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This study aims to dye silk with natural pigments extract of Coreopsis tinctoria, by treating the fabrics with appropriate mordant under suitable dyeing conditions, to achieve good dyeing depth, fastness and ultraviolet (UV) protection.

Firstly, single factor experiments were used to determine the basic dyeing conditions of Coreopsis tinctoria. The optimal process conditions for direct dyeing were determined through orthogonal experiments. After that, the dyeing with mordant was used. Based on the previously determined optimal process conditions, silk fabrics were dyed with different mordanting methods, with different mordants and mordant dosages. The dyeing results were compared, in terms of the K/S values of the dyed fabrics, to determine the most appropriate dyeing conditions with mordant.

The extract of Coreopsis tinctoria can dye silk fabrics satisfactorily. Good dyeing depth and fastness can be obtained by using suitable dyeing methods and dyeing conditions, especially when using the natural mordant pomegranate rind and the rare earth mordant neodymium oxide. The silk fabrics dyed with Coreopsis tinctoria have good UV resistance, which allows a desirable finishing effect to be achieved while dyeing, using a safe and environmentally friendly method.

The composition of Coreopsis tinctoria is complex, and the specific composition of colouring the silk fibre has not been determined. There are many factors that affect the dyeing experiment, which have an impact on the experimental results.

The results of this study may help expand the application of Coreopsis tinctoria beyond medicine.

To the best of the authors’ knowledge, this paper is the first report on dyeing silk with the extract of Coreopsis tinctoria achieving good dyeing results. Its depth of staining and staining fastness were satisfactory. Optimum dyeing method and dyeing conditions have been identified. The fabric dyed with Coreopsis tinctoria has good UV protection effect, which is conducive to improving the application value of the dyeing fabric. The findings help offer a new direction for the application of medicinal plants in the eco-friendly dyeing of silk.

This study aims to observe the coloring efficacy of graphite (G) and nano bentonite clay (BCNPs) on the adsorption of Basic Blue 5 dye from residual dye bath solution.

Some factors that affected the adsorption processes were examined and found to have significant impacts on the adsorption capacity such as the initial concentration of G and/or BCNPs (Co: 40–2,320 mg/L), adsorbent bath pH (4–9), shaking time (30–150 min.) and initial dye concentration (40–200 mg/L). The adsorption mechanism of dye by using G and/or BCNPs was studied using two different models (first-pseudo order and second-pseudo order diffusion models). The equilibrium adsorption data for the dye understudy was analyzed by using four different models (Langmuir, Freundlich, Temkin modle and Dubinin–Radushkevich) models.

It has been found that the adsorption kinetics follow rather a pseudo-first-order kinetic model with a determination coefficient (R²) of 0.99117 for G and 0.98665 for BCNPs. The results indicate that the Freundlich model provides the best correlation for G with capacities q_max = 2.33116535 mg/g and R² = 0.99588, while the Langmuir model provides the best correlation for BCNPs with R² = 0.99074. The adsorbent elaborated from BCNPs was found to be efficient and suitable for removing basic dyes rather than G from aqueous solutions due to its availability, good adsorption capability, as well as low-cost preparation.

There is no research limitation for this work. Basic Blue 5 dye graphite (G) and nano bentonite clay (BCNPs) were used.

This work has practical applications for the textile industry. It is concluded that using graphite and nano bentonite clay can be a possible alternative to adsorb residual dye from dye bath solution and can make the process greener.

Socially, it has a good impact on the ecosystem and global community because the residual dye does not contain any carcinogenic materials.

The work is original and contains value-added products for the textile industry and other confederate fields.

This study aims to synthesize pigment and resin from agro-wastes and use them in the formulation of eco-friendly surface coatings.

The pigments and resin were synthesized through a chemical modification of agro-wastes. The pigments were characterized by infrared spectroscopy (FTIR) and were screened for their antimicrobial activities. The physicochemical characteristics of the cashew nutshell liquid (CNSL)-modified resin were evaluated. These precursors and other natural additives were used to formulate surface coatings, and their drying and adhesive properties were evaluated using international testing methods.

It was observed that the curing of the CNSL-modified resin depended on time and temperature. The pigments exhibited antimicrobial activity against E. coli and S. aureus and had high melting points, affirming their stability. The chemically modified precursors successfully yielded surface coatings with acceptable drying times and adhesion to the base substrate.

The use of agro-wastes as the main components of the surface coatings implies waste valorization, a reduction in production costs and the creation of job opportunities for sustainable development. To increase the chemical, physical, corrosion resistance and antimicrobial qualities of paint compositions, chemically modified peanut skin extracts and CNSL can be used as pigments and resins, respectively. This could be a green approach to achieving the targets of Sustainable development goals 11 and 12.

The paper outlines a prospective approach to use unwanted waste (peanut skin, cashew nutshells) and other natural additives as industrial raw materials. These novel surface coating precursors are cost-effective, readily available, eco-friendly and could replace conventional precursors.

