Dengue, chikungunya and Zika are arboviruses transmitted by mosquitos of the genus Aedes and have caused several outbreaks in world over the past ten years. Morphological identification of mosquitos is currently restricted due to the small number of adequately trained professionals. We implemented a computational model based on a convolutional neural network (CNN) to extract features from mosquito images to identify adult mosquitoes from the species Aedes aegypti, Aedes albopictus and Culex quinquefasciatus. To train the CNN to perform automatic morphological classification of mosquitoes, we used a dataset that included 4,056 mosquito images. Three neural networks, including LeNet, AlexNet and GoogleNet, were used. During the validation phase, the accuracy of the mosquito classification was 57.5% using LeNet, 74.7% using AlexNet and 83.9% using GoogleNet. During the testing phase, the best result (76.2%) was obtained using GoogleNet; results of 52.4% and 51.2% were obtained using LeNet and AlexNet, respectively. Significantly, accuracies of 100% and 90% were achieved for the classification of Aedes and Culex, respectively. A classification accuracy of 82% was achieved for Aedes females. Our results provide information that is fundamental for the automatic morphological classification of adult mosquito species in field. The use of CNN's is an important method for autonomous identification and is a valuable and accessible resource for health workers and taxonomists for the identification of some insects that can transmit infectious agents to humans.

Virtual reality experiences are frequently created using game engines, yet they are not simple for novices and unskilled professionals who do not have programming and 3D modeling skills. Concurrently, there is a knowledge gap in software project design for intuitive virtual reality authoring tools, which were supposed to be easier to use. This study compiles design guidelines derived from a systematic literature review to contribute to the development of more intuitive virtual reality authoring tools. We searched the Scopus and Web of Science knowledge databases for studies published between 2018 and 2021 and discovered fourteen articles. We compiled fourteen requirement and feature design guidelines, such as Visual Programming, Immersive Authoring, Reutilization, Sharing and Collaboration, Metaphors, and Movement Freedom, among others. The gathered guidelines have the potential to either guide the development of new authoring tools or to evaluate the intuitiveness of existing tools. Furthermore, they can also support the development of the metaverse since virtual content creation is one of its bases.

One of the difficulties of artificial intelligence is to ensure that model decisions are fair and free of bias. In research, datasets, metrics, techniques, and tools are applied to detect and mitigate algorithmic unfairness and bias. This study examines the current knowledge on bias and unfairness in machine learning models. The systematic review followed the PRISMA guidelines and is registered on OSF plataform. The search was carried out between 2021 and early 2022 in the Scopus, IEEE Xplore, Web of Science, and Google Scholar knowledge bases and found 128 articles published between 2017 and 2022, of which 45 were chosen based on search string optimization and inclusion and exclusion criteria. We discovered that the majority of retrieved works focus on bias and unfairness identification and mitigation techniques, offering tools, statistical approaches, important metrics, and datasets typically used for bias experiments. In terms of the primary forms of bias, data, algorithm, and user interaction were addressed in connection to the preprocessing, in-processing, and postprocessing mitigation methods. The use of Equalized Odds, Opportunity Equality, and Demographic Parity as primary fairness metrics emphasizes the crucial role of sensitive attributes in mitigating bias. The 25 datasets chosen span a wide range of areas, including criminal justice image enhancement, finance, education, product pricing, and health, with the majority including sensitive attributes. In terms of tools, Aequitas is the most often referenced, yet many of the tools were not employed in empirical experiments. A limitation of current research is the lack of multiclass and multimetric studies, which are found in just a few works and constrain the investigation to binary-focused method. Furthermore, the results indicate that different fairness metrics do not present uniform results for a given use case, and that more research with varied model architectures is necessary to standardize which ones are more appropriate for a given context. We also observed that all research addressed the transparency of the algorithm, or its capacity to explain how decisions are taken.

With the introduction of new devices, industries are turning to virtual reality to innovate their product development processes. However, before the technology’s possibilities can be fully harnessed, certain constraints must be overcome. This study identifies the benefits and challenges of virtual-reality-based usability testing and design reviews in industry through a patents and articles review. We searched Derwent Innovation, Scopus, and Web of Science and identified 7 patent filings and 20 articles. We discovered an increase in patent filings since 2016 and strong development in the technology space, offering opportunities to enter an area while it is still young. The most frequently researched field is the automotive industry and the most used device is the HTC VIVE head-mounted display, which is frequently paired with motion capture systems and Unity 3D game engines. Virtual reality benefits design reviews and usability testing by providing the visualization of new angles that stimulate novel insights, increasing team engagement, offering more intuitive interactions for non-CAD specialists, saving redesign cost and time, and increasing participants’ safety. The challenges faced by virtual-reality-based prototypes are a lack of realism due to unnatural tactile and visual interactions, latency and registration issues, communication difficulties between teams, and unpleasant symptoms. While these constraints prevent virtual reality from replacing conventional design reviews and usability testing in the near future, it is already a valuable contribution to the industrial product development process.

O objetivo deste estudo foi analisar a produção científica sobre integração do gerenciamento de dados dos processos de controle da biocarga em indústrias farmacêuticas e verificar a contribuição dessa integração para a melhoria do controle de qualidade. Foi realizada uma revisão sistemática da literatura em bases de dados científicos, considerando os trabalhos publicados entre 2016 e 2021. Foram identificados 302 trabalhos, mas após aplicação dos critérios de exclusão, foram selecionados 10 trabalhos de seis países. Conclui-se que, para implementar um sistema integrado de controle de dados relacionados à biocarga em uma indústria farmacêutica, devem ser considerados os sistemas de gestão da qualidade e a necessidade de integração entre os diversos sistemas de controle, permitindo acesso conveniente a dados operacionais brutos para ajudar a rastrear a conduta e o desempenho do processo.