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Air pollution is one of the biggest problems affecting large urban areas. Better monitoring of regions suffering from this type of pollution is in the interest of public health. Although many cities employ sensors to monitor air pollution, a current concern is how to establish the ideal number of sensors to monitor a given geographical region. To address this concern, this research proposes a method to optimize the number of sensors in an air pollution monitoring network to cover a given region efficiently and precisely and uses the metropolitan region of São Paulo, Brazil, and CO sensors as an example. The model of Fragmentation into Groups via Routes is proposed to distribute sensors within micro-regions that display similar air pollution characteristics. A network of virtual sensors is created, and the output of each sensor is established using a method of spatial interpolation called IDW. To identify the optimum sensor configuration, a genetic algorithm is used to assess the topology with the lowest variance of data spread. A lesser number of sensor stations to be treated leads to faster responses to sudden changes in urban conditions. Therefore, municipality authorities can take quick measures to improve the population's wellness. [ABSTRACT FROM AUTHOR]

Featured Application: A new algorithm that uses self-organizing maps and entropy calculus to generate a tree structure based on XAI software principles. Data classification is an automatic or semi-automatic process that, utilizing artificial intelligence algorithms, learns the variable and class relationships of a dataset for use a posteriori in situations where the class result is unknown. For many years, work on this topic has been aimed at increasing the hit rates of algorithms. However, when the problem is restricted to applications in healthcare, besides the concern with performance, it is also necessary to design algorithms whose results are understandable by the specialists responsible for making the decisions. Among the problems in the field of medicine, a current focus is related to COVID-19: AI algorithms may contribute to early diagnosis. Among the available COVID-19 data, the blood test is a typical procedure performed when the patient seeks the hospital, and its use in the diagnosis allows reducing the need for other diagnostic tests that can impact the detection time and add to costs. In this work, we propose using self-organizing map (SOM) to discover attributes in blood test examinations that are relevant for COVID-19 diagnosis. We applied SOM and an entropy calculation in the definition of a hierarchical, semi-supervised and explainable model named TESSOM (tree-based entropy-structured self-organizing maps), in which the main feature is enhancing the investigation of groups of cases with high levels of class overlap, as far as the diagnostic outcome is concerned. Framing the TESSOM algorithm in the context of explainable artificial intelligence (XAI) makes it possible to explain the results to an expert in a simplified way. It is demonstrated in the paper that the use of the TESSOM algorithm to identify attributes of blood tests can help with the identification of COVID-19 cases. It providing a performance increase in 1.489% in multiple scenarios when analyzing 2207 cases from three hospitals in the state of São Paulo, Brazil. This work is a starting point for researchers to identify relevant attributes of blood tests for COVID-19 and to support the diagnosis of other diseases. [ABSTRACT FROM AUTHOR]

A 300 kg, 11-month-old quarter horse filly was presented with a joint dislocation in the right anterior limb. The animal was attended at the Federal University of Pará (UFPA) Veterinary Hospital in September 2016, where it was reported that the horse was injured on a fence. On clinical examination, the dislocation of the metacarpophalangeal joint with a rupture of the joint capsule and exposure of bone surfaces was observed, however, with no rupture of the extensor and flexor tendons. Surgery was recommended and the patient was anaesthetised with detomidine (Dormiun V; Agener União, São Paulo, Brazil; 0.04 mg/kg), ketamine (Cetamin; Syntec, São Paulo, Brazil; 0.03 mg/kg/h) and guaiacol glyceryl ether (JA Saúde Animal, São Paulo, Brasil, 0.5 ml/kg/h) i.v. The reconstitution of the joint capsule, lateral collateral ligament (LCL) and the other soft tissues around the injury was performed. A chemical arthrodesis was performed, then the tissue approximation and finally the skin suture. Bandages were applied on the wound with hydrophobic cotton and a Robert-Jones dressing. Systemic treatment was performed with enrofloxacin (Zelotril 10%; Agener União, São Paulo, Brazil; 5 mg/kg) i.m., meloxicam (Maxicam 2%; São Paulo, Brasil; 0.6 mg/kg) i.v. during 10 and 20 days, respectively, then replacing the antibiotic with ceftiofur (Cef 50; Agener União, São Paulo, Brasil; 2.2 mg/kg) i.m. for another 22 days. An iodine solution was applied to the joint optimising arthrodesis. After three and a half months, the lesion was entirely healed and with good movement, so the patient was discharged. This case demonstrates that, with adequate treatment and attention, it is possible to recover the patient's quality of life. [ABSTRACT FROM AUTHOR]