The purpose of the project was to find out whether humans can tell the difference between human-generated text and artificial intelligence (AI) chatbots-generated text and to identify how Al-generated text differs from human-generated text. The dependent variable was the results given by the participants (whether the paragraphs were Al-generated texts or human-generated text). The Independent variable was the participants in the experiment and the controlled variable was the type of paragraphs (both the Al-generated texts and human-generated texts) and time used to test each participant. The hypothesis for this experiment was that the participants were not going to be able to differentiate between AI-generated text and human-generated text. In this descriptive and mixed-method study, participants were presented with questionnaires. Each participant needed to state whether they thought each paragraph was human-generated or AI-generated. At the end of the questionnaire, the participants were asked to briefly explain what assisted them in differentiating between the two. They were given 60 seconds to decide. A stopwatch was used to time them. A sample of 456 participants took part in this project. They were not told how many AI-generated passages and human-generated passages there were in the selection. They only knew the total number of passages. Over 99% of the participants could not correctly differentiate between AI-generated text and human-generated text in all passages. Only four participants were able to get 100% of the questions correct. All four learners attend schools located in urban areas. After I did my analysis, I discovered that my hypothesis was incorrect. Four of the participants were able to get 100% of the questions correct this indicates that not all the participants were not able to tell the difference between Al-generated text and human-generated text. This rejects my hypothesis. However, the chances of humans differentiating between the two are very low. All four learners attend schools located in urban areas; this indicates that it is easier for learners attending urban area schools to recognise AI-generated texts than learners attending schools located in rural areas.

本研究透過3D列印製作消波塊與自製造浪機，並用海浪沖刷粉筆模擬岸邊侵蝕效果，尋找減緩海浪岸侵蝕方法。 本研究製作兩種造浪機。大型版：壓克力板製水缸，用活塞推動造浪板，因漏水問題，改良小型版；小型版：採現成魚缸、TT馬達配合連桿帶動造浪板。小型版波形更流暢。 首先，確定粉筆泡水25分後重量趨穩定；在單排測試中，林克塊減浪效果最佳。實驗亦發現消波塊離岸越近、裸露體積越多，減浪效果越好。增加排數方面，小浪排數越多效果越佳，大浪下則需至少兩排以上才具顯著效果，綜合成本與效益，建議使用兩排設計。 針對家鄉情境，設計A至D四種配置，D型適合冬季東北季風大浪條件，如東北向海岸；C型則適用於港口等平靜海面。

為揭開「為何有的麵團彈牙、有的卻軟趴趴」的謎團，從發酵時間下手，結果發現時間越長，彈性和韌性就像漏氣的氣球般漸漸消失。那加多一點酵母呢？實驗後麵團反而更無力，像睡過頭一樣軟趴趴，我們驚覺，發酵速率才是關鍵！ 轉向冷藏發酵，意外發現經過低溫「歷練」後的麵團更有韌性。接著加小蘇打，結果像打散的沙堡，彈性全失，推測是麵團原本的結構被破壞。 於是我們改走「澱粉之路」，發現加入富含支鏈澱粉的糯米就像讓麵團裝上彈性肌肉，彈性與韌性大大提升！而後經多次實驗調整比例，成功找到最佳配方。 最後設計「嚼勁實驗」，結果證實彈性與韌性與實際咀嚼感呈正相關。我們也將持續探索這條「麵團之路」，發掘更多有料又好咬的美味秘密！

近年來溫室效應日益嚴重，我們注意到可利用氣體作為正極的空氣電池，並思考是否能將工業排放的CO2儲存後作為正極氣體並轉換成電能。本研究重點在基本電池和正極氣體兩部分，首先思考如何製作效能高的鋁空氣電池，再嘗試以CO2取代空氣，並找出增進鋁二氧化碳電池效能的方法，研究出能消耗CO2並轉換成電能的電池模組。實驗結果顯示：(1)最佳電解液為鹼性3.0 M KOH(aq)；(2)純氧為最佳單一氣體；(3)三乙醇胺為最佳改質劑；(4)驗證CO2能參與反應；(5)CO2 80%時電壓最高；(6)設計出鋁空氣電池組能長時間穩定輸出5.7 V；(7)最終製作出的鋁二氧化碳電池組能長時間平均輸出電壓2.62 V。

