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Although Public Research Institutions (PRIs) are large technology producers, they lack automated information tools that follow technical and scientific criteria for assessing and valuing patents. The assessment and valuation processes are stages of technology transfer (TT) that make it possible to obtain productive arrangements and guide the efforts of those involved in the development, maintenance and negotiation. This study aims to analyze the hybrid model of assessment and valuation of technologies by Soares (2018), applying the ‘Valorativo' software. In addition to patent value and indicator scores, the methods allow an understanding of the technology portfolio and its management.

This research is quali-quantitative, following an approach of applied nature and descriptive objectives. The research has bibliographical, documental and case study features based on the software development methodologies described in the study and the theoretical framework.

The Valorativo software assisted in the analysis of ten patents on PRIs. With the data collection and patent analysis, PAT1 scored highest among engineering patents, PAT3 scored highest among pharmaceutical patents and PAT10 scored highest among biotechnology patents. Five of the assessed patents resulted in a surplus of net present value (NPV), final net present value (NPVF) and royalties; revenue expectations outpaced investments.

The authors based the developed software on Soares’s (2018) methodology, with additional calculations and graphs. The Web software and the spreadsheet with Visual Basic for Application (VBA) were developed to deal with the patents assessment and valuation, helping in the analysis of their Legal Value, Technological Value and Market Conditions in the assessment process, and the Discounted Cash Flow and NPV in the valuation process.

The software helps with patent analysis and can generate indicators for traders, technology holders and researchers. Thus, it was necessary to understand and develop a theoretical-applied framework to outline and replicate the methodology clearly and easily.

This paper aims to propose a structured model based on a data mining algorithm that can calculate, based on business association (BA) attributes, the probability of micro and small enterprises (MSEs) becoming a new member of a BA. Another goal is the probability of a BA attracting new members.

As a methodological procedure, the authors used the Naive Bayes data mining algorithm. The collected data were analyzed both quantitatively and qualitatively and then used to define the model, which was tested randomly, while allowing for the possibility of future validation.

The findings suggest a structured model based on a data mining algorithm. The model can certainly be used as a management tool for BAs concentrating their efforts on those businesses that are certainly potential new recruits. Further, for an MSE, it serves as a means of evaluating a BA, indicating the possible advantages in becoming a member of a particular association.

This paper is not intended to be generalized, considering that it only analyzes the BAs of Rio Grande do Sul, Brazil. In this way, when applying this model to other situations, the attributes listed here can be revised and even modified to adapt to the situation in focus.

The use of the proposed model will make it possible to optimize the time of BA managers. It also gives MSE greater reliability in choosing BA.

Using this model will provide better decision-making and better targeting, thus benefiting both the BAs and the MSEs, which can improve their management and keep jobs.

This paper contributes to the literature because it is the first to connect BAs, MSEs and Naive Bayes. Also, this study helps in better management for BA managers in their daily activities and provides a better choice of BA for MSE managers. Also, this study contextualizes BAs, MSEs and data mining in an objective way.

This paper aims to concentrate on recent innovations in flexible wearable sensor technology tailored for monitoring vital signals within the contexts of wearable sensors and systems developed specifically for monitoring health and fitness metrics.

In recent decades, wearable sensors for monitoring vital signals in sports and health have advanced greatly. Vital signals include electrocardiogram, electroencephalogram, electromyography, inertial data, body motions, cardiac rate and bodily fluids like blood and sweating, making them a good choice for sensing devices.

This report reviewed reputable journal articles on wearable sensors for vital signal monitoring, focusing on multimode and integrated multi-dimensional capabilities like structure, accuracy and nature of the devices, which may offer a more versatile and comprehensive solution.

The paper provides essential information on the present obstacles and challenges in this domain and provide a glimpse into the future directions of wearable sensors for the detection of these crucial signals. Importantly, it is evident that the integration of modern fabricating techniques, stretchable electronic devices, the Internet of Things and the application of artificial intelligence algorithms has significantly improved the capacity to efficiently monitor and leverage these signals for human health monitoring, including disease prediction.