為了探討蓋斑鬥魚的攝食偏好以及找出主導攝食行為的感官系統，我們採用了3種實驗槽，17隻蓋斑鬥魚，設計9項實驗來進行證明我們的假設。結果發現，蓋斑鬥魚偏好吃顏色鮮艷味道濃郁紅蟲 (48.3%)明顯高於顏色灰黃、味道較淡的乾燥紅蟲(22.5%)。蓋斑鬥魚無法正確分辨真假餌（1：1），假餌偏好紅色＞棕色＞白色。只要有微弱光線就能成功攝食，達78.64%。但只靠嗅覺的黑暗中只有10%成功率，而明亮環境中找到有味道水體的比例為68%。在只能依靠視覺的情況下確有更高的正確攝食率，選擇顏色鮮豔卻未無味道紅假餌有82%，且在近距離時主要依靠視覺攝食，遠距離時，則會加入嗅覺輔助。綜合以上，蓋斑鬥魚的攝食行為主導感官是以視覺為主，嗅覺為輔。

The ceRNAs as a class of RNAs act by competitively binding to miRNAs and limiting their regulatory effect on the target genes. Increasing evidences point to the role of ceRNAs in glioma cancer. So far, limited studies have been reported on the role of ceRNA in the development in glioma cancer. In this study, we have analyzed online RNA sequencing data in order to predict the ceRNAs which are putative regulators of in glioma cancer.

本研究利用蝶豆花果凍進行電解。透過將電解質水溶液製作成果凍的方式，使溶液具有固定的形狀，不會隨意流動，且利用蝶豆花作為酸鹼指示劑，方便觀察電解時電極處所產生的酸性物質和鹼性物質，而酸性和鹼性物質讓指示劑顏色改變，藉此觀察果凍膠體內部離子的移動情形。透過電解蝶豆花果凍，能在電解實驗中觀察果凍明顯的變色，使人更加了解離子的流動方向，也能觀察電解實驗在電壓大小、電阻不同、路徑長短不同、同時有不同高低電壓在同一果凍時，離子的流動情形。我們發現電壓愈大，會使電解的速率加快，但是不會影響反應途徑。而在不同電壓中電解，離子會選擇往最高電壓方向移動。

食品快篩廣泛運用在生活當中，而受到這種機制的發想，本實驗想運用阻抗頻譜推估酵母菌的未知濃度，讓民眾了解自己平日所攝取的物質是否有過量而影響身體健康。本實驗利用網版印刷做出四種不同規格的銀膠晶片感應溶液的阻抗及導電率，用LCR掌上型電錶測量不同濃度的強電解質NaCl（食鹽水）確認晶片能有效的使用，再測量弱電解質醋酸的平衡常數，最後擴大至大分子味精及生物酵母菌，成功得到生物溶液酵母菌的阻抗頻譜和檢量線，如指叉數目為8、指叉間隔為0.1mm的指叉電極，其對酵母菌溶液的檢量線R2值為0.9958，靈敏度為3.08x10-6 ± 1x10-8，在98%信心區間下之偵測極限為0.0125 (顆/1mm2)，為生物快篩檢測提供了新的可能。

本作品主要為發現水面上漂浮物易被外物吸引，甚至發生排斥或旋轉的現象，藉由研究探討此現象的多種特色。表面張力雖常被討論，但表面張力所產生之漂浮移動現象卻從不曾在我們所查證的科展題目、網路資料、書籍、甚至是科學文章中被研究過，富有創新、獨特性，於是由不同親疏水試棒、試片的交互作用中，探討其移動規則及可能引起之機制。從實驗結果可知，疏水邊易與疏水相吸與親水相斥，其細微現象與水面凹凸、折角試片俯面仰面有莫大關連，過程中則以光影輔助推論，進而由表面張力使液體表面積趨向越小特性，了解一系列運動原因並加以論證。此現象甚至也發生在試片間與槐葉萍的葉子上。透過本研究的發現和討論，或許未來可用來解釋一些自然現象。

