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This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.

This paper aims to explore the citation aging pattern of Library and Information Science (LIS) and further investigate the impact of interdisciplinary citation on the aging of scientific literature.

The study examines LIS journal articles published between 2016 and 2020. Articles were retrieved from the Web of Science (WoS) and were organized using Scopus's discipline classification system. Citation aging patterns within LIS are described using literature aging indicators. The study examines the effect of interdisciplinary citations on the literature aging pattern by comparing the half-life of LIS literature and the median age of interdisciplinary citations.

The study results show that the citation aging rate of LIS in the last five years has been slow, and the rate of slowing down is decreasing. Interdisciplinary citations are sourced from various disciplines, focusing on computer science, social sciences and business. The proportion of self-citations is declining. The Reference Diversity Index (RDI) increases from 0.690 to 0.724 between 2016 and 2020. Currently, the median age of interdisciplinary citations is higher than the LIS's half-life. It has a diminishing effect on the citation aging rate. But the median age of interdisciplinary citations is decreasing. The interdisciplinary citation may contribute to the literature aging rate in the future. The effect of interdisciplinary citation on literature aging needs to be judged dialectically.

This study still has some limitations. Due to the wide variety of citation journals in LIS, there is no database to cover all journals, so it is impossible to match all citation journals with disciplines. Therefore, it is still feasible to analyze interdisciplinary citations based on the two-eight principle for large-scale data. This approach necessarily sacrifices some of the precision of the study. However, the results of this paper can still be helpful for the development of the discipline. In addition, LIS is a discipline with solid cross-cutting properties, and this paper concludes only with this interdisciplinary discipline in mind. It is necessary to test the applicability of the findings to other disciplines.

The study explores the impact of interdisciplinary citation on literature aging from a professional communication perspective. The results reveal underlying reasons for the aging of scientific literature. These findings further enrich the study of the effect of interdisciplinary communication.

Social mediametrics is a subfield of measurement in which the emphasis is placed on social media data. This paper analyzes the trends and patterns of paper comprehensively mentions on Twitter, with a particular focus on Twitter's mention behaviors. It uncovers the dissemination patterns and impact of academic literature on social media. The research has significant theoretical and practical implications.

This paper explores the fundamental attributes of Twitter mentions by means of analyzing 9,476 pieces of scholarly literature (5,097 from Nature and 4,379 from Science), 1,474,898 tweets and 451,567 user information collected from Altmetric.com database and Twitter API. The study uncovers assorted Twitter mention characteristics, mention behavior patterns and data accumulation patterns.

The findings illustrate that the top academic journals on Twitter have a wider range of coverage and display similar distribution patterns to other academic communication platforms. A large number of mentioners remain unidentified, and the distribution of follower counts among the mention users exhibits a significant Pareto effect, indicating a small group of highly influential users who generate numerous mentions. Furthermore, the proportion of sharing and exchange mentions positively correlates with the number of user followers, while the incidence of supportive mentions has a negative correlation. In terms of country-specific mention behavior, Thai scholars tend to utilize supportive mentions more frequently, whereas Korean scholars prefer sharing mentions over communicating mentions. The cumulative pattern of Twitter mentions suggests that these occur before official publication, with a half-life of 6.02 days and a considerable reduction in the number of mentions is observed on the seventh day after publication.

Conducting a multi-dimensional and systematic analysis of Twitter mentions of scholarly articles can aid in comprehending and utilizing social media communication patterns. This analysis can uncover literature's distribution patterns, dissemination effects and social significance in social media.

The constitutive models determine the mechanical response to the defined loading based on model parameters. In this paper, the inverse problem is researched, i.e. the identification of the model parameters based on the loading and responses of the material. The conventional methods for determining the parameters of constitutive models often demand significant computational time or extensive model knowledge for manual calibration. The aim of this paper is to introduce an alternative method, based on artificial neural networks, for determining the parameters of a viscoplastic model.

An artificial neural network was proposed to determine nine material parameters of a viscoplastic model using data from three half-life hysteresis loops. The proposed network was used to determine the material parameters from uniaxial low-cycle fatigue experimental data of an aluminium alloy obtained at elevated temperatures and three different mechanical strain rates.

