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This study aims to provide a comprehensive overview of thin-film temperature sensors (TTS), focusing on the interplay between material properties and fabrication techniques. It evaluates the current state of the art, addressing both low- and high-temperature sensors, and explores the potential applications across various fields. The study also identifies challenges and highlights emerging trends that may shape the future of this technology.

This study systematically examines existing literature on TTS, categorizing the materials and fabrication methods used. The study compares the performance metrics of different materials, addresses the challenges encountered in thin-film sensors and reviews the case studies to identify successful applications. Emerging trends and future directions are also analyzed.

This study finds that TTS are integral to various advanced technologies, particularly in high-performance and specialized applications. However, their development is constrained by challenges such as limited operational range, material degradation, fabrication complexities and long-term stability. The integration of nanostructured materials and the advancement of wireless, self-powered and multifunctional sensors are poised to drive significant advancements in this field.

This study offers a unique perspective by bridging the gap between material science and application engineering in TTS. By critically analyzing both established and emerging technologies, the study provides valuable insights into the current state of the field and proposes pathways for future innovation in terms of interdisciplinary approaches. The focus on emerging trends and multifunctional applications sets this review apart from existing literature.

Nanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.

Review of a series of state of the art research papers on the subject matter.

This paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.

As a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).

PPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.

When used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.

NT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.

This paper aims to provide a full introduction, new classification, comparison and investigation of the challenges as well as applications of layerless 3D printing, which is one of the industry 4.0 pioneers.

Given the significance and novelty of uniform 3D printing, more than 250 publications were collected and reviewed in an unbiased and clear manner.

As a result, the majority of uniform parts printed in polymer form are known up to this point. In a novel division for better researchers’ comprehension, uniform printing systems were classified into three categories: oxygen inhibition (OI), liquid lubrication (LL) and photon penetration (PP), and each was thoroughly investigated. Furthermore, these three approaches were evaluated in terms of printing speed, precision and accuracy, manufacturing scale and cost.

The parameters of each approach were compared independently, and then a practical comparison was conducted among these three approaches. Finally, a variety of technologies, opportunities, challenges and advantages of each significant method, as well as a future outlook for layerless rapid prototyping, are presented.

People’s economic behaviors are often embedded in their social networks. Based on social embeddedness theory, this study investigates how customers’ social network embeddedness (structural and relational embeddedness) affects their online referral intentions through four studies.

This study adopted a vignette experimental design and used both offline and online channels to collect data. The respondents were undergraduate students and consumers, covering different age groups and occupation types. We used the ANOVA and bootstrap methods of SPSS to conduct data processing and hypothesis testing.

We found that when fewer (vs more) people in a customer’s social network participated in reward referral programs (RRPs), the customer’s online referral intention was also lower because of higher perceived social costs and lower perceived control. However, when the tie strength, reciprocity and normative influence between customers and people who have participated in RRPs were high, customers’ online referral intention also increased, ignoring the influence of participants’ network size. In these relationships, perceived social costs and perceived control played important mediating roles.

The results provide practical implications for enterprises to develop efficient RRPs in the context of the increasing popularity of the mobile Internet and intelligent terminals.

This study offers insights into the influence mechanism of social network embeddedness on referral likelihood, contributing to the field of RRPs and social embeddedness theory.

The chapter articulates the transition to and evolution of the commercialisation of Chinese professional football. It is periodised based on major turning points. The research yielded two major findings. First, there exists a distinct ‘Chinese way’ of commercialising football. However, it does not indicate that Chinese football doggedly avoided the Western governance model. For the club governance, Chinese football authorities set about recommending privately operated enterprises, large and medium-sized state-owned enterprises. Second, the commercialisation transitions of professional football in China were triggered by exogenous policy shifts, rather than endogenous changes in market structures, resulting in higher horizontal financial fragility than is associated with the commercialisation model adopted in more developed Western markets. The applicability to voluntary football clubs is assessed as well.

Intellectual experiences focus on users’ information processing and critical thinking toward stimuli. The deployment of humanoid service robots as novel stimuli in tourism and hospitality has influenced users’ perceptions and may affect their intellectual engagement. This paper aims to connect four contemporary theoretical concepts: the service robot acceptance model, technological fear, the uncanny valley theory and the stereotype content model, to investigate users’ perceptions and intellectual experiences toward humanoid service robots.

Scale development procedures were conducted: literature review, checking face and content validity, factorizing items and dimensions, achieving construct and criterion validity and testing predictive validity.

