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This paper aims to propose a method for automatically labelling an affective lexicon with intensity values by using the WordNet Similarity (WS) software package with the purpose of improving the results of an affective analysis process, which is relevant to interpreting the textual information that is available in social networks. The hypothesis states that it is possible to improve affective analysis by using a lexicon that is enriched with the intensity values obtained from similarity metrics. Encouraging results were obtained when an affective analysis based on a labelled lexicon was compared with that based on another lexicon without intensity values.

The authors propose a method for the automatic extraction of the affective intensity values of words using the similarity metrics implemented in WS. First, the intensity values were calculated for words having an affective root in WordNet. Then, to evaluate the effectiveness of the proposal, the results of the affective analysis based on a labelled lexicon were compared to the results of an analysis with and without affective intensity values.

The main contribution of this research is a method for the automatic extraction of the intensity values of affective words used to enrich a lexicon compared with the manual labelling process. The results obtained from the affective analysis with the new lexicon are encouraging, as they provide a better performance than those achieved using a lexicon without affective intensity values.

Given the restrictions for calculating the similarity between two words, the lexicon labelled with intensity values is a subset of the original lexicon, which means that a large proportion of the words in the corpus are not labelled in the new lexicon.

The practical implications of this work include providing tools to improve the analysis of the feelings of the users of social networks. In particular, it is of interest to provide an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyberbullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children.

This work is interested in providing an affective lexicon that improves attempts to solve the problems of a digital society, such as the detection of cyberbullying. In this case, by achieving greater precision in the detection of emotions, it is possible to detect the roles of participants in a situation of cyber bullying, for example, the bully and victim. Other problems in which the application of affective lexicons is of importance are the detection of aggressiveness against women or gender violence or the detection of depressive states in young people and children.

The originality of the research lies in the proposed method for automatically labelling the words of an affective lexicon with intensity values by using WS. To date, a lexicon labelled with intensity values has been constructed using the opinions of experts, but that method is more expensive and requires more time than other existing methods. On the other hand, the new method developed herein is applicable to larger lexicons, requires less time and facilitates automatic updating.

In the recent international scenario, the rise of emerging economies, in particular, Brazil, Russia, India, China and South Africa (BRICS) has gained ample of attention. The global trade flows of the BRICS countries have significantly increased during the last one-and-a-half decade. The purpose of this paper is to examine the intra-BRICS and BRICS–EU trade flows.

To study the intensity of trade among BRICS countries and with EU, the Trade Intensity Index is employed for the period 2001–2015. Balassa’s revealed comparative advantage (RCA) index is computed for the assessment of comparative advantages of exports by BRICS countries in the year 2015 in the global markets. A comparative analysis of export similarity is done for India and other BRICS countries in EU.

The findings of trade intensity showed large bilateral trade flows among BRICS member. Russia has emerged as the main trading partner with EU in BRICS. For the year 2015, the comparative study of RCA at HS-two digits and HS-four digits classification highlights marginal structural changes in the export composition of these countries. The analysis revealed that Brazil and Russia have comparative advantages in natural resource-based products, while India and China possessed comparative advantages in manufactured and processed products. The export similarity index shows the presence of competition between India and China in EU.

This paper highlights the need for closer cooperation to promote intra-BRICS trade and to make structural transformations in the basket of trading products by them to have trade benefits at large.

Numerous studies are available on bilateral trade of BRICS members. However, limited studies are available to get a holistic view of intra-BRICS trade. This paper is an attempt to examine the BRICS countries trade profile both at global levels and within the group.

The energy efficiency policy is a priority component of the overall economic policy of different countries striving to ensure the competitiveness and sustainability of national economic development. The improvement of energy efficiency represents an important economic task for the post-Soviet countries, characterized by excessive energy intensity of the economy, and the solution of this task requires proper information and analytical support: a system for accounting and analyzing energy consumption indicators. The present research is aimed at developing the tools to support decision-making in the sphere of evaluation and estimation of performance of the State energy efficiency policy of territories and testing these tools on the example of Russian regions. The study has been carried out using the methods of statistics, economic, mathematical and econometric modeling, structural, dynamic and comparative analyses. The following tools have been proposed: the method for differentiated accounting of various factors’ influence on the dynamics of energy consumption in the regions and for estimating the index of technological efficiency of electricity consumption; the method for the empirical classification of territories by types of their energy and economic development. We’ve revealed the general trend and typological features in the dynamics of electricity consumption efficiency indicators in the constituent entities of the Russian Federation and carried out the decomposition factor and comparative analysis of energy consumption patterns of the Volgograd region over 2005–2014 on the basis of the proposed tools.