This research presents a novel approach to understanding the curling and uncurling behavior of tracing paper when exposed to water, identifying limitations in traditional diffusion-based models like Fick’s second law. While Fick's model adequately represents the uncurling phase, where water content is stable, it falls short during the curling phase due to its inability to account for dynamic changes in diffusivity. Our study identifies capillary action, modeled through Richards' equation, as the primary mechanism in the curling phase, where diffusivity varies with water content due to capillary-driven water movement through the paper's porous structure. Experimental data align well with the Richards' equation model, highlighting a saturation point where curvature peaks, governed by evaporation's impact on moisture balance. To simulate this phenomenon, we developed a finite difference approximation scheme based on Richards' equation, discretizing the spatial domain for detailed control over moisture dynamics and incorporating the Robin boundary condition with virtual points. This approach, combined with evaporation considerations, produces simulation results consistent with observed data, emphasizing evaporation’s role in steady-state moisture gradients and the subsequent deformation mechanics. Our findings further reveal that factors like paper thickness, temperature, and salt concentration significantly influence curling behavior. We established linear correlations between peak time and thickness reciprocal, as well as between peak curvature and thickness squared, supporting theoretical models. Temperature affects both peak curvature and curling rate due to changes in viscosity and surface tension, and higher temperatures prevent full uncurling due to sustained evaporation effects. Increased salt concentration heightens peak curvature without altering expansion ratio, suggesting additional variables in play.

在電影場景中的流沙是可以吞沒人體的自然現象，但實際上流沙能覆蓋人體的深度並不深。從文獻報告顯示，流沙是非牛頓流體的一種，但表現出來與太白粉所製的非牛頓流體不同。利用咖啡渣來建立流沙的受力模型，發現不論在阻力的表現、表面出水的現象與物體沉入的狀況與流沙都極其相似，以咖啡渣作為流沙的模型，可以減少使用玉米粉或麵粉所造成的浪費，同時可控制粒徑大小，測量受困者與流沙間的受力關係，立新的研究模型。在定力的實驗中，對物體施以垂直拉力，瞬間拉力約為物重的80％即可拉起；若沒有瞬間拉力，則需物重的120％以上。在動態的實驗中，物體所受的阻力會因著受力擠壓、含水量多寡、接觸面積、力量作用時間的不同而有差異。

本研究主要探討鳳梨各部位的蛋白酵素活性差異並評估其潛在應用價值。實驗針對鳳梨的果肉、皮、冠芽、葉及芯等部位進行分析，透過分解明膠實驗與酵素活性測定，探討其在不同溫度與酸鹼值環境下的酵素活性變化。結果顯示，鳳梨尾的蛋白酵素活性最高，其次為鳳梨頭、皮與芯，冠芽與葉的活性則相對較低，但整體差異不大。抑菌實驗顯示，各部位皆具有抑制細菌生長的效果，表示在食品保存或抗菌應用上有一定的潛力。本研究證實鳳梨不同部位皆含有蛋白酵素，顯示葉、冠芽、皮等副產物具有再利用的可能性。若能進一步改良萃取方式、提升酵素濃度，未來可望增加鳳梨副產物的應用價值，為食品加工與農業廢棄物再利用提供重要的參考依據。

本研究探討胭脂樹籽（婀娜多色素）的特性及其應用，並分析其在不同環境中的行為。結果顯示，人工紅色色素如紅色40號與蘇丹紅顏色鮮豔且穩定，但可能對健康有害；相較之下，天然色素較安全，但易受氧化、光照與溫度影響，穩定性較低，保存期較短。實驗發現，胭脂樹籽色素在60-80°C顯色最佳，高於100°C則顏色變淺；其在酸鹼性溶液中隨pH值變化而改變顏色，最適環境為中性至弱鹼性。光譜分析證實其具橙紅色特徵，適用於食品、化妝品及纖維染色，並兼具環保與無毒優勢。綜合而言，婀娜多色素在天然染料應用上潛力廣泛，能提供穩定且安全的色澤效果。

