In all experiments for detection of WIMP dark matter, it is essential to develop a classifier that can distinguish potential WIMP events from background radiation. Most often, clas- sifiers are developed manually, via physical modeling and empirical optimization. This is problematic for two reasons: it takes a great deal of time and effort away from developing the experiment, and the resulting classifiers often perform suboptimally (which means that a greater amount of expensive run time is required to obtain a confident experimental result). Machine learning has the potential to automate this and accelerate experimentation, and also to detect patterns that humans cannot. However, two major challenges, which are shared among several dark matter experiments, stand in the way: impure calibration data, which hinders training of models, and unpredictable physical dynamics within the detector itself. My objective was to develop a set of machine learning techniques that address these two problems, and thus more efficiently generate highly accurate classifiers. I was able to obtain raw data for two dark matter experiments which exhibit these challenges: the PICO-60 bubble chamber [2], and the DEAP-3600 liquid argon scintillator [1]. For each experiment, I developed and compared three general-purpose algorithms intended to resolve its inherent challenge (impurity and unpredictable dynamics, respectively). In PICO-60, background alpha and WIMP-like neutron calibration datasets are used for training; however, there is an impurity of 10% alphas in the neutron set. While a conventional classifier was developed (and is believed to be 100% accurate), machine learning in the form of a supervised neural network (NN) has also been previously explored, because of the benefits of automation. Unfortunately, it achieved a mean accuracy of only 80.2% – not usable as a practical replacement for conventional methods in future iterations of the experiment. In DEAP-3600, photons are absorbed by a wavelength shifting medium and re-emitted in an unpredictable direction, before being detected by one of 255 photomultiplier tubes (PMTs) around the spherical detector. The randomness severely limits the accuracy of conventional classifiers; in a simulation, the best so far removes 99.6% of alpha background, while also (undesirably) removing 91.0% of WIMP events. Because of physical limitations, simulated data is used for calibration, with 30 real-world experimental events available for testing. I have written a research paper [11] about my work on PICO-60, which has been approved by the PICO collaboration and pre-published at https://arxiv.org/abs/1811.11308. It is currently undergoing peer review for publication in Computer Physics Communications. All PICO researchers are listed on my paper for their work on the original PICO-60 experi- ment. They did not contribute to this study; I completed and documented it independently.

¬生物光伏電池(BPV)是一種利用光合自營生物進行光電能量轉換的發電裝置。本研究利用共生菌G76創造不須補充培養基的固態複合型BPV。我們以陽極只含小球藻的BPV為控制組(X-C)，發現在實驗開始24小時之後，BPV電壓開始隨著光照週期產生規律變化，前三個光週期電壓高峰平均值為116.23±2.92 mV, 谷底平均值為87.96±4.48 mV，波動幅度28.27 mV。實驗組為陽極有小球藻與共生菌G76的複合型BPV (X-CG)，同時期電壓高峰平均值為109.23±2.45 mV, 谷底平均值為100.63±0.9 mV，波動幅度8.6 mV。與X-C相比，添加G76會使電壓高峰下降6.02%，但提高谷底電壓14.4%且縮小電壓波動幅度69.58%。目前X-CG已運轉超過1032小時，電壓高峰為95.2mV，衰減幅度19.35%。同時期控制組X-C電壓高峰已下降至61.1mV，衰減幅度90.22%。實驗過程中我們發現在X-C及X-CG組別運作73小時後, 在陰極區都出現了紫黑色微生物(PB1)，同時這些被汙染的BPV的電壓明顯比其他組別更高，將BP1單獨培養並引入陰極後(P-CG), 此一BPV的電壓高峰平均值高達179.3±3.66 mV，谷底平均值則為162.37±1.38 mV，都比X-CG組提高近六成。更重要的是X-CG與P-CG分別能保持日間電壓的92.13%與90.56%，都是非常穩定的BPV。 由以上結果可知, 將共生菌G76加入BPV陽極能提高日夜間的供電穩定度並延長裝置使用壽命，而將PB1引入陰極則能使BPV電壓提高六成以上。若能進一步優化應用這些共生菌, 此種低成本複合型生物光電轉換裝置將有潛力建構出一套不須儲電系統的太陽能發電系統。

