本實驗研究螺線圈數、擴大倍率對黃金螺線與阿基米德螺線交點數的影響，並預測交點座標。研究發現： 1.透過趨勢線預測圈數變化時的交點數，分別使用第一至第四象限預測，發現四個象限各別形成的趨勢線預測值總和會有較高的準確率。另外，我們也利用兩螺線圈數來推導出預測交點數的公式。 2.透過趨勢線預測兩螺線比例變化時的交點數，當黃金螺線和阿基米德螺線擴大倍率的比值越大，交點數越多。 3.前25個交點距離、夾角及圍成三角形面積所形成的趨勢線，用以預測第26個交點之後的數據，誤差率在5.16 %以內。 4.預測交點座標第26點以後，發現預測越接近x軸的交點，y座標偏差率越高，x座標偏差率越低，反之亦然。

生物時鐘可對生物體的行為與生理造成影響，在探討晝夜節律特徵的差異時，過去研究常侷限於北美大陸的品系，缺少赤道及南半球品系的晝夜節律特徵探討。有鑒於黑腹果蠅在全球各大洲的廣泛分佈，因此我們以黑腹果蠅（近赤道與中高緯度品系）為材料，研究果蠅是否因緯度而有相異的晝夜節律特徵？結果顯示不同緯度的果蠅品系展現出相異的晝夜節律特徵。赤報品系在全暗狀態下仍維持原本光暗12小時的穩定節律，而南北半球的中高緯度品系則具有相似節律特徵，即在全暗狀態下的節律不對齊原本正常光源的穩定節律，其他如活動量、週期、及節律強度等皆有著品系間的差異。更進一步比對實驗中各個品系基因序列，研究發現per和tim在調控區段有許多SNP變異，顯示其與晝夜節律特徵的關係，有助於後續尋找更多造成晝夜節律特徵差異的可能遺傳變異並探討。

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

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.

本次實驗以地熱溫泉區的土壤中所篩選出的8隻耐熱菌種，並加以純化、液態培養，將8種菌液稀釋成度三種不同濃度(100X、200X、500X)，以恆溫培養箱控制環境溫度(25℃、30℃、35℃)模擬環境變因，並測試和定量菌株的溶磷、固氮能力，亦測試載鐵、抗番茄萎凋菌能力及對於聖女番茄及桃紅番茄所帶來的影響。本研究發現 LC26 及 LC28 可於熱逆境下顯著促進聖女番茄生長，此外LC03亦能於熱逆境下明顯促進桃紅番茄生長。本研究也發現LC03、LC26及LC28對玉米及胡蘿蔔有最好的促進生長效果，而對青江菜有最明顯的抑制生長效果，可作為輪耕作物之參考。最後本研究發現這三隻菌種皆對大花咸豐草之生長有抑制情形，其中以LC26(500X)抑制效果最佳。

現今醫療中，黃疸的早期偵測對肝臟疾病的預防與治療至關重要，但多數人難以在症狀輕微時察覺。我們希望藉由智慧手機影像結合機器學習進行黃疸檢測，提升民眾自我監測的能力。Su 等人（2021）曾使用深度學習和機器學習進行黃疸預測，但其方法依賴專業色卡進行色彩校正，成本高且限制應用範圍。本研究提出以白平衡演算法中的白色補丁法與灰界演算法，搭配深度學習模型 DCCNM1和2 取代色卡，提升黃疸檢測的普及性與便利性。經黃疸偵測效果評估顯示，DCCNM2 在無色卡模型中表現最佳，雖然各指標略低於色卡校正，但其展現出優異的穩定性和準確性，證明其作為無色卡黃疸篩檢方案的可行性。本方法將能提供便捷的居家黃疸檢測途徑，尤其對偏鄉地區居民而言，不僅提升早期發現的機會，還能有效減輕醫護人員的負擔，推動大眾健康管理。

本實驗主要透過程式模擬及數據分析，探討受力材料之裂紋走向。透過模擬，我們找出會影響裂紋發展的因素，如原斷裂紋的長寬比。於不同的的材料會影響裂紋走向，我們將材料設置為單一材料與兩種材料組成的複合材料進行探討，並將結果進行分類。此實驗有助不我們去理解同的初始裂紋對不材料後續的裂紋關係，目前也正在嘗試利用cGan系統預測複合材料與裂紋的關係，希望能預測出準確的結果。

