本研究聚焦於籃球員的慣用動作分析，透過深度學習技術開發了一套籃球動作分析系統，旨在準確分析籃球員在籃球運動中的個人動作特徵來進行動作辨識。我們透過自行蒐集籃球動作的影片，並使用MMAction2這個資源庫來進行動作辨識模型的訓練，將訓練好的動作辨識模型用開發慣用動作分析系統。系統流程首先使用滑動視窗（Sliding Window）的機制將即時拍攝的影像變成有序列的連續影像片段，再即時傳送至進攻動作辨識的深度學習模型中，來辨識出連續影像片段中的動作序列屬於何種特定動作，藉此將多個連續影像片段中的動作序列各自轉換為單一動作單元並依次輸出。最終，系統基於前述單一動作資料進行綜合分析，以統計使用者的籃球慣用動作。此分析系統能為籃球愛好者提供清晰的動作偏好資料，具有提升訓練成效的潛力，同時為籃球技術分析與訓練提供了一個精確的數據分析工具。

本研究旨在探討如何利用地衣共生藻類與共生真菌天然的互利性來建構長效的微生物電池，此實驗將培養出的地衣共生真菌與藻類利用海藻酸鈉（SA）進行固化，並進一步製成不須添加質子交換膜的晶球地衣電池，並觀察其發電量。經觀察，本研究之地衣電池電壓高峰為0.497 V，且目前已維持運作1038小時，電壓仍有0.3 V。由上述可知，利用海藻酸鈉固化之方式能製作出穩定且高效能的地衣電池；而地衣取自於自然環境，亦不需添加質子交換膜，故對成本低廉且環境友善成本低廉，符合永續發展目標（SDGs）中的目標七：確保所有的人都可取得負擔的起、可靠、永續及現代的能源。期許未來能夠發展為具備實用性且低成本的綠色能源。

水凝膠被廣泛應用於生醫材料方面，其特性可用來吸收大量溶劑、生物流體，或是吸附水樣中的物質，其中應用於吸附金屬離子以淨水為最具發展潛力。本計畫將N-異丙基丙烯醯胺與幾丁聚醣反應形成共聚物NIPAAm-g-chitosan水凝膠，此合成水凝膠具有 生物可降解性、溫度敏感性，探討不同嫁接程度之共聚物對金屬離子之吸附效率。目前研究從FTIR確認已成功合成出NIPAAm-g-chitosan水凝膠，且膨潤率可高達約900%。此NC水凝膠比幾丁聚醣更具廣泛的pH溶脹範圍，且在150℃高溫下不會熱裂解，顯示其穩定性佳。對於金屬離子的吸附，NC水凝膠的吸附率皆較幾丁聚醣 為佳，對於鈷離子的吸附率更可高達75%，應用在工業廢水的檢測會是一大效益。

本研究將不同比例的硫.與二氯化錫，在固定溫度200℃、加熱時間9小時，所合成出的壓電材料SnS2用來降解羅丹明染料，實驗結果發現以莫耳比1：4為最佳，降解率可達到96%。接著固定莫耳比1：4及溫度200℃下，發現在不同水熱時間時以水熱九小時的SnS2觸媒降解效果最好。最後本實驗以固定比1：4及水熱時間9小時在不同水熱溫度所合成的SnS2觸媒，以水熱溫度200℃時的降解效率最好，可達到96%。接著我們將最佳合成條件的觸媒對不同濃度的羅丹明進行降解，可發現當羅丹明濃度達到50ppm時，只需要2秒降解率即可達到99%，即使濃度達到70ppm時濃度降解效果仍可達到86%。最後對SnS.觸媒進行SEM分析發現顆粒大小為微米等級，而在PL分析發現本觸媒具有活性的能力且符合實驗結果。

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.

Wind energy is one of the most promising and rapidly growing sources of renewable energy, although maximizing its efficiency while minimizing noise remains a challenge and limits its widespread adoption. The emergence of toroidal propellers, which have gained popularity for producing comparable thrust levels to traditional drone propellers while producing less noise, could mitigate this. This study aimed to develop a small-scale toroidal HAWT and compare its power and noise output to a conventional rotor design under similar wind velocity conditions. 15-centimeter diameter models of the toroidal and conventional rotors were created in Fusion 360 and simulated using Ansys Fluent to identify the significant aerodynamic characteristics that positively affect the blades’ power coefficient. The toroidal design with the greatest simulated power output at low tip speed ratios (TSRs) was then 3D printed and physically tested in a wind tunnel against the control rotor. The experimental results confirmed that the toroidal design had greater power coefficients at lower TSRs compared to the control rotor. The toroidal rotor started operating at a wind velocity of 3 m/s compared to the control rotor’s 6 m/s, which indicates superior start-up characteristics. While the toroidal rotor produced half the power output of the control at the highest tested wind speed of 7 m/s, it emitted 18 decibels less noise and showed a reduction in discernible noise between frequencies of two to five kilohertz. The results from this study show its potential in low-noise wind turbines within low-wind velocity environments.

