螢光奈米鑽石(Fluorescent Nanodiamond, FND)主要應用於生物定位，其結構中與一個氮原子相鄰的晶格缺陷部分(Nitrogen-Vacancy, NV^-)照射波長532nm的雷射會發出637nm的螢光，對FND施加磁場會使螢光強度減弱。由於在低濃度溶液中螢光訊號會被溶液的背景雜訊掩蓋而難以偵測，因此設計實驗對FND施加穩定變換的磁場，此動作能夠使螢光強度也進行相同週期的變化。針對此週期進行快速傅立葉轉換(Fast Fourier Transformation, FFT)得出的數值會與螢光強度呈正比，進而推知FND濃度，有效排除不隨磁場變動的背景雜訊。研究結果顯示，施加磁場並使用FFT能夠成功排除牛血清蛋白(Bovine Serum Albumin, BSA)、碘化丙啶(Propidium Iodide, PI)、水、血液的背景輻射，且FND在高離子濃度溶液中會沉澱，在表層包覆BSA則可以有效地改善此現象。FND不易受到血液的背景輻射干擾螢光測定。

本專題利用混沌電路本身的不可預測性及混亂程度，製造出作為硬體安全實現的密鑰生成器。從實現基本的蔡氏電路，並探討蔡氏電路作為硬體安全的相關應用為基礎，進而實現進階的研究改造。透過電阻、電感、電容、運算放大器等電子元件，焊接在電路板上，實現一個「四渦漩狀」的混沌電路。 本研究的最大特點，就是會製作兩個具備相同電子元件及構型的多吸引子混沌電路，但天生的元件變異性會產生「類蝴蝶效應」，再利用數位乘法器，將兩個電路之類比訊號相乘，而這個操作會形成一個密鑰生成器的混沌系統。 最後會透過軟體MATLAB 確認電路操作和分析電路輸出信號，及所提出密鑰生成器的安全程度，將會驗證隨機性和獨特性這兩組特徵，分別計算平均及交叉相關函數來做為驗證的依據。最後從實驗中證明了「四渦漩狀」的混沌電路所產生的密鑰具有快速產生及高安全性。

常見模式生物檢測化學環境壓力後無法重複使用。本研究探討對環境變化敏感的大生熊蟲抗化學環境壓力機制，評估其作為重複使用模式生物可行性。得知多數大生熊蟲於硝酸鹽、酸鹼值改變與殺蟲劑環境壓力下仍能活動或隱生。以亞甲藍簡易染色可確認化學環境壓力下大生熊蟲是否受傷害，如體表角質層被破壞會導致完全染色。分析大生熊蟲於常見硝酸鹽環境壓力下體內脂質含量、總蛋白質單體表現量與總抗氧化能力，推測出大生熊蟲對抗硝酸鹽環境壓力機制：1.藉由> 20 kDa持續性活化蛋白抗硝酸鹽環境壓力。2.活動大生熊蟲增加體內脂質含量隔絕環境硝酸鹽。3.隱生大生熊蟲以抗氧化系統降低硝酸鹽產生的氧化壓力傷害。以上結果得知大生熊蟲具潛能檢測常見化學環境壓力並作為重複檢測之模式生物。未來將持續評估大生熊蟲可檢測的化學環境壓力並為人類健康把關。

本研究利用RNA干擾與基因過度表現兩種方法，觀察基因表現對果蠅蕈狀體神經突導向的影響。我們以RNA干擾方式降低基因表現，標定dally、octβ2r、sifr與frazzled基因；而基因過度表現則選定frazzled基因。利用果蠅GAL4-UAS系統使神經細胞表現的綠色螢光蛋白，觀察神經突導向情形。由干擾果蠅腦部蕈狀體PPL1-α′2α2神經細胞上述基因，觀察到dally、octβ2r基因在降低表現後，此細胞分別呈現失去神經支配與異常神經支配。sifr基因在兩種不同RNA干擾序列下，表現出不同之結果，一是和wild type的神經突型態相似、另一則是失去神經支配。frazzled基因干擾後，細胞在蕈狀體α2區失去神經支配。frazzled基因在過度表現下，神經突和wild type相比，出現異常導向。綜言之，由我們研究發現透過操控基因表現，果蠅腦部神經突會產生異常的導向與神經支配，顯示果蠅腦部神經系統在分化或發育上，基因扮演極為重要的角色。

This research aims at exploiting civil and pre-treated industrial wastewaters that go into the purifier and those that come out of it after various treatments in order to build a galvanic cell with the goal of producing clean electric energy. Our background hypothesis is that it is possible to exploit the existing potential difference between these two types of water to generate electricity. In fact, the water sent for purification contains elements (carbon, nitrogen, sulphur, phosphorus, etc.) in a predominantly "reduced" state and its oxygen level is scarce. On the other hand, the water coming out of the process contains the same elements in a mostly "oxidized" state and it is rich in oxygen. Those chemical discrepancies should get the job done. In order to simulate the two types of water, two different solutions were prepared. The first one is highly concentrated with pollutants and gaseous nitrogen is insufflated in it to reproduce its anoxic environment. The second one’s pollution level is based on the Italian legislative limits of chemical contaminants for superficial waters (Legislative Decree 152/2006) and the semi-cell is insufflated with gaseous oxygen.

