全國中小學科展

2025年

探討影響臺灣周遭海域波浪能蘊藏量的關鍵因素 English Title:Investigation of Key Factors Influencing Wave Energy Potential in the Surrounding Waters of Taiwan

團隊透過分析臺灣台灣周遭八個處海洋浮標測站資料,自2021年1月到2024年10月為止之示性波高、平均週期、平均風速、海溫等資料間之相關性,並試從不同位置測站之海洋條件與大氣因素,來綜合探討影響臺灣周遭海域波浪能蘊藏量的關鍵因素。並透過分析海溫與波浪能變化的關係,試圖瞭解全球暖化平均海溫上升,對臺灣周遭波浪能蘊藏的變化趨勢。 團隊發現影響臺灣周遭海域波浪能蘊藏的因素,除了季節性季風的影響,黑潮主流以及澎湖水道的黑潮支流湧升流,也都 可能 是影響臺灣周遭海域波浪能蘊藏的重要因素。團隊也發現,在臺灣周遭海域波浪能蘊藏與海溫變化有負相關的趨勢,此現象與臺灣中央研究院針對過去70年,全球波浪能的變化趨勢並不一致。其原因可能是臺灣所屬地理位置環境的關係,也可能是分析的數據資料僅有4年無法準確看出趨勢變化。

線蟲土壤食物網監測模式建立與功能性調節 The Establishment of Nematode Food Web Monitoring Model and Regulation of Soil Functions

為了永續利用土壤生態系服務,本研究分析線蟲族群變化監測土壤食物網,探討線蟲食物網與土壤養分調節相關性,實踐 SDGs中第 2項消除飢餓與第15項陸域生態。首先使用文獻分析法,建構模式觀察線蟲功能群演替,監測線蟲食物網組成評估土壤生態系服務,改善與結合過去僅探討環境干擾方式。觀察線蟲對土壤養分影響,結果顯示線蟲功能群多樣性、族群增長與交互作用 (資源重疊與演替等)可能提升土壤無機氮;不過推測因族群交互作用減弱或微生物過度被捕食,氨化能力在食物網發展初期(六週提升 37%)與後期 (六週僅提升16%)不同,需探討如何延續其氨化能力。將結合植物生長觀察線蟲食物網對植物影響。期望未來新模式進一步評估與標準化,用於監測土壤線蟲食物網組成並調節土壤,在農業管理與生態復育方面做出貢獻,為土壤永續利用提出新的可能。

大氣常壓微電漿合成共價有機框架應用於光催化降解汙染物

為了解決水污染問題,本研究探討共價有機框架(COF)作為光催化劑的應用。COF具備高度可調孔洞、高穩定性及選擇性吸附等優勢,有助於有效去除水中污染物,對未來具有前景。本實驗採用大氣常壓微電漿合成COF,此方法能在室溫下以水為溶劑,無需高溫或有毒化學品,並僅需一小時即可完成合成,具有綠色化學優勢。實驗結果顯示,成功合成的COF能有效降解水中常見染料污染物(結晶紫及亞甲藍),證明了COF的高效光催化性。在紫外-可見光光譜中,隨著光催化反應的進行,染劑吸收波峰顯著減弱並幾乎完全褪色,確認了COF優異的降解能力。掃描電子顯微鏡圖像顯示,COF的高度有序孔洞結構提升了其催化活性與穩定性。這項技術不僅能高效處理水中有機污染物,還具備廣泛應用潛力,有望為全球水污染治理與環保提供新思路。

羽轉綠肥-自製肥料對蔬果生長的影響

羽毛廢棄物是畜產類廢棄物排名第二大宗,為了提高廢棄羽毛的實用價值與效益,我們利用啤酒酵母菌進行雞羽毛分解。經啤酒酵母分解一個月後的雞毛液肥,含有胺基酸濃度約為0.17 M,是市售肥組的5.67倍。以雞毛液肥灌溉高經濟價值的彩椒及福山萵苣,彩椒果實總質量比市售肥組高出84.6%,果實中含有葡萄糖濃度為23.8%,比市售肥組多出49.7%。碘量法的抗氧化能力試驗中,發現雞毛液肥灌溉的彩椒抗氧化能力比市售肥組高出91.3 %。清除DPPH自由基的能力實驗中,雞毛液肥組的彩椒果實汁液清除自由基能力約是市售肥組的2.82倍。雞毛液肥灌溉的福山萵苣的葉片總質量比市售肥組多出116.2%。可以發現啤酒酵母分解的雞毛液肥,確實可取代市售肥料,當作彩椒及福山萵苣的養分。希望藉此研究能將廢棄雞毛再利用,減少環境負擔,讓農業永續發展。

圓緣相連—關於忍者通道性質之探討

本作品由2023年IMO的第五題出發,希望探索在忍者通道中的其他性質,首先思考改變每排中放入的球數並觀察規律,進而推廣到三維圓圈塔中的性質,最後使用hyper-cube(超立方體)的情況進行一般化的推廣與構造的優化,完成最小值問題的求解,另外也對於特例部分探索解的總數。