Certain specially structured ceramics, which can be used as biomaterials to replace bone, have recently started being utilized in the medical field. The aim of this study is to produce high-bioactivity silica from corn cob waste, a widely available organic material in nature, and combine it with calcium oxide (CaO) obtained by grinding organic mussel shell waste with high bioactivity. This combination is intended to synthesize dicalcium silicate (2CaO.SiO₂) to develop an alternative tissue scaffold with high bioactivity, capable of replacing bone, for existing titanium alloys. The goal is to incorporate this scaffold into PEEK (polyether ether ketone), a novel tissue scaffold material, at varying percentages to create a next-generation innovative bone substitute material. An additional objective is to demonstrate through biocompatibility tests that the produced ceramic-polymer biocomposite exhibits antibacterial activity against Staphylococcus aureus.

Impostor Syndrome, a psychological phenomenon where individuals doubt their abilities despite evident achievements, can hinder personal and academic development. This study aims to evaluate the effectiveness of the interactive journal “I am Great Because of Me”, integrated with artificial intelligence (AI), in addressing Impostor Syndrome among high-performing students. The journal features innovative tools such as the Clance Impostor Phenomenon Scale (CIPS) test accessible via QR code, self-acknowledgement pages, and virtual consultations powered by AI chatbots. These elements aim to support self-reflection, provide real-time diagnostics, and deliver actionable recommendations for users. The ADDIE model was employed for the journal's development, incorporating feedback from experts and users. Likert scales and Cohen’s D analysis were used to evaluate satisfaction, usability, and impact. Results showed that 90.1% of students expressed high satisfaction with the journal’s accessibility, interactivity, and capacity to enhance self-awareness and motivation. A pre-test and post-test conducted on the intervention group revealed a significant reduction in Impostor Syndrome by 42.5%, with an effect size of 2.84, categorized as "very large." Features such as the self-acknowledgement worksheets helped students recognize their strengths, while AI consultations offered additional psychological support. Expert validation emphasized the journal’s clarity, relevance, and objectivity, noting the absence of bias in AI-driven suggestions. The journal was praised for its accurate content, ease of use, interactivity, and the protection of user data, ensuring a safe and private environment for self-development. Students found the journal beneficial not only for addressing Impostor Syndrome but also for fostering personal growth and self-confidence. This research demonstrates the potential of combining psychological theories with AI-driven tools in education. The journal “I am Great Because of Me” effectively aids students in overcoming Impostor Syndrome and improving self-perception. It serves as a scalable solution for schools and individuals aiming to tackle similar psychological challenges. Future studies are encouraged to explore its application in broader contexts to maximize its impact.

Fungal infections, particularly those caused by resistant strains like Candida auris and Candida glabrata, pose a significant threat to global health. The widespread use of azole antifungals, such as fluconazole, has driven the emergence of multidrug-resistant strains, undermining the efficacy of existing treatments. These challenges necessitate the development of novel antifungal agents with enhanced activity and reduced resistance profiles. To address resistance mechanisms, we designed and synthesized hybrid molecules combining triazole and thiazolidine-2,4-dione (TZD) pharmacophores. This strategy leverages dual mechanisms of action: inhibiting fungal CYP51, a key enzyme in ergosterol biosynthesis, and disrupting fungal cell wall integrity. The structural versatility of hybrid molecules allows for targeted modifications to enhance antifungal potency, binding specificity, and pharmacokinetics. Using a stepwise synthetic approach, triazole-containing piperazine derivatives were first prepared and coupled with TZD-based carboxylic acids via optimized condensation reactions. The structures of the synthesized compounds were confirmed through advanced spectroscopic methods, including 1D/2D NMR and high-resolution mass spectrometry. The antifungal activity of these hybrids was evaluated in vitro against clinical and reference strains of Candida spp. and Aspergillus fumigatus. Among the synthesized compounds, 6a demonstrated notable activity against Candida parapsilosis (MIC 0.06 μg/mL), comparable to voriconazole. Compound 4b exhibited moderate activity against C. parapsilosis (MIC 1–2 μg/mL) and A. fumigatus (MIC 8 μg/mL). However, most compounds showed limited efficacy against highly resistant strains such as C. albicans 8R and C. krusei. This study highlights the potential of hybrid triazole-TZD molecules in overcoming resistance and improving antifungal efficacy. While promising, further optimization is required to broaden the spectrum of activity and enhance efficacy against multidrug-resistant pathogens. These findings contribute to the growing field of antifungal drug development, emphasizing hybrid approaches as a viable solution for combating fungal resistance.

