一等獎

本研究是評估大生熊蟲( Macrobiotus sp.)檢測小白菜混合化學壓力的應用潛能。目前已建立大生熊蟲檢測實際環境化學壓力方法，若以正常活動樣本檢測化學壓力需24小時獲得結果；但乾燥隱生樣本則能在2小時內獲得檢測結果，每週檢測1次，至少可重複6次檢測，所以隱生大生熊蟲最適合在符合生物倫理準則之下檢測化學壓力。 探討大生熊蟲檢測實際化學壓力時耐受機制，發現大生熊蟲在小白菜萃取液隱生時，其100、50與40~50 kDa蛋白質單體表現量顯著增加，另外自乾燥隱生恢復活動時40~50 kDa單體表現量亦顯著提升，未來將以LC MS/MS分析其蛋白質種類與功能。藉由加熱實驗的總抗氧化能力數據確認大生熊蟲能以酵素與非酵素抗氧化系統對抗實際化學壓力，未來將探討對抗常見抗氧化物質的單一酵素活性。目前尚未成功分析大生熊蟲常見抗氧化基因表現量，本研究會持續改良設計出合適的primer與目標基因黏合，分析表現量。檢測其脂質含量則發現，大生熊蟲在實際化學壓力下隱生、乾燥隱生以及自隱生恢復活動階段體內脂質含量顯著增加。

神經細胞成熟的過程中可分成數個階段，每個階段間的轉換都伴隨著蛋白質的種類，RNA異構體、細胞結構與功能等全面性的轉變。但控制神經細胞在確切的時序下成熟的分子機制尚待研究。本研究發現 Paxillin 的新功能：當腦神經細胞在活體外培養至第七天時， Paxillin 的位點 Serine119 會被磷酸化 (p-PaxillinS119)，並從細胞質轉位進入至細胞核。我們使用 N2a 細胞以神經分化的模式來探討 p-PaxillinS119 進核的分子機制與功能，發現 p-PaxillinS119 進入細胞核需要位點 Serine119 被磷酸化，且分析後確認 Paxillin 的 LIM 結構域中帶有 PY-NLS 序列，分別為 P516/Y517 及 P575/Y576。我們發現 Paxillin 藉由轉運蛋白 Importin β2 辨識其 PY-NLS序列，進行蛋白間的交互作用後進入細胞核中。從螢光影像的分析，我們觀察到神經細胞的 p-PaxillinS119 在細胞核中會呈現顆粒狀，並與 RNA 剪接因子 P-SR 共定位在核斑點上。經由免疫共沉澱與細胞轉染的方式，我們證實位點 Serine119 突變，會影響 Paxillin 與 RNA 剪接因子的交互作用，及降低細胞分化與 RNA 剪接的程度。

生物時鐘可對生物體的行為與生理造成影響，在探討晝夜節律特徵的差異時，過去研究常侷限於北美大陸的品系，缺少赤道及南半球品系的晝夜節律特徵探討。有鑒於黑腹果蠅在全球各大洲的廣泛分佈，因此我們以黑腹果蠅（近赤道與中高緯度品系）為材料，研究果蠅是否因緯度而有相異的晝夜節律特徵？結果顯示不同緯度的果蠅品系展現出相異的晝夜節律特徵。赤報品系在全暗狀態下仍維持原本光暗12小時的穩定節律，而南北半球的中高緯度品系則具有相似節律特徵，即在全暗狀態下的節律不對齊原本正常光源的穩定節律，其他如活動量、週期、及節律強度等皆有著品系間的差異。更進一步比對實驗中各個品系基因序列，研究發現per和tim在調控區段有許多SNP變異，顯示其與晝夜節律特徵的關係，有助於後續尋找更多造成晝夜節律特徵差異的可能遺傳變異並探討。

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。

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.

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.

Chronic kidney disease (CKD) represents a significant public health challenge, often referred to as a “silent disease” due to its asymptomatic progression during early stages (1–2). Consequently, most diagnoses occur during advanced stages (3 and beyond), where treatment options are more complex and outcomes are less favorable. Globally, CKD affects over 850 million individuals, with 434.3 million cases in Asia alone. Despite its prevalence, early-stage awareness remains alarmingly low, with only 5% of affected individuals aware of their condition. Existing screening methods are predominantly hospital-based, expensive, and time-intensive, limiting their accessibility, particularly in resource-constrained settings. This underscores an urgent need for more accessible and efficient diagnostic tools to enable early intervention. In response to this critical problem, we developed KidneyLifePlus+, an AI-powered platform that leverages advanced machine learning models, including U-net, ResNet-50, and YOLO v8, to analyze retinal images for early CKD detection. These models are integrated to ensure high screening accuracy by identifying subtle biomarkers indicative of CKD progression. Complementing the software, we designed proprietary hardware capable of capturing high-resolution retinal images, delivering performance comparable to hospital-grade equipment. By ensuring affordability and ease of use, the system extends screening capabilities beyond clinical environments, making it suitable for deployment in community healthcare settings. KidneyLifePlus+ addresses key limitations of traditional methods by offering a rapid, cost-effective, and highly accurate diagnostic solution. The platform’s potential to enhance early detection rates could significantly improve clinical outcomes and quality of life for CKD patients. Furthermore, this innovation contributes to global efforts to reduce the burden of CKD by promoting equitable access to diagnostic services, particularly in underserved regions.

