本研究主要了解(一)人工培育孔雀魚交配偏好、(二)人工培育孔雀魚與野生孔雀魚的交配偏好是否有所不同。透過觀察母魚選擇不同體色或不同熟悉程度的公魚，了解母魚的交配偏好。有以下幾點重要發現(1)對於各種不同體色的母魚而言，牠們沒有偏好特定體色的公魚；(2)在熟悉彼此體色的個體間，母魚偏好與自身體色相同的公魚；(3)熟悉度對母魚的交配偏好有顯著的影響，不同體色的母魚有相似的偏好，不熟悉的公魚最受偏愛，其次是熟悉的稀有公魚，熟悉的常見公魚最不受青睞；(4)人工培育母魚交配偏好比野生母魚更為強烈，暗示有較強的性擇作用(5)人工培育的母魚會偏好較鮮艷的人工培育公魚，而野生的母魚沒有較偏好人工培育或野生公魚。

本報告旨在探討不同形狀的物體在振動液面上的運動現象，通過改變物體形狀、漂與浮的狀態，及實驗時的振動條件，觀察物體的行為，並以液體表面張力、漂體與液面夾角的變化、振動模式與流場狀態解釋。研究發現：疏水性漂體因表面張力漂在振動液面上時，其重力會造成液面凹陷，由於漂體形狀對稱性質與質量分布差異，造成各端點與液面夾角不同，液面為漂體提供不同方向與大小的作用力，並產生不同的流場，使其移動與轉動，其(角)速度受液體種類、振幅、頻率、漂體質量影響。此外，在振動液面上移動的漂體與其他漂、浮體間會因為液面狀態互相影響產生交互作用力，進而出現吸引、排斥、繞圈的現象。

當以磁鐵做為擺錘的物理擺，並讓它在金屬面附近作週期振盪，經由實驗量測與理論擬合分析發現，本裝置會因電磁感應而在金屬面上產生渦電流，並對擺產生阻尼作用，其渦電流阻尼力是空氣阻尼力的18.4倍，進而使物理擺較快停止。進一步研究，發現渦電流阻尼作用力大小亦與金屬片材質、厚度、擺與金屬距離、磁鐵強度等因子相關，再者，因金屬的集膚效應，渦電流大小在金屬內的分布會隨著深度而呈現指數衰減；且此大小會受到金屬層間隙或是金屬面的不完整而進一步劣化。最後針對「減震」研究，經由磁鐵物理擺產生的總阻尼力以消除物體的振動能量增益達112%，較同質量的物理擺增益亦達25%，可實現較輕質量塊的物理擺式阻尼器在建築物防震領域上的有效應用。

此研究建構一套校園智慧疏散系統—GuideRoad Live，以協助學校規劃逃生路線，並在緊急狀況下迅速提供疏散建議。該系統具備精準定位、壅塞回饋、避難追蹤、聰明疏散等四大功能。在精準定位方面，系統進行地圖建模，結合校園WiFi數據庫，分析教室GPS資料，實現個人定位。在壅塞回饋方面，系統透過監視器分析樓梯壅塞情況，即時回傳數據、更新路線，並設計蜂鳴器警報，降低學生恐慌與推擠風險。在避難追蹤方面，系統匯入地震級數，提供即時警示預估逃生時間，提供逃或躲的疏散意見，並對外援助與給予使用者關懷。最後，系統根據節點、邊、人流速度、壅塞回饋的即時數據等因素進行權重計算，達到聰明疏散的目標。

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.

