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This paper investigates how two characteristics of language, abstractness vs concreteness and narrativity, influence user engagement in communication exercises on innovation targeted to the general audience. The proposed conceptual model suggests that innovation fits well with more abstract language because of the association of innovation with imagination and distal construal. Moreover, communication of innovation may benefit from greater adherence to the narrativity arc, that is, early staging, increasing plot progression and climax optimal point. These effects are moderated by content variety and emotional tone, respectively.

Based on a Latent Dirichlet allocation (LDA) application on a sample of 3225 TED Talks transcripts, the authors identify 287 TED Talks on innovation, and then applied econometric analyses to test the hypotheses on the effects of abstractness vs concreteness and narrativity on engagement, and on the moderation effects of content variety and emotional tone.

The authors found that abstractness (vs concreteness) and narrativity have positive effects on engagement. These two effects are stronger with higher content variety and more positive emotional tone, respectively.

This paper extends the literature on communication of innovation, linguistics and text analysis by evaluating the roles of abstractness vs concreteness and narrativity in shaping appreciation of innovation.

This paper reports conceptual and empirical analyses on innovation dissemination through a popular medium – TED Talks – and applies modern text analysis algorithms to test hypotheses on the effects of two pivotal dimensions of language on user engagement.

Automatic DNA-binding protein (DNA-BP) classification is now an essential proteomic technology. Unfortunately, many systems reported in the literature are tested on only one or two datasets/tasks. The purpose of this study is to create the most optimal and universal system for DNA-BP classification, one that performs competitively across several DNA-BP classification tasks.

Efficient DNA-BP classifier systems require the discovery of powerful protein representations and feature extraction methods. Experiments were performed that combined and compared descriptors extracted from state-of-the-art matrix/image protein representations. These descriptors were trained on separate support vector machines (SVMs) and evaluated. Convolutional neural networks with different parameter settings were fine-tuned on two matrix representations of proteins. Decisions were fused with the SVMs using the weighted sum rule and evaluated to experimentally derive the most powerful general-purpose DNA-BP classifier system.

The best ensemble proposed here produced comparable, if not superior, classification results on a broad and fair comparison with the literature across four different datasets representing a variety of DNA-BP classification tasks, thereby demonstrating both the power and generalizability of the proposed system.

Most DNA-BP methods proposed in the literature are only validated on one (rarely two) datasets/tasks. In this work, the authors report the performance of our general-purpose DNA-BP system on four datasets representing different DNA-BP classification tasks. The excellent results of the proposed best classifier system demonstrate the power of the proposed approach. These results can now be used for baseline comparisons by other researchers in the field.

Banks play a crucial role in the sustainable development of exports as they finance much of the trade. Additionally, in Brazil's case, banks provide exporting companies with advisory and training services, which facilitate the internationalization process. This work aims to analyze the role of public and private banks in the export process of companies in Brazil.

Interviews are conducted with a sample of 318 Brazilian exporting companies. Two research questions are posed: What type of export services do companies use from public and private banks in Brazil? Is exporting companies' access to credit, as a type of banking service, related to their size or export experience? A descriptive study of the functions of public and private banks in helping Brazilian exports is presented. Hypotheses are proposed regarding companies' access to credit and its relationship with their size and export experience.

It is found that public and private banks in Brazil provide exporting companies with banking services, other services related to technical aspects, and export consulting. There are significant differences in access to credit in both public and private banks, depending on the exporting company's size.

This work contributes to the internationalization literature on the role of banks in supporting exports in an emerging country like Brazil.

This literature review explores the potential of gamification in workplace learning beyond formal training. The study also highlights research gaps and opportunities for scholars to develop new theories and methodologies to enhance the understanding and application of gamification in workplace learning. It provides guidance for managers to use gamification to enhance learning and engagement. Ultimately, this review presents gamification as a promising field of study to increase individual and organizational performance.

Literature review of 6625 papers in the timeframe 1990–2020, with an update to include papers published in 2023.

This article examines the impact of gamification beyond formal learning and its potential to enhance employee productivity and well-being in the workplace. While there has been extensive research on gamification in formal learning contexts, little is known about its impact on informal learning. The study argues that the context of gamification is crucial to extending its effects and discusses the role, antecedents and consequences of game design elements in the workplace. The article also explores how the learning context relates to employee learning during work. Further research is necessary to investigate the impact of individual characteristics on work experience and performance.

Intended contribution of the present study is the development of a theoretical framework exploring the benefits of gamification in a work context.

For practicing managers, this paper shows how to use gamification to increase workplace learning and employee engagement, not just in the context of formal learning—as some companies already do today—but also systematically, in the context of informal learning.

This study explores the impact of gamification on informal workplace learning and emphasizes the significance of the context of gamification in extending its effects to improve individual and organizational performance.

Diagnosing pain in neonates is difficult but critical. Although approximately thirty manual pain instruments have been developed for neonatal pain diagnosis, most are complex, multifactorial, and geared toward research. The goals of this work are twofold: 1) to develop a new video dataset for automatic neonatal pain detection called iCOPEvid (infant Classification Of Pain Expressions videos), and 2) to present a classification system that sets a challenging comparison performance on this dataset. The iCOPEvid dataset contains 234 videos of 49 neonates experiencing a set of noxious stimuli, a period of rest, and an acute pain stimulus. From these videos 20 s segments are extracted and grouped into two classes: pain (49) and nopain (185), with the nopain video segments handpicked to produce a highly challenging dataset. An ensemble of twelve global and local descriptors with a Bag-of-Features approach is utilized to improve the performance of some new descriptors based on Gaussian of Local Descriptors (GOLD). The basic classifier used in the ensembles is the Support Vector Machine, and decisions are combined by sum rule. These results are compared with standard methods, some deep learning approaches, and 185 human assessments. Our best machine learning methods are shown to outperform the human judges.