Abstract
The estimation of the frequency of sinusoids has been the object of intense research for more than 40 years. Its importance in classical fields such as telecommunications, instrumentation, and medicine has been extended to numerous specific signal processing applications involving, for example, speech, audio, and music processing. In many cases, these applications run in real-time and, thus, require accurate, fast, and low-complexity algorithms. Taking the normalized Cramer-Rao lower bound as a reference, this paper evaluates the relative performance of nine non-iterative discrete Fourier transform-based individual sinusoid frequency estimators when the target sinusoid is affected by full-bandwidth quasi-harmonic interference, in addition to stationary noise. Three levels of the quasi-harmonic interference severity are considered: no harmonic interference, mild harmonic interference, and strong harmonic interference. Moreover, the harmonic interference is amplitude-modulated and frequency-modulated reflecting real-world conditions, e.g., in singing and musical chords. Results are presented for when the Signal-to-Noise Ratio varies between -10 dB and 70 dB, and they reveal that the relative performance of different frequency estimators depends on the SNR and on the selectivity and leakage of the window that is used, but also changes drastically as a function of the severity of the quasi-harmonic interference. In particular, when this interference is strong, the performance curves of the majority of the tested frequency estimators collapse to a few trends around and above 0.4% of the DFT bin width.

Abstract
The recent pandemic crisis has greatly impacted startups, and some changes are expected to be long-lasting. Small businesses usually have fewer resources and are more vulnerable to losing customers and investors, especially during crises. This study investigates how startups’ business processes were affected and how entrepreneurs managed this sudden change brought by the COVID-19 outbreak. Data were analyzed using qualitative research methods through in-depth interviews with the co-founders of eighteen startups. Results show that the three core business processes affected by the COVID-19 crisis were marketing and sales, logistics and operations, and organizational support. The way to succeed is to be flexible, agile, and adaptable, with technological knowledge focusing on digital channels to find novel opportunities and innovate. Additionally, resilience, self-improvement, education, technology readiness and adoption, close relationship with customers and other stakeholders, and incubation experience seem to shield startups against pandemic crisis outbreaks.

Abstract
Bin picking based on deep learning techniques is a promising approach that can solve several analytical methods problems. These systems can provide accurate solutions to bin picking in cluttered environments, where the scenario is always changing. This article proposes a robust and accurate system for segmenting bin picking objects, employing an easy configuration procedure to adjust the framework according to a specific object. The framework is implemented in Robot Operating System (ROS) and is divided into a detection and segmentation system. The detection system employs Mask R-CNN instance neural network to identify several objects from two dimensions (2D) grayscale images. The segmentation system relies on the point cloud library (PCL), manipulating 3D point cloud data according to the detection results to select particular points of the original point cloud, generating a partial point cloud result. Furthermore, to complete the bin picking system a pose estimation approach based on matching algorithms is employed, such as Iterative Closest Point (ICP). The system was evaluated for two types of objects, knee tube, and triangular wall support, in cluttered environments. It displayed an average precision of 79% for both models, an average recall of 92%, and an average IOU of 89%. As exhibited throughout the article, this system demonstrates high accuracy in cluttered environments with several occlusions for different types of objects.

Abstract
Serious games are increasingly present in higher education and many researchers are reflecting on how to use them in the development and training of new skills. However, an unexplored area is the analysis of the impact that serious games have on students’ academic performance in an entrepreneurship course. In this sense, this study simultaneously seeks to explore the impact of the use of a serious game, titled FLIGBY, on the development of hard and soft skills through the use of a mixed methods approach, in which quantitative and qualitative methods are combined by adopting the convergent parallel design model. The findings did not allow us to establish a correlation between the parameters assessed in the FLIGBY and the students’ academic performance. However, it was possible to identify several benefits in the development of soft skills with potential impact on the students’ academic and professional careers.

Abstract
Th chapter presents an empirical study on a Brazilian cyber-activism movement on Instagram associated with the hashtag #CorpoLivre (#FreeBody in Portuguese). This movement, which was established in 2018, has published more than 3,000 posts and has over 400,000 followers, disseminates anti-fatphobia and real body discourses, and promotes a positive relationship between women and their bodies beyond traditional beauty standards. The study analyses the posts made by the feminist movement on Instagram in December 2022, with a sample size of 101 posts. The study adopted the framework developed by Khurana and Knight for the analysis, which enables the classification of the sample posts in terms of message appeal, orientation, engagement, popularity, and image characteristics. This framework was used to examine the relationship between content characteristics and engagement. Additionally, the study includes a content analysis of the posts' comments, specifically evaluating the valence (positive, negative, or neutral) to assess the effectiveness of the characteristics of the posts. © 2023, IGI Global. All rights reserved.

Abstract