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This study aims to investigate the impact of regular Kung Fu and Taekwondo practice on the health and quality of life among elderly individuals in the Maule region, Chile.

The authors designed a 12-week Kung Fu and Taekwondo workshop with activities suitable for their age. Through semistructured interviews (at the beginning and the end of the workshop), along with periodic monitoring of vital signs and cardiovascular components, the authors observed an improvement in participants’ physical (strength, speed of reaction and flexibility) and psychological conditions (self-esteem and resilience), quality of life (relationships with family and friends and ability to deal with stressful events in working life) and health (waist circumference, percentage of oxygen saturation in blood, blood pressure, among other values).

From these results, the authors affirm that this workshop improves health and physical condition and helps the participants develop the coping capacity to deal with stressful situations and complicated interpersonal relationships. In this sense, the authors conclude that Kung Fu and Taekwondo as regular sports activities can benefit senior citizens’ aging process.

This research is based on an original study project.

Proenvironmental hotels and hotels with green initiatives are emerging as a method to address environmental issues and respond to tourists’ environmental concerns. To better understand what can encourage reservations in proenvironmental hotels, this study aims to investigate the connection between the performing arts watching experience and the preference for such a hotel.

Five scenario-based experiments were conducted. A total of 1,024 participants for the five studies were recruited with the help of Credamo, a commonly used Chinese data collection platform.

The results indicated that viewing performing arts could increase tourists’ preferences for proenvironmental hotels. This phenomenon occurred due to the fact that performing arts watching experience can induce a psychological state of self-transcendence in individuals, which, in turn, can raise their levels of altruism, and ultimately lead to proenvironmental hotel choices. This effect will not occur, however, when people watch performing arts with either an extrinsic motivation or in an analytical state.

The findings of this study provide hotel managers with a novel approach to market the proenvironmental attributes of their hotels and to promote tourists’ proenvironmental behaviors.

This study proposes performing arts viewing experiences as a novel way to encourage proenvironmental hotel choice. To the best of the authors’ knowledge, this is the first study to explore the impact of the performing arts watching experience on tourist behavior.

Building information modeling (BIM) is a striking development in the architecture, engineering and construction (AEC) industry, which provides in-depth information on different stages of the building lifecycle. Real estate valuation, as a fully interconnected field with the AEC industry, can benefit from 3D technical achievements in BIM technologies. Some studies have attempted to use BIM for real estate valuation procedures. However, there is still a limited understanding of appropriate mechanisms to utilize BIM for valuation purposes and the consequent impact that BIM can have on decreasing the existing uncertainties in the valuation methods. Therefore, the paper aims to analyze the literature on BIM for real estate valuation practices.

This paper presents a systematic review to analyze existing utilizations of BIM for real estate valuation practices, discovers the challenges, limitations and gaps of the current applications and presents potential domains for future investigations. Research was conducted on the Web of Science, Scopus and Google Scholar databases to find relevant references that could contribute to the study. A total of 52 publications including journal papers, conference papers and proceedings, book chapters and PhD and master's theses were identified and thoroughly reviewed. There was no limitation on the starting date of research, but the end date was May 2022.

Four domains of application have been identified: (1) developing machine learning-based valuation models using the variables that could directly be captured through BIM and industry foundation classes (IFC) data instances of building objects and their attributes; (2) evaluating the capacity of 3D factors extractable from BIM and 3D GIS in increasing the accuracy of existing valuation models; (3) employing BIM for accurate estimation of components of cost approach-based valuation practices; and (4) extraction of useful visual features for real estate valuation from BIM representations instead of 2D images through deep learning and computer vision.

This paper contributes to research efforts on utilization of 3D modeling in real estate valuation practices. In this regard, this paper presents a broad overview of the current applications of BIM for valuation procedures and provides potential ways forward for future investigations.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.

This study aims to evaluate the thermal performance of sodium alginate (SA) aerogel attached to nano SiO₂ and its radiative cooling effect on firefighting clothing without environmental pollution.

SA/SiO₂ aerogel with refractory heat insulation and enhanced radiative cooling performance was fabricated by freeze-drying method, which can be used in firefighting clothing. The microstructure, chemical composition, thermal stability, and thermal emissivity were analyzed using Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analyzer and infrared emissivity measurement instrument. The radiative cooling effect of aerogel was studied using thermal infrared imager and thermocouple.

When the addition of SiO₂ is 25% of SA, the prepared aerogel has excellent heat insulation and a high radiative cooling effect. Under a clear sky, the temperature of SA/SiO₂ aerogel is 9.4°C lower than that of pure SA aerogel and 22.1°C lower than that of the simulated environment. In addition, aerogel has more exceptional heat insulation effect than other common fabrics in the heat insulation performance test.

SA/SiO₂ aerogel has passive radiative cooling function, which can efficaciously economize global energy, and it is paramount to environment-friendly cooling.

This method could pave the way for high-performance cooling materials designed for firefighting clothing to keep maintain the wearing comfort of firefighters.

SA/SiO₂ aerogel used in firefighting clothing can release heat to the low-temperature outer space in the form of thermal radiation to achieve its own cooling purpose, without additional energy supply.

Using a sample of manufacturing firms listed in China between 2007 and 2019, first, this paper aims to examine whether peer firms influence corporate trade credit supply. Next, the authors examine the channels through which peer firms influence corporate trade credit supply by testing the predictions of rivalry and information theories. Furthermore, the authors examine the heterogeneity of the industry peer effect on corporate trade credit supply. Finally, the authors examine the economic consequences of the industry peer effect on corporate trade credit supply.