原始生命的形成被推論是由化學分子透過自發性化學反應組成簡單的代謝路徑，再由這些路徑組合成為複雜的代謝網絡，最終形成原始的生命。由於自營性的代謝路徑可將無機性的CO₂固定為有機物可謂形成生命的前提，因此推論在自營性微生物所擁有的一種生物固碳路徑，可將CO₂固定進入三羧酸循環的「逆向三羧酸循環」被認為是原始細胞最初形成的代謝路徑之一。然而，以化學反應的自由能考量，自2-Oxoglutarate到Isocitrate 此固碳反應並非自發性反應，並由異檸檬酸脫氫酶催化。異檸檬酸脫氫酶在逆三羧酸循環中是決定固碳反應速率的關鍵酵素之一，在當今正向三羧酸循環中亦具重要的調節細胞能量代謝的功能。有鑑於此，本研究探討源自古老地球環境的嗜熱自營菌Aquifex aeolicus 中異檸檬酸脫氫酶的酵素性質，並期待本研究成果對生命起源的探究及發展新穎固碳技術能有所助益。

本研究探討翠葉蘆薈汁液對植物生長的影響及其變紅現象的成因。實驗結果顯示，翠葉蘆薈與中華蘆薈汁液皆能抑制黴菌生長，且翠葉蘆薈在促進綠豆發芽與水生植物（霓虹水竹草、左手香）生根方面具潛力。 觀察到蘆薈汁液變紅後，進行顯微鏡分析，確認變色細胞為保衛細胞。為了了解變色的原因，進行多項變因測試。結果顯示：單獨的氧氣、加入酸鹼反應、金屬接觸及溫度皆無法造成明顯變色，唯有「 在氧氣環境中的照射紫外線」時，才會誘發汁液快速變紅。此外，臭氧氣體亦能在短時間內促使翠葉蘆薈汁液變紅，顯示臭氧可能為促使色素反應的重要因素。

本研究透過結合局部敏感雜湊（Locality-Sensitive Hashing, LSH）與知識圖譜（Knowledge Graph, KG）以改善檢索增強生成技術（Retrieval-Augmented Generation, RAG）在檢索的時候難以在精確度與效率之間取得平衡的問題。透過使用LSH 將資料分桶，接著利用知識圖譜進一步篩選資料，以提高檢索的精確度與效率。 實驗結果顯示，結合知識圖譜與LSH後的系統，在精確度（precision）上可達到91%，相較於VectorRAG 的84%提升約8.33%，與GraphRAG的96%則僅有5.21%的差距。此外，本系統在檢索時間上較GraphRAG降低了95.38%。由此可以證實，透過結合LSH及知識圖譜能在保持高精確度的同時，顯著提高檢索效率。

本實驗探討黏菌對不同條件所產生的趨性。七年級生物課時看過介紹黏菌的影片，引發我們對這種奇妙生物的興趣，想透過本研究了解環境因素如何會影響其移動。一、放在不同角度傾斜面的黏菌，都表現出對於重力的趨性。二、黏菌對於磁鐵具有正趨性，在磁鐵N上S下的環境會做順時針旋轉，反之S上N下做逆時針旋轉，在磁場強的環境下旋轉速度較快。 三、黏菌移動受到1mA或以上電流抑制。四、黏菌會避開黴菌生長區域。五、在某些環境條件下黏菌會循著其分泌的黏液移動。 近年來科學家對於黏菌的移動策略有一些研究，我們希望以簡單的實驗方式，透過控制單一變因，為黏菌存在的爭議或研究空缺找到答案，認識黏菌潛藏的適應策略與行為特性。