Increasing energy needs alongside the urgent issues of chemical pollution has prompted the need for developing novel green energy sources. Nitrogen-based fertilizers are of fundamental importance for the ecosystem as their usage has increased eight times in the last fifty years [1]. On the other hand , increased use of nitrogenous fertilizers is followed by higher ammonia emissions, which are dangerous pollutants responsible for deterioration in biodiversity by means of eutrophication, acidification of soil and water, and climate change [2]. Ammonia has the2apacityy to bond with other pollutants including sulfur oxides and nitrogen oxides to create particles that cause smog, which is associated with lung disease. Ammonia also increases frost sensitivities and causes necrosis of many plant species [3.] Therefore, there is a need to properly manage the ammonia-rich nitrogen waste to decrease the environmental threat factors. Of the possible approaches suggested for ammonia waste treatment, the ammonia electro-oxidation reaction (eAOR) has various promising features for application in the energy sector. It is economically appealing because Ammonia can serve as an excellent hydrogen carrier due to its storage capabilities and existing transport infrastructure alongside having no net carbon emissions. Apart from this, it requires 95% less of the theoretical energy [4] to perform the process. But the reaction is kinetically slow [5], which has been a research obstacle during the development of (eAOR), due to factors ofmslow reaction rate and large catalytic overpotential that this process consumes an unnecessary amount of power [6]. Nickel-based catalysts are a promising solution to these problems, they are cheaper , more stable and easier to produce than electrocatalysts for water electrolysis which makes it highly energy efficient for widespread use on the industrial scale. N films deposited on the anodic side also allow the creation of N-containing products such as (NH42SO3) and nitrates, which can be converted into fertilizers or renewed into the nitrogen cycle to make the process more environmentally friendly while enhancing the (eAOR) process [7,8]. Compared to Pt and Ir which are the most used noble metals, they are less poisoned on the potentials less than 0.65V and are more stable [9,10]. However , noble metals are scarce, and their cost is high for industrial applications as well as the energy they waste during (eAOR) [11].

1.1 Short project summary My project involves the conceptualization and development of an innovative approach to modular self-assembling robotic systems. Through its ability to form any complex configuration, the system is highly adaptable to various scenarios and environments. Before delving deeper into the details of my project, I will provide an overview of my background and motivations. 1.2 Background Ever since I first watched the movie "Big Hero 6", I felt amazed by the applications of the so called “microbots”. From that point on, it made me always wonder what would be possible in the real world. When I did the research, I stumbled upon this field of modular robotics. Initially, I was unsure whether to embark on a project focused on electronics and robotics due to my background in programming. On the other side, this year gave me a chance to see the incredible performances of various projects at different science expos. Besides, I took part in the program of CANSAT LU and learned a lot during it, such as microchips, the control of miniature robotics, and the sensors of it. Finally, at school, I took the option Electronics where we dig into similar topics. With this accumulated knowledge and experience I felt confident enough to start this project.

發展安全且可靠的身份辨識技術是當今的重要議題，而指靜脈因其高安全性及難以偽造特性成為我們的主題。本研究提出一種基於Transformer模型架構的指靜脈辨識模型稱為GLA-FD，旨在解決現有技術對指靜脈影像特徵表示與提取的局限性。透過開發特徵解耦與重建模組(FDRM)，模型能夠有效區分指靜脈的背景資訊與紋理特徵，並將其重新組合以提升辨識準確度。此外，本研究開發的全域-局部注意力模組(GLAM)能同時捕捉影像的全域與局部特徵，進一步強化模型對指靜脈特徵的理解。GLA-FD在FV-USM、PLUSVein-FV3、MMCBNU-6000、UTFVP、NUPT-FPV 資料集中的正確辨識率(CIR)達到100%、98.47%、99.75%、96.11%、99.82%，展現卓越的穩定性與泛化能力。此外，本模型在處理不同年齡層、國籍與影像模糊度的資料下，仍能保持高辨識準確度，顯示其在需要高安全性辨識的應用場景中具備廣泛的實用性。