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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.

A “state-driven” approach suggests that colonists use census categories to rule. However, a “society-driven” approach suggests that this state-driven perspective confers too much power upon states. A third approach views census-taking and official categorization as a product of state–society interaction that depends upon: (a) the population's lay categories, (b) information intellectuals' ability to take up and transform these lay categories, and (c) the balance of power between social and state actors. We evaluate the above positions by analyzing official records, key texts, travelogues, and statistical memoirs from three key periods in India: Indus Valley civilization through classical Gupta rule (ca. 3300 BCE–700 CE), the “medieval” period (ca. 700–1700 CE), and East India Company (EIC) rule (1757–1857 CE), using historical narrative. We show that information gathering early in the first period was society driven; however, over time, a strong interactive pattern emerged. Scribes (information intellectuals) increased their social status and power (thus, shifting the balance of power) by drawing on caste categories (lay categories) and incorporating them into official information gathering. This intensification of interactive information gathering allowed the Mughals, the EIC, and finally British direct rule officials to collect large quantities of information. Our evidence thus suggests that the intensification of state–society interactions over time laid the groundwork for the success of the direct rule British censuses. It also suggests that any transformative effect of these censuses lay in this interactive pattern, not in the strength of the British colonial state.

The purpose of this paper is to define the evaluation criteria for Chinese female lower body shapes and simplify the evaluation process of shapewear, including girdles, shaping pants, etc.

The study utilized a machine learning algorithm based on support vector regression and optimized by a genetic algorithm to construct an evaluation model for the contour beauty of Chinese female lower body shapes. A total of 64 virtual 3D models of women were measured. These models were rated by 42 raters using the Likert 9 psychological scale and data was obtained from 310 female samples.

Eight factors were selected and used to create a regression prediction model. The model achieved an accuracy of 84.7% for the training samples and 86.6% for the test samples.

The model can be utilized to assess the aesthetic appeal of the female lower body and to evaluate the shaping impact of shapewear. The research and evaluation of shapewear for the female lower body are of great significance, particularly in enhancing production efficiency.

Self-Service Technology (SST) is a disruptive technology that has reshaped customer interactions, increased efficiency, and enabled data-driven decision-making. Its impact continues to evolve as technology advances and customer expectations change, making it a key consideration for businesses in a dynamic landscape. This chapter delves into critical findings regarding the adoption and implications of SST in tourism and hospitality. The relevant studies are sourced from the Scopus database. A mixed literature review methodology was employed to review papers. The literature review findings show facets of SST adoption, shedding light on the intricate relationships between consumer readiness variables, context-specific influences, preferred SST features, and psychological attributes. The study reveals consumer preferences, including convenience, ease of use, and speed of service, as primary drivers of the adoption of SST. The bibliometric analysis reveals the scope for developing SST literature in tourism and hospitality. Collaborations among scholars, research and funding institutions could help provide the impetus. Research in SST security, sustainability, and resilience could help enhance the SST literature. Comparative studies evaluating SST's social and economic implications are also suggested.

Agriculture is an activity that plays an important role in human life. Similarly, the agricultural sector plays an important role in the national economy. One of the biggest problems of the agricultural sector is the carbon gas it produces during production. Fertilizing activities and tools used in plowing the fields cause this gas to be produced. The release of the said gas into nature causes serious damage to the environment. Therefore, carbon emissions in the agricultural sector are of vital importance. In line with this purpose, it is aimed to determine the most appropriate strategy for carbon emission in this study. As a result of the DEMATEL analysis, it was seen that the most appropriate strategy was effective regulations and auditing.

Artificial intelligence (AI) has transformed the field of hiring, enabling employers to collect and analyse massive amounts of data to understand and predict the suitability of candidates. However, AI can also have subconscious effects on candidates’ and employers needs through biased data, which can stem from human biases, algorithmic errors or external factors. For example, Amazon scrapped an AI-based recruitment programme that favoured male candidates over female candidates due to the historical patterns in the resumes it analysed. This paper examines how AI can shape candidate's needs through biased data from various sources and types, and what are the consequences for candidate's welfare and rights. We review the literature on AI applications in hiring, the origins and kinds of bias in AI systems, and the potential risks and benefits for candidates. We also suggest some guidelines for reducing bias in AI and enabling candidates to make informed and ethical choices online. We argue that AI can be a double-edged sword for candidate's needs and that more research and regulation are required to ensure its fair and accountable use.

This paper summarizes the design thinking of pleated clothing, uses virtual software to simulate the pleated clothing process and clothing effect and applies the fractal geometry principle to conduct relevant experiments, which verifies the possibility of simulation software assisting pleated clothing design, improves research and development efficiency, lays a foundation for future process parameter collection to upgrade the pleated clothing database and realizes the diversity of digital innovative design of pleated clothing.

According to the principle of self-similarity and fractal dimension in fractal geometry, the design thinking of pleated clothing is put forward and the simulation folding of 3D digital technology is expanded. The design method takes traditional craft and digital technology as an example. The two-dimensional design thinking of pleated clothing is geometric crease thinking of fabric swatch and geometric expansion of basic clothing pattern. Three-dimensional modeling design thinking, such as based on the human body shape, geometric shape and folding mode. As well as digital design thinking, such as the angle of the fabric element and the perspective of garment pattern change.

This paper analyzes the Y structure of the similar shape “binary tree” in origami art, the principle of infinite folding with 45? As the unit and the point set capacity theory of Cantor concentric circles in the fractal dimension principle expands the design thinking of pleated clothing, applies a large number of experimental cases of design thinking inspired by the fractal geometry of pleated clothing and validates the possibility of simulation software assisting pleated clothing design.

