2025年

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。

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。

Carbon dioxide (CO2) is an important greenhouse gas that helps trap heat in our atmosphere; without it, our planet would be inhospitably cold [1]. It is the fourth most abundant gas in the Earth's atmosphere. It is a byproduct of normal cell function when breathed out of the body, and produced when fossil fuels and organic wood compounds are burned [2]. However, an increase in CO2 concentration in the atmosphere can contribute to climate change and ocean acidification, and exposure to high levels of CO2 can produce a variety of health effects [3]. Human progress and economic innovation have led to increased emissions, causing climate change and affecting all living creatures. Current levels are 36.8 Gt CO2 in 2023 and are expected to reach 54-56 Gt CO2 by 2030 [4]. Figure 1 displays the current atmospheric CO2 measurements at Mauna Loa Observatory without seasonal variations [5].

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.

Due to their pollination services, honeybees are one of the most ecologically vital animals, being singlehandedly responsible for nearly 80% of global agricultural pollination [1]. However, in recent years, they have experienced large declines in populations, and as a survey reported roughly 50% of beekeepers in the US lost their honeybee colonies [2]. These losses are experienced globally due to a combination of many factors, including but not limited to habitat loss, pesticides, climate change, and other invasive species [3, 4]. One of the biggest factors attributed to the decline of honeybee colonies is the usage of pesticides, specifically neonicotinoids [3-6]. Neonicotinoid compounds have been used globally since their introduction in the early 1990s [4]. Studies have shown that neonicotinoids can have both sublethal and lethal effects on honeybees, depending on the dosages that they are exposed to, as neonicotinoids bind to nervous system receptors of honeybees [7]. These effects can range from behavior changes to altered motor functions [7-9]. Among the reported effects, one of the more significant ones is the effect of neonicotinoids on honeybee learning and memory [10, 11]. Additionally, there is a lack of availability for methods of monitoring of honeybee hives, essentially meaning that the only methods to track honeybee health are through obtrusive physical methods of inspection. This paper aims to develop a novel AI-based honeybee health assessment system, able to monitor beehives using the following functions: continuous temperature and humidity monitoring both inside and outside the hive, as well as video and audio recording to assess honeybee health as well as population. In addition, this system can be used for honeybee-related studies such as nutrition effects and evaluation on health, learning, and memory. To do this, four types of nutrition have been studied and their effects have been analyzed by a deep learning approach.

本研究記錄人類進行「剪刀石頭布」遊戲時的決策行為，也設計T型迷宮建立鼠婦之負趨光行為作為動物模式，探討行為偏好與決策依賴性等特性。我們發現「出石頭」的機率較高，且時間間隔縮短後，「出剪刀」的機率增加而「出石頭」的機率減少，並會展現負相關的決策經驗依賴性，其中「慢出組」更為明顯，代表出拳間隔縮短而減少意識作用，負相關的決策經驗依賴性即會減弱。另一方面，鼠婦在負趨光性刺激剛消失後，仍呈現負趨光性的選擇方向，具有習慣性。鼠婦在選擇行走方向多次後，會呈現與前次選擇的正向相關性。在負趨光性的環境刺激後，上述的現象會先消失，而後再現。若負趨光性刺激方向轉換，則原先的趨光行為消失，應是因方向選擇的習慣性干擾了負趨光性的選擇。

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.

Fungal infections, particularly those caused by resistant strains like Candida auris and Candida glabrata, pose a significant threat to global health. The widespread use of azole antifungals, such as fluconazole, has driven the emergence of multidrug-resistant strains, undermining the efficacy of existing treatments. These challenges necessitate the development of novel antifungal agents with enhanced activity and reduced resistance profiles. To address resistance mechanisms, we designed and synthesized hybrid molecules combining triazole and thiazolidine-2,4-dione (TZD) pharmacophores. This strategy leverages dual mechanisms of action: inhibiting fungal CYP51, a key enzyme in ergosterol biosynthesis, and disrupting fungal cell wall integrity. The structural versatility of hybrid molecules allows for targeted modifications to enhance antifungal potency, binding specificity, and pharmacokinetics. Using a stepwise synthetic approach, triazole-containing piperazine derivatives were first prepared and coupled with TZD-based carboxylic acids via optimized condensation reactions. The structures of the synthesized compounds were confirmed through advanced spectroscopic methods, including 1D/2D NMR and high-resolution mass spectrometry. The antifungal activity of these hybrids was evaluated in vitro against clinical and reference strains of Candida spp. and Aspergillus fumigatus. Among the synthesized compounds, 6a demonstrated notable activity against Candida parapsilosis (MIC 0.06 μg/mL), comparable to voriconazole. Compound 4b exhibited moderate activity against C. parapsilosis (MIC 1–2 μg/mL) and A. fumigatus (MIC 8 μg/mL). However, most compounds showed limited efficacy against highly resistant strains such as C. albicans 8R and C. krusei. This study highlights the potential of hybrid triazole-TZD molecules in overcoming resistance and improving antifungal efficacy. While promising, further optimization is required to broaden the spectrum of activity and enhance efficacy against multidrug-resistant pathogens. These findings contribute to the growing field of antifungal drug development, emphasizing hybrid approaches as a viable solution for combating fungal resistance.

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。

本研究開發一種新穎孔洞性吸附材料：金屬有機骨架 (MOF)。MOF 在反應溶液中自組裝形成孔洞結構，透過物理吸附有效捕捉氣相乙酸分子。研發出綠色、快速可在常溫常壓下大量合成三種MOF（HKUST-1(Cu)、UTSA-280(Ca) 及 A520(Al)）方法。此外，為提升材料機械強度和應用價值，採用 PVA 聚合技術製備 MOF 複合物，使其造粒型化更易處理，提升商業和環境應用價值。吸附實驗結果顯示，HKUST-1(Cu) 粉末對乙酸移除率高達98%，而HKUST-1(Cu) PVA 複合物達93%，對比活性碳及其PVA複合物（移除率分別為85%和78%）表現更優異。MOF憑藉優異吸附性能和可大量生產低成本優勢，成為極具潛力有機無機氣體吸附劑，可為半導體產業提供一種維持高標準製程環境精密且簡便解決方案。