A reasonable correlation between experimental and numerical data was achieved using the determined material parameters.

This paper fulfils a need to research alternative methods of identifying material parameters.

This research paper aims to analyze the stock exchanges of developed, emerging and developing countries to investigate the volatility in stock markets and to evaluate the rate of mean reversion.

The stock exchanges included in the research are NASDAQ, Tokyo stock exchange, Shanghai stock exchange, Bombay stock exchange, Karachi stock exchange and Jakarta stock exchange. Secondary daily data from Bloomberg are used to conduct the research for the period from January 2011 to December 2018. Generalized autoregressive conditional heteroskedasticity (GARCH) (1,1) model was applied to examine volatility and the half-life formula was used to calculate mean reversion in days.

The research concluded that all the stock exchanges included in the research satisfy the assumptions of mean reversion. Developing countries have the lowest volatility while emerging countries have the highest volatility which means that the rate of mean reversion is fastest in developing countries and slowest in emerging countries.

Future studies can determine the reasons for fastest rate of mean reversion in developing countries and slowest rate of mean reversion in emerging countries.

Developing countries show the lowest mean reversion in days while the emerging countries show the highest mean reversion in days indicating that developing countries take less time to revert to their mean position.

The majority of previous studies on univariate volatility models are mostly on applications of the models. Only a few researchers have taken the robustness of the models into account when applying them in emerging countries and not in developed, developing and emerging countries in one place. This makes the current study unique and more rigorous.

In the textile dyeing industry, the foam dyeing has been recognized as a significantly sustainable alternative for the cotton fabrics. However, this efficient technology undergoes the many issues related to the foam generation, foam optimization and the required performance of the resultant fabrics. The purpose of this paper is to address these issues through the development and optimization of the novel reactive foam dyeing recipes for the cotton fabrics.

The foam dyeing recipes were generated and optimized using the different stabilizers, foaming agents and three primary colors of reactive dyes. The different recipes were applied onto the cotton fabric using laboratory scale foam coating machine. The performance of the foam coated and padded fabrics was evaluated using different criteria including the shade depth, rubbing fastness, air permeability, washing fastness, perspiration fastness, light fastness and tear strength. Then, a complex decision-making approach, namely, analytic hierarchy process (AHP), was applied for the ranking of the key recipes based on the main criteria.

The newly optimized foam dyeing recipes were found very competitive with the conventional pad dyeing process with respect to the shade-depth and the other performance properties. The optimization of foaming parameters and addition of stabilizers have advanced the foam dyeing process, which would accelerate the implementation of foam dyeing methods in the textile industry. Furthermore, significant water and energy savings would be achieved as compared to the conventional foam dyeing. AHP model offered a comprehensive and rational way to identify the most important recipes amongst the selected recipes.

In this research, novel foam dyeing recipes have been developed for the cotton fabrics through the optimization of the different stabilizers, foaming agents and the three primary colors of reactive dyes. Until now, the exiting literature has not reported the combination of these stabilizers with the different foaming agents and three primary reactive dyes for the improvement of sustainable foam cotton dyeing process.

Polyethylene terephthalate (PET) as a fiber molding polymer is widely used in aerospace, electrical and electronic, clothing and other fields. The purpose of this study is to improve the thermal insulation performance of polyethylene terephthalate (PET), the SiO₂ aerogel/PET composites slices and fibers were prepared, and the effects of the SiO₂ aerogel on the morphology, structure, crystallization property and thermal conductivity of the SiO₂ aerogel/PET composites slices and their fibers were systematically investigated.

The mass ratio of purified terephthalic acid and ethylene glycol was selected as 1:1.5, which was premixed with Sb₂O₃ and the corresponding mass of SiO₂ aerogel, and SiO₂ aerogel/PET composites were prepared by direct esterification and in-situ polymerization. The SiO₂ aerogel/PET composite fibers were prepared by melt-spinning method.