本研究探討太陽活動極大期與極小期臺灣周遭海域海溫與太陽黑子數的關聯。將極大期與極小期次年的海溫依季節分組，分析各季海溫距平與太陽黑子數的關聯。發現在太陽活動極大期次年的冬季與秋季，臺灣海峽區域表面海溫與太陽黑子數的關聯最顯著。推測係因在太陽活動極大期時西伯利亞高壓強度改變，導致臺灣附近的季風強弱產生變化，進而使臺灣海峽表面海溫與太陽黑子數較具關聯性。將極大期與極小期次年的海溫數據依各月分組後，發現結果大致與前述研究相符，但能發現許多依季節分組無法觀察到的現象，例如關聯性較高的海域範圍常呈帶狀分布，應是受洋流流向影響所致；在太平洋西部海域，具顯著性區域常呈團狀分布，顯示應與渦旋的生成及移動有關。

本研究探討倒立擺在不同鉛直振動頻率下的擺動軌跡、擺動時間、擺動範圍及自主校正之情形。我們發現單一倒立擺是否能夠達成穩定自主校正主要受鉛直振動頻率影響，且隨著鉛直振動頻率增加，倒立擺擺動範圍會縮小。當倒立擺的長度增加時，擺達穩定所需之角頻率上升，相同頻率下的擺動範圍增加。接著再將不同長度的擺頭尾以螺絲相連組合，觀察雙擺之運動情形，我們發現在雙擺的情形下，下擺與上擺的長度會影響擺是否能穩定，在上下長度相同與下長上短時，擺在適當頻率下即可達成平衡，但在下短上長的狀況下，下擺及上擺長度則須達特定比例才可平衡。

本研究主要想了解田中吊鐘姬蛛生活史過程中能量之權衡及其特殊巢穴之功能，經相關研究發現，蜘蛛生活史過程中在能量分配上明顯具有權衡現象，捕獵能量的投資隨著齡期的增加明顯減少，例如，捕捉絲的數量及捕捉區的角度逐漸減少，而防禦能量的投資隨著齡期的增加明顯增加，例如，巢穴長度及巢穴重量逐漸增加，尤其到了成體後更加明顯；關於蜘蛛對築巢材料形狀的選擇明顯偏好粒狀材料，但顏色方面，蜘蛛對淺色及深色材料並無明顯選擇偏好性；另外，巢穴對蜘蛛來說避敵的功能大於避光，且巢穴內有保溫功能，有利於卵的孵化；最後，巢穴上黏附蜘蛛捕食後的螞蟻屍體有利於吸引其他螞蟻靠近，增加捕食機會，但無明顯抑菌效果。

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。

股市投資涉及多元的因素和快速變動的市場趨勢，為了降低投資風險，我們希望找出個股適用的技術指標投資策略，因此本研究採用跨學科領域的科學研究方法，將財經和資訊科技兩大領域結合，使指標和程式交易相互配合，提高投資效率並降低成本。本研究使用個股適用的參數和組合進行回測，若回測有效，則系統將自動開啟網站進行交易。實作最初面臨策略難以靈活應變市場快速變化的挑戰，因此將原設計系統多次改良，以資料訓練的方式進行大數據分析，接著經由多階段篩選提高策略穩定性及降低投資風險。整體而言，本研究強調跨領域學習的應用，希望透過不同領域的知識，找出適合以技術指標判斷投資策略的個股，有效因應瞬息萬變的金融市場。

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.

本研究探討利用含蚵殼粉的海藻酸鈣晶球去除水中磷酸鹽的可行性。我們設計正向與反向晶球，加入不同重量蚵殼粉，觀察其對磷酸根的去除效果。實驗使用RGB比色法建立檢量線判讀磷酸鹽濃度，自製Arduino微型電導度計，追蹤即時離子變化，數據與EDTA滴定一致。反向晶球（4克蚵殼粉）於50分鐘內達80.8%去除率，優於其他組。Ca²⁺濃度變化為磷酸鹽反應指標，呈先降後升趨勢，初期與磷酸根結合沉澱，後期因磷酸根減少而釋放量累積。推估晶球在1至12小時期間可去除約0.007 mmol/L磷酸根，具後續吸附潛力。另外，透過煅燒法與CO₂排水集氣法反推分析蚵殼粉中CaCO₃純度，約為81.8%。證實晶球具化學沉澱與物理滲透雙重機制，具水質淨化與教學應用價值。