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.

利用鏡檢大生熊蟲形態檢測蔬菜中硝酸鹽壓力，常有形態判別問題，本研究想利用其自體螢光開發新型檢測模式，利用硝酸鹽壓力下其活動與隱生比例差異與自體螢光強度關係，檢測硝酸鹽濃度。顯示其自體螢光最佳激發波長為488 nm，製作檢量線(R2=0.99)與自製裝置使用470nm波長激發以壓克力濾光(R2=0.97)可檢測0〜156 mg/L硝酸鹽，可改善鏡檢缺點，並嘗試應用，發現蔬菜硝酸鹽 (小白菜492mg/L)，超出其自體螢光檢測極限，且蔬菜萃取液會影響大生熊蟲自體螢光，目前能進行定性分析，後續將分析蔬菜中造成干擾物質，繼續評估其應用性。探討其螢光機制，利用組織切片，探討大生熊蟲自體螢光強度與表皮層厚度在隱生和活動狀態下，是否具有相關性，發現脫水樣本自體螢光強度與螢光面積較活動樣本無差異(p>0.05)，推測自體螢光強度會受到其隱生時體表收縮程度有關。

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。

本研究以電化學二氧化碳還原反應(CO2RR)技術將二氧化碳還原成高經濟能源燃料，使用水相合成法製備Cu/Zn銅鋅雙金屬奈米觸媒，改變金屬間的比例: Cu2Zn1、Cu1Zn1、Cu1Zn2以及通入N2/O2/H2 熱處理改變觸媒氧化態，而改變氧化態可以在化學性質、催化活性、電子結構等方面有重要影響使其催化出不同反應路徑，改變產物生產效率和選擇性。用能量散射光譜儀、X光繞射儀鑑定奈米觸媒間金屬比例和晶型;線性掃描伏安法和氣相層析儀探討二氧化碳還原法拉第效應和生產效能。結果發現Cu2Zn1-N2能產生最多的CH4，因改變氧化態使其效能高達53.03%; Cu1Zn2產生最多的CO，效能為44.99%，推論為鋅的比例較高所致。

The DIVE&CLEAN project is an educational and innovative initiative aimed at addressing a significant environmental challenge: marine pollution. With oceans covering over 70% of the Earth’s surface and providing a home to 50–80% of life on the planet, their health is critical. However, marine ecosystems are under threat due to plastic pollution, which impacts wildlife, coastal communities, and global biodiversity. This project centers around the idea of introducing underwater trash bins, especially in areas frequented by recreational divers. While most divers explore the seas without specific tools to collect trash, they could contribute significantly with the right infrastructure. The vision of DIVE&CLEAN is to inspire behavioral change, encourage collaboration, and promote actionable solutions to reduce ocean pollution. Using interactive robotics and storytelling, the project tells the story of divers rescuing animals entangled in plastic and collecting trash from the ocean floor using underwater bins. Through creative performances, it seeks to educate and motivate individuals, resorts, and authorities to adopt sustainable practices.

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.

阿茲海默症(AD)是導致失智症的主因，影響全球數千萬人。然而，AD目前的藥物大多昂貴且療效有限。目前已知腦內β類澱粉蛋白(Aβ)斑塊為AD的病理特徵，且大腦清除系統被認為對AD的治療具有重要性。先前研究發現非侵入性迷走神經刺激術(nVNS)增加腦脊髓液循環，但在神經退化疾病中的機制和應用尚不明確。本研究旨在探討nVNS增強大腦清除系統來作為AD新療法之成效，使用Aβ誘導之AD小鼠模型，利用巨視顯微鏡和免疫組織化學染色評估其膠淋巴系統功能，並以新奇事物測試評估認知功能。本研究發現於AD小鼠中，給予nVNS使大腦清除系統之水通道蛋白-4顯著增加、促進膠淋巴系統，進而改善認知功能。本研究首次發現nVNS可通過增強大腦清除系統功能，進而改善AD病理引起的失智症狀，支持nVNS作為AD新療法的可行性。

光柵作為常見的分光元件，應用於許多光學儀器中。然而，傳統光柵彈性較差且硬度較高，限制了其應用範圍。本研究利用具有高彈性和易形變特性的 PDMS 作為柔性光柵的材料，對不同厚度和彎曲程度的光柵進行一系列測試。為了探討厚度和彎曲曲率對繞射效果的影響，進行了不同厚度柔性光柵的繞射點分析實驗。實驗結果顯示，增加柔性光柵的厚度會提升其彎曲時第一亮紋的變化率；相反，減少厚度則會降低該變化率。隨後的研究進一步探討不同施力方式是否會影響柔性光柵的分光效果。通過拉伸和壓縮光柵，發現拉伸會使光柵的軌距變大，而壓縮則會使軌距變小。此外，研究還探討了利用 PDMS 複製類似光柵的結構是否具有分光效果。實驗結果顯示，複製的指紋確實展現了類似特性，期望未來能夠將這些特性實際應用於相關領域。

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.

The equation of an ellipse and quadratic residues are well-known concepts in elementary geometry and number theory, respectively. While the properties of ellipse equations in Euclidean space have been extensively studied, many characteristics of quadratic residues, such as consecutive quadratic residues, have also been explored in past research. In this study, we discovered the characteristic polynomial of the equation of an ellipse over finite fields Fp, a single-variable polynomial that shares the same roots as the ellipse. Furthermore, by examining the parallels between the equation of an ellipse and the pairs of residues and nonresidues, we derived a characteristic polynomial for this concept and demonstrated its connection to the Catalan number, a significant sequence in combinatorics. This research was conducted through the following steps. First, the power sums of the roots of the ellipse in Fp were calculated using the Legendre symbol and Euler’s criterion. Next, the characteristic polynomial of the ellipse was determined using Newton’s identity, generating functions, and Vieta’s theorem. Finally, leveraging the equivalence between the equation of the ellipse and the pairs of residues and nonresidues, we established the main results connecting these two concepts with Catalan numbers.

The quantum mechanical description of the four fundamental forces of nature is very important for the decryption of the rules which underlie our world. While Quantum Electrodynamics (QED) describes the electromagnetic force in great detail, it also uses complