成就證書

Road safety remains a global concern, with driver-related factors like distraction, drowsiness, and medical conditions such as stroke being leading causes of accidents. In this paper, we propose a real-time ensemble learning framework that leverages transfer learning for the detection of stroke, drowsiness, and distracted driving. Our model integrates multiple Convolutional Neural Networks (CNNs) fine-tuned for each specific task, and employs a stacking method to combine the predictions of these models using a meta-classifier. Notably, the model is optimized to enhance stroke detection, minimizing false negatives— an essential aspect for timely medical intervention. Experimental evaluations on diverse datasets demonstrate the efficacy of our approach, achieving an overall accuracy of 92.5%. The results emphasize the model’s potential for real-time driver monitoring, offering critical safety features that could reduce accidents and save lives.

Electricity has become an essential part of modern life, powering homes, businesses, and industries. However, the misuse of electricity or malfunctioning electrical systems can lead to hazardous situations such as electrical fires, shocks, and significant energy wastage. This project focuses on creating a Comprehensive Electrical Security System to protect users and properties from the risks associated with electricity. The system is designed to prevent electrical malfunctions, ensure safety in various scenarios, and monitor energy consumption effectively. It integrates a variety of sensors and safety mechanisms to detect dangers and take preemptive action

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.

According to the 印尼n Association of Olefin Aromatic and Plastic Industries/INAPLAS, 2019 national plastic consumption still relies on plastic packaging at 65% and surprisingly, around 60% of plastic waste is absorbed by the food and beverage industry. The waste has been widely sought to be environmentally friendly, one of which is by developing biodegradable packaging. The purpose of this research is to make durian peel cellulose nanocomposites impregnated with TiO2 NPs, to form antibacterial properties against E. coli wild type and resistance. In this research, there are research methods consisting of nanocomposite synthesis, PSA test, FTIR, physical characteristics test and resistance test. The results analyzed that the nanocomposite nanocellulose-TiO2 NPs was successfully made using a 1:1 ratio and had a particle size of 458.7 nm based on the PSA test, which is classified as a nano size. The success of nanocomposite synthesis was proven by the results of FTIR analysis, which showed the formation of 698.65cm-1 and 1633.99cm-1 spectra, indicating the peak of TiO2 NPs and O-H functional groups on TiO2 NPs, as well as 1028.98cm-1 and 1158.42cm-1 showing C-O and C-O-C bonds in cellulose. The antibacterial test performed showed no significant activity in disc diffusion and well diffusion tests against E. coli wild type and resistance. This is potentially caused by inhomogeneous particle size variation. Physical characteristics test showed that the tensile strength test (0.075 > 0.0125 MPa) Durio Nano-Pack is superior to styrofoam, but the compressive strength test (0.125 > 0.875 MPa) shows the opposite. In this study, nanocomposite has a potential innovation that provides good mechanical properties and has a dual function mechanically as bio-based food packaging and chemically as antibacterial. Further research is needed to improve the particle size homogeneity of nanocomposites, modify the impregnation method, so that it has the potential to develop multifunctional materials that excel in various applications.

This paper presents a simplified but mostly accurate model for the acoustic mechanism of Seebeck sirens. We investigate the impact of key parameters, including the number and size of holes, as well as the angular speed of the disk, on the characteristics of the produced sound. The disk is fabricated using fused deposition modelling 3D printing, and we used a brushless motor, an air compressor, and a shotgun microphone to capture the generated sound. An order of magnitude analysis was conducted on the Navier-Stokes equation to formulate a simplified version. These simplifications allowed for a low computational intensity model relating volume flow rate to sound pressure level, which is used to predict the waveform of sound produced. Our findings reveal that the fundamental frequency of the sound can be precisely predicted by only the rotational frequency of the disk and the number of holes, a relationship validated experimentally. Notably, observed asymmetry in the waveform was attributed to skin drag effects, and this hypothesis was experimentally verified. Our model computes a solution in less than half a second on average: far less than the 21h 47min needed for a k−ω turbulent model to compute the same phenomenon. The research presents and verifies a simplified model of acoustic mechanics for the sound generated by rotating systems that require little computational resources, which can prove useful in situations where absolute precision is not required, in exchange for ease of computation. For more precise systems, this model serves as a foundation for quickly generating an initial design, paving the way for subsequent iterations using more comprehensive models. The developed model not only serves as a foundation for efficient preliminary designs but also contributes valuable insights into the intersection of fluid dynamics and sound production.

In the upcoming years, both population and energy consumption are expected to increase dramatically [1]. Industrialization has led to a dramatic shift in the energy environment [2], with predictions of a 57% increase in demand for energy between 2002 and 2025 [3]. In addition to organic materials like trees and solid waste, fossil fuels like coal, natural gas, and oil provide more than 90% of the world's energy needs. Their overuse has resulted in the release of climate-altering greenhouse gases like carbon dioxide (CO2) and methane (CH4) into the atmosphere [4]. Scientists and other stakeholders are putting more emphasis on finding solutions to global warming, increasing energy production in order to meet increasing demands, and decreasing emissions of greenhouse gases. Using greenhouse gasses to make useful chemicals or fuels is one solution to both problems [5]. This motivated researchers to investigate the potential of CO2 and CH4 as clean energy sources. The process of dry reforming of methane (DRM) has been identified as a potentially successful strategy for transforming CO2 into marketable syngas with a balanced H2/CO composition [6], [7], [8], [9]. The economic viability of DRM, the reactor type, the availability of raw materials, and the intended use of the produced syngas are all-important considerations. Though DRM is gaining popularity, maintaining its long-term stability is difficult due to carbon accumulation from CO disproportionation and methane degradation [10], [11]. The catalyst used, as well as other parameters like as pressure, temperature, feed concentration, and reactor size, are critical to the process's effectiveness. In this scenario, a nickel catalyst on a La2O3/SiO2 substrate with microspheres and a core-shell structure will be developed to improve the conversion of greenhouse gases into profitable syngas. This catalyst is projected to improve the efficiency and performance of the DRM process significantly.

The country of Nepal, although beautiful, is facing many challenges due to its geography, lying between the towering Himalayas and the vast plains of Terai. The narrow mountain roads, prone to landslides and poor infrastructure, often result in frequent accidents. This situation is worsened by the delayed emergency response, as accidents are often reported much later than the time they occur. In the past ten years, over 15 major bus accidents have killed hundreds of people, and in 2024 alone, more than 80 deaths were reported. In response, the "Third Life" project was developed to improve emergency response time and save lives.The project has two main components: first, a device equipped with GSM (Global System for Mobile Communications), a GPS module (Global Positioning System), a gyroscopic sensor, and a microcontroller to detect accidents in real-time within seconds of the incident. Second, once an accident is detected, live coordinates are sent directly to emergency services and police stations for immediate assistance.This project is not only vital for Nepal but also for countries with similar terrain and infrastructure challenges. The "Third Life" project aims to save many lives that are lost due to delayed reporting, ensuring quicker emergency responses.A tragic example of this was the 2024 Trishuli bus accident, where many lives were lost when the bus plunged into the river. To date, the bus has not been recovered. Our project aims to create a waterproof device that, when connected to a satellite, will send live coordinates to emergency services, ensuring 100% reliability. This device could help locate the bus, which is still missing, within seconds.Ultimately, this initiative offers more than just safety it restores peace of mind and hope for the families of victims, providing them with a chance for a better future despite the tragedy.

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.

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.

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.