四等獎

One of the main factors that contribute to fire incidents and the excessive heat people feel during a heat wave is the building materials used, and one such material that possesses durable and heat-resistant properties is sandwich panels. A possible structure that can be used to model sandwich panels is honeycomb structures; however, further research has yet to be conducted on its applications as a heat-resistant urban construction material. This study aims to design a three-dimensional model of a honeycomb sandwich panel and simulate its performance under different thermal and structural stressors. A 3D model of the honeycomb sandwich panel was generated using Autodesk Fusion 360. Then, multiple versions of the panel were generated with varying heat-resistant core materials—namely, aluminum, nickel, nickel-copper alloy 400, and copper—along with polystyrene as the core material for the control model. The following properties of every panel were assessed using finite element analysis (FEA): static deformation, stress distribution, strain distribution, total heat flux, and thermal gradient. Results showed that when subjected to varying structural loads (2 kN, 5 kN, 7 kN), the nickel-core panel demonstrated the best results in terms of static deformation and strain distribution due to its relatively lower deformation and elongation values, respectively. Meanwhile, under the same structural loads, the aluminum-core panel performed better than other core materials in terms of stress distribution due to it having the relatively highest difference between its simulated von Mises stress and its yield strength. The honeycomb sandwich panels have also shown to possess heat-resistivity when subjected to a thermal load of 90°C, with polystyrene being the most promising material overall in terms of heat-resistance due to its relatively lower heat flux and thermal gradient. The results from this study would contribute to future research on honeycomb sandwich panels and may be used in real-life applications.

The aim of this project is to enhance the mobility of individuals with disabilities, particularly aiding them in navigating stairs and challenging terrains. Across the world, powered wheelchair employ various methods, primarily categorized into two: 1) tracked mechanisms and 2) robotic wheelchair utilizing intricate robotic systems. The design presented by our team belongs to the latter category, which is recognized for its lighter build when contrasted with the former. However, despite its lightweight structure, this wheelchair design incorporates equipment that renders it more cost-effective and practical than conventional designs within the same category. Our design integrates three distinct mechanisms to adjust the height and center of mass of the passenger during stair climbing and maintain balance and surface contact. Utilizing an array of sensors, it continuously monitors the position of the person on the wheelchair and the wheelchair on the surface. This data guides adjustments in the mechanisms, ensuring stability. This innovation harbors the potential for enhancing various functionalities, including: GPS integration for user navigation. Real-time monitoring of vital signs (e.g., heart rate, blood pressure, body temperature). In an emergency, this data can be transmitted to ambulance centers to pinpoint the individual's location and immediate assistance. A simplified ambulance request system, accessible via a single button press. Overall, this innovative wheelchair prototypes aims to revolutionize accessibility, granting enhanced mobility and independence to individuals with disabilities.

This thesis presents the design and construction of a small-scale humanoid robot, covering all aspects from 3D modeling to electronics design and programming. The robot is built entirely from custom 3D-printed components, with a new servomotor developed specifically to meet the project’s requirements. During the robot’s development, custom electronics were also designed, leading to a modular platform that enables easy interaction with diverse modules like servomotors and inertial measurement unit (IMU) modules. This modular approach allows these components to be programmed and controlled with minimal adjustments, as well as making development of potential future modules straightforward. The robot is operated via a computer application that includes a graphical user interface for displaying real-time data from the robot.

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.

本研究先觀察著名的密克定理（Miquel theorem）與密克點（Miquel point），我們創新給出了新的研究項目，關注密克點𝑃與密克三角形的頂點所構成直線和原三角形𝐴𝐵𝐶三邊直線的其餘六個交點，這是前人沒有觸及的研究項目，從而定義旁接三角形與衍伸三角形。 我們先針對特殊型（直角）的構圖，發現滿足兩個衍伸三角形的有向面積 [𝐴1𝐵1𝐶1]=±[𝐴2𝐵2𝐶2] 時，𝑃 點形成的軌跡為原三角形的 Kiepert hyperbola 與外接圓，這個是有趣且重要發現，我們也進一步給出其幾何必然性。進一步考慮 [𝐴1𝐵1𝐶1]=𝑟[𝐴2𝐵2𝐶2] 時，則刻劃出 𝑃 點軌跡為圓錐曲線系。在前面的基礎下，再針對一般型（任意角）的構圖，若 𝑃 點位於原三角形外接圓及Kiepert hyperbola 與 Steiner circumellipse 的線性組合曲線上，此時兩個衍伸三角形 𝐴1𝐵1𝐶1 與 𝐴2𝐵2𝐶2 的有向面積比值為定值，且兩者恆為相反數。

Infections by the Dengue virus (DENV) cause a disease amonghumansreferred to as Dengue fever, which causes thousands of fatalities globally. There is no existing treatment as of yet that successfully targets DENV. Among the factors thatdeterminetheentry of the virus and severity of the disease is the envelope(E) protein of DENV. This study aimed to examine forty antiviral phytochemicals enumeratedinpaststudiesaspossibleinhibitorstotheEprotein of DENV to provide candidates to aid in drug discovery against DENV. The phytochemicals were screened for their likelihood of inhibition of the E protein using AutoDock Suite and LigPlot+. Seven phytochemicals produced favorable binding affinities to the E protein, which are based on the interactions between the phytochemicals and amino acidsintheactivesiteoftheEprotein.Lipinski’s rule of 5 was then used to screen the seven phytochemicals for oral bioavailability. Glabridin has a binding affinity of -7.6 kcal/mol and was predicted to be orally bioavailable. This phytochemical interacts with amino acids in the E protein active site through hydrogen bonds to Asn355, andPhe337, as well as ten hydrophobic interactions. These interactions ensure that glabridin is able to specifically target and fit intotheactivesiteoftheEprotein, preventing its binding to the host cell and activating its viral proliferation. Glabridin is known to be found in the roots of licorice plants, providing anatural source for a possible cure for Dengue fever.

Over 30 million souls live in a world of darkness, a number greater than the populations of both Norway and Sweden combined. Every individual deserves the chance to embark on a journey across our magnificent blue planet. Yet, regrettably, little has been done to assist them. With this project, we’re lighting the way for the blind to explore our beautiful world independently, breaking free from dependence and embracing boundless horizons. In order to put our theory of the project into practice & explore the use of artificial intelligence & computer science, we started by collecting the required materials for our project such as micro-controllers, sensors, a pair of glasses, a laptop, and a miniature camera. Then we moved onto creating the project itself in which the digital software programmed onto the hardware plays the key-role, as the sensors and the camera will record the details and information from the surroundings and send it to the laptop for further processing. The camera would be the backbone of our project, as it will stream real-time footage to the laptop which will be analyzed by an open-source object detection model ‘YOLOv8’ for identifying objects. After finishing the base model of our project, we tested it in-front of objects such as toy cars, bikes, people, etc, and the results of the object-detection would be shown on the laptop. To observe this data, we created a device which has different modules and integrations for different functions. For example, we will use our camera and then stream it onto a laptop so the reading and the data can be processed on the laptop by AI using YOLOv8. As mentioned in the start, many people do not possess the ability to see, to assist them we have thought of this device which uses all readings and its analytical skills to analyze data and help them navigate, travel or simply, live a better life.

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.

Agro-guard aims to revolutionize disease identification and community-based projects in the field of agriculture. Integrating Machine learning, Computer vision, clustering, and community-based technology, this project helped farmers to detect their plant disease with their solution and for early warning of plant disease which was spreading in their community which helped in crop management. The research project is divided into three parts.First,Integrating Machine learning to detect and classify plant disease with their solutions.Second,Integrating Density-Based Spatial Clustering of Applications with Noise (DBSCAN),to identify disease and analyze the pattern within agricultural regions.Third,Establishing notification system to notify real-time alerts to farmers about disease spreading in particular region.The research is crucial because it solve one of the crucial problem of our community which is untimely detection of disease.The finding of the research highlight the effectiveness of Agro-Guard framework in early disease detection and community detection.The machine learning models achieved high accuracy in identifying common plant disease and clustering results the pattern in diseases that were very important for notifying the community.The significance of these findings is that it can build powerful system which will overall grow the production of crops and plants due to timely update of the disease prevailing in the community.It contributes in sustainability production of crops and plants which ultimately ensure the good livelihood of farmer.

Space Medicine and relevant sciences are still considered a new era; the first humankind steps toward the space took place since less than 60 years. It has been noticed the adverse effects of microgravity on the human body in different aspects, our concern here is the musculoskeletal aspect. On the ground we didn’t notice how we can stand up, or how our muscles and bones of the lower limbs can keep us standing up right. This is by a complicated process including the bones, the equilibrium, and the anti-gravitational muscles of the lower limbs which occurred without thinking about it. The force of Earth gravity against our bones of the lower limbs makes them harder and makes the muscles stronger, because they are interfacing the earth gravitational force every moment we are standing up, as per Newton’s third law (for every action in nature there is an equal and opposite reaction), such forces are unavailable in space and its effect being obvious on arrival to earth after long stay space flights, so being unable to keep standing upright easily on their arrival. On return to earth the routine medical examinations revealed loss of astronaut muscle mass and bone density particularly of their lower extremities because they did not use them in space for a long time. Currently, astronauts on board of ISS (International Space Station) they accomplish daily tasks including resistive exercises ARED “Advanced Resistive Exercise Device” in form of treadmill, ergometer, and weightlifting machine, to decrease the loss of bone density and muscle mass of their lower limbs. Despite their discipline to those exercises they still lose 1-2% of the muscle mass and bone density that give importance to add some protective measures to keep their muscles and bones healthy. Through this article, the idea is to make a device such AACT (Automated Alternative Compression/Traction) to be applied daily to the astronauts lower limbs as part of their daily exercise during space flight to give push/traction forces to astronauts lower limbs to prevent or at least decrease such loss, by AACT we are mimicking the gravitational force of earth on astounds lower limbs during long space flights to let them be healthy till they come back.