This paper aims to investigate the responses of single piles and pile groups due to tunneling-induced ground movements in a two-layered soil system. The analyses mainly focus on the additional single pile responses in terms of bending moment, lateral deflection, axial force, shaft resistance and pile settlement. Subsequently, a series of parametric studies were carried out to better understand the responses of single piles induced by tunneling. To give further understanding regarding the pile groups, a 2 × 2 pile group with two different pile head conditions, namely, free and capped, was considered.

Using the PLAXIS three-dimensional (3D) software, a full 3D numerical modeling is performed to investigate the effects of ground movements caused by tunneling on adjacent pile foundations. The numerical model was validated using centrifuge test data found in the literature. The relevance of the 3D model is also judged by comparison with the 2D plane strain model using the PLAXIS 2D code.

The numerical test results reveal that tunneling induces significant displacements and internal forces in nearby piles. The magnitude and distribution of internal forces depend mainly on the position of the pile toe relative to the tunnel depth and the distance between the pile and the vertical axis of the tunnel. As the volume loss increases from 1% to 3%, the apparent loss of pile capacity increases from 11% to 20%. By increasing the pile length from 0.5 to 1.5 times, the tunnel depth, the maximum pile settlement and lateral deflection decrease by about 63% and 18%, respectively. On the other hand, the maximum bending moment and axial load increase by about 7 and 13 times, respectively. When the pile is located at a distance of 2.5 times the tunnel diameter (Dt), the additional pile responses become insignificant. It was found that an increase in tunnel depth from 1.5Dt to 2.5Dt (with a pile length of 3Dt) increases the maximum lateral deflection by about 420%. Regarding the interaction between tunneling and group of piles, a positive group effect was observed with a significant reduction of the internal forces in rear piles. The maximum bending moment of the front piles was found to be higher than that of the rear piles by about 47%.

Soil is a complex material that shows differently in primary loading, unloading and reloading with stress-dependent stiffness. This general behavior was not possibly being accounted for in simple elastic perfectly plastic Mohr–Coulomb model which is often used to predict the behavior of soils. Thus, in the present study, the more advanced hardening soil model with small-strain stiffness (HSsmall) is used to model the non-linear stress–strain soil behavior. Moreover, unlike previous studies THAT are usually based on the assumption that the soil is homogeneous and using numerical methods by decoupled loadings under plane strain conditions; in this study, the pile responses have been exhaustively investigated in a two-layered soil system using a fully coupled 3D numerical analysis that takes into account the real interactions between tunneling and pile foundations. The paper presents a distinctive set of findings and insights that provide valuable guidance for the design and construction of shield tunnels passing through pile foundations.

From a technological determinist perspective, machine learning (ML) may significantly contribute towards sustainable development. The purpose of this study is to synthesize prior literature on the role of ML in promoting sustainability and to encourage future inquiries.

This study conducts a systematic review of 110 papers that demonstrate the utilization of ML in the context of sustainable development.

ML techniques may play a vital role in enabling sustainable development by leveraging data to uncover patterns and facilitate the prediction of various variables, thereby aiding in decision-making processes. Through the synthesis of findings from prior research, it is evident that ML may help in achieving many of the United Nations’ sustainable development goals.

This study represents one of the initial investigations that conducted a comprehensive examination of the literature concerning ML’s contribution to sustainability. The analysis revealed that the research domain is still in its early stages, indicating a need for further exploration.

The compressive strength of concrete depends on many interdependent parameters; its exact prediction is not that simple because of complex processes involved in strength development. This study aims to predict the compressive strength of normal concrete and high-performance concrete using four datasets.

In this paper, five established individual Machine Learning (ML) regression models have been compared: Decision Regression Tree, Random Forest Regression, Lasso Regression, Ridge Regression and Multiple-Linear regression. Four datasets were studied, two of which are previous research datasets, and two datasets are from the sophisticated lab using five established individual ML regression models.

The five statistical indicators like coefficient of determination (R²), mean absolute error, root mean squared error, Nash–Sutcliffe efficiency and mean absolute percentage error have been used to compare the performance of the models. The models are further compared using statistical indicators with previous studies. Lastly, to understand the variable effect of the predictor, the sensitivity and parametric analysis were carried out to find the performance of the variable.

The findings of this paper will allow readers to understand the factors involved in identifying the machine learning models and concrete datasets. In so doing, we hope that this research advances the toolset needed to predict compressive strength.

This paper aims to develop a stable gel-type lubricant emulating commercial conditions. This encompassed rheological and tribological assessments, alongside field trials on the Medellín tram system.

The gel-type lubricant with graphite and aluminum powder is synthesized. Rheological tests, viscosity measurements and linear viscoelastic regime assessments are conducted. Subsequently, tribological analyses encompassing four-ball and twin disc methods are executed. Finally, real-world testing is performed on the Medellín tram system.

An achieved lubricant met the stipulated criteria, yielding innovative insights into the interaction of graphite and aluminum powder additives under varying tests.

