Abstract
Biomedical engineering has been targeted as a potential research candidate for machine learning applications, with the purpose of detecting or diagnosing pathologies. However, acquiring relevant, high-quality, and heterogeneous medical datasets is challenging due to privacy and security issues and the effort required to annotate the data. Generative models have recently gained a growing interest in the computer vision field due to their ability to increase dataset size by generating new high-quality samples from the initial set, which can be used as data augmentation of a training dataset. This study aimed to synthesize artificial lung images from corresponding positional and semantic annotations using two generative adversarial networks and databases of real computed tomography scans: the Pix2Pix approach that generates lung images from the lung segmentation maps; and the conditional generative adversarial network (cCGAN) approach that was implemented with additional semantic labels in the generation process. To evaluate the quality of the generated images, two quantitative measures were used: the domain-specific Frechet Inception Distance and Structural Similarity Index. Additionally, an expert assessment was performed to measure the capability to distinguish between real and generated images. The assessment performed shows the high quality of synthesized images, which was confirmed by the expert evaluation. This work represents an innovative application of GAN approaches for medical application taking into consideration the pathological findings in the CT images and the clinical evaluation to assess the realism of these features in the generated images.

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Abstract
As being environmentally responsible is a potential source of competitive advantage, incorporating genuine environmental practices across the supply chain may help firms capitalize on the growing demand for corporate accountability and consumer awareness. Therefore, it is important to understand to what extent firms are using greenwashing to mislead their stakeholders in the supply chain. The purpose of this paper is to review the existing literature regarding greenwashing in supply chain management (SCM) to shed light on the main thematic groups addressed in the literature, understand its challenges and develop a framework that highlights the key drivers that companies need to tackle to prevent greenwashing in supply chains. For this purpose, we have conducted a systematic literature review, following a three-stage method. It was possible to identify possible solutions to prevent greenwashing across four main dimensions of SCM: consumers/customers; relationships between focal firms and suppliers; certification programs and reporting assessment; and corporate leadership. We provide a framework to help firms develop their sustainable strategy and prevent greenwashing along the supply chain. This paper synthesizes the challenges that firms face when implementing a sustainable supply chain, suggests solutions to prevent greenwashing and provides future research avenues.

Abstract
Cricket sounds are usually regarded as pleasant and, thus, can be used as suitable test signals in psychoacoustic experiments assessing the human listening acuity to specific temporal and spectral features. In addition, the simple structure of cricket sounds makes them prone to reverse engineering such that they can be analyzed and re-synthesized with desired alterations in their defining parameters. This paper describes cricket sounds from a parametric point of view, characterizes their main temporal and spectral features, namely jitter, shimmer and frequency sweeps, and explains a re-synthesis process generating modified natural cricket sounds. These are subsequently used in listening tests helping to shed light on the sound identification and discrimination capabilities of humans that are important, for example, in voice recognition. © 2023 IEEE.

Abstract
Immersive Virtual Reality (IVR) provides a platform where the real world can be replicated to a point where users can act and react in the virtual world as they would in reality. However, rendering visual stimuli is computationally heavy. Thus, optimizations must be done to take advantage of computational systems by studying our perception of reality. This study investigated parameters related to light rendering (Global Illumination, Ambient Occlusion, Screen Space Reflections (SSR) and Direct Shadows) in real-time in a virtual replica of a real place using IVR. Participants experienced both virtual and real rooms with only one flashlight and changed the quality settings of the considered parameters so that their sense of reality would be the closest to the one they felt when they experienced the real room. Participants were given a budget to drive them to prioritize what parameters, and their level of quality, are the most important for their sense of reality. Results indicated that participants considered Global Illumination the most important factor, closely followed by Direct Shadows. Ambient Occlusion and Reflections (Screen Space Reflections) were the less prioritized parameters. We conclude that in a lighting setting where only dynamic lights are used, Global Illumination and Direct Shadows should be prioritized over SSR Reflections and Ambient Occlusion when computational power is limited.