Abstract
Digital transformation is a broad description of efforts to introduce new technologies within and across organizations with the potential to revolutionize the way they function and perform. Digital transformation may be addressed at multiple levels of analysis, and this paper focuses on the enterprise level. This includes the organization, its people, systems, tools and technologies, and suppliers and partners that combined create valued outcomes that sustain the enterprise and advance its objectives. Collectively, this is a complex sociotechnical system (STS), and digital transformation is an intervention in a STS of potentially profound scope. Classical STS theory emerged from analysis of individuals and work groups and principles have been defined for the design of work systems at that level. We explore how STS design principles may be applied to the enterprise-level challenges associated with digital transformation. We present an enterprise-level framework that describes a process and methods that are consistent with STS design principles and illustrates how existing systems analysis methods and artifacts may be used to design an enterprise level STS. We review some artifacts employed in digital transformation efforts, including enterprise reference architectures, to better understand how they might function as means to foster communication and collaboration across multiple disciplines and domains in the STS design process.

Abstract
Background: The high salt concentration is the major factor limiting microbial growth at salterns, along with solar radiation, temperature, and pH. These environmental factors play key roles in the acquisition of unique genetic adaptations for the survival of microorganisms in salterns, which can result in the production of interesting secondary metabolites. The main goal of the present work was to isolate and compare the culturable microbiota from two geographically distant salterns in Portugal and access their biotechnological potential. Methods: Culturomics approaches using different culture media were applied for microbial isolation. All isolates were identified either by 16S rRNA or ITS genes sequencing, and their biotechonological potential was assessed by PCR. Results: Overall, 154 microbial isolates were recovered that were phylogenetically assigned to 45 taxa from 9 different phyla. From these, 26 isolates may represent putative new taxa. The predominant genera obtained were Penicillium (41 isolates, 26.6%), Streptomyces (13 isolates, 8.4%) and Sinomicrobium (11 isolates, 7.1%). Moreover, the polyketide synthase I gene was present in 64 isolates, the nonribosomal peptide synthethase gene in 16 isolates, and both genes in 23 isolates. Conclusions: This study adds up valuable knowledge on the culturable microbiota of Portuguese salterns and on its potential for production of secondary metabolites. In the long run, this study provides a widely diverse microbial collection for future works. Data public repository: All DNA sequences were deposited in the GenBank database at National Centre for Biotechnology Information (NCBI) web platform under accession numbers OK169439-OK169485, OK216020-OK216124, OK287059 and OK326927.

Abstract

Abstract
Using a Markov-perfect equilibrium model, we show that the use of customer data to practice intertemporal price discrimination will improve monopoly profit if and only if information precision is higher than a certain threshold level. This U-shaped relationship lends support to a popular view that knowledge is good only if it is sufficiently refined. When information accuracy can only be achieved through costly investment, we find that investing in profiling is profitable only if this allows to reach a high enough level of information precision. Consumers expected surplus being a hump-shaped function of information accuracy, we show that consumers have an incentive to lobby for privacy protection legislation which raises the cost of monopoly's investment in information accuracy. However, this cost should not dissuade firms to collect some information on customers' tastes, as the absence of consumers' profiling is actually detrimental to consumers.

Abstract
A tecnologia de metaverso tem se destacado recentemente a partir de duas abordagens. A primeira compreende os metaversos como sistemas multiusuários de criação de mundos em 2D/3D. A segunda propõe o “Metaverso do mundo ‘real’” e utiliza realidade aumentada no mundo físico para ampliar e potencializar experiências humanas. Baseados nesse contexto, buscamos discorrer sobre como os metaversos desafiam as práticas comunicativas e a interação na educação. Apresentamos as principais abordagens do metaverso com o propósito de evidenciar novos habitares e potencializar a Educação On-LIFE. Inspirados no método cartográfico de pesquisa-intervenção, identificamos pistas que permitem inferir que o metaverso favorece a passagem da compreensão de comunicação para um novo modo, trazendo um habitar comunicativo onde comunicar, interagir e cocriar implicam habitar um habitat.

Abstract
Cardiac auscultation is one of the most cost-effective techniques used to detect and identify many heart conditions. Computer-assisted decision systems based on auscultation can support physicians in their decisions. Unfortunately, the application of such systems in clinical trials is still minimal since most of them only aim to detect the presence of extra or abnormal waves in the phonocardiogram signal, i.e., only a binary ground truth variable (normal vs abnormal) is provided. This is mainly due to the lack of large publicly available datasets, where a more detailed description of such abnormal waves (e.g., cardiac murmurs) exists. To pave the way to more effective research on healthcare recommendation systems based on auscultation, our team has prepared the currently largest pediatric heart sound dataset. A total of 5282 recordings have been collected from the four main auscultation locations of 1568 patients, in the process, 215780 heart sounds have been manually annotated. Furthermore, and for the first time, each cardiac murmur has been manually annotated by an expert annotator according to its timing, shape, pitch, grading, and quality. In addition, the auscultation locations where the murmur is present were identified as well as the auscultation location where the murmur is detected more intensively. Such detailed description for a relatively large number of heart sounds may pave the way for new machine learning algorithms with a real-world application for the detection and analysis of murmur waves for diagnostic purposes.