Novel findings are unveiled regarding the interaction of graphite and aluminum powder additives in tribological, rheological and real-world trials. In addition, the wear behavior of polymers is observed, along with the potential utilization of such additives in tramway systems.

Concrete-filled double skin steel tubes (CFDST) columns are taken more attention due to their ability to withstand high structural loads in structures such as high-rise buildings, bridges' piers, offshore and marine structures. This paper is intended to improve the CFDST column's capacity without the need to increase the column's size to maintain its lightweight by filling it with self-compacted concrete (SCC) containing nanoclay (NC).

First, experimental investigation is conducted to select the optimal NC percentage that improves the mechanical properties. Different mixing method, mixture ingredients, cement content, and NC percentage are considered. Then, slender and short CFDST columns are tested for axial capacity to investigate the effect of adding the optimum NC percentage on column's capacity and failure mode.

The test results show that adding 3% NC by cement weight using dry mixing method to SCC is the optimum ratio. It is concluded that adding 3% NC by cement weight increased the CFDST column's capacity, especially the specimens with higher slenderness ratio. Moreover, it is concluded that more specimens should be tested under various geometric and reinforcement details.

Recently, CFDST tube columns solve many structural and architectural problems that engineers have encountered in traditional systems. Therefore, more studies are required to design high-performance columns capable of carrying complex loads with high efficiency since the traditional design could not achieve the required performance. Since concrete contributes to a large portion in the axial capacity of the CFDST columns, it is proposed to improve the CFDST column's capacity without the need to increase the column's size to maintain its lightweight by filling it with (SCC containing NC. Previous research has affirmed the effectiveness of employing nanoclay in the concrete's workability, durability, microstructures, and mechanical properties.

Increased ethanol accumulation during ethanol fermentation generates stress in yeast cells, which finally reduces the fermentation performance and efficiency. Trehalose, a potential stress protectant, has been reported to regulate the response of yeast to diverse environmental stresses. This study aimed to explore how exogenous trehalose application affects the survival, transcriptome and antioxidant enzymes of Wickerhamomyces anomalus grown under ethanol stress conditions.

Exogenous trehalose was applied to the growth condition of W. anomalus, and optical densitometric method was used to detect contents of intracellular trehalose and MDA and activities of CAT and SOD. The survival was evaluated using spot analysis. Differentially expressed genes (DEGs) were identified through transcriptomics analysis.

The results showed that ethanol stress induced the accumulation of intracellular trehalose, with further exogenous trehalose application improving the survival and alleviating oxidative stress in ethanol-stressed W. anomalus. Transcriptomic results showed that trehalose has pleiotropic regulating effects on ethanol-stressed W. anomalus since most DEGs annotated to energy metabolism, amino acid metabolism, translation, folding, sorting and transport were affected post trehalose addition. Therefore, it is found that trehalose protected W. anomalus against ethanol stress, and these findings provide interesting insights into the mechanistic role of trehalose in improving ethanol stress tolerance of W. anomalus.

(1) Protective effect of exogenous trehalose addition on the survival of ethanol-stressed W. anomalus was proved. (2) Exogenous trehalose addition could partly alleviate oxidative stress induced by ethanol stress and affect transcriptome in W. anomalus.

Foundry produces cast metal components and parts for various industries and drives manufacturing excellence all over the world. Assuring quality of these components and parts is vital for the end product quality. The complexity in foundry operations increases with the complexity in designs, patterns and geometry and the quality parameters of the casting processes need to be monitored, evaluated and controlled to achieve expected quality levels.

The literature addresses quality improvement in foundry industry primarily focusing on surface roughness, mechanical properties, dimensional accuracy and defects in the cast parts and components which are often affected by numerous process variables. Primary data are collected from the experts working in sand and investment casting processes. The authors perform machine learning analysis of the data to model the quality parameters with appropriate process variables. Further, cluster analysis using k-means clustering method is performed to develop clusters of correlated process variables for sand and investment casting processes.

The authors identified primary process variables determining each quality parameter using machine learning approach. Quality parameters such as surface roughness, defects, mechanical properties and dimensional accuracy are represented by the identified sand-casting process variables accurately up to 83%, 83%, 100% and 83% and are represented by the identified investment-casting process variables accurately up to 100%, 67%, 67% and 100% respectively. Moreover, the prioritization of process variables in influencing the quality parameters is established which further helps the practitioners to monitor and control them within acceptable levels. Further the clusters of process variables help in analyzing their combined effect on quality parameters of casting products.

This study identified potential process variables and collected data from experts, researchers and practitioners on the effect of these on the quality aspects of cast products. While most of the previous studies focus on a very limited process variables for enhancing the quality characteristics of cast parts and components, this study represents each quality parameter as the function of influencing process variables which will enable the quality managers in Indian foundries to maintain capability and stability of casting processes. The models hence developed for both sand and investment casting for each quality parameter are validated with real life applications. Such studies are scarcely reported in the literature.