巴西

The Information Technology (IT) area has shown great growth in recent years, even with the economic recession that 巴西 has been through and the impact of the coronavirus pandemic. It is estimated that by 2024 the area will have a deficit of more than 290 thousand professionals. However, companies still face other difficulties in hiring, especially people who are looking for their first job in the Information Technology area. Most part of these difficulties are lack of qualified manpower and high prerequisites to fill internship or junior positions. As a result, the objective of this project is: to develop a platform that connects people who seek guidance, improvement or professional relocation in the Information Technology area with professionals that already have the experience they are seeking. The first step was a research and analysis of similar platforms in the market, whose proposal involves mentoring or professional connections, and it concluded that there are no services that fully meet the project’s proposal. In the second step, a research was done about mobile development, highlighting Flutter and Firebase platform. The third step defined the application’s features, such as suggestion of users and mentors, search for users, become a mentor, private chat, video calls, Portuguese and English languages, light and dark themes and profile customization. The suggestion of users and mentors is done by a match with the registered users, relating their areas of work (where the user has experience) and the areas of interest of each one. For the coding of the project, Flutter and Firebase technologies were used. To design the app, it followed Material Design specifications. For testing and distribution, the app was published on Play Store, Google’s Android application platform. The tests were performed by both the researcher and a selected group of users to verify if the functionalities were in accordance to what was defined in the beginning of the project. Perceiving the correct functioning of the application, the project achieved the proposed objective. In addition, it expanded its reach area, because it is possible to find users and mentors from any other area of the market.

The present research has as main objective to degrade the post-cured photopolymer of the stereolithography 3D printer resin using Galleria mellonella larvae. It is necessary to consider that the use of materials from 3D printers tends to increase considerably and in approximately seven years about 10% of everything that will be produced in the world will come from this type of printing. Considering also that the increase in population growth and technological development are directly linked to the increase of solid waste on the planet, in particular to polymeric materials, there is a need to degrade and give an adequate end to waste, avoiding a notorious accumulation along the time. For this purpose, Galleria mellonella larvae will be used because of it's comprovated capacity to degrade polyethylene, to find out if it is capable of biodegrading the post-cured resin of the printer. To carry out the research, compositional tests were done in partnership with the SENAI Institute for Innovation in Polymer Engineering, located in São Leopoldo, Rio Grande do Sul, and the creation of the larvae and degradation of the photopolymer will be carried out in partnership with the University Federal University of Health Sciences of Porto Alegre (UFCSPA). The data analysis will be based on the crystallinity determination tests by differential scanning calorimetry (DSC), thermogravimetric analysis (TGA) and attenuated total reflectance spectroscopy (ATR) that will also be applied in the larvae feces after contact with the polymer to assess for degradation. As a result of the compositional tests, the ATR showed predominantly characteristic absorptions of acrylic resin; in the TGA test, the loss of mass described in the test is related to the loss of mass of organic material, mainly polymer. Finally, in the DSC test a thermal event was observed in the heating of the sample, with peaks at 125 ° C (Tpm), characteristic of fusion, and a thermal event in the cooling of the sample, in 112 ° C (Tpc), characteristic of crystallization. Based on the analysis of the results obtained, it is possible to infer that most of the composition of the photopolymer is acrylic resin, widely used in stereolithography 3D printers. The research has the future objective of isolating the substance into the larvae responsible for degradation so that it can be degraded on industrial scales. The research started in March 2020 and is still under development due to the COVID-19 pandemic, which compromised the planned tests.

The Aedes aegypti mosquito is one of the main transmitters of viral diseases in countries close to the equator. This vector promotes a series of generalized endemics that are difficult to control and prevent in these regions. Furthermore, the presence of bacteria in the environment favors the proliferation of mosquito larvae, which increases the probability of Aedes aegypti reproductive success. The Araçzeiro (Psidium guineense Sw.) is a plant present throughout the Brazilian Atlantic Forest and has in its composition, especially in the leaves, several substances that can be used to solve problems. Thus, we sought to verify the activity of flavonoids and polyphenols in terms of their antibacterial potential and the performance of saponins in their larvicidal potential, as well as surfactant, in order to prevent the accommodation of the mosquito in the water at the time of egg deposition and larvae respiration. The saponins were extracted from the araçazeiro leaf using a hydroalcoholic solvent and the flavonoids/polyphenols using methanol, the latter being subsequently rotaevaporated to maintain the non-toxic nature of the extract. Through the aqueous extracts, the content of total saponins by UV-VIS spectrophotometry, surfactant activity, larvicidal activity and toxicity were determined. In relation to the ethanolic extracts, the content of polyphenols and total flavonoids by UV-VIS spectrophotometry and high performance liquid chromatography (HPLC), antibacterial activity and toxicity were determined. The results showed that the aqueous extract has a satisfactory amount of saponins, as well as a surfactant potential due to the formation of foam and larvicidal activity in the two highest concentrations of the extracts. Ethanol extracts showed phenolic acids, especially gallic and ellagic acid, and flavonoids, especially catechin and quercetin, and antibacterial activity in most of the worked concentrations. Both extracts (aqueous and ethanolic) showed a dominant nontoxic character, which favors their use without risk to the environment, having an alternative and sustainable potential for controlling the proliferation of the Aedes aegypti mosquito.

