Gathering channel data to test telecommunication systems is an essential step to guarantee the quality of the product. However, it can be a slow process and demand a considerable amount of effort and investment since it is costly to make field measurements of mmWaves. Having a ready dataset at disposal make things way faster and cheaper, allowing a developer to focus on more specific tasks. This paper presents an entire multimodal dataset with different kinds of information like channel communication, urban traffic and obstacles position, got from two realistic computer simulations made in two different city models: Beijing and Rosslyn. It also includes detailed information on how each data is stored.

ABSTRACT. Breeding programs currently use statistical analysis to assist in the identification of superior genotypes at various stages of a cultivar's development. Differently from these analyses, the computational intelligence approach has been little explored in genetic improvement of cotton. Thus, this study was carried out with the objective of presenting the use of artificial neural networks as auxiliary tools in the improvement of the cotton to improve fiber quality. To demonstrate the applicability of this approach, this research was carried out using the evaluation data of 40 genotypes. In order to classify the genotypes for fiber quality, the artificial neural networks were trained with replicate data of 20 genotypes of cotton evaluated in the harvests of 2013/14 and 2014/15, regarding fiber length, uniformity of length, fiber strength, micronaire index, elongation, short fiber index, maturity index, reflectance degree, and fiber quality index. This quality index 2 E.G. Silva Júnior et al. Genetics and Molecular Research 16 (3): gmr16039798was estimated by means of a weighted average on the determined score (1 to 5) of each characteristic of the HVI evaluated, according to its industry standards. The artificial neural networks presented a high capacity of correct classification of the 20 selected genotypes based on the fiber quality index, so that when using fiber length associated with the short fiber index, fiber maturation, and micronaire index, the artificial neural networks presented better results than using only fiber length and previous associations. It was also observed that to submit data of means of new genotypes to the neural networks trained with data of repetition, provides better results of classification of the genotypes. When observing the results obtained in the present study, it was verified that the artificial neural networks present great potential to be used in the different stages of a genetic improvement program of the cotton, aiming at the improvement of the fiber quality of the future cultivars.

e19119 Background: There is growing interest in enhancing symptoms monitoring during routine cancer care using patient-reported outcomes (PROs). The quality of evidence that demonstrates clinical benefits of this type of assistance is also increasing. However, the best method for this approach is evolving and there are many barriers to implement these tools in the real world scenario. We aim to describe a pilot study about a chatbot with artificial intelligence developed to collect PROs and to optimize adherence to systemic cancer treatment. Methods: This is a case series that reports the first patients in a private oncology clinic in Belo Horizonte, Southeast Brazil who consecutively underwent regular conversations with a chatbot based on artificial intelligence. Data was collected from February the 23th to December the 3rd 2019. The virtual assistant interacted with the patient in four different ways, being the first three of these an active search from the chatbot: a) search for adverse events, b) adherence to oral treatment c) screening for depression d) spontaneous patient demand (not an active search). Results: Interaction with the chatbot was offered to 193 patients. A total of 107 patients were included. Of these 74% were female, 55% older than 60 years and 22% had at least 7 years education.194 protocols of treatment were analysed, 66% of these being chemotherapy regimens and 23% hormone therapy. Oral drugs corresponded to 23% of the protocols. The main adverse events reported were fatigue 20%, nausea 13%, pain 11%, diarrhea 10%, lack of appetite 6%. Adverse effects were classified by patients as grade III or IV approximately 24% of the time. For patient safety the system runs a script twice a day to detect any adverse effect and send it to the service attended. A total of 3883 dialogues were initiated, the majority of which (3772) was carried out by the machine. Only 3% of the dialogues were spontaneously initiated by the patients. Once the conversation started, adherence was considerably satisfactory since engagement was 73% for questions about adherence to oral medications and 76% of people reported at least one adverse event. Conclusions: An initial barrier must be surpassed since the chatbot was offered to 193 patients and 86 (44%) did not register for use. Once the contact started, we understand that the use of AI is promising since the engagement rates were very good. It is important to highlight the potential capacity for early identification of symptoms since most dialogues were initiated by the virtual assistant.

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Digital representations of the real world are being used in many applications, such as augmented reality. 6G systems will not only support use cases that rely on virtual worlds but also benefit from their rich contextual information to improve performance and reduce communication overhead. This paper focuses on the simulation of 6G systems that rely on a 3D representation of the environment, as captured by cameras and other sensors. We present new strategies for obtaining paired MIMO channels and multimodal data. We also discuss trade-offs between speed and accuracy when generating channels via ray tracing. We finally provide beam selection simulation results to assess the proposed methodology.