The results showed that the SiO₂ aerogel was uniformly dispersed in the PET matrix. The thermal insulation coefficient of PET was significantly reduced by the addition of SiO₂ aerogel, and the thermal conductivity of the 1.0 Wt.% SiO₂ aerogel/PET composites was reduced by 75.74 mW/(m · K) compared to the pure PET. The thermal conductivity of the 0.8 Wt.% SiO₂ aerogel/PET composite fiber was reduced by 46.06% compared to the pure PET fiber. The crystallinity and flame-retardant coefficient of the SiO₂ aerogel/PET composite fibers showed an increasing trend with the addition of SiO₂ aerogel.

The SiO₂ aerogel/PET composite slices and their fibers have good thermal insulation properties and exhibit good potential for application in the field of thermal insulation, such as warm clothes. In today’s society where the energy crisis is becoming increasingly serious, improving the thermal insulation performance of PET to reduce energy loss will be of great significance to alleviate the energy crisis.

In this study, SiO₂ aerogel/PET composite slices and their fibers were prepared by an in situ polymerization process, which solved the problem of difficult dispersion of nanoparticles in the matrix and the thermal conductivity of PET significantly reduced.

We expand the recent literature on the dynamics of capital structure decisions by investigating the impact of national culture on firms' optimal debt ratios and their dynamic re-adjustment process. To this end, we aim at estimating firm-specific speeds of leverage adjustment, allowing for heterogeneous dynamics in firms' capital structure.

We use dynamic panel data estimators to analyze the impact of cultural factors on the dynamics of debt ratios.

We show that national culture affects the optimal level of leverage and the dynamic rebalancing of debt ratios, both directly and indirectly, by altering the effect of firm characteristics and macroeconomic factors on firms' financing behavior. Firms converge faster towards the optimal leverage in countries with a stronger attitude to conform with the norm, while they are slower where there is a higher propensity to intellectual autonomy. A higher risk aversion and long-run propensity induce over-levered firms to reduce leverage faster, making the adjustment process strongly asymmetric. Moreover, national culture also produces indirect effects by mitigating the impact of asymmetric information on capital structure decisions. Indeed, firms in more individualistic countries display a lower speed of adjustment and a stronger effect of firm characteristics associated with higher agency costs. On the contrary, firms in countries with a higher tendency to conform to social norms, less individualistic and more long-term oriented have a higher adjustment speed and appear to suffer less from agency issues. Our results therefore highlight how national culture affects agency problems within firms, thus suggesting the adoption of country-specific corporate governance provisions accounting for the effects of local cultural traits on managers' behavior.

We expand the capital structure and governance literature by showing how cultural traits impact on the dynamics of debt ratios. In particular, we show how cultural traits may mitigate or exacerbate the role of agency issues on firms' behavior, hence suggesting that cultural factors may interact with governance rules in shaping firms' decisions. Therefore, our work highlights how policy-makers should include cultural aspects when defining regulation concerning corporate governance.

The device, amplifies and sub-regionally transmits the current generated by the body temperature thermoelectric generator through a smart body temperature sensor.

The present study designs a wearable smart device regarding the relationship between temperature and emotion.

Experimental results show that the device can accurately detect changes in human body temperature under hilarious, fearful, soothing and angry emotions, so as to achieve changes in clothing colors, namely blue, red, green and brown.

Different areas of clothing produce controllable and intelligent color, so that adult emotions can be understood through changes in clothing colors, which is conducive to judging their moods and promoting social interaction.

We examine the twin deficits and the direction of its movement for the case of Bolivia, a natural resource-dependent country, using the database of (Kehoe et al., 2019) from 1960 to 2019.

We combine a structural vector autoregression (SVAR) model with a dynamic stochastic general equilibrium (DSGE) model to understand the transmission mechanisms.

Our results suggest the existence of twin deficits in Bolivia; however, causality in the Mundell-Fleming sense does not hold. While fiscal policy shocks explain current account deficits, current account shocks have a stronger effect over fiscal deficit. In fact, only 23% of the variance of current account forecast errors is explained by fiscal policy shocks; in contrast, 45% of the variance of the fiscal deficit is explained by current account shocks.

The study is for a specific case, which is a limitation; however, other country samples can be included.

Based on the results of the work, policies can be recommended and designed to cushion the effects of external shocks.

According to the literature available for the Bolivian case, our work constitutes a significant contribution and, therefore, is original for this specific case.