The sample includes all manufacturing firms listed on both the Shanghai and Shenzhen securities exchanges for the sample period from 2007 to 2019, and the data come from the China Stock Market & Accounting Research database. The authors use the fixed effects method to examine the industry peer effect on trade credit supply. The results are robust to a series of robustness tests. To address the potential endogeneity problem, the authors adopt appropriate instruments by estimating instrumental variable models (two-stage least square). The authors use Heckman’s two-stage model to mitigate the sample selection bias.

The authors provide strong empirical evidence showing that the industry peer effect on trade credit supply exists in the manufacturing sector. It is also found that both competitive rivalry-based and information-based theories can provide explanations of the industry peer effect on trade credit supply. This process is both active imitation and passive reaction. Additional analysis suggests that the industry peer effect on trade credit supply is more pronounced for state-owned firms, firms with low customer concentration and firms with high geographical proximity. The amplification effect and spillover effect are the economic consequences of the industry peer effect on trade credit supply. In other words, the trade credit supply based on peer effect will not only increase the liquidity risk of the firm per se but also induce and increase the liquidity risk of the industry.

The study makes some important contributions. First, the authors find robust evidence that peer firms’ trade credit supply is an important factor in explaining corporate trade credit supply, which extends the literature by connecting the firm’s trade credit supply with the peer effect. Second, the study provides a new micro-perspective for understanding that firms use trade credit supply as a tool of competition, which proves the importance of rivals’ decision-making as a determinant of corporate decisions. Third, the authors examine the industry peer effect on trade credit supply, which not only helps to guide firms to pay more attention to the potential risk and spillover effects of the trade credit supply decision-making relevance but also helps to clarify the industry interaction phenomenon of corporate decision-making behavior. It is an important practical significance to play a role as a bridge between the microlevel of the firm and the meso-level of the industry. Finally, the study provides inspiration for the formulation of industry norms and policies.

The authors propose to estimate a varying coefficient panel data model with different smoothing variables and fixed effects using a two-step approach. The pilot step estimates the varying coefficients by a series method. We then use the pilot estimates to perform a one-step backfitting through local linear kernel smoothing, which is shown to be oracle efficient in the sense of being asymptotically equivalent to the estimate knowing the other components of the varying coefficients. In both steps, the authors remove the fixed effects through properly constructed weights. The authors obtain the asymptotic properties of both the pilot and efficient estimators. The Monte Carlo simulations show that the proposed estimator performs well. The authors illustrate their applicability by estimating a varying coefficient production frontier using a panel data, without assuming distributions of the efficiency and error terms.

The study aims to draw on the self-congruity theory to investigate the relationship among destination personality (DP), self-congruity and tourists’ pro-environmental behavioral intention (BI) among Guangzhou citizens who have experienced nature-based tourism (NBT).

The survey-based quantitative research was divided into two rounds, namely, a preliminary study exploring the dimensions of DP and the verification of whether the DP dimensions that significantly affect pro-environmental BI in step one would be selected for the main research to validate the conceptual model.

The results suggest that wholesome, one of the destination personalities, strongly predicts tourists’ pro-environmental BI, while actual self-congruity plays a mediating role between sincere, another DP, and tourists’ pro-environmental BI.

In practice, it offers multidimensional knowledge and robust evidence-based recommendations for the sustainable development and destination branding of NBT destinations in the post-epidemic era.

The study presents pioneering work that reveals previously underestimated factors influencing pro-environmental BI.

The purpose of this study is to predict the thermal protective performance (TPP) of flame-retardant fabric more economically using machine learning and analyze the factors affecting the TPP using model visualization.

A total of 13 machine learning models were trained by collecting 414 datasets of typical flame-retardant fabric from current literature. The optimal performance model was used for feature importance ranking and correlation variable analysis through model visualization.

Five models with better performance were screened, all of which showed R2 greater than 0.96 and root mean squared error less than 3.0. Heat map results revealed that the TPP of fabrics differed significantly under different types of thermal exposure. The effect of fabric weight was more apparent in the flame or low thermal radiation environment. The increase in fabric weight, fabric thickness, air gap width and relative humidity of the air gap improved the TPP of the fabric.

The findings suggested that the visual analysis method of machine learning can intuitively understand the change trend and range of second-degree burn time under the influence of multiple variables. The established models can be used to predict the TPP of fabrics, providing a reference for researchers to carry out relevant research.

The findings of this study contribute directional insights for optimizing the structure of thermal protective clothing, and introduce innovative perspectives and methodologies for advancing heat transfer modeling in thermal protective clothing.

In the context of investment decisions, the intricate interplay between exogenous shocks and their influence on investor confidence significantly shapes their behaviors and, consequently, their outcomes. Investment decisions are influenced by uncertainties, exogenous shocks as well as the sentiments and confidence of investors, factors typically overlooked by decision-makers. This study will meticulously examine these multifaceted influences and discern their intricate hierarchical nuances in the sentiments of industrial entrepreneurs during the COVID-19 pandemic.

Employing the robust framework of the generalized linear latent and mixed models (GLLAMM), this research will thoroughly investigate individual and group idiosyncrasies present in diverse data compilations. Additionally, it will delve deeply into the exogeneity of disturbances across different sectors and regions.

Relevant insights gleaned from this research elucidate the adverse influence of exogenous forces, including pandemics and financial crises, on the confidence of industrial entrepreneurs. Furthermore, a significant discovery emerges in the regional analysis, revealing a notable homogeneity in the propagation patterns of industrial entrepreneurs' perceptions within the sectoral and regional context. This finding suggests a mitigation of regional effects in situations of global exogenous shocks.

Within the realm of academic inquiry, this study offers an innovative perspective in unveiling the intricate interaction between external shocks and their significant impacts on the sentiment of industrial entrepreneurs. Furthermore, the utilization of the robust GLLAMM captures the hierarchical dimension of this relationship, enhancing the precision of analyses. This approach provides a significant impetus for data-informed strategic directions.