinusoidal curvature beam sensor is insensitive to the change of temperature and is more likely to produce chirp effect. The sensitivity is about 317 pm/g, and the natural frequency is 56 Hz.

This paper fulfils an insensitive to temperature changes sensor which has effectively solved the temperature cross-sensitivity problem in building structure health monitoring.

Traditional three-dimensional numerical methods require a long time for transient computational fluid dynamics simulation on oil-filling process of hydrodynamic braking. The purpose of this paper is to investigate reconstruction and prediction methods for the pressure field on blade surfaces to explore an accurate and rapid numerical method to solve transient internal flow in a hydrodynamic retarder.

Dynamic braking performance for the oil-filling process was simulated and validated using experimental results. With the proper orthogonal decomposition (POD) method, the dominant modes of transient pressure distribution on blades were extracted using their spatio-temporal structural features from the knowledge of computed flow data. Pressure field on blades was reconstructed. Based on the approximate model (AM), transient pressure field on blades was predicted in combination with POD. The causes of reconstruction and prediction error were, respectively, analyzed.

Results show that reconstruction with only a few dominant POD modes could represent all flow samples with high accuracy. POD method demonstrates an efficient simplification for accurate prediction of the instantaneous variation of pressure field in a hydrodynamic retarder, especially at the stage of high oil-filling rate.

The paper presents a novel numerical method, which combines POD and AM approaches for rapid and accurate prediction of braking characteristics during the oil-filling period, based on the knowledge of computed flow data.

Drawing on complementarity theory, this paper aims to examine the type and effect of interdependencies/interaction (i.e. complementarity or substitutability) between the supply chain capabilities of agility, adaptability and alignment.

A survey research design is adopted to collect primary and secondary data from 182 international firms. The complementarity (or substitutability) of three As (agile, adaptable and aligned) were analyzed in three-way and pairwise interactions; both, correlation and performance differences methods of testing the type of interactions among the system’s elements were used. Supply chain-centric and firm-centric performance metrics were used to examine the interaction types.

The study did not find empirical evidence of three-way complementarity between the three As. However, this paper did find evidence of complementarity in bivariate interactions for alignment and adaptability. Moreover, in the performance difference method, the study found a substitute relationship between all pairs of As.

The findings related to the substitutability between the three As offer managers guidance on how to allocate their limited resources to avoid unnecessary over-or under-investing in either one of the three As.

This study helps refine prior findings related to the three As by offering evidence that firms can still achieve their performance-related goals with reduced investment commitments by taking advantage of the substitutability relationship existent between these capabilities. That is, instead of concomitantly developing all three As as past studies have suggested, managers can use the findings to determine how to prioritize their resource allocation better. Furthermore, understanding the actual interaction among the supply chain variables generally provide insights for designing the supply chain, change management in the supply chain, developing supply chain strategy and adopting best practices in the supply chain.