以往人類對抗癌症只能依靠手術、化療、電療等治標不治本的手段殺死癌細胞，不僅效率差且有強烈的副作用，而CAR-T免疫療法透過活化病人自身免疫細胞來專一性的治療癌症。 本研究旨在發展能治療實質固態瘤的CAR-T免疫療法，透過活化由人血中所分離出來的T細胞，使之能辨識在癌細胞中大量表現的上皮細胞黏附因子（EpCAM），並建立一套體外細胞毒殺測試的模型來測試CAR-T殺死腫瘤細胞的能力，以篩選出具有發展潛力CAR-T細胞。 本研究從外周血單個核細胞（PBMC）中分離T細胞，透過基因重組技術與lentivirus來改造T細胞，使T細胞表現CAR基因，再利用Dynabeads Human T-Activator CD3/CD28活化，並以大腸癌細胞株HCT116當作癌細胞目標，測試CAR-T是否有能力殺死腫瘤細胞，另外也以不表現EpCAM的A549肺癌細胞株、H460肺癌細胞株進行毒殺能力測試，結果顯示此CAR-T細胞有高度專一性，證明此CAR-T細胞具有潛力成為新一代的CAR-T免疫療法。

This research project focuses on developing a web-based multi-platform solution for augmenting prognostic strategies to diagnose breast cancer (BC), from a variety of different tests, including histology, mammography, cytopathology, and fine-needle aspiration cytology, all in an automated fashion. The respective application utilizes tensor-based data representations and deep learning architectural algorithms, to produce optimized models for the prediction of novel instances against each of these medical tests. This system has been designed in a way that all of its computation can be integrated seamlessly into a clinical setting, without posing any disruption to a clinician’s productivity or workflow, but rather an enhancement of their capabilities. This software can make the diagnostic process automated, standardized, faster, and even more accurate than current benchmarks achieved by both pathologists, and radiologists, which makes it invaluable from a clinical standpoint to make well-informed diagnostic decisions with nominal resources.

尿酸氧化酶參與嘌呤代謝，然人類尿酸氧化酶基因已退化，易使過量尿酸沉積於關節造成痛風，近年來微生物源尿酸氧化酶之酵素工程改良，逐漸被應用於尿酸檢測與降解藥物，因此具極高研發價值。 本研究針對微生物源尿酸氧化酶進行基因體探勘，篩選出抗輻射奇異球菌(Deinococcus radiodurans)及耐熱雙球桿菌(Thermobispora bispora)源尿酸氧化酶基因，以蛋白質異源表現與金屬螯合層析法純化取得重組尿酸氧化酶，進行酵素動力學、熱穩定分析、結構解析、金屬離子耐受性分析與尿酸檢測應用。在最佳反應條件下，抗輻射奇異球菌源酵素於30 ℃之Km與Kcat為679.03 μM, 30.33 s-1；耐熱雙球桿菌源酵素於70 ℃之Km與Kcat為191.31 μM, 12.85 s-1。此外，我們已解析耐熱雙球桿菌源尿酸氧化酶結構，發現其羧基端之特異性構型可能與熱穩定性有關。本研究以此兩種尿酸氧化酶為酵素工程改良標的，盼未來能研發作為快速篩檢與臨床治療之生物替代藥物。

澎湖牡蠣養殖受扁形動物危害嚴重但缺乏相關研究。本研究首次採集活體澎湖蚵蛭Stylochus ( Imogine ) orientalis splendida Bock, 1913進行捕食行為研究。觀察澎湖蚵蛭捕食過程分為攻擊期、捕食期和消化期，並首次報導攻擊期中發現新型的離體咽。離體咽具負趨光性( P <0.01 ** )能朝向牡蠣殼內暗處移動，使其開閉殼頻率與死亡率增加。離體咽也顯著影響文蛤死亡率 ( P <0.01** )，20條以上離體咽即可導致文蛤死亡率 60% 以上，造成文蛤外套膜萎縮，且與數量呈高度正相關 ( R2 = 0.964 )，外套膜切片顯示離體咽可導致外套膜肌肉變細且形成許多空洞。經離體咽均質和硫酸銨沉澱法萃取蛋白質後，通過SDS蛋白質電泳比較澎湖蚵蛭離體咽、咽、與其他部位的粗萃物，分離出目標蛋白質，以MALDI-TOF質譜儀分析分子量約為10 kDa。證據顯示離體咽是蚵蛭成功捕食牡蠣的重要關鍵，亦是海洋扁蟲從未被報導過的新行為。