本研究延續上屆台灣東部海岸鐵砂與磁鐵砂含量分析，於2022年9月至2023年4月進行，目的是希望透過親自動手操作以探討東部海岸鐵砂煉鐵之可能性。 經文獻探討得知，花蓮崇德有煉鐵遺址並參考十三行博物館之煉鐵方法，於是進行13次實驗，實驗於每週2小時之獨立研究課進行，受時間限制，採分階段實驗，初階實驗：熟悉煉鐵觀念及實驗操作，進階實驗：探討碳粉及鐵砂之較佳比例，高階實驗：實際模仿十三行博物館建窯煉鐵。 研究發現，台灣東部海岸鐵砂（含崇德海砂），經冶煉後，都可得有金屬光澤之鐵球及鐵渣，並對比地質圖，東部海岸以火成岩為主，崇德地區屬花岡片麻岩及綠色片岩區，都含鎂鐵等重礦物，可見東部海岸鐵砂煉鐵值得進一步調查分析。

田菁是台灣重要的綠肥作物，甚至也有些研究顯示田菁具有可以移除鹽害土壤中過多鹽分的能力；綠豆是常用作物，也有人針對其耐鹽性做相關研究，讓我們很好奇同樣身為豆科植物，兩者對鹽逆境的抗性有差異嗎？本研究透過比較田菁與綠豆兩種常見豆科植物，深入了解並比較其耐鹽機制。我們透過改變鹽逆境濃度，使植株生長，並觀察其外表及生長情形，發現一、田菁耐鹽能力優於綠豆，二、田菁抗氧化能力優於綠豆，三、田菁將能量分配於根部使其生長複雜，以應對環境壓力。因此，在面對環境壓力時，田菁具有較好的適應方式，並更容易存活。希望本研究結果也可以在未來應用於農業與後續研究當中。

With the introduction of the transformer architecture by Vaswani et al. (2017), contemporary Text Generation Models (TGMs) have shown incredible capabilities in generating neural text that, for humans, is nearly indistinguishable from human text (Radford et al., 2019; Zellers et al., 2019; Keskar et al., 2019). Although TGMs have many potential positive uses in writing, entertainment and software development (Solaiman et al., 2019), there is also a significant threat of these models being misused by malicious actors to generate fake news (Uchendu et al., 2020; Zellers et al., 2019), fake product reviews (Adelani et al., 2020), or extremist content (McGuffie & Newhouse, 2020). TGMs like GPT-2 generate text based on a given prompt, which limits the degree of control over the topic and sentiment of the neural text (Radford et al., 2019). However, other TGMs like GROVER and CTRL allow for greater control of the content and style of generated text, which increases its potential for misuse by malicious actors (Zellers et al., 2019; Keskar et al., 2019). Additionally, many state-of-the-art pre-trained TGMs are available freely online and can be deployed by low-skilled individuals with minimal resources (Solaiman et al., 2019). There is therefore an immediate and substantial need to develop methods that can detect misuse of TGMs on vulnerable platforms like social media or e-commerce websites. Several methods have been explored in detecting neural text. Gehrmann et al. (2019) developed the GLTR tool which highlights distributional differences in GPT-2 generated text and human text, and assists humans in identifying a piece of neural text. The other approach is to formulate the problem as a classification task to distinguish between neural text and human text and train a classifier model (henceforth a ‘detector’). Simple linear classifiers on TF-IDF vectors or topology of attention maps have also achieved moderate performance (Solaiman et al., 2019; Kushnareva et al., 2021). Zellers et al. (2019) propose a detector of GROVER generated text based on a linear classifier on top of the GROVER model and argue that the best TGMs are also the best detectors. However, later results by Uchendu et al. (2020) and Solaiman et al. (2019) show that this claim does not hold true for all TGMs. Consistent through most research thus far is that fine-tuning the BERT or RoBERTa language model for the detection task achieves state-of-the-art performance (Radford et al., 2019; Uchendu et al., 2020; Adelani et al., 2020; Fagni et al., 2021). I will therefore be focussing on attacks against a fine-tuned RoBERTa model. Although extensive research has been conducted on detecting generated text, there is a significant lack of research in adversarial attacks against such detectors (Jawahar et al., 2020). However, the present research that does exist preliminarily suggests that neural text detectors are not robust, meaning that the output can change drastically even for small changes in the text input and thus that these detectors are vulnerable to adversarial attacks (Wolff, 2020). In this paper, I extend on Wolff’s (2020) work on adversarial attacks on neural text detectors by proposing a series of attacks designed to counter detectors as well as an algorithm to optimally select for these attacks without compromising on the fluency of generated text. I do this with reference to a fine-tuned RoBERTa detector and on two datasets: (1) the GPT-2 WebText dataset (Radford et al., 2019) and (2) the Tweepfake dataset (Fagni et al., 2021). Additionally, I experiment with possible defences against these attacks, including (1) using count-based features, (2) stylometric features and (3) adversarial training.

