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.

Aspirin is one of the most used and well-known medicines world-wide. It can be synthesized by reacting acetic anhydride and salicylic acid in a warm temperature of around 60-80°C. This reaction is usually catalyzed by sulfuric or phosphoric acid. This paper will investigate alternative catalysts, safer and more environmentally friendly, as well as compare different synthesis methods with different heat mediums, one using a water bath and the other amicrowave. By doing so, the effects of the catalyst and the method of synthesis on the yield, purity and environmental consequence of crude aspirin synthesis will be deduced. The targeted utcome is to find the alternative method as more energy efficient, and to find a greener safer catalyst to sulfuric and phosphoric acid. Further background information, exploration, and explanation is in the appendix. The targeted outcome will be to find a viable alternative catalyst that is safer and more environmentally friendly, and to find that the microwave synthesis method consumes less energy.

Face pose estimation has many possible applications, ranging from driver attention measurement systems to applications in the metaverse, which this project will be focused on. Rather than using a more traditional landmark-to-pose method where the head pose is estimated via keypoints, our method trains a simple convolutional neural network, using the dataset 300W_LP, where the images are simply inputted into the network. The model is fitted with three fully connected layers that are linked to the each of the three Euler angles (yaw, pitch, and roll), alongside multiple loss functions, which improve the robustness of the network.

癌症治療多半會傷害到人體的健康，所以國民大多較偏好以較養生的方法來治療癌症，例如中藥。本實驗以山藥萃取物原薯蕷皂苷抑制腎癌細胞 A498 及 786-O ，期望能達抑制腎臟癌細胞增生之目的。 實驗方法包括以 MTT 試驗、細胞菌落試驗來觀測腎癌細胞受原薯蕷皂苷作用後的活性及存活量，再透過西方墨點法及流式細胞儀來了解腎臟癌細胞死亡途徑。實驗結果顯示將原薯蕷皂苷抑制人類腎癌細胞株786-O及A498的增生能力具有抑制的能力。再透過流式細胞儀的分析，顯示原屬蕷皂苷可誘發兩種腎臟癌細胞的凋亡作用，並且透過西方墨點法觀察出是抑制Bcl-2蛋白、增加Bax蛋白和caspase-9/PARP蛋白的表現，進而導致腎臟癌細胞株產生細胞凋亡。 本研究是在實驗室中進行，且只是利用細胞株來觀測此項研究結果。或許未來可以透動物實驗以及臨床實驗，確認原薯蕷皂苷抗癌之功效，並推廣至全球以造福全人類之健康。

The development of rapid and efficient synthetic approaches to the bioactive cyclic and polycyclic azaheterocycles is one of the most important challenges in organic synthesis. In this work effective and simple synthetic approaches to polysubstituted pyrrolidin-2-ones 2 and isoindolines 3 from donor-acceptor cyclopropanes 1, bearing the ester group as the one of acceptor substituents, and amines were developed. The γ- pyrrolidone based skeletons and isoindoline ring system is a constituent of many biologically active molecules, both natural and synthetic, and a key component of clinically relevant entities (Fig.1,2) [1,2]. The synthesis of pyrrolidin-2-ones 2 includes Lewis acid-catalyzed opening of the donor-acceptor cyclopropane with primary amines (anilines, benzylamines, etc.) to γ-amino esters, followed by in situ lactamization and dealkoxycarbonylation. The reaction has a broad scope of applicability; a variety of substituted anilines, benzylamines, and other primary amines as well as a wide diversity of donor-acceptor cyclopropanes bearing (hetero)aromatic or alkenyl donor groups and various acceptor substituents, can be involved in this transformation. In this process, donor-acceptor cyclopropanes react as 1,4-C,C-dielectrophiles, and amines as 1,1- dinucleophiles. The resulting di- and trisubstituted pyrrolidin-2-ones can be also used in subsequent chemistry to obtain various nitrogen-containing polycyclic compounds of interest to medicinal chemistry and pharmacology, such as benz[g]indolizidine derivatives. The synthesis of the substituted isoindolines 3 is based on the domino-reaction between donor-acceptor cyclopropanes, bearing in ortho-position of aromatic substituent a bromomethyl group, and different primary amines (e.g., anilines, benzylamines, cycloalkylamines) was developed. The reaction involves the generation of secondary amine followed by nucleophilic ring opening of cyclopropane with amino group. Moreover, this process provided a new practical method for the rapid synthesis of benzo[b]pyrrolizidinone 4 from readily available starting materials.

”The broken pick-up sticks problem” is proposed by T. Kyle Petersen and Bridget Eileen Tenner in 2020. We solve the problem by considering the discrete version using random variables, and the limit behaviour of the discrete version gives us a combinatorial solution to the original problem. We also evaluate the probabilities of the triangles formed by the broken/pick-up sticks satisfying some specific geometric conditions with various techniques, including calculus and elementary number theory.

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.

There have been numerous studies exploring the applications of graph theory in traffic management, often finding ways to reduce traffic congestion and make traveling more efficient. Such studies will be beneficial when applied to heavily congested areas such as España Boulevard, one of the busiest thoroughfares in Manila. This paper aimed tooptimize the road map of España Boulevard using graph theory. The current road map of España Boulevard was represented as a directed graphand subjected to the mutation method of edge removal, wherein an edge isremoved in each mutation based on a computed fitness function, F(G),which depicts better efficiency at lower values. Edges were removed until the graph got disconnected, which was tested using the Floyd-Warshall algorithm. The 28th mutation resulted in a minimum F(G) value of 144.4; this is a 50.18% decrease from the F(G) of the original graph, which is 290. After the 28th mutation, the removals resulted in an increase in the F(G). As a result, the final mutation resulted in an F(G) of 311.89, which characterized a less efficient graph. This study was able to apply graph theory concepts to optimize the España Boulevard road map using the mutation method, minimizing its F(G) by at most 50.18%. For future studies, the practicality of the alternate road map may be tested in simulations to examine its efficiency when other factors, such as traffic volume, are introduced.

為了解地震對地球自轉速率變化之影響，本研究使用自製地球模型、模擬板塊裝置，並運用 Tracker 等程式，模擬地震後地球自轉變化情形。 自製地球轉動時角速度有週期變化，可當作模擬地震發生的背景資料。研究結果顯示，加重板塊負重，角速度無明顯變化趨勢，但自轉一圈所需時間皆增加。改變板塊位置，北緯 22.5 度組角速度圖形高峰值及振幅顯著增加，赤道、北緯 45 度組變化則不明顯。 板塊移動與球體旋轉同向時，角速度變化振幅明顯加大，反向則不明顯。在角速度相對小時移動板塊，角速度趨勢往下，平均角速度減少；反之，在角速度相對大時移動板塊，角速度趨勢往上，平均角速度增加。 本模型模擬之地震所引發之日長改變量，經由換算相當於自轉週期 24 小時的地球改變了 36 分鐘。