Tlaolli Onilli

Most people in the country tend to consume soda as part of their daily lives, without thinking about the health consequences that this entails. And the fact is that 墨西哥 is considered the main consumer of soda in the world (Universidad Nacional Autónoma de México [UNAM], 2019), and not only that, it also tops the lists of obesity and diabetes (Procuraduría Federal del Consumidor [PROFECO], 2018). A bad diet is one of the main causes of these medical conditions, so it is necessary to rethink the foods of daily consumption. In this project, the development of a carbonated beverage with three main ingredients: purple corn, hibiscus, and pineapple, which contain diverse nutritional properties in addition to having significant amounts of antioxidants (Secretaría de Cultura,2020; Sumaya et al., 2014; Kongsuwan et al., 2009), is established as an objective. In addition, antioxidants act as regulators of cellular aging and prevent chronic diseases (Vilaplana, 2007). For this reason, the elaboration of a beverage with these characteristics is considered harmless and a good option for people with a chronic disease or who are prone to it, as well as a healthy alternative to the consumption of soft drinks. In this project, an experimental methodology was followed to prepare the nutritional beverage, which had nutritional properties, such as vitamin and antioxidant content, in addition to having a pleasant taste and texture.

Utilizing Sparse Optimal Linear Feedback Control to Design Targeted Therapeutic Strategies for Enhancing Gut Microbiome Stability

According to the 2024 American Cancer Risk Survey, one in 24 individuals is at high risk of developing colon cancer. This condition is linked to gut microbiome instability. Consequently, there is a pressing need for a more effective and precise approach to maintaining gut microbiome stability, which this research aims to solve by finding the most crucial bacteria species in maintaining the stability of the gut microbiome through the application of Optimal Linear Feedback Control. Two of its variants being applied in this research are Sparsity Promoting Linear Quadratic Regulator (LQRSP) with a variety range of  (0.05, 44.58, and 49.84) and Linear Quadratic Regulator (LQR) ( = 0) along with other supporting methods; Controllability Gramian and Network Theory (graph analysis). The finding in this research shows that bacteria species Bacteroides hydrogenotrophica, Bacteroides uniformis, Bacteroides vulgaris, Bacteroides thetaiotaomicron, Escherichia lenta, and Dorea formicigenerans have an important role for preventing and medicating a variety of gut-related diseases. This conclusion is reinforced by the analysis conducted using the Controllability Gramian, displaying five of the chosen bacteria with the highest controllability index, which demonstrates that the system can be effectively controlled. This finding suggests a potential for enhancing therapeutic strategies, rendering them more precise and systematic. To gain deeper insights into the relationship between each bacteria and the rationale behind the selection of these bacteria by LQRSP, this study also employs network theory, which successfully elucidates the choice of Bacteroides uniformis despite its low controllability index. Additionally, to further validate the efficacy of these bacteria, the research develops a simulation that compares the controlled system with the uncontrolled system, utilizing two types of disturbances. The results indicate a significant difference in robustness against disturbances between the controlled and uncontrolled systems. The findings from this research can be used as a foundation for a more efficient and systematic intervention strategy findings. By researching gut microbiome composition regulation using a mathematical approach, it opens new opportunities for new method discoveries aiming to increase the health of the gut microbiome which is beneficial for the medical field and prevention of gut related diseases.

分子結構語言與熔沸點性質的人工智慧預測

背景:預測分子性質如溶解度、毒性及熔沸點對於基礎科學至關重要。然而,實驗測量這些性質耗時且昂貴,因此本研究使用多種機器學習模型藉由調整變相來準確預測熔、沸點。 方法:本研究使用超過一萬筆數據及兩種類型的機器學習方法:淺度與深度學習。淺度學習由 PyCaret實現,並以Mordred作為分子描述器;深度學習使用圖神經網路,包括(CMPNN和GCN),並調整隱藏層參數。 結果:CMPNN在目前嘗試的模型中表現最佳。發現影響沸點預測的關鍵特徵是piPC1,與鍵級相關;熔點則是AATS0d,與σ電子的 Moreau-Broto自相關有關。 結論:CMPNN模型在沸點與熔點預測中均表現最佳。沸點中深度學習模型優於淺度學習模型(p<0.05)。此外,使用SHAP成功找出piPC1和AATS0d對最關鍵。本研究不僅得出了高準確性的模型,還發現了影響分子性質的關鍵特徵,且可擴展至其他預測。

在量子電腦上模擬量子諧振子隨時間演化

量子電腦是近年來新發展的科技,利用量子糾纏態的量子位元進行計算。本文希望可以利用量子電腦計算諧振子隨時間演化算符。而這也是我第一次在量子電腦上模擬諧振子隨時間演化系統。首先我找出可以用於諧振子算符的合適算符矩陣大小、空間步長(Δ𝑥)、質量(m)、角頻率(ω)並且在位置基底下表現時間演化算符矩陣。設計並簡化量子電路後,使用IBM公司提供的量子電腦模擬並計算數值。我透過矩陣修正減少修正輸出錯誤產生的誤差,達到較精確的結果。模擬出在一個時間單位內的數值與理論值大致相符,未來希望可以利用此量子電路尋找矩陣的特徵值或是模擬更大型的系統。

猜拳與轉向中的運籌帷幄- 探討人類與鼠婦在連續決策行為 的偏好與決策經驗依賴等特性

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。