Cultivating plants from seeds or cuttings is a fundamental aspect of gardening and agriculture. While traditional methods have been practiced for centuries, there is a persistent need for innovative and efficient approaches to enhance plant growth and development. This section explores the challenges associated with traditional propagation methods and examines potential solutions offered by emerging technologies and materials. Plant propagation is necessary to allow efficient multiplication and distribution of desirable plant varieties (Sorensen & Garland, 2024). Plant propagation is the process of creating new plants. There are two primary methods of propagation: sexual and asexual. ．Sexual propagation involves the union of pollen and egg, drawing genetic material from two parent plants to create a new, genetically diverse offspring. This process utilizes the floral parts of a plant. ．Asexual propagation, on the other hand, involves taking a part of a single parent plant and inducing it to regenerate into a new plant. The resulting offspring is genetically identical to its parent. This method utilises the vegetative parts of a plant, such as stems, roots, or leaves. One emerging technology that has garnered attention in this field is the use of cocopeat, a sustainable growing medium derived from coconut husks (Pane et al. 2021). Cocopeat has been extensively studied as a potential alternative to peat moss in plant propagation (Gericke, 1940). It offers a favourable balance between air porosity and water holding capacity, promoting root development and nutrient uptake (Kalaivani and Jawaharlal, 2019). Furthermore, cocopeat is a renewable and environmentally-friendly resource, making it an attractive option for sustainable seedling cultivation. Research has shown that the use of cocopeat as a growing medium can enhance the growth of both vegetables and various ornamental plants, such as Impatiens. The biostimulant effect of the Trichoderma atroviride fungus, which can readily colonize coir, has been observed to increase aboveground biomass, flower production, pigments, and nutrient concentration in these plants (Traversari et al., 2024).

本研究報告，從酪梨葉利用柱層分析技術萃取出葉綠素-a、葉綠素-b以及類胡蘿蔔素，用來研究其物理性質以及測量非線性折射率(n2)。 當雷射光束照射在置於比色管中的樣本時，中央軸上的強度最高，導致溶液產生了溫度梯度和折射率梯度。雷射光束穿過溶液後，在屏幕上產生了遠場繞射圖樣。這些繞射圖樣的最大半徑(Rm)和暗條紋的數目(N)隨雷射光的功率(P)、光徑長度 (𝓵)、溶液的熱吸收係數(μ)和溶劑的熱光係數(dn/dT)變化。從N對𝓵和N對P的關係圖中，可以計算出溶液的n2。 在本研究中，從酪梨葉中萃取的色素濃度分別是從菠菜和朱槿葉中萃取色素濃度的4.0倍和3.1倍。更令人驚訝的是測得的n₂ 值比石墨烯大100倍。結果顯示，該樣品具有顯著的非線性折射率，使其成為各種光學開關應用的理想材料。

在超大質量黑洞周圍，偶爾能觀測到潮汐破壞事件的發生，而這也是一個能夠探測黑洞的手段。潮汐破壞事件是一種特殊的現象，當一個天體進入到所謂的「洛希極限」半徑範圍內時，因為受到的潮汐力超越了自身的重力而遭到撕裂。當這個事件發生時，會因為黑洞在吸積的過程中產生明顯的亮度變化，因此可以透過一系列的亮度變化觀測潮汐破壞事件，並可以推算黑洞的各項參數，因此潮汐破壞事件在天文學的發展上有其重要性。 因此，我們想要嘗試模擬潮汐破壞事件的演化過程。我們學習Linux語言以及如何使用Mcluster和PeTar等模擬軟體，並透過Python分析模擬結果，然後與理論預測值進行比較，以了解我們有那些地方需要修正。