我們觀察到蓋斑鬥魚會攀爬到漂浮水面的葉片，這獨特行為引起我們的興趣。為探討蓋斑鬥魚攀葉行為，本研究開發「鬥魚攀葉行為AI監測系統」，可準確、穩定地收集攀葉行為歷程，並直接提供研究數據。我們探討了競爭者數量與葉片特徵對蓋斑鬥魚攀葉行為之影響。 研究結果顯示，蓋斑鬥魚間的競爭行為是影響攀葉行為的重要因素。競爭後，弱者會有兩種不同的避敵行為，一是積極攀葉，二是消極側躺在角落不動。此外，當鬥魚攀上吃水較淺的葉片上，需要以側躺姿勢攀附，但這也是一個相對安全的避敵空間。我們曾記錄到一天高達92%的攀葉時間。 本研究找到了蓋斑鬥魚攀葉行為的原因，提出競爭後的弱者避敵模式，也為動物行為監測技術提供了一個可能性。

Triage is the process by which nurses manage hospital emergency departments by assigning patients varying degrees of urgency. While triage algorithms such as the Emergency Severity Index (ESI) have been standardized worldwide, many of them are highly inconsistent, which could endanger the lives of thousands of patients. One way to improve on nurses’ accuracy is to use machine learning models (ML), which can learn from past data to make predictions. We tested six ML models: random forest, XGBoost, logistic regression, support vector machines, k-nearest neighbors, and multilayer perceptron. These models were tasked with predicting whether a patient would be admitted to the intensive care unit (ICU), another unit in the hospital, or be discharged. After training on data from more than 30,000 patients and testing using 10-fold cross-validation, we found that all six models outperformed ESI. Of the six, the random forest model achieved the highest average accuracy in predicting both ICU admission (81% vs. 69% using ESI; p<0.001) and hospitalization (75% vs. 57%; p<0.001). These models were then added to an Android application, which would accept patient data, predict their triage, and then add them to a priority-ordered waiting list. This approach may offer significant advantages over conventional triage: mainly, it has a higher accuracy than nurses and returns predictions instantaneously. It could also stand-in for triage nurses entirely in disasters, where medical personnel must deal with a large influx of patients in a short amount of time.

高雄巨山蟻是社會型動物，有族群勢力範圍和首領。外敵入侵，蟻群會迅速啟動有紀律防禦(接觸、攻擊、護卵、殲敵)，並在最短時間完全殲敵(平均18分13秒)。蟻后是防禦領導者兼主要殺手，殲敵率78%。服用鎮定劑一個月後，工蟻、蟻后領域概念低，蟻群出現防禦瓦解，殲敵率僅11%(60分鐘內)，蟻后殲敵率「零」，完全失去領導力，但仍保有領導地位。服用興奮劑一個月後，工蟻、蟻后個別育幼行為雖明顯提高，但領域防禦降低，外敵最終由工蟻完全殲滅(平均52分52秒)。蟻后殲敵率降為56%，工蟻不再餵食蟻后，已喪失領導地位。停藥二個月後，興奮劑組蟻后有恢復領導地位，但兩組在60分鐘內，皆未完全殲敵，族群領域防禦仍未恢復，明顯已造成不可逆傷害。

一般將螢光粉覆蓋於LED上，受光激發而產生各色光，直接照明的光轉換效率高，缺點是極刺眼。本實驗研究LED的間接照明，利用已發展千年的釉藥調製技術和材料，以塞格式、一維二元...常見調釉藥比例的方式，結合現代科技分析方法，應用於LED以產生漂亮又不刺眼的間接燈光，亦具有高藝術價值及實用性。 研究結果成功依需求自製低色溫(暖色系)或高色溫(冷色系)的「反射式」高演色性熒光釉，避免接觸高溫晶片而使螢光粉劣化，得到間接燈光的最佳化曲線。陶瓷耐高溫、易散熱而且釉藥能保持 長時間不會退色、千年不壞。釉藥學千年來多應用於瓷器上，近年部分用於LED的螢光粉。本實驗堅持傳統用於瓷器藝術，又援以新科技LED，使其兼具現代實用需求及工藝美學。