Based on NEA Waste Statistics and Overall Recycling Rate for 2017, 809,800 tonnes of food waste and 815,200 tonnes of plastic waste was generated. Both food waste and plastic waste account for more than 10% of the total waste generated in Singapore in 2017 respectively. However only 16% of the food waste and 6% of plastic waste was recycled, the rest of it was disposed at the incineration plants and then the landfill. Such action will eventually lead to 2 major environmental issues that Singapore will face in near future: 1)Semakau landfill is our only landfill left and it is expected to run out of space in near future 2)The burning of food waste results in the release of methane (CH4), a greenhouse gas that has over 25 times the impact in trapping excess heat in the atmosphere as compared to Carbon Dioxide (CO2). This will increase carbon footprint and contribute to greenhouse effect and global warming in due course. According to the Sustainable Singapore Blueprint 2015, Singapore is working towards becoming a Zero Waste Nation by reducing our consumption, reusing and recycling all materials. A national recycling rate target of 70% has been set for 2030 with an aim to increase domestic recycling rate from 20% in 2013 to 30% by 2030 and non-domestic recycling rate from 77% in 2013 to 81% by 2030. As part of our total commitment towards waste management and sustainability effort, the purpose of doing this research project is to investigate whether food waste can be recycled and made into biodegradable plastics. First of all, chitosan will be derived from shrimp shells and be dissolved in acetic acid and lactic acid produced by probiotic fermentation of fruit and/ or vegetable waste for synthesis of biodegradable plastics.

我們的視覺能力是大腦將感官所觀察到的物體進行辨認，由於物體受到形狀、線條和顏色的變化，加上人們的生理、心理原因，而產生與實際不符合的視錯覺。為探討眼睛對不同顏色赫曼方格的視錯覺，我們以標準化的情境和RGB 色環中的對比色、相近色和互換色定義電腦上赫曼方格顏色，進行實驗。研究發現黑白配色所看到鬼影人數最多，而綠紅配色卻較少人看到鬼影。因此我們用側抑制現象與感光細胞進行討論、分析，得出傳統黑白赫曼方格，受到側抑制作用的影響最為明顯，而其餘顏色變因的赫曼方格，對於紅藍綠視錐細胞和桿狀細胞會有不同程度的刺激，產生更複雜生理錯覺。此外我們延伸去探討不同顏色的格子襯衫對於受試者消費行為的影響，研究發現生理視錯覺會影響受試者的消費行為，錯覺較少比較多人願意購買；除此之外社會觀感與年齡層皆會影響受試者的消費行為。

Heavy metals contaminate many bodies of water, posing a health risk to not only organisms that live and use the water in these areas, but also to the humans that live nearby. Chlorella vulgaris, a microalga, is one organism whose chlorophyll a fluorescence can indicate the presence of these substances, detecting any changes in concentrations using fluorescence microscopy and other fluorescence devices. The study explores the sensitivity of C. vulgaris to the heavy metal zinc where the algae was exposed to five concentrations of zinc: 0 ppm, 5 ppm, 10 ppm, 50 ppm, and 100 ppm. The fluorescence of the samples was observed with a fluorescence microscope on days 0, 4, 7, and 12, where the algal samples were adapted to the dark for 5 minutes, then exposed to light for 90 seconds. The values of the minimal and maximal fluorescence of the samples in the dark were noted. There is a significant difference in the values of the minimal fluorescence, maximal fluorescence, and maximum quantum yield, a value derived from the minimal and maximal fluorescence, at the highest concentration, 100 ppm, from the other treatments for the entirety of the experiment. The significantly low values at 100 ppm and the calculated EC50 of 75.70 ppm indicate that C. vulgaris is indeed a viable indicator for zinc detection at this and higher concentrations of zinc.

Unaffordable healthcare and excessive plastic waste are both alarming issues that are plaguing modern society. Recent studies conducted by the World Health Organisation (WHO) report that about 15% of the world's population suffer from a form of disability, of which 50% of the demographic cannot afford adequate health care. Furthermore, 8 million metric tons of plastic annually enter our oceans (apart from the 150 metric tons that currently circulate our oceans!). In conjunction to the global plastic pollution crisis, unnecessary invasive surgery is currently being done on amputees. Many of these desperate patients are forced to pay exorbitant prices in order to live a normal life with bionic prosthetics. The solution… Project Limbs - an EEG, 3D printed prosthetic printed from recycled plastic. Signal processors will be implemented to build an affordable and easy-to-use ‘mind controlled prosthetic hand’, that requires no invasive surgery.