Energy has had an enormous impact on the development of technology and is a main factor in humans’ advancement towards an evolved society. Nevertheless, nonrenewable energy resources – which are the most effective in everyday application - have led to changes in the climate, environment, human health, and the world in general [1], which has encouraged researchers to switch to the use of renewable energy sources. Solar Cells are one of the most effective resources that rely on renewable energy. They come in a variety of types, operation methods, and efficiency as shown in Figure 1, including Dye-Sensitized Solar Cells (DSSC), which, inspired by photosynthesis in plants, uses photo-sensitive dye to capture sunlight and generate electricity. DSSCs were proved to have generated a great deal of interest and are one of the most promising solar cells among third-generation PV technologies, due to their low cost, simple preparation, good performance, and environmental friendliness compared to conventional photovoltaic devices [3]. However, their efficiency is quite insufficient for everyday use. Previous studies proved that Tandem DSSCs – which are two dye-sensitized cells stacked on top of each other – are able to enhance cell performance. The light absorption range of a tandem cell is increased because the bottom cell behind the top one absorbs and uses the incident light that was not absorbed by it [4]. It operates as shown in Figure 2, where the light photons excite the electrons of the dye molecules. The electrons are then transported to the FTO (conductive glass) by the semiconductor, which is used in the figure as TiO2 nanoparticles. The electrons pass through the circuit to perform the work, then move to the counter electrode (shown as Platinum). They are then transported by the electrolyte (I-/I3-) back to the dye molecules, and the process is repeated.

In this fast-paced digital economy, customers' judgment is based on their experience with a company’s products and services. Customer reviews become a vital source of information for companies because this information can be used to enhance their products, understand customer wants and needs, improve brand reputation, and provide a competitor’s advantage. A company can understand customer needs and wants by going through reviews. Customers are encouraged to leave not only their opinion but also their ideas for the development of the product or service. By understanding these reviews, a company can actively respond and engage with a reviewer or problem. Failure of companies who don't answer customer queries may negatively impact customer loyalty. Customers will feel neglected by these companies and will choose competing companies to handle their needs. Additionally, customers may speak negatively about a company that does not respond to reviews. The AI-based customer sentiment dashboard can help gain a company's competitive advantage by identifying weaknesses in themselves and others. Companies will be enabled to understand where they succeed and where improvement is needed compared to their competitors, leveraging businesses to address strengths and weaknesses before competitors do. Through AI-based customer sentiment dashboards, a company can analyze its competitor’s reviews and use that information as leverage to make improvements to its products and services. Customers are increasingly leaving reviews on popular apps like Google Play, Stamped.io, Yapto, and Judge.me, Loox, Qualaroo, and Yelp. The reviews are rich in customer sentiments offering valuable insights into user satisfaction and pointing out the areas for improvement that are crucial to every company no matter how big or small. Despite their value, manually processing these reviews is a challenging task due to the large volume of unstructured data. Manual processing is also vulnerable to bias and human error, leading to inaccurate information. Traditional methods such as surveys have been proven to be ineffective if the main focus is targeted feedback and have low responses compared to reviews. The advances in artificial intelligence like Natural Language Processing (NLP) help us interpret and analyze human language and generate outputs like predicting what type of sentiments are in reviews. This project proposes developing an AI-based sentiment analysis model to evaluate customer feedback on two widely used taxi applications. Natural Language Processing libraries, such as the Valence Aware Dictionary and Sentiment Reasoner (. The model aims to categorize customer reviews into positive, negative, and neutral sentiments.

This thesis presents the design and construction of a small-scale humanoid robot, covering all aspects from 3D modeling to electronics design and programming. The robot is built entirely from custom 3D-printed components, with a new servomotor developed specifically to meet the project’s requirements. During the robot’s development, custom electronics were also designed, leading to a modular platform that enables easy interaction with diverse modules like servomotors and inertial measurement unit (IMU) modules. This modular approach allows these components to be programmed and controlled with minimal adjustments, as well as making development of potential future modules straightforward. The robot is operated via a computer application that includes a graphical user interface for displaying real-time data from the robot.

The present research work aimed to assess the impact of biochar (BC) amendment (5%) and foliar supplementation of titanium (Ti) at a concentration of 50 mg L-1 TiO2 on the growth, chlorophyll content, and biochemical parameters of wheat (Triticum aestivum L). The results demonstrated significant improvements in several aspects of wheat physiology due to these treatments, both individually and in combination. Plant height, as well as fresh and dry weight of wheat, exhibited substantial increases when subjected to Ti and BC treatments, with the highest enhancements observed in plants treated with both Ti and BC. Furthermore, chlorophyll content, including chlorophyll a, chlorophyll b, total chlorophylls, and carotenoids, showed marked increases in response to individual Ti and BC treatments, with even greater improvements when both treatments were combined. In terms of biochemical parameters, the content of proline, sugars, and free amino acids significantly increased in plants grown in soils amended with BC. Additionally, foliar Ti treatment led to elevated levels of these biochemical constituents. The combined treatment of Ti and BC resulted in the most pronounced effects. Moreover, oxidative damage parameters, such as hydrogen peroxide, lipid peroxide, and electrolyte leakage, were notably reduced in plants subjected to Ti and BC treatments, either individually or together. The activity of antioxidant enzymes, including superoxide dismutase, catalase, and ascorbate peroxidase, exhibited significant increases in response to Ti and BC treatments, further emphasizing their beneficial effects on wheat plants. Overall, this investigation shows that biochar amendment and titanium foliar supplementation both have beneficial effects on wheat development and biochemical parameters; these findings may be relevant to efforts to increase crop productivity and stress tolerance.

