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This paper explores what factors influence household textile washing behaviour and how these factors relate to greenhouse gas emissions during the textile use stage.

A questionnaire survey related to textile summer washing and care behavior was conducted among households in 16 administrative districts of Shanghai. This study used the modified Consumer Lifestyle Approach framework of the washing and care ecosystem. The research hypotheses were established by selecting related factors from four aspects: household demographic characteristics, economy and consumption characteristics, washing machines and detergents characteristics.

First, we have demonstrated how some course factors do not significantly affect greenhouse emissions. None of the demographics, detergent-related activities, economy and consumption constructs significantly affect greenhouse emissions. Second, we have identified that washing machine and related activities has a direct positive effect on GHG emissions. The washing machine is not only the de facto carrier of all washing activities but also the core of washing activities. Washing machine is crucial in reducing greenhouse emissions and adjusting consumer behaviors.

This paper conducts a study related to the washing and care behavior of households in Shanghai. The paper examines the factors influencing household washing behavior and the relationship between these factors and greenhouse gas emissions during the textile use phase.

This study aims to evaluate how the structure of medical compression stockings, including three compression levels and five cross-sections from the ankle to the thigh part, will be changed after washing in different conditions and further investigate the effect of the washing parameters on the medical compression stockings.

By washing medical compression stockings in different conditions and measuring the structures (including the density, the girth, the transversal and lengthwise dimension, the weight per unit area and the thickness) of medical compression stockings from the knee to the thigh part.

It was concluded that the density, the weight per unit and the thickness increase and the girth, the transversal and lengthwise dimension, the weight per unit and the thickness decrease. The change degree of Class one and Class two is greater than Class 3. Moreover, the washing temperature is the most significant factor affecting all the structures of medical compression stockings. Meanwhile, the mechanical actions of the washing machine, like drum speed and washing time, also influence different medical compression stockings structures to different degrees.

The washing parameter not only includes the temperature and washing cycles but also has other factors, such as the drum speed and washing time. In addition, different kinds of factors will be influenced by each other.

This study can provide consumers advices on the washing of medical compression stockings, and attribute to the optimization of materials and structures to maintain its properties for manufacturers.

This study aims to explore human–machine interactions in the process of adopting artificial intelligence (AI) based on the principles of Taylorism and digital Taylorism to validate these principles in postmodern management.

The topic has been investigated by means of a case study based on the current experience of Carrozzeria Basile, a body shop born in Turin in 1970.

The Carrozzeria Basile’s approach is rooted in scientific management concepts, and its digital evolution is aimed at centring humans, investigating human–machine interactions and how to take advantage of both of these.

The research contributes to both Taylorism management and the literature on human–machine interactions. A unique case study represents a first step in comprehending the phenomenon but could also represent a limit for the study.

Practical implications refer to the scientific path to facilitate the implementation and adoption of emerging technologies in the organisational process, including employee engagement and continuous employee training.

The research focuses on human–machine interactions in the process of adopting AI in the automation process. Its novelty also relies on the comprehension of the needed path to facilitate these interactions and stimulate a collaborative and positive approach. The study fills the literature gap investigating the interactions between humans and machines beginning with their historical roots, from Taylorism to digital Taylorism, in relation to an empirical scenario.

Drawing upon extended-self theory, this research aims to understand how different access modes of consumption and product type influence consumers' perceptions of self-product connection and sense of individual and collective ownership.

Two experiments assessed the impact of access modes (temporary vs permanent) and product types (tangible vs intangible) on individual and collective psychological ownership (IPO and CPO) as well as the mediating role of self-product connection in these relationships.

Temporary access reduces IPO compared to permanent access. Moreover, self-product connection mediates the relationship between access modes and both IPO and CPO. Product type significantly moderates the degree of psychological ownership, with intangible products having a notable influence on the impacts of access modes on IPO and CPO.

Understanding the nuances of access-based consumption and its effects on psychological ownership can inform marketers and businesses in designing more effective strategies for product offerings and access modes. Highlighting self-product connection can aid in cultivating stronger consumer relationships and loyalty in access-based consumption contexts.

This study contributes to the existing literature on consumer access-based consumption by shedding light on the changing dynamics of material possessions and the psychological mechanisms underlying ownership perceptions in access-based models.

