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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 price of a painting is determined by multiple variables, including color-related variables. Colors are important in an economic analysis. This study aims to analyze the paintings executed by Jean-Michel Basquiat (the famous street artist from the 1980s' New York City) and sold at auction to study the potential effect of color intensity, luminosity and color contrast on the prices of his paintings. The authors also study the case of the op art master, Carlos Cruz-Diez, as a robustness analysis to the main results. The analysis that the authors present may be of interest to academicians and to participants in the art market.

The authors run a hedonic regression model considering 306 paintings (executed by Basquiat alone), and 41 works painted collaboratively between Basquiat and Warhol and sold at auction (2003–2017). The data and the images corresponding to each painting were hand-collected from the websites of several auction houses and complemented with information obtained from the Artprice and Blouin websites.

Increases in color intensity, luminosity and color contrast have a positive effect on art prices. The authors also find that color intensity is even more recognized (as reflected by higher prices) for paintings belonging to Basquiat's most appreciated (understood as most expensive) artistic period (1980–1983) and during the second part of the sample period (2011–2017). The authors find similar results for Cruz-Diez. The authors also estimate that Basquiat's artworks made collaboratively with Andy Warhol are worth 55% less than those made only by Basquiat. An investment in Basquiat's paintings generated an average annual compounded return of 16.81% (2003–2017), clearly overperforming the S&P 500.

The main limitation of the hedonic regression method lies in the need to have a significant and varied sample to identify the true effect of each variable on the price of the good. Another limitation is that we are only able to use art prices from auctions, as this is the only comprehensive source of data that is publicly available. These two limitations are common to all studies that use the hedonic pricing model. This paper has implications not only for the art pricing (and more generally, asset pricing) literature, but also for the fields of psychology and marketing.

This is the first paper that highlights the importance of analyzing the price impact of color intensity throughout the artistic periods of a painter, finding that color intensity is even more appreciated (as reflected by higher prices) for paintings belonging to an artist's most recognized period. In the case of color contrast, the authors present a novel way to estimate this variable.

El precio de una pintura está determinado por múltiples variables, incluidas las relacionadas con el color. Los colores son importantes en el análisis económico. En este trabajo, se analizan las obras realizadas por Jean-Michel Basquiat (el famoso artista callejero latino de la escena artística neoyorquina de los años 80) y vendidas en subasta, para estudiar el efecto potential que la intensidad del color, la luminosidad y el contraste de sus pinturas pueden tener sobre sus precios También estudiamos el caso del maestro de op-art, Carlos Cruz-Diez, como análisis de robustez a nuestros principales resultados. El análisis que presentamos puede ser de interés tanto para académicos como para participantes del mercado del arte.

Realizamos un modelo de regresión hedónica considerando 306 obras (pintadas solo por Basquiat), y 41 obras realizadas de manera colaborativa entre Basquiat y Warhol y vendidas en subasta (2003–2017). Los datos y las imágenes correspondientes a cada obra fueron recolectados a mano de los sitios web de varias casas de subastas y complementados con información de los sitios web de Artprice y Blouin.

Incrementos en la intensidad, luminosidad y contraste del color (variable para la que proponemos una nueva medida), inciden positivamente en los precios del arte. También encontramos que la intensidad del color es aún más reconocida (como se refleja en los precios más altos en las subastas) para pinturas pertenecientes al período artístico más apreciado de Basquiat (1980–1983), entendido como el más caro, y durante la segunda parte del período muestral (2011–2017). Además, hallamos resultados similares para Cruz-Diez. De igual forma, estimamos que las obras de arte de Basquiat realizadas en colaboración con Andy Warhol valen un 55% menos que las realizadas solo por Basquiat. Una inversión en las pinturas de Basquiat generó un rendimiento compuesto anual promedio del 16,81% (2003–2017), claramente superando el rendimiento total del índice S&P 500.

La principal limitación del método de regresión hedónica radica en la necesidad de contar con una muestra significativa y variada para identificar el verdadero efecto de cada variable sobre el precio del bien en estudio. Otra limitación consiste en que solo es posible utilizar los precios de las obras de arte que son vendidas en subastas, ya que esta es la única fuente completa de datos que está disponible públicamente. Estas dos limitaciones son comunes a todos los estudios que utilizan el modelo de precios hedónicos. El artículo tiene implicaciones no solo para la literatura sobre precios de arte (y, más en general, sobre los precios de los activos), sino también para la literatura sobre psicología y mercadeo, en donde el estudio del impacto de los colores en las emociones y en el atractivo de un producto son de suma importancia.

Este es el primer estudio que destaca la importancia de analizar el impacto en precios de la intensidad del color a lo largo de los períodos artísticos de un pintor, debido a que se determinó que la intensidad de color es aún más apreciada (como se refleja en los precios más altos en las subastas), para las pinturas pertenecientes al período artístico más reconocido de un artista. En el caso del contraste de color, se propuso y utilizó una forma novedosa de estimar esta variable.

This paper aims to provide empirical evidence on formal stakeholder participation (or “lobbying”) in the early phase of the International Sustainability Standards Board’s (ISSB’s) standard-setting.

Drawing on a rational-choice framework, this paper conducts a content analysis of comment letters (CLs) submitted to the ISSB in response to its first two exposure drafts (published in 2022) to investigate stakeholder participation across different groups and jurisdictional origins. The analyses examine participation in terms of frequency (measured using the number of participating stakeholders) and intensity (measured using the length of CLs).

Preparers and users of sustainability reports emerge as the largest participating stakeholder groups, while the accounting/sustainability profession participates with high average intensity. Surprisingly, preparers do not outweigh users in terms of participation frequency and intensity; and large preparers outweigh smaller ones in terms of participation intensity but not participation frequency. Internationally, stakeholders from countries with a private financial accounting standard-setting system participate more frequently and intensively than others. In addition, country-level economic wealth and sustainability performance are positively associated with more participating stakeholders.

This study is of interest for organizations and stakeholders involved in or affected by standard-setting in the field of sustainability reporting. The finding of limited participation by investors and from developing countries suggests the ISSB take actions to enhance the voice of those stakeholders.

The imbalances in stakeholder participation that were found pose potential threats to an important aspect of the input legitimacy of the ISSB’s standard-setting process.

To the best of the authors’ knowledge, this paper is the first to explore stakeholder participation by means of CLs with the ISSB in terms of frequency and intensity.

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.

This paper aimed to examine the relationship between green logistics management (GLM) practices and export intensity of Thai SMEs in food and drinks manufacturing. The research involved carrying out in-depth interviews with ten experts to shed light on factors of GLM practices. The authors used a questionnaire to survey the data amongst 89 SME exporters through purposive sampling with valid 52 responses. The managerial issues related to export intensity, GLM practices, with regards to whether firms followed ISO 14001 and whether they had an employee(s) responsible for environmental management. The findings suggest that greater GLM practices being applied lead to higher export intensity in SME. The results indicate that most SMEs apply GLM practices in all dimensions to gain the benefits from the export market. It also emerged that those following ISO 14001 and/or having an employee(s) were associated with having higher export intensity than those who did not engage in these practices.

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.