本研究記錄人類進行「剪刀石頭布」遊戲時的決策行為，也設計T型迷宮建立鼠婦之負趨光行為作為動物模式，探討行為偏好與決策依賴性等特性。我們發現「出石頭」的機率較高，且時間間隔縮短後，「出剪刀」的機率增加而「出石頭」的機率減少，並會展現負相關的決策經驗依賴性，其中「慢出組」更為明顯，代表出拳間隔縮短而減少意識作用，負相關的決策經驗依賴性即會減弱。另一方面，鼠婦在負趨光性刺激剛消失後，仍呈現負趨光性的選擇方向，具有習慣性。鼠婦在選擇行走方向多次後，會呈現與前次選擇的正向相關性。在負趨光性的環境刺激後，上述的現象會先消失，而後再現。若負趨光性刺激方向轉換，則原先的趨光行為消失，應是因方向選擇的習慣性干擾了負趨光性的選擇。

This report presents a novel AI-driven tool for aiding musical composition through the generation of chord progressions. Data acquisition and analysis are discussed, uncovering intriguing patterns in chord progressions across diverse musical genres and periods. We developed a range of deep learning models, from basic recurrent networks to sophisticated Transformer architectures, including conditional and style-based Transformers for improved controllability. Human evaluation indicates that, within the context of our specific data processing methods, the chord sequences generated by the more advanced models are practically indistinguishable from real sequences. The models are then integrated into a userfriendly open-source web application, making advanced music composition tools accessible to a broader audience.

When a leaf of a plant encounters stress, how does the plant convey the stress signal to other tissues and manage nutrient distribution? This field of study has been largely unexplored. However, the unique interconnected frond structure of Lemna trisulca, along with the use of a divided Petri dish, is very suitable for handling localized stress and investigating the mechanisms of intracellular signaling and nutrient distribution. Research has shown that when the mother leaf experiences localized stress, it releases healthy daughter leaves to minimize collateral damage to the daughter leaves. Conversely, when the daughter leaves face localized stress, the mother leaf chooses to retain them and continues supplying them with nutrients to support their survival. In-depth studies revealed that stressed daughter leaves accumulate Reactive Oxygen Species (ROS), triggering nutrient distribution by sending a distress signal to the mother leaf. This prompts the mother leaf to use Ca2+ as a signaling molecule to deliver nutrients to the daughter leaves. Selective detachment is regulated and triggered by the interaction between Ca2+ and ROS within the mother leaf. When the mother leaf undergoes stress, Ca2+ acts upstream to induce ROS accumulation at the nodes, sending a unidirectional detachment signal to the daughter leaves. This causes ROS accumulation at the daughter leaf nodes, inducing detachment and thereby reducing the collateral damage the daughter leaf could experience due to the mother leaves.

Carbon capture and storage technology (CCS) has tremendous potential to enable the use of fossil fuels while reducing the emissions of CO₂ into the atmosphere, and, consequently, combating climate change. CCS faces several challenges such as energy consumption, cost, low practical applications and environmental friendliness. In this work, a new approach to carbon capture that is not energy intensive is proposed.

本研究先觀察著名的密克定理（Miquel theorem）與密克點（Miquel point），我們創新給出了新的研究項目，關注密克點𝑃與密克三角形的頂點所構成直線和原三角形𝐴𝐵𝐶三邊直線的其餘六個交點，這是前人沒有觸及的研究項目，從而定義旁接三角形與衍伸三角形。 我們先針對特殊型（直角）的構圖，發現滿足兩個衍伸三角形的有向面積 [𝐴1𝐵1𝐶1]=±[𝐴2𝐵2𝐶2] 時，𝑃 點形成的軌跡為原三角形的 Kiepert hyperbola 與外接圓，這個是有趣且重要發現，我們也進一步給出其幾何必然性。進一步考慮 [𝐴1𝐵1𝐶1]=𝑟[𝐴2𝐵2𝐶2] 時，則刻劃出 𝑃 點軌跡為圓錐曲線系。在前面的基礎下，再針對一般型（任意角）的構圖，若 𝑃 點位於原三角形外接圓及Kiepert hyperbola 與 Steiner circumellipse 的線性組合曲線上，此時兩個衍伸三角形 𝐴1𝐵1𝐶1 與 𝐴2𝐵2𝐶2 的有向面積比值為定值，且兩者恆為相反數。

Due to their pollination services, honeybees are one of the most ecologically vital animals, being singlehandedly responsible for nearly 80% of global agricultural pollination [1]. However, in recent years, they have experienced large declines in populations, and as a survey reported roughly 50% of beekeepers in the US lost their honeybee colonies [2]. These losses are experienced globally due to a combination of many factors, including but not limited to habitat loss, pesticides, climate change, and other invasive species [3, 4]. One of the biggest factors attributed to the decline of honeybee colonies is the usage of pesticides, specifically neonicotinoids [3-6]. Neonicotinoid compounds have been used globally since their introduction in the early 1990s [4]. Studies have shown that neonicotinoids can have both sublethal and lethal effects on honeybees, depending on the dosages that they are exposed to, as neonicotinoids bind to nervous system receptors of honeybees [7]. These effects can range from behavior changes to altered motor functions [7-9]. Among the reported effects, one of the more significant ones is the effect of neonicotinoids on honeybee learning and memory [10, 11]. Additionally, there is a lack of availability for methods of monitoring of honeybee hives, essentially meaning that the only methods to track honeybee health are through obtrusive physical methods of inspection. This paper aims to develop a novel AI-based honeybee health assessment system, able to monitor beehives using the following functions: continuous temperature and humidity monitoring both inside and outside the hive, as well as video and audio recording to assess honeybee health as well as population. In addition, this system can be used for honeybee-related studies such as nutrition effects and evaluation on health, learning, and memory. To do this, four types of nutrition have been studied and their effects have been analyzed by a deep learning approach.