The purpose of this work is to study the relationship between the fabric’s mechanical properties such as tear strength (T_S), breaking strength (B_S) and cloth’s dimensional stability (Sh), particularly, after industrial launderings (stone wash, enzyme wash, mixed wash and rinse). Hence, we select the most interrelationships using the principal component analysis (PCA) technique. In this study, the treatments of finishing garments during washing are the important parameters influencing the cloth’s dimensional and the fabric’s mechanical properties. To improve the obtained results, the selected significant inputs are also analyzed within their influence on shrinkage. The polynomial regression model relating the tear strength and the shrinkage of denim fabric proves the effectiveness of the PCA method and the obtained findings.

To investigate the matter, the type of washing, and their contributions to shrinkage, four types of fabrics manufactured into pants were used. These fabrics differ not only by their basis weights (medium and heavy weight fabrics) but, also by their compositions (within and without elastane) and their thread count (warp and weft yarn count, twist and density. To evaluate significant results, a factorial design analysis based on an experimental design was established. The choice of these treatments, as well as their design mode, led us to make a complete factorial experimental design.

According to the results, the prediction of shrinkage behavior as a function of the process washing input parameters seems significant and useful in our experimental design of interest. As a consequence, it was also concluded that after these input parameters, we can find the relationship between the shrinkage (Sh_warp and Sh_weft) and the mechanical properties such as tear strength (T_Swarp and T_Sweft) and breaking strength (B_Swarp and B_Sweft). Thanks to the PCA, it is very easy to reduce the number of the influent output parameters, and knowing these significant parameters, the prediction of mechanical properties knowing the shrinkage of denim garment, during the process of washing seems successful and can undoubtedly help industrial to minimize the poor workmanship of the finishing quality.

This study is very interesting for finishing denim garments. The shrinkage is very important for correcting measures in sewing, considering that a high shrinkage may cause the cancellation of the fit from the client. This type of defect cannot be repaired in the major part of the cases and causes a big loss for the company, moreover the mechanical properties. For this reason, analyzing the value of shrinkage before starting the production cycle is of great importance to apply the right balance to the pattern. The model of predicting the mechanical properties behaviors as a function of the shrinkage denim garment leads manufacturers to eliminate the test of mechanical properties that remain as destructive tests. Moreover, according to the results obtained, it may be concluded that prediction is still accurate through the shrinkage test which is an inevitable test. Even though, these results can bring a huge gain for the garment wash industries.

This work presents the first study predicting a relationship between the mechanical properties and denim garment shrinkage, applying the PCA technique to minimize the all-output parameters that are not significant or correlated with each other. Besides, it deals with the relationship developed between the fabric’s mechanical properties such as tear strength (T_S), breaking strength (B_S) and cloth’s dimensional stability (Sh), particularly, after industrial launderings (stone wash, enzyme wash, mixed wash and rinse). Moreover, it is notable to mention that the originality of this study is to let to the garment wash industries to save in production time of orders and also in quality.

The widespread use of washing machines in textile dyeing and finishing boosts product quality while leading to significant wastewater production. This wastewater poses environmental risks due to the textile industry's high pollution levels and water consumption. Sustainability hinges on minimizing water usage and treating wastewater for reuse. This study employs Matlab R2020a and Python 2023 to model experimental designs for treating textile production wastewater using the Fenton oxidation method, aiming to address sustainability concerns in the industry.

The Fenton oxidation process's efficacy and optimal operating conditions were determined through experimental sets employing the Box–Behnken design. Assessing machine learning algorithms on the data, Matlab R2020a utilized an artificial neural network (ANN), while Python 2023 employed support vector regression (SVR), decision trees (DT), and random forest (RF) models. Evaluation of model performance relied on regression coefficient (R2) and mean square error (MSE) outcomes. This methodology aimed to refine the Fenton oxidation process and identify the most efficient parameters, leveraging a combination of experimental design and advanced computational techniques across different programming platforms.

The study identified optimal conditions: pH 3, Fe+2 concentration of 0.75 g/L, and H2O2 concentration of 5 mM, yielding 87% COD removal. The Box–Behnken design achieved a high R2 of 0.9372, indicating precise predictions. Artificial neural networks (ANN) and support vector regression (SVR) exhibited successful applications, notably achieving an R2 of 0.99936 and low MSE of 0.00416 in the ANN (LOGSIG) model. However, decision trees (DT) and random forests (RF) proved less effective with limited datasets. The findings underscore technology integration in treatment modeling and the environmental imperative of wastewater purification and reuse.