In Brazil, approximately sixty million people suffer from or acquire some type of disease daily. However, the average time for blood count diagnoses, used to identify many of these diseases, remains very lengthy. This can lead to the worsening of conditions and delays in care, as well as a decrease in the patients’ quality of life. Moreover, in some cases, the waiting period can result in irreversible situations and even the death of the affected individuals. In this landscape, technological tools such as artificial intelligence software can help reduce the time taken for diagnostic reporting. In light of this, the project involves developing software to assist in the analysis of blood counts and optimize medical diagnoses. For this purpose, the methodology was divided into three stages. In the first, titled ”Medical Standardization”, a survey of the standard variables related to diseases that can be identified with the help of blood counts was conducted. Among the findings, diabetes, anemia, leukemia, dengue, polycythemia, tuberculosis, leprosy, meningitis, chlamydia, schistosomiasis, spotted fever, and malaria were the main diseases detected. Furthermore, hemoglobin, leukocytes, platelets, glucose, cholesterol, ions, and hormones were the key findings concerning the primary blood indicative factors for the mentioned diseases. In the second phase, the theoretical and practical foundations of the software were developed, based on artificial neural networks. In Python, regression models were also crafted to check the feasibility of the analyses. Finally, the last stage consisted of testing with real datasets, based on 1,227 anonymized blood counts. Among the artificial intelligence algorithm models tested, Support Vector (0.02) and Multiple Linear (0.61) had the lowest performances, while Polynomial (0.97), Random Forest (1.0), and Decision Tree (1.0) showed the best results. Given that the Random Forest and Decision Tree regression models achieved an accuracy of 1.0, while the Polynomial model scored 0.97, Support Vector 0.02, and Multiple Linear Regression 0.61, it is concluded that the blood count analysis system, with Python tools like regression, proved to be highly efficient. The closer the R² value is to 1.0, the better the programming fits the model, ensuring accurate analyses. Aside from that, in order to expand the number of analysis possible to do be done we decided to use a second tool called ”classification”, with which we made a bigger dataset to be used as a model to identify blood related diseases and the behavior of complex and diverse diseases. With that in mind, we performed a second evaluation of the models by doing an accuracy test, scored 87 percentage points and with a confusion matrix. With those results, we verified that the high performance of the tests indicates that Artificial Intelligence can be avaunt-guard to the elaboration of more efficient medical diagnosis, improving people’s lives quality and, overall, lowering the number of deaths in our country.

MAGALH˜AES, Eduardo De Mˆonaco. PiezoPioggia: Energy Harvesting with Raindrops. 2024. 24 p. Research report – Scientific Apprentice Program, Col´egio Dante Alighieri, S˜ao Paulo, 2024. This project wishes to study and analyze the possibility of generating clean and accessible energy with the plain impact of droplets in the ground. Therefore, it was necessary to use piezoelectric devices in order to convert the kinetic energy of each droplet into electric energy throughout piezoelectric energy harvesting processes, (PEH). Piezoelectricity is a method of clean and sustainable energy generation, developed and explored by several scientists worldwide. Thus, while studying the proprieties of those devices, the project evaluates the present situation of electricity harvesting in Brazil, the benefits of piezoelectric technology and the possibilities it presents to economy and society. Throughout the development the project builds itself upon mathematical equations and experimental results, analyzing the deformation and generated tensions of piezos. Brand new data on the behavior of rain, as well as about the potential it presents for PEH are highlighted throughout the research, reinforcing the value of such process as a sustainable energy generation method alongside with its investment potential, both from governmental and private institutions. The project also deeply characterizes the piezoelectric device studied, diving deeply in its characteristics and evaluating the deformation of the device and treating the data sets with statistical analysis methods, in order to improve the precision of the data presented. All in all, the opportunities of piezoelectric energy harvesting in the rain, nella pioggia, shall be discussed profoundly throughout the project.

