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The purpose of this research is to study the performance of piezoresistive pressure sensors using polysilicon as the piezoresistive material, which is typically used to measure pressure in high-temperature environments.

The performance of this sensor is enhanced by studying the influence of multi-turn configuration at which the piezoresistors are arranged. Different configurations are studied and compared by laying down their analytical solution.

The validation of analytical results is accomplished through finite element analysis using the software COMSOL Multiphysics. The best configuration, which uses a partial triple-turn configuration, was able to achieve a sensitivity of 116.00 mV/V/MPa over a simulated pressure range of 0 to 500 KPa.

The literature shows the study of single-turn and double-turn meander-shaped configuration of micro-electromechanical systems piezoresistive pressure sensor but multi-turn meander-shaped configuration using a square silicon diaphragm has not been reported. Its study has reflected promising results than its counterparts based on key performance parameters such as sensitivity and linearity and are more effective to be used for automotive, aviation, biomedical and consumer electronics applications.

The purpose of this paper is to examine how over-reliance on buyer-supplier relational capital (created through the interconnected supply chain and social network) impacts firm performance in the context of the emerging market, i.e. India.

The study uses the Prowess database (on Indian firms) to identify the firms that rely heavily on relational capital and employs panel data regression analyses to test the effect of relational capital on firm performance (supply chain performance and financial performance).

The results show that over-reliance on relational capital leads to lower supply chain performance (proxied by supply chain cycle) and financial performance (proxied by Tobin's Q). The results also reveal that supply chain performance mediates the relationship between over-reliance on relational capital and financial performance. Together, these results indicate that over-reliance on relational capital created through the interconnected supply chain and social network for supply chain management may negatively affect a firm's competitive advantage, which in turn can significantly impede its financial performance.

In light of the supply chain literature and relevant theories, the study develops an objective understanding of over-reliance relational capital created through the interconnected supply chain and social network, by relying on a large panel dataset of manufacturing firms and hence contributes to the supply chain literature. Also, it presents a novel idea to operationalize the measure for relational capital using the Prowess database.

The purpose of this paper is to study the spillover effects of foreign direct investment (FDI) on skilled–unskilled wage inequality in the Indian manufacturing industries.

The authors show theoretically with a model of spillover that if foreign firms (receiving FDI) have a negative spillover effect on domestic firms (not receiving FDI), then the level of capital and skilled workers in the domestic firms falls down. Consequently, the authors conduct an empirical analysis by using system GMM estimation technique on the firm-level data of the Indian organised manufacturing sector.

The authors show that wage inequality worsens when there is negative spillover effects like competition spillover or skill spillover effect of FDI in India.

To the best of the authors’ knowledge, this is the first attempt to measure the various spillover effects of FDI on the wage inequality in the Indian manufacturing industries by using firm-level data.

This study examines the performance effect of working capital for a large sample of Indian manufacturing firms in light of supply chain disruption, i.e. the COVID-19 pandemic.

This study is based on secondary data collected from the Prowess database on Indian manufacturing firms listed on the Bombay Stock Exchange (BSE) 500. Panel data regression analyses are used to estimate all models. Moreover, this study has employed robust standard errors to consider for heteroscedasticity concerns.

The results challenge the current notion of working capital investment and reveal that higher working capital has a positive and significant impact on firm performance. Further, it highlights that Indian manufacturing firms suffered financially post-COVID-19 as they significantly lack the working capital to run day-to-day operations.

This research contributes to the scant literature by examining the association between working capital financing and firm performance in light of the COVID-19 pandemic, representing typical developing economies like India.

The performance of oil-filled pressure cores is very much affected by the corrugated diaphragm and the oil filling volume. The purpose of this paper is to show the effects of different corrugated diaphragms, different oil filling volumes and different treatments of the corrugated diaphragms on the performance of pressure sensors.

Pressure-sensitive cores with different diaphragm diameters, different diaphragm ripple numbers and different oil filling volumes are produced, and thermal cycling is introduced to improve the diaphragm performance, and finally the performance of each pressure-sensitive core is tested and the test data are analyzed and compared.

The experimental results show that the larger the diameter of the corrugated diaphragm used for encapsulation, the better the performance. For pressure-sensitive cores using smaller diameter corrugated diaphragms, the performance of one corrugation is better than that of two corrugations. When the number of corrugations and the diameter are the same size, the performance of the outer ring of the diaphragm with concave corrugations is better than that with convex corrugations. At the same time, the diaphragm after thermal cycling treatment and appropriate reduction of encapsulated oil filling can improve the performance of the pressure-sensitive core.

By exploring the effects of corrugated diaphragm and oil filling volume on the performance of oil-filled pressure cores, the design of oil-filled pressure sensors can be guided to improve sensor performance.

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.

This work aims to explore the effects of (equity and non-equity) strategic alliances between banks and FinTechs on FinTechs' online visibility.

For a sample of 124 Italian FinTechs, the authors measured online visibility through their website ranking (Google PageRank) and website traffic (Google Trends). Consistent to the historical depth of these measures, the authors separately investigated the effect of equity and non-equity (contractual) agreements on online visibility by means of ordinal logistic regressions and diff-in-diff analysis.

Strategic alliances with banks enhance FinTechs' online visibility. Although both equity and contractual agreements positively influence the popularity of FinTechs' website achieved through the activity of internal and external online content creators (websites ranking), only equity agreements are effective in attracting Internet users (website traffic).

When deciding to interact with banks, FinTechs' managers should consider that equity agreements may be a powerful strategic choice for enlarging the customer base and boosting visibility of FinTechs.

Fostering strategic alliances between banks and FinTechs contributes to FinTechs' growth, generating virtuous mechanisms of innovation, financial inclusion and better allocative efficiency of the financial system.

This work expands marketing knowledge and literature regarding online visibility determinants, by investigating the benefits of strategic alliances and cooperation in the market, while providing an empirical strategy replicable by future marketing studies.