Through literature review and free-response tasks, 43 measurement items were generated. Next, 1,006 samples from two cross-cultural groups refined the scale. Finally, a reliable and valid scale with four dimensions measuring users’ perceptions of humanoid service robots was determined.

Humanoid service robots should be designed to enhance functionality and innovativeness while minimizing stiffness, inflexibility, unsafety and danger to improve users’ intellectual engagement.

This study provides a novel examination of users’ intellectual experiences toward humanoid service robots by connecting four contemporary theories of users’ perceptions. This study enriches human–robot experience through an integrated perspective and presents a rigorous examination of the scale’s psychometric properties. A reliable and valid scale for measuring users’ perceptions toward humanoid service robots fills the gaps and serves as an effective predictor of intellectual experience in human–robot literature.

Purpose/Objective: The shadow economy problem comes from the need for governments to increase their revenues, while citizens expect better social protection schemes such as universal basic income, increased spending for social security, and better living standards, including a sustainable and green environment. Our study analyses a complex and multifaced relationship between the shadow economy and the environment.

Design/Methodology/Approach: The analysis refers to the shadow economy (% in GDP) and three environmental proxies (Agriculture Methane Emissions, Methane Emissions for the Energy Sector, and Tax on Pollution) for the EU countries over the 2009–2019 period. Firstly, we describe the evolution of the indicators, and then we observe the correlation between the indicators. The last stage considers simple linear regression models, with shadow economy as the independent variable and environmental proxies as dependent variables.

Significance/Implications/Conclusions: Our research found that the evolution of the shadow economy is on a downtrend, considering that the fight against corruption developed after the 2009 financial crisis and especially after the Euro crisis 2014. Countries such as Romania, Bulgaria, Italy, Portugal, and Spain have the highest shadow economy due to their political electorate’s constant mismanagement and low credibility. On the contrary, countries such as Hungary, Estonia, and Lithuania have a low shadow economy.

Limitations: The reliance on limited data and the models applied.

Future Research: Future studies could expand the analysis by incorporating additional indicators, considering other econometric models, and incorporating more comprehensive and up-to-date data.

New quality productive forces (NQPF) are critical for high-quality economic development. As digital mergers and acquisitions (M&As) gain prominence in corporate digital transformation, understanding their impact on NQPF is essential. This study explores whether digital M&As enhance NQPF in firms and identifies key mechanisms that drive this effect.

This study investigates the impact of corporate digital M&As on NQPF using a multi-period difference-in-difference (DID) methodology. Analyzing a sample of Chinese listed firms from 2011 to 2021, the study explores how digital M&As contribute to NQPF, identifying firm innovation and data assets as key mechanisms. It also examines how external factors, such as industrial structure, urban human capital and economic policy uncertainty, moderate the effect of digital M&As on NQPF.

The study reveals three key findings: (1) Digital M&As significantly enhance corporate NQPF; (2) innovation and data assets serve as key mechanisms through which digital M&As drive NQPF and (3) external factors, including industrial structure, urban human capital and economic policy uncertainty, amplify the positive effects of digital M&As on NQPF.

Firms should leverage digital M&As as a strategic tool for improving NQPF, focusing on innovation and data assets. Policymakers can support this transformation by fostering an environment that enhances the positive impact of digital M&As on economic development.

This paper introduces a novel NQPF index, offering a comprehensive measurement of the concept. It provides new insights into how digital M&As affect NQPF, filling a gap in the literature on digital transformation and offering actionable recommendations for firms and policymakers.

Previous research mainly uses graph neural networks on syntactic dependency graphs, often neglecting emotional cues in sarcasm detection and failing to integrate image features for multimodal information effectively. To address these limitations, this study proposes a novel multimodal sarcasm detection model based on the directed graph isomorphism network with sentiment enhancement and multimodal fusion (DGIN-SE-MF).

The approach extracts image and text features through vision transformer and BERT, respectively. To deeply integrate the extracted features, the author develops a text-guided multi-head attention fusion mechanism module. Subsequently, a directed graph is constructed through SE and the multimodal factorized bilinear pooling method to integrate image features into the graph. The DGIN then fuses the image and text features, using a weighted attention mechanism to generate the final representation.

The model is validated on three datasets: English, Chinese and an Indonesian–English dataset. The results demonstrate that the proposed model consistently outperforms other baseline models, particularly on the Chinese and English sarcasm datasets, achieving F1 scores of 88.75 % and 83.10 %, respectively.

The proposed model addresses the inadequacies of previous methods by effectively integrating emotional cues and image features into sarcasm detection. To the best of the authors’ knowledge, this is the first work to leverage a DGIN-SE-MF for this task, leading to significant improvements in detection performance across different languages.