The theme of this project is to develop software to facilitate and innovate the design of low-voltage industrial electrical circuits. The goal is to develop a program that makes projects more efficient in terms of time, accuracy, and sustainability, automating dimensions such as calculating conductor cross-sections, protections, single-line diagrams, and analyzing with AI at which points industrial electrical circuits can be more sustainable. The 2023 Electric Energy Yearbook of the Energy Research Company describes that electricity consumption increases 2% per year in Brazil, and industrial installations represent the largest part of the national electrical sector (36.2%). As stated in standard NBR 5410/2004, when developing an installation project, an electrical professional works with several processes, depending on several criteria and calculations to present a reliable electrical installation. Minimal errors in calculations can cause damage to equipment, conductors, and individuals present in the installation. Using software to model these circuits optimizes time and brings more confidence to the project. This work aims to differentiate itself in this field by filling in the gaps in existing solutions for the industry, providing support for Brazilian standards, automatically generating single-line diagrams and presenting suggestions for sustainability in the circuits. The program is developed in Python, based on NBR 5410/2004 and engineering works. The software developed allows the user to size different distribution boards, motors and circuits, calculating the cross-section of the conductors/electrical protections, a particular transformer, and generating a single-line diagram in CAD. The program also presents suggestions aimed at sustainability to reduce material/energy costs. Tests were carried out with electrical engineering companies and students in the technical area, where the software presented high precision and very positive feedback from the interviewees, and it can be said that the work achieved its objectives.

GROSSMANN, João Miguel Sastre. ReCiPla - Cyclic Soil Microplastic Remover: A way to remove microplastics from soil using electrostatics. 2023. 28 p. Research report – Scientific Apprentice Program, Colégio Dante Alighieri, São Paulo, 2023. Microplastics are the largest form of physical pollution on the planet. Affecting everything from terrestrial and aquatic environments to the air, compounds up to 1 micrometer in size are present inside the human body and can intoxicate the main organs in which they are found, such as the lungs, spleen, liver, and heart. Therefore, methods of removing these compounds from nature are essential, which is why this research is based on electrostatically removing MP from the soil. To this end, a vibrating conveyor belt was designed that would act in conjunction with a plate electrified by a Van de Graaff generator to separate the plastic compound using electric field induction. After characterization tests to quantify the voltage produced by the generator, which produced an average of 95 kV, the vibrating belt was made and will be used later in conjunction with the electrostatic method. This methodology suggests that it’s a success even after the electrified plate was applied to its structure. It carried out the proposed processes, such as moving the test masses, vibrating them, and fully supporting the electrified plate. In addition, the electrostatic removal method was tested to verify its efficiency and applicability. It was found that the removal of microplastics ranged it from 10 to 20% efficiency, suggesting it to be an effective method for separating microplastics. It should be noted that these statistics will be improved as the research progresses. In this way, the research proved capable of establishing an electrostatic removal method, as well as a process for transporting the material to be removed, thus achieving the objectives it set out to achieve. Finally, it should be noted that this research is still under development, with a view to applying the process in conjunction with the conveyor belt to carry out sample tests, as well as improving the removal process in the future to make it more efficient.

Medication errors in patients are a global problem. They can negatively affect patients and be costly for hospitals and medical clinics. In 2021, a 28-year-old man with heart problems was admitted to a hospital in Porto Alegre. Due to a pharmacy error and insufficient monitoring in the administration, he received a dose 10 times higher than prescribed. This caused serious and probably irreversible damage to the patient. Reading the news and following the case in the media has encouraged research in scientific databases, searching for information and data on medication errors, as well as emerging technologies to reduce the occurrence of adverse medication events. Based on the findings of an English study that proved that errors occur at the drug prescription stage, the first stage of this research focused on drug dosage errors. The aim of this study is to develop an application based on artificial intelligence that can recognise these errors and help prevent them. The application uses a neural network to analyse prescriptions and warn of possible cases of incorrect dosage. The computer program was developed using a neural network and the drug dosage error recognition system using Python and Keras. The system was trained with 10 drugs and correct and incorrect dosage cases. A graphical interface was created to input and display new case data. Neural networks with different configurations were tested to obtain high accuracy with the training and validation data. A confusion matrix was used to assess the accuracy of the network for cases not used for training. The accuracy was approximately 96%, but problems were found in certain intervals. The errors are due to the need for more training, higher processing capacity and a cloud server. The results of the first stage of the research indicate the feasibility of using a neural network to recognise medication dosage errors and thus preventing the associated risks. Such a method could prevent cases like the one in Porto Alegre. Future studies could incorporate more types of drugs, allergies, drug interactions, pre-existing illnesses and other relevant factors into the system.