隨機漫步是數學、物理學、化學、經濟學上常需要涉及和探討的問題，其中探討回到原點的方法數和機率是常見的研究方向。本研究嘗試列出不同維度之間回到原點的方法數遞迴關係，發現不同維度移動相同次數時，回到原點方法數為特定的多項式。 參考了文獻Counting Abelian Squares後，本研究證明了特殊的對應關係，得到了多維空間中回到原點方法數的漸近式。儘管並沒有直接以其他較困難的數學探討方法計算，但依據本研究之結論，已可算出多維度下回到原點之方法數 至於在有限空間中回到原點的方法數，本研究僅完成二維平面下，超出邊界不同次數各種情況的討論，並經由程式檢驗公式的正確性。

當高鐵行經地層下陷明顯區域、或土壤液化中高潛勢的沖積層時，行車的車身震動會更劇烈嗎？本研究基於這樣的動機，經由測量高鐵沿線不同區段的震動訊號，了解各段的震動特徵。 本次研究使用微機電加速計（MEMS sensor）來量測高鐵行經左營至台北路線上車廂內之震動，並利用QCN Live軟體分析波形，分析行經各站間之頻率與振幅特徵，結果發現，行經台南到嘉義、嘉義至雲林、雲林至彰化三段路線的震動，在濾波0.5-4.0 Hz之平均振幅大於其他非地層下陷與土壤液化地段，同時我們也發現低頻振動的最大振幅在列車行經土壤液化區時有顯著的增加。

This is a first approach in the development of beekeeping and the preserving of bees, a crucial and important species in the balance of ecology on our planet. This project consists in designing and building a small affordable device that will help beekeepers keep an eye on their hives and prevent theft whenever and wherever they are by providing them with instant and continuous data and information about their beehive status through a mobile application. This IOT approach will rely on many physical variables especially the sound frequency of the bee buzz, which appears to be a way for the bees to communicate with each other in special circumstances. That is why; we aimed to analyze the sound frequencies of the bee buzz to detect beehive behavioral changes. Many other factors are also important for the keeping of a healthy beehive such us temperature, humidity, weight and fly activity. And as for security measures we are going to add a GPS tracker to the system to keep track of the hives and alert the beekeeper if there is any kind of danger. The development of this real time beehive monitoring system will not only help the beekeeper keep track of his hive and collect useful data but also increase the honey production and avoid many colony losses and thus preserve the bees and ensure their well-being.

Infectious diseases caused by bacteria from biological pollutants pose a great burden in terms of diagnosis and treatment, and millions of people worldwide die from bacterial infections. Detection of bacteria plays a critical role in clinical diagnosis and control of contamination, but is not accessible due to the high cost, complex devices and equipment required. In the project, an alternative to existing methods, a paper-based biosensor for the detection of model organism E. coli bacteria, which is visible, low cost, easy to use, can be integrated with a smartphone, is based on rapid color change in the exposed environments, drinking and pool water, wastewater, beverage products. platforms were developed. For the specific detection of E.coli bacteria, two different biosensors have been developed that can perform colorimetric detection in a user-friendly origami design, minimizing microchip and processing steps based on antibody-bound PVDF membrane and filter paper-based immunological method. In the presence and absence of target bacteria E.coli, the lowest detection limit of the biosensors obtained by using paper-based platforms that create a distinctive color on them, depending on the concentration, was 0.9x103 bacteria/ml for origami biosensor, 2.7x103 bacteria/ml for microchip biosensor and the widest dynamic linear operating range was calculated as 103-107 bacteria/ml. With the biosensor platforms we have developed, the use of only one smartphone for both qualitative and quantitative, visible results and analysis within minutes constitutes the originality of our project. With these promising results, the biosensors we have developed can also be used for the detection of different biological pollutants, do not contain complex devices and can be easily produced in large scales. We believe that the biosensors we have developed for the detection of biological pollutants in water and beverages, especially in regions where test laboratory infrastructure is not available, will contribute to the literature, public health, health economy and sustainable development goals such as clean water and sanitation, health and quality life, and life in water.

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.