本研究利用乙二醇處理後的聚對苯二甲酸(PET)及氯氧化鋯，以創新的無溶劑方式”solvent-free PET-to-MOF conversion”合成一類金屬有機框架材料(MOF) ── UiO-66(Zr)，並發現此材料可用於催化Pechmann condensation、以間苯二酚和乙醯乙酸乙脂合成羥甲香豆素。於190℃之環境下，乙二醇處理6小時的EG-PET可合成出晶型最接近模擬模型的UiO-66晶體；而我們開發的合成方法中，最佳的反應條件為反應物質量比(氯氧化鋯:PET)= 2:1、130℃合成24小時。 對於合成出的UiO-66(Zr)，我將其進行了XRD、FTIR、BET、SEM檢測，結果符合UiO-66(Zr)的特徵；唯比表面積僅有約900 平方公尺/克，推測此方法合成出的材料之有機linker數量和一般UiO-66(Zr)有所差異。 對於催化合成羥甲香豆素之反應，我們首先發現UiO-66(Zr)可作為非勻相催化劑參與此反應；且實驗結果顯示，相較於溶劑熱合成的UiO-66(Zr)，以本研究的方法所得之UiO-66(Zr)作為催化劑可得到更高產率的羥甲香豆素。

本研究探討隨機生成數列的長度期望值。一個籤筒中有n支籤，編號分別為1,2,3,…,n，每抽出一支籤，就將抽取的編號寫在紙上，形成一個數列。數列只能向左右兩端添加項，不能從中插入。抽出的籤若大於目前數列的最大項，則將抽出的數寫在目前數列右邊；抽出的籤若小於目前數列的最小項，則將抽出的數寫在目前數列左邊；抽出的籤若介於目前數列的最小與最大項之間，則操作結束。基於此想法，研究者將數列依照添加項的方向分為「單向數列」與「雙向數列」兩類。顧名思義，單向數列只能向一端延伸（本研究不失一般性討論往右延伸），雙向數列代表可以向左右兩端延伸。此外，研究者又將數列分為「嚴格遞增減」和「非嚴格遞增減」兩類。在生成原理上，嚴格遞增減等價於「抽後不放回」；非嚴格遞增減等價於「抽後放回」。在這樣的規則下，本研究探討了n支籤抽完放回與不放回時，單雙向隨機生成數列的長度期望值之通解，並成功證明了一些恆等式及性質。

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.

本研究結合奈米合成技術與生物醫學， 利用表沒食子兒茶素沒食子酸酯 (Epigallocatechin gallate, EGCG) 作為載體 調控摻雜Cu2+/Cu3+與 Fe2+/Fe3+之含量 並以π-π交互作用力附載缺氧性抗癌藥物替拉扎明 (Tirapazamine, TPZ) 成功製備出多功能金屬多酚配位奈米顆粒簡稱為EFeCuTPZ。 材料經紫外-可見光譜 (UV-vis)，、動態光散射 (DLS) 及掃描式電子顯微鏡 (SEM) 確認其粒徑大小、形貌學與穩定性。利用808 nm和671 nm雷射分析其光熱轉換效率 評估光熱療法效果，。在腫瘤微酸性環境下， EFeCuTPZ可利用高濃度之H2O2行芬頓反應 (Fenton Reaction) 產生高活性之氫氧自由基 (•OH)， 展現化學動力療法 (Chemo dynamic-therapy, CDT)，。同時， 藉由材料中的Cu²⁺與腫瘤環境中的穀胱甘肽 (Glutathione, GSH)反應減少高活性物質 (Reactive oxygen species, ROS) 的消耗 增強CDT之療效。酸性條件下 TPZ顯著釋放 有助於腫瘤治療。 另外， 細胞實驗顯示EFeCuTPZ具有高生物相容性與治療效果， 成功開發出具CDT，、CT及PTT功能之奈米複合材料 為醫學新興藥物材料提供可能性。