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In this paper we investigate why the process of structural change in Brazil was growth accelerating before 1980 and why it was growth reducing after this year.

We investigate the causes of this change in behavior using the shift-share decomposition method.

The results indicate that in the first period there were high productivity gains as result of improvement in economic fundamentals such as the quality of capital and of labor and innovations. In this way, reallocation of workers between sectors, that is part of the process of structural change, was an inducer of economic growth. However, after 1980, mainly between 1991 and 2011, sectors that achieved productivity gains did so by reducing labor, which was absorbed by sectors with poor performance in terms of productivity growth. Furthermore, factors such as the deindustrialization that developed countries have been undergoing, the international situation, the stage of Brazilian economic development and its possible premature deindustrialization contributed to a growth reducing structural change.

Our differential to the matter is applying the shift-share methodology without combining any of the ten sectors analyzed, adopting a slightly different time frame than similar studies and presenting the shift-share results in a graphically manner in addition to the traditional numbers. By representing graphically how much each of the ten sectors is contributing to the structural change in the economy we are emphasizing the specificities of each of these sectors instead of just considering the aggregated view like manufacturing industry versus other industries or modern services versus traditional services.

This paper examines the efforts of National Health Service (England) (NHSE) to respond to employee-based racial inequalities via its Workforce Race Equality Standard (WRES). The WRES constitutes a hybridised accountability initiative with characteristics of the moral and imposed regimes of accountability.

The study conceptualises the notion of responsive race accountability with recourse to Favotto et al.’s (2022) moral accountability model and critical race theory (CRT), and through it, examines the enactment of WRES at 40 NHSE trusts using qualitative content analysis.

Despite the progressive nature of the WRES that seeks to nurture corrective actions, results suggest that trusts tend to adopt an instrumental approach to the exercise. Whilst there is some evidence of good practice, the instrumental approach prevails across both the metric reporting that trusts engage in to guide their actions, and the planned actions for progress. These planned actions not only often fail to coalesce with the trust-specific data but also include generic NHSE or equality, diversity and inclusion initiatives and mimetic adoptions of best practice guidance that only superficially address racial concerns.

Whilst the WRES is a laudable voluntary achievement, its moral imperative does not appear to have translated into a moral accountability within individual trusts.

Responding to calls for more research at the accounting-race nexus, this study uniquely draws on CRT to conceptualise and examine race accountability.

The purpose of this study is to investigate and demonstrate the advancements achieved in the field of chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Typical convolutional neural networks (CNNs) are unable to capture both local and global contextual information effectively and apply a uniform operation to all pixels in an image. To address this, we propose an innovative approach that integrates a dynamic convolution operation at the encoder stage, improving image encoding quality and disease detection. In addition, a decoder based on the gated recurrent unit (GRU) is used for language modeling, and an attention network is incorporated to enhance consistency. This novel combination allows for improved feature extraction, mimicking the expertise of radiologists by selectively focusing on important areas and producing coherent captions with valuable clinical information.

In this study, we have presented a new report generation approach that utilizes dynamic convolution applied Resnet-101 (DyCNN) as an encoder (Verelst and Tuytelaars, 2019) and GRU as a decoder (Dey and Salemt, 2017; Pan et al., 2020), along with an attention network (see Figure 1). This integration innovatively extends the capabilities of image encoding and sequential caption generation, representing a shift from conventional CNN architectures. With its ability to dynamically adapt receptive fields, the DyCNN excels at capturing features of varying scales within the CXR images. This dynamic adaptability significantly enhances the granularity of feature extraction, enabling precise representation of localized abnormalities and structural intricacies. By incorporating this flexibility into the encoding process, our model can distil meaningful and contextually rich features from the radiographic data. While the attention mechanism enables the model to selectively focus on different regions of the image during caption generation. The attention mechanism enhances the report generation process by allowing the model to assign different importance weights to different regions of the image, mimicking human perception. In parallel, the GRU-based decoder adds a critical dimension to the process by ensuring a smooth, sequential generation of captions.