The pressure exerted on the body by clothes is one important factor affecting the comfort of clothing, it is an effective method to evaluate pressure comfort by physiology and psychology. The purpose of this paper is to measure, electroencephalography (EEG), an index of brain activity in order to examine the effect on brain activity conditions caused by oppression exerted by clothing on the body.

EEG power spectrum analysis was conduct to verify the electrophysiological characteristic of brain caused by pressure on the body by girdle.

Experimental results showed that the intensity of α waves in the pressure condition is decreased compared to the non-pressure condition, and the somatosensory activated by pressure of girdle mainly in occipital, frontal and parietal region of brain.

It was clarified that it is impossible to evaluate the clothes pressure by physiological technique of EEG, this study has enriched methods of evaluation pressure comfort.

This paper aims to clarify the relationship between foreign direct investment (FDI) and carbon intensity. This study uses the dynamic panel data model to study and provide fresh evidence for the issue.

This study first uses the dynamic panel data model to consider the endogeneity problem, and applies a system-generalized method of moments estimator to study the effect of FDI on carbon intensity using the panel data of 188 countries during 1990-2013.

The result shows that FDI has a significant negative impact on carbon intensity of the host country. After considering the other factors, including share of fossil fuels, industrial intensity, urbanization level and trade openness, the impact of FDI on carbon intensity is still significantly positive. In addition, FDI also has a significant negative impact on carbon intensity of high-income countries and middle- and low-income countries.

This paper offers two contributions to the literature on the effect of FDI on carbon intensity. From a methodological perspective, this paper is the first to apply a dynamic panel data model to study the effect of FDI on carbon intensity using worldwide panel data. Second, this paper is the first to analyze the effect of FDI on carbon intensity in different countries with different income levels separately.

The purpose of this paper is to provide an input-output life cycle assessment model to estimate the carbon footprint of US manufacturing sectors. To achieve this, the paper sets out the following objectives: develop a time series carbon footprint estimation model for US manufacturing sectors; analyze the annual and cumulative carbon footprint; analyze and identify the most carbon emitting and carbon intensive manufacturing industries in the last four decades; and analyze the supply chains of US manufacturing industries to help identify the most critical carbon emitting industries.

Initially, the economic input-output tables of US economy and carbon footprint multipliers were collected from EORA database (Lenzen et al., 2012). Then, economic input-output life cycle assessment models were developed to quantify the carbon footprint extents of the US manufacturing sectors between 1970 and 2011. The carbon footprint is assessed in metric tons of CO₂-equivalent, whereas the economic outputs were measured in million dollar economic activity.

The salient finding of this paper is that the carbon footprint stock has been increasing substantially over the last four decades. The steep growth in economic output unfortunately over-shadowed the potential benefits that were obtained from lower CO₂ intensities. Analysis of specific industry results indicate that the top five manufacturing sectors based on total carbon footprint share are “petroleum refineries,” “Animal (except poultry) slaughtering, rendering, and processing,” “Other basic organic chemical manufacturing,” “Motor vehicle parts manufacturing,” and “Iron and steel mills and ferroalloy manufacturing.”

This paper proposes a state-of-art time series input-output-based carbon footprint assessment for the US manufacturing industries considering direct (onsite) and indirect (supply chain) impacts. In addition, the paper provides carbon intensity and carbon stock variables that are assessed over time for each of the US manufacturing industries from a supply chain footprint perspective.

The purpose of this paper is to investigate which sectors are more vulnerable to human capital depreciation, with an emphasis on potential differences in skills and in ICT intensities.

The authors estimate an extended Mincerian earnings equation based on Neuman and Weiss’s (1995) model using the EU-KLEMS international database for 15 sectors for the period from 1980 to 2005. The authors also test structural ruptures in earnings and human capital depreciation in the labor market per decade controlling by technological intensity.

Human capital depreciation ranges from 1 to 6 percent. It is mainly significant in skill-intensive sectors regardless of the sector’s technological intensity. The analysis of structural breaks shows that human capital value indeed changed from decade to decade. It even appreciated in low skill-intensive sectors in the 1980s and in the high skill-intensive during the 1990s. Appreciation though, was mainly skill-biased.

