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The purpose of this paper is to determine the effects of clothing ease and body postures on the size and distribution of the air gap as well as the body coverage with the clothing.

Visual and quantitative analyses were conducted using a 3D body scanner and Geomagic Software. The air gap size and clothing area factor (f_cl) in three test coverall and seven selected postures were calculated and compared.

The results indicated that both the clothing ease and body postures had a strong effect on the air gap and clothing coverage, especially the more complex the postures, the wider the range of influence. Nevertheless, these effects varied over body regions, being stronger at the lower body than the upper body. The air gap size at the left side of the body was generally larger than the right side. It was also found that the clothing coverage was linearly correlated with the air gap size and could be employed as an indicator to evaluate clothing protective capabilities.

The findings suggested that greater attention should be paid to the protection and flexibility at the lower body and asymmetrical distribution of the air gap should be considered in the future air gap modeling.

The outcomes provided useful information to improve the protective clothing and develop more realistic air gap models to simulate the heat and mass transfer.

The purpose of this paper is to investigate the status quo of digital poverty among adolescents in China, analyze the characteristics and the causes, then propose countermeasures to provide reference for alleviating digital poverty among adolescents.

The study developed an initial scale of digital poverty among adolescents and used survey data to revise the scale, on this basis, formed a questionnaire, which was distributed to nationwide adolescents. The study developed its findings from the 837 valid questionnaire respondents.

The digital poverty among adolescents is mainly shown in the poverty of digital ability, digital psychology and digital environment and presents the following characteristics, that is, insufficient information seeking ability and information selection ability needing to be improved; equipped with basic information awareness but lack of information evaluation ability; lack of patience in obtaining information and inclined to the principle of least effort; imperfect knowledge structure and immature psychological emotions and vulnerable to external interference; having a certain relationship with the information environment, but not significantly affected by regional economic differences. Finally, the study puts forward countermeasures to alleviate digital poverty among adolescents.

Understanding of the digital poverty among adolescents will likely demand rethinking into a number of issues ignored by information poverty studies.

Few studies focus on digital poverty among adolescents. This study developed an initial scale of digital poverty among adolescents and revised it by survey data, then conducted an empirical study through questionnaire, which could expand the understanding of information poverty in the field of library and information science.

The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions.

In this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines.

The authors’ proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines.

This paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors’ method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors’ knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.

The purpose of this paper is to present a novel framework for strategic decision making using Big Data Analytics (BDA) methodology.

In this study, two different machine learning algorithms, Random Forest (RF) and Artificial Neural Networks (ANN) are employed to forecast export volumes using an extensive amount of open trade data. The forecasted values are included in the Boston Consulting Group (BCG) Matrix to conduct strategic market analysis.

The proposed methodology is validated using a hypothetical case study of a Chinese company exporting refrigerators and freezers. The results show that the proposed methodology makes accurate trade forecasts and helps to conduct strategic market analysis effectively. Also, the RF performs better than the ANN in terms of forecast accuracy.

This study presents only one case study to test the proposed methodology. In future studies, the validity of the proposed method can be further generalized in different product groups and countries.

In today’s highly competitive business environment, an effective strategic market analysis requires importers or exporters to make better predictions and strategic decisions. Using the proposed BDA based methodology, companies can effectively identify new business opportunities and adjust their strategic decisions accordingly.

This is the first study to present a holistic methodology for strategic market analysis using BDA. The proposed methodology accurately forecasts international trade volumes and facilitates the strategic decision-making process by providing future insights into global markets.

Exchange rate uncertainty leads to an indecisive environment for imports and exports that would condense international trade, foreign direct investment, trade earnings, trade volumes, economic growth and welfare. This study aims to examine, empirically, the effect of exchange rate uncertainty on bilateral trade performance, focusing on eight SAARC member economies using the popular modified gravity model of trade.

The paper includes eight SAARC members – Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan and Sri Lanka panel data set over the period 2005–2018. The authors consider both standardized value (standard deviation) and conditional variance model to determine volatility of exchange rate. Primarily, ordinary least squares, random effects and fixed effects estimation techniques are employed to investigate the impact of exchange rate volatility. Endogeneity and robustness of the findings have been tested using the simultaneity-adjusted model and dynamic panel data two-step system GMM estimation techniques.

Empirical findings endorse the view that exchange rate volatility lowers trade flows in the SAARC regions. However, this adverse effect of exchange rate uncertainty on trade is pretty small. The negative correlation between exchange rate volatility and bilateral trade remains consistent and significant after controlling of simultaneous causality, autocorrelation, year effects, country-pair heterogeneity and endogeneity irrespective of panel data estimation techniques and different measures of volatility.

The present paper is original work.

The purpose of this paper is to develop weights of key performance areas (KPAs) and performance indicators for public private partnerships (PPPs) in Bangladesh. Since a variety of PPP arrangements is observable, different performance measurement approaches exist in the literature. However, analysing the relative importance of indicators influencing the performance score of particular projects using the perspective of developing countries remains unexplored.

The authors’ method involves application of the analytical hierarchy process (AHP) to develop weights for eight KPAs for which 41 contributing performance indicators have been developed. In total, 68 respondents (62 per cent of the PPP practitioners in Bangladesh) participated in a structured questionnaire survey and their judgements have been found to be consistent, using consistency ratios, a geometric consistency index and one-way ANOVA test.

“Feasibility analysis”, “life cycle evaluation and monitoring” and “optimal risk allocation” are the most significant performance indicators in Bangladesh. “Financing” is the most important KPA, followed by “planning and initiation” and “transparency and accountability”. Interestingly, unlike the cost, time and quality measures of the public sector comparator analysis used in most developed countries, a different set of indicators and KPAs are found dominant.

This suggests that performance indicators and their weights may differ for developing countries. Future research could usefully focus on testing this model in different countries and applying it to derive performance scores for individual PPPs.

An application of AHP in determining weights of the performance indicators represents a major contribution to the literature on PPP performance measurement in the developing countries including Bangladesh.

Medical device industry in India is a niche sector with few key players but it possesses huge potential for both domestic and international trade. In recent years, a number of regulatory relaxations have been provided to medical device manufacturers in India to enhance production and further trade especially exports. Though the industry is highly dependent on imports, the purpose of this paper is to identify key medical devices using the revealed comparative advantage, which can be exported from India by identifying new markets.

For the selected medical devices, India’s exports to the world and the newly identified markets are forecasted using the autoregressive integrated moving average model of regression.

It is seen that three major medical devices emerge to be the ones where India has the capacity and potential to manufacture and export. These medical devices are electro-cardiographs, magnetic resonance imaging apparatus and oscilloscopes and oscillographs being exported to the USA, Australia; China and the USA, respectively, which is rising in recent years.

As the forecasted values indicate that there is an increasing potential in exports from India to the world of the selected medical devices, there is an urgent need to develop this industry and enhance exports from India. Very few studies have been carried out to examine and forecast exports from specific sectors or industries which is the need of the hour now.

The paper also provides suggestions to exporters and policymakers on leveraging the future export potential of selected medical devices.