The findings of this study highlight the significant advancements achieved in chest X-ray image captioning through the utilization of dynamic convolutional encoder–decoder networks (DyCNN). Experiments conducted using the IU-Chest X-ray datasets showed that the proposed model outperformed other state-of-the-art approaches. The model achieved notable scores, including a BLEU_1 score of 0.591, a BLEU_2 score of 0.347, a BLEU_3 score of 0.277 and a BLEU_4 score of 0.155. These results highlight the efficiency and efficacy of the model in producing precise radiology reports, enhancing image interpretation and clinical decision-making.

This work is the first of its kind, which employs DyCNN as an encoder to extract features from CXR images. In addition, GRU as the decoder for language modeling was utilized and the attention mechanisms into the model architecture were incorporated.

In order to achieve the desired macroscopic mechanical properties of woven fiber reinforced polymer (FRP) materials, it is necessary to conduct a detailed analysis of their microscopic load-bearing capacity.

Utilizing the representative volume element (RVE) model, this study delves into how the material composition influences mechanical parameters and failure processes.

To study the ultimate strength of the materials, this study considers the damage situation in various parts and analyzes the stress-strain curves under uniaxial and multiaxial loading conditions. Furthermore, the study investigates the degradation of macroscopic mechanical properties of fiber and resin layers due to fatigue induced performance degradation. Additionally, the research explores the impact of fatigue damage on key material properties such as the elastic modulus, shear modulus and Poisson's ratio.

By studying the load-bearing mechanisms at different scales, a direct correlation is established between the macroscopic mechanical behavior of the material and the microstructure of woven FRP materials. This comprehensive analysis ultimately elucidates the material's mechanical response under conditions of fatigue damage.

This study aims to demystify the mediating mechanism behind the relationship between green human resource management (HRM) and an organization’s environmental performance with the support of resource-based view theory and social exchange theory. Specifically, this study investigates the sequential mediation of green work engagement and green innovation on the direct effect of green HRM on environmental performance.

This quantitative study collected data from 311 employees working in various Indian manufacturing firms using an online survey. Structural equation modeling was used to determine the model fit of the serial mediation model, and PROCESS macro was used to test the hypotheses.

The findings of the study revealed the following important results. First, green HRM positively affects an organization’s environmental performance. Second, green work engagement mediates the effect of green HRM on environmental performance. Third, green innovation mediates the effect of green HRM on environmental performance. Fourth, green work engagement and green innovation sequentially mediate the green HRM–environmental relationship.

This study offers the following practical implications. First, it improves the managerial comprehension of the processes in enhancing environmental performance. Second, it implies that managers need to implement green HRM in their organizations as they play a pivotal role in improving employees’ green work engagement, organizations’ green innovation and environmental performance.

The present study is one of the primary research works that examined the serial mediating effect of green work engagement and green innovation in the relationship between green HRM and environmental performance. This study enriches the existing literature on green HRM and environmental performance by uncovering the mediating mechanism of green work engagement and green innovation.

Aspect-based sentiment analysis (ASA) is a task of sentiment analysis that requires predicting aspect sentiment polarity for a given sentence. Many traditional techniques use graph-based mechanisms, which reduce prediction accuracy and introduce large amounts of noise. The other problem with graph-based mechanisms is that for some context words, the feelings change depending on the aspect, and therefore it is impossible to draw conclusions on their own. ASA is challenging because a given sentence can reveal complicated feelings about multiple aspects.

This research proposed an optimized attention-based DL model known as optimized aspect and self-attention aware long short-term memory for target-based semantic analysis (OAS-LSTM-TSA). The proposed model goes through three phases: preprocessing, aspect extraction and classification. Aspect extraction is done using a double-layered convolutional neural network (DL-CNN). The optimized aspect and self-attention embedded LSTM (OAS-LSTM) is used to classify aspect sentiment into three classes: positive, neutral and negative.

To detect and classify sentiment polarity of the aspect using the optimized aspect and self-attention embedded LSTM (OAS-LSTM) model. The results of the proposed method revealed that it achieves a high accuracy of 95.3 per cent for the restaurant dataset and 96.7 per cent for the laptop dataset.

The novelty of the research work is the addition of two effective attention layers in the network model, loss function reduction and accuracy enhancement, using a recent efficient optimization algorithm. The loss function in OAS-LSTM is minimized using the adaptive pelican optimization algorithm, thus increasing the accuracy rate. The performance of the proposed method is validated on four real-time datasets, Rest14, Lap14, Rest15 and Rest16, for various performance metrics.

