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.

Explorations in mathematics are limited due to the negative image and perspective about Mathematics itself in high school. However, some topics in mathematics are found interesting in high schools such as geometry and sequences. Therefore, this research will look at the explorative development of Ford Circle that creates some interesting results while combined with other theories and geometry. The main focus of this research is to address and explore the ford circle with its connection to the Sierpinski Triangle in hyperbolic geometry. The investigation and exploration will focus on the properties of geometry in the hyperbolic plane, the fractal geometry of Ford Circle and Euclidean fractals through a hyperbolic perspective that brings a fascinating correlation between all the topics discussed in this research.

「少數決遊戲」就是針對N個玩家詢問一些只能回答是或否的問題，而問題回答不必符合實際狀況，由少數一方獲勝，這個部分的定義與少數派賽局（Minority Game）中的定義相同，不同處為獲勝者須進入下一輪的問題，直到剩下一位或兩位玩家為止，由剩下玩家獲得最後的N單位獎金，但所有人需償還原來遊戲開始時所付出1單位的代價。前作「詐欺遊戲之少數決」[1]即對該問題作詳細的探索，但僅限於一組結盟人數。本作品是將前作內的獲利期望值與演算法作進一步的發展討論，並對結盟人數超過必勝結盟人數時的期望值變化做討論，得到賽局理論中的「少數派博弈」類似的結論。本作品更進一步討論兩組結盟人數的結果與期望值，後續的變化有些類似賽局理論。

Aim: The aim of this study is to make a disease-predicting model trained on data from weather stations and API using machine learning that gives the farmer the ability to predict crop diseases before they set in, allowing them to take timely preventative measures and reduce wastage. Materials and Methods: In this study the Internet of Things (IoT) sensors throughout agricultural fields of potato crops in Jafferabad, Depalpur Punjab. The sensors collect real-time data on environmental conditions, such as precipitation, air temperature, relative humidity, wind speed, and direction, Dew Point, VPD, and the Delta T values, to identify subtle disease indicators and patterns within the environmental data. Our novel machine-learning program makes use of the data collected by the weather station and analyses them. Results: Using the data, one predictive statistical method using Python 3.8.0 was created which uses the data from the weather station which can predict diseases before they set in.

本研究旨在利用組合數來分析第二類斯特靈數在模質數下的性質。研究分為五階段：第一階段我們參考O-Yeat Chan等人的論文，並改良了其證明過程，得到一個同餘組合數。第二階段利用第一階段的奇偶性定理發現(3n n)與𝑆(2𝑛,𝑛)同餘mod2並改良論文證明，使得證明更易推廣。第三階段我們更推廣到更一般的結論：滿足𝑆(𝑝𝑛, 𝑛)≡(p'n n)(𝑚𝑜𝑑2)時，p與p'是線性關係。第四階段與第五階段繪製熱圖時，我們發現圖中存在謝爾賓斯基三角形，對此進行了研究，並成功證明斯特靈數三角形在模2與模3下具有碎形結構與謝爾賓斯基三角形，這是文獻中都沒有探討過的。

雲彩是其中一個在世界上最奇妙的自然現象。在其中也隱藏著巨大的觀光經濟利益。因此，我們想要建立一個系統以預測晚霞雲彩出現的時間，以幫助台灣的觀光業。 本研究將藉由柯西公式、折射反射相關定理以及其他由論文貢獻的輔助公式提出一個計算模型，以計算預測晚霞雲彩出現的時間以及光的路徑。自動化的部分，包含溫度、壓力以及濕度，我們藉由政府的公開資料平台以及衛星公開資料進行靜態網頁爬蟲抓取。雲層高度我們則是透過動態網頁爬蟲，逐一從AccuWeather公開網站上爬取相關資料以利計算。我們將爬取的資料以及所提出的模型計算後以15分鐘作爲一個區隔，提供使用者準確的時間觀賞雲彩。 透過此模型以及爬蟲擷取資料計算得到的結果，我們可以得到接近90%準確率的預測結果。因此，我們能夠準確地為用戶提供正確日落雲彩出現的時間。

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.

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.

2023年9月數學雜誌《Crux Mathematicorum》刊登有趣的三角形內心的幾何問題，我們先證明了原命題的長度性質，再創新刻劃出有趣的面積不變量。隨後將內心推廣到旁心、垂心與外心的建構，並且證明僅此四心的建構下才有長度與面積不變量。值得一提的是，除了前述的定量項目外，我們也發現四種建構下的三線共點之定性性質，同時刻劃四種建構的關聯性是漂亮的等角結構，這是本研究亮點。推廣到多邊形，我們發現本質的幾何結構為截線的角平分線性質（內心與旁心的結合），從而將此問題轉換成一般性問題，並給出了豐富的等長、等角、等面積之性質，以及連線多邊形恆為圓外切多邊形。

我們研究的問題源自於〝棋盤上的蛇〞(Snakes on a chessboard) ，是由教授Richard Stanley所提出。問題如下：在 棋盤形格子上，蛇由任意一格出發，但蛇的走法只能往右→，往上↑，或停住。若此蛇已停住，將由另一條蛇來走，且不同蛇走過的格子不可重疊。證明：將 棋盤形格子完全覆蓋的總方法數為費氏(Fibonacci)數列某些項的乘積。 我們以〝生成格〞概念來解決問題，藉由生成格建立二維棋盤形格子〝蛇填充數〞與費氏關係，並試圖拓展三維空間棋盤情形，在過程中發現藉由〝生成矩陣〞可以組成空間棋盤的〝生成格〞，並以此解決p×q×r的空間棋盤問題。 2022年9月，在網站The On-Line Encyclopedia of Integer Sequences上發現由教授 Greg Dresden及其學生Aarnav Gogri提出的數列，與我們2022年3月於高雄市發表的科展作品中的一組數列完全對應，甚而對此數列的原問題Tiling a Hexagonal Strip with Triangles and Diamonds，我們的作品還能做進一步延伸探討。

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 this project, we created a function integrated onto a Lingao Chassis that allows the robot to use Slam and Gmapping to successfully navigate its way to the most convenient destination for the user, while avoiding any obstacles on the way, improving the default Gmapping errors.