This study, in which water use and wastewater treatment are evaluated with technological integration such as machine learning and data management, reveals how to contribute to targets 6, 9, 12, and 14 within the scope of UNEP 2030 sustainable development goals.

Due to e-commerce growth, technological advancements and environmental concerns, developing a more nuanced service portfolio has become a critical issue for last-mile logistics service providers. Concurrently, consumers are adopting new modes of consumption. This paper aims to investigate the potential for last-mile logistics service providers to act as intermediaries in access-based consumption and to revitalise their service offerings through product-service systems – a pioneering strategy not executed in the market yet.

This strategic customer foresight study uses a quantitative survey of 1,000 respondents and an online focus group comprising 10 early adopter consumers to investigate emerging last-mile service models. Potential service concepts were identified through the survey, and two distinct concepts were subsequently selected for evaluation and co-development within the focus group. The research was conducted in partnership with an SME logistics company in Finland.

The consumers expressed selective interest in access-based consumption related to the proposed offering of essential household goods. Young adults and consumers in early middle age living in the city centre emerged as the most potential user groups. Economic reasons and short-term needs were the primary motivations for adopting access-based consumption.

The study showed that engaging consumers in a customer foresight process is viable for SMEs innovating their offerings and demonstrates how the process works in practice.

Documented cases of customer integration into foresight processes are rare in earlier research, and this paper extends the knowledge base through a multidisciplinary examination of future consumer behaviour in the last-mile logistics domain. The paper also expands the limited literature on the role of logistics in access-based consumption.

The purpose of this paper is to generate nitrogen-containing groups in the cotton fabric surface via low-temperature nitrogen plasma as an eco-friendly physical/zero-effluent process. This was done for rendering cotton dye-able with Acid Blue 284, which in fact does not have any direct affinity to fix on it.

Dyeing characteristics of the samples such as color strength (K/S), fastness properties to light, rubbing and perspiration and durability, as well as tensile strength, elongation at break, whiteness, weight loss and wettability in addition to zeta potential of the dyed samples, were determined and compared with untreated fabric. Confirmation and characterization of the plasma-treated samples via chemical modifications and zeta potential was also studied using Fourier transform infrared spectroscopy (FTIR) and Malvern Zetasizer instrumental analysis.

The obtained results of the plasma-treated fabric reflect the following findings: FTIR results indicate the formation of nitrogen-containing groups on cotton fabrics; notable enhancement in the fabric wettability, zeta potential to more positive values and improvement in the dyeability and overall fastness properties of treated cotton fabrics in comparison with untreated fabric; the tensile strength, elongation at break, whiteness and weight % of the plasma treated fabrics are lower than that untreated one; and the durability of the plasma treated fabric decreased with increasing the number of washing cycles.

The novelty addressed here is rendering cotton fabrics dye-able with acid dye via the creation of new cationic nitrogen-containing groups on their surface via nitrogen plasma treatment as an eco-friendly and efficient tool with a physical/zero-effluent process.

The efforts of researchers in the 21st century have been devoted to developing novel approaches to leave planet earth green for future generations. This study aims to report the synthesis of microcapsules from natural essential peppermint oil and their application to a bleached polyester and cotton (PC) blended fabric.

Microcapsules were prepared by a complex coacervation process and applied through the conventional pad-dry-cure method. The liquid suspension of the microcapsules was examined by optical microscopy to investigate the surface morphology of the microcapsules. Scanning electron microscopy was used to examine the surface morphology of the fabric after the application of the microcapsules. The finished fabric was checked for its mosquito repellent activity at the lab scale using a standard test protocol (cage test) by inserting a human arm and hand enfolded with microcapsules treated fabric.

PC fabric treated with 6% microencapsulated peppermint oil at zero wash showed 95.3% repellency against mosquitoes, and after 30 washes, the repellency was 85.8% which confirmed the durability of the developed finished fabric. The finished samples exhibited excellent air permeabilities and absorbencies.

This study successfully developed peppermint oil microencapsulated fabric with excellent efficacy against three mosquito species.