The purpose of this study is to develop an integrated conceptual framework to better understand the psychological pathways connecting consumer perceptions to purchasing intentions for premium sustainable products.

The study develops a conceptual model that theorizes relationships between consumer perceptions of a firm’s innovation, competitive advantage, sustainable practices and stakeholder orientation. It proposes that stakeholder orientation mediates the effects of these perceptions on consumers’ willingness to purchase premium sustainable products. Additionally, lifestyle is hypothesized as a moderator. The model advances knowledge through eight testable propositions.

The conceptual framework specifies indirect, mediated and moderated relationships that have not been fully captured by past literature. It theorizes that perceptions of a firm’s innovation, competitive advantage from sustainable practices and stakeholder orientation indirectly influence purchase willingness through the mediating role of stakeholder orientation. Lifestyle is proposed to moderate these relationships.

This conceptual model offers insights for cultivating consumer perceptions that strengthen a firm’s stakeholder image and endorsement of premium sustainable products. Its validated theoretical lens and propositions can provide strategic guidance for addressing the challenges of higher price points for sustainable products through capturing psychological drivers of values-based decision-making. Future empirical assessment is recommended to validate the specified relationships in the model.

This study aims to understand the perception of young people regarding different communication strategies to promote proenvironmental disposal behavior. Based on the attention-interest-desire-action (AIDA) model, the study analyses how university students react to different communication approaches used by a university aiming at the correct disposal of waste.

The authors conducted a qualitative exploratory research in two steps: (i) a narrative bibliographic review and (ii) a case study. The (i) bibliographic review was conducted about proenvironmental behavior and disposal of solid waste and response hierarchy models, with emphasis on the AIDA model. The (ii) case study was executed through an in-depth interview with a manager of the environmental sanitation area and a qualitative survey with undergraduate students from a university in southern Brazil.

The findings reveal that young people tend to prefer communication strategies related to triggers for long-term memory, especially when evaluating the cognitive stage of the response hierarchy. For example, the provision of bins identified with stickers and colors is a communication strategy that leads to a memory model of associative networks. By viewing a certain color or image of an object, the individual can quickly retrieve information already known about the act of properly disposing of waste. Additionally, convenience is a key factor for the behavioral intention of properly disposing of waste to become a reality.

Few studies have identified the most effective communication strategies to promote proper disposal behavior among young people in universities. This study addresses this gap, based on the AIDA model.

The viewpoint essay focusses on the significance of integrated care (IC) for chronic pain in India, in an attempt to reflect on how pain management and care can be made more accessible and available to patients.

This reflective essay invites looking at chronic pain beyond biomedical perspectives. Insights from the medical humanities and the social sciences are used to emphasise chronic pain as a psychosocial and socio-political phenomenon and not just a biomedical category.

The essay argues that there are several challenges and barriers to the recognition and validation of chronic pain as a speciality.

IC has not received sufficient attention in the Indian context, where medical curricula and training do not sufficiently include an understanding of the multi-faceted aspects surrounding chronic pain. By highlighting the role of humanistic approaches to effectively bridge the gap, this viewpoint essay illustrates the significance of drawing on an integrated or holistic healthcare framework.

The purpose of this paper is to examine the mediating role of e-trust and e-satisfaction in the relationship between e-service quality and e-loyalty in the context of e-government services.

The data were collected via an online questionnaire of Jordanian citizens. The structural equation model based on partial least squares was used to test hypotheses.

The findings showed that e-service quality has a positive and significant effect on e-loyalty. E-service quality has a positive and significant effect on both e-trust and e-satisfaction. E-trust and e-satisfaction have a positive and significant effect on e-loyalty. E-trust has a positive effect on e-satisfaction. Finally, regarding the mediating effect of e-trust and e-satisfaction, e-trust and e-satisfaction partially mediate the relationship between e-service quality and e-loyalty in the context of e-government services, and hence all hypotheses were accepted.

The results of this research aid governmental policymakers in implementing information and communication technology strategies that streamline citizens’ transactions and promote their active engagement in e-government initiatives. Additionally, the government has suggested improving awareness campaigns and providing training for employees to enhance the quality of e-services provided to citizens.