Information about on-the-job-training and non-cognitive skills that can also affect human capital depreciation are not included due to lack of data.

To prevent human capital from depreciating in particular sectors and periods educational systems should provide the tools for ongoing lifelong learning at all skills levels. Education is subject to dynamic effects that should be addressed to increase the potential benefits of technological change.

First, instead of using cross-section analysis which is considered to be a pitfall in studying the depreciation of knowledge, the authors observe its dynamic on a longitudinal basis. Second, the international macro-sectoral approach goes beyond limited micro-sectoral analysis in certain countries.

The purpose of this paper is to propose a new inspecting algorithm for defect detection on PCB circuits.

PCB circuit images were processed by a radon transformation. A Radon histogram was formed and utilized to establish a texture directional characteristic similarity function. Then, a region of the image which contained the same texture directionality feature was segmented. Furthermore, a directionality estimation method is presented. As the circuit was damaged, the directionality was weakened correspondingly. According to principle, the concept of directional intensity was proposed and then used to measure directionality through analysis of the Radon histogram fluctuation. Finally, the defect was detected based on directional intensity.

The method has been applied to an inspecting system used in practice and it achieved a higher accuracy and efficiency in comparison with similar methods.

Although work on highly intensive PCB circuitry inspection and flaw detection is presented, defect classification was not involved although this is also a very important requirement of inspection.

The paper provides a new way to detect PCB circuitry defects based on texture directionality and proposes evaluating the similarity between image texture directionalities using a radon transformation to search the inspected area. As the inspected region was located, the concept of directional intensity was defined to measure texture directionality to identify defects. The new algorithm performs stably and efficiently and is fit for practical application.

The purpose of this paper is to evaluate a shiitake mushroom extract as a potential natural taste enhancer in low-sodium beef burgers by means of sensorial and physico-chemical assays because nowadays there is a trend in the market for the development of clean-label products.

Ten formulations of beef burgers were developed, varying in the percentage reduction in NaCl (0-75 per cent) and mushroom water extracts (obtained from a 5, 12.5, or 20 per cent mushroom homogenate). Sensory characterisation was performed by time-intensity (TI) and acceptance tests. In addition, physico-chemical analyses (pH, yield, shrinkage, shear force, and colour) were conducted.

Extracts obtained from 5, 12.5, or 20 per cent mushroom homogenate (E1, E2, and E3, respectively) did not enhance the salty taste in formulations with a 0 or 75 per cent reduction in NaCl. In formulations with a 50 per cent reduction in NaCl, all the extracts enhanced salinity perception, with E3 being the most effective. E3 also increased acceptance of colour, aroma, texture, flavour, and overall perception, although it caused changes in some physico-chemical characteristics (pH, yield, shrinkage, shear force, and colour).

The shiitake mushroom extract is a natural ingredient with a potential to serve as a taste enhancer in meat and other food products, for the purpose of reducing sodium content without compromising sensory acceptability. Therefore, this extract will enable the development of healthier products (owing to a reduction in sodium) with preserved sensory quality and will meet consumers’ requirements for the minimal use of chemical additives in food.

This paper aims to introduce a compact and low-cost robotized system and corresponding processing method for automatically identifying and de-stacking circulation boxes under natural stacking status.

The whole system is composed of an industrial robot, a laser scanner and a computer. Automated de-stacking requires comprehensive and accurate status information of each box. To achieve this goal, the robot carries the laser scanner to perform linear scanning to describe a full depth image for the whole working area. Gaussian filter is applied to the image histogram to suppress the undesired noise. Draining and flooding process derived from classic algorithm identifies each box region from an intensity image. After parameters calculation and calibration, the grasping strategy is estimated and transferred to the robot to finish the de-stacking task.

Currently, without pre-defined stack status, there is still manual operated alignment in stacking process in order to enable automatic de-stacking using robot. Complicated multi-sensor system such as video cameras can recognize the stack status but also brings high-cost and poor adaptability. It is meaningful to research on the efficient and low-cost measurement system as well as corresponding common data processing method.

This research presents an efficient solution to automated de-stacking task and only tests for three columns stack depending on the actual working condition. It still needs to be developed and tested for more situations.

Utilizing only single laser scanner to measure box status instead of multi-sensor is novel and identification method in research can be suitable for different box types and sizes.