MEDTEC - Artificial Intelligence Software for medical diagnosis optimization and analysis
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.
Safe CrossWalk (SCW)
Safe CrossWalk (SCW) is an innovative solution designed to enhance pedestrian safety at crosswalks, addressing the alarming issue of 270,000 pedestrian fatalities worldwide each year. By integrating advanced sensors, artificial intelligence, and real-time communication, SCW creates safer and more efficient urban environments. The system comprises three key components: SCW Strisce, a smart crosswalk device that detects pedestrian movement; SCW Car, a vehicle-integrated system that alerts drivers; and SCW AI, which processes data to optimize traffic flow and safety measures. SCW offers a proactive approach to reducing accidents through detection, alerts, and data-driven optimization. The solution not only improves safety but also supports urban planning by providing valuable insights into pedestrian and vehicle behavior. SCW aligns with the growing demand for AI-driven technologies in Smart Cities, presenting a scalable and cost-effective model for implementation. By fostering collaboration with municipalities and insurance companies, Safe CrossWalk aims to transform urban mobility, saving lives and creating smarter, safer cities.
Utilization of Nano cellulose from date palm waste for improvement of thermal stability in epoxy composite
Nano additives is becoming popular trends nowadays due to its nanosize (1-100 nm). Incorporating nano additives in polymer could increase different properties such as mechanical, physical, electrical and thermal stability (1, 2). Different nano additives has been used such as nano copper oxide, nano silica, nano zinc oxide, nano titanium dioxide but most of these come from synthetic or metal oxides that considered as non-environmentally friendly and harmful to human when exposed or inhaled (3). One of the green materials that become attention by researchers is nano cellulose. Nano cellulose can be extracted in different methods and sources such as from wood, and non-woody resources such as kenaf, jute, bamboo as well as from bacteria such as Acetobacter species(4). This making nano cellulose abundantly available in resources. Nano cellulose can be in the form of nano crystalline cellulose (CNC) or NCC or can be in form of nano fibrillated cellulose (NFC) and bacterial nanocellulose (BNC)(5). This nanocellulose has many advantages that can give improvement in different applications such as mechanical, physical, thermal and improving the biodegradation when added together in different matrices (6, 7). Polymers have a problem in thermal stability while processing. It hard to control and maintain the thermal stability of polymer during processing and most polymers considered to have low in thermal stability except for thermosetting polymers such as epoxy. Epoxy has been widely used in many fields such as coating, adhesive, laminates, castings and many more (8). But the drawbacks of epoxy while using is hard to maintain and controll the thermal properties when processing of this materials and used for long period due to aging and attack by free radicals causing by UV radiation (9, 10). In this study we are incorporating nano additives into epoxy as polymer matrix to enhance and improve the thermal stability of composite by crosslinking the polymer chains with the nano additives. Furthermore, the nano additive used is come from nano cellulose extracted from date palm waste and thus to create an environmentally friendly and sustainable nano additives products.
Investigating the Effects of Temperature and Carbon Dioxide Levels on Nannochloropsis oceanica Using a Hemocytometer Counting Method
Climate changes that include ocean acidification and global warming are serious problems in the ecosystem, affecting marine phytoplankton, including Nannochloropsis oceanica. In the effort to further explore the impact of rising temperature and carbon dioxide (CO₂) concentrations on oceanic ecosystems, the phytoplankton Nannochloropsis oceanica was used as a model organism. This study explored the effect of temperature change and CO₂ concentration on the growth of Nannochloropsis oceanica, achieving 243 samples that were tested with three different temperatures (24 degrees Celsius (°C), 28°C, 32°C) and CO₂ concentrations (0 milliliter (ml)/min, 0.4 ml/min, 0.6 ml/min), utilizing a hemocytometer counting method. Results indicate that the CO₂ concentration has a significant effect on the population of Nannochloropsis oceanica. But the temperature doesn't affect a lot. The Nannochloropsis oceanica in the lowest temperature and highest concentration of CO₂ in its environment had the highest population growth, and in the highest temperature and lowest concentration of CO₂, it had the lowest population growth. Results show the serious negative effect of climate change on the cosystem and the importance of environmental protection. Population blooms due to excess CO₂ taking up ocean resources causing dangerous ecological imbalances.