We argue here that digital technology should continue to be used in a similar way for research and development in other clothing design-related processes in the future. When it comes to the possibilities for digital innovation in relation to pleated clothing, there is a lack of data for modeling the characteristics of such clothing and establishing the processing requirements. Thus, there is scope here for innovation breakthroughs in terms of both the art and the technology associated with clothing design, not to mention how the two are integrated. This paper has laid some potential foundations for this through its elaboration of fractal-inspired design in the context of pleated garments.

Numerous experiments were undertaken and digital simulation software was used to explore various potential designs in the paper. This confirmed the possibility of using simulation software to assist in the design of pleated clothing, which offers the scope for improved research and more efficient development. However, if the processing parameters for changes in the pleated fabric are not set in the software, it is impossible to automatically generate auxiliary clothes. The collection of processing parameters and the creation of a more comprehensive pleated clothing database should therefore be the focus of future research.

The paper is oriented toward future clothing design. There is some impact on research, practice and/or society. This opens up the possibility of using fractal geometry in the design of the folds and dimensional energy in pleated clothing. On the whole, the innovation point is clear and the idea is novel. By seeking the principles of science and technology, sensing the infinite possibilities, improving the design efficiency and inspiring innovative design thinking methods, in order to meet the diversity and personality needs of the external shape of the human body.

The concave and convex texture of pleated fabric is geometric and decorative. It has a direct reference value for pleated clothing design by using the concepts of partial and whole in fractal geometry and the quantity and energy conversion in fractal dimension space. Due to the influence of clothing intelligent manufacturing policy, there is a lack of data sorting for the modeling characteristics and process requirements of pleated clothing, and there is innovative design thinking that needs to be integrated with technology and art, which lays a foundation for the future collection of process parameters to upgrade the pleated clothing database.

Implementing big data analytics and client customization programs is causing a significant revolution in the insurance sector. This study examines how big data analytics may revolutionize the insurance industry, emphasizing how consumer customization can improve customer experiences, maximize risk assessment, and spur company expansion.

An empirical study with statistical analysis using tools like correlation and regression was carried out to ascertain the relationships between the various sets of variables—personalized customer experiences and customer satisfaction and customer profiling leads to more effective targeting of marketing efforts. We explore essential ideas like client segmentation, profiling, and retention via a thorough analysis of the literature and case studies, showcasing best practices and inspirational tales from top insurers.

The empirical study found that there is a very high correlation between transparency in data and stakeholders' trust. The study found that insurers may preserve their innovation-driven culture, strengthen customer relationships, and achieve sustainable development in a competitive market by embracing future technological innovations and resolving current challenges.

Insurance companies may seize new chances for individualized client experiences and long-term success in a market that is becoming increasingly competitive by utilizing cutting-edge technology like artificial intelligence and the Internet of Things. To effectively manage the changing terrain of consumer customization in the digital age, insurance professionals, academics, and legislators will find this study highly insightful.

The study is an original contribution based on literature and case studies analysis, showcasing best practices and inspirational tales from top insurers.

The purpose of this study is to examine the acceptance of artificial intelligence devices (AIDs) by customers in banking service encounters using the Artificially Intelligent Device Use Acceptance (AIDUA) model and thus test the validity of the AIDUA model in the context of the banking sector as well as extending the AIDUA model by incorporating two moderator variables, namely technology anxiety and risk aversion by regarding the nature of banking services, which are considered highly risky and technology-intensive.

About 575 valid face-to-face self-administered surveys were gathered using convenience sampling among real bank customers in Turkey. The structural equation modelling was used to test hypotheses involving both direct and moderation effects.

The current study has demonstrated that the AIDUA model is valid and reliable for the acceptance of AIDs in banking service encounters by modifying it. The study results have shown that the acceptance process of AIDs for bank customers consists of three phases. Furthermore, the study’s findings have demonstrated that technology anxiety and risk aversion have adverse moderation effects on the relationship between performance expectancy and emotion as well as on the relationship between emotion and willingness to accept AIDs, respectively.

The current study validates the AIDUA model for the banking industry. In addition, the present study is unique compared to other studies conducted in the literature since it applies the AIDUA model to the setting of banking services for the first time by considering the potential effects of two moderators.

Logistics sector is recognized as one of the core enablers of the economic development of a nation. However, inefficiency in logistics operations impedes the achievement of intended targets by increasing the cost of doing business. Also, it is difficult to improve the efficiency of a country’s logistics operations without a metric for evaluating and understanding logistics capabilities and efficiency. Therefore, the present study has developed In-country Logistics Performance Index (ILP Index) to propose a benchmarking tool to measure the in-country logistics competitiveness, particularly in the setting of emerging economies, i.e. India.

This study has developed a unified index using principal component analysis and quintile approach. In addition, the proposed index relies on several dimensions that are developed and illustrated using quantitative secondary panel data.

The findings of this study reveal that the quality of infrastructure, economy, and telecommunications are the three most important dimensions that may significantly support the growth of the transportation and logistics sector. The results reveal that Gujarat, Tamil Nadu, and Maharashtra are the top performers whereas, Bihar, Jharkhand, and Jammu and Kashmir scores the least due to the insufficient logistics infrastructure as compared to other Indian states.

Given the extensive focus on international-level logistics index (like World Bank’s LPI) in the existing literature, this study intends to develop in-country logistics index to evaluate the logistics capabilities at the regional and state level. In addition, unlike prior studies, this study utilizes quantitative secondary data to eliminate cognitive and opinion bias. Moreover, this benchmarking tool would assist decision-makers in idealizing standard practices toward sustainable logistics operations. Additionally, the ILP index could serve the international investors in crucial decision-making, as it provides valuable insights into a country’s logistics readiness, influencing their investment choices and trade preferences. Finally, the proposed approach is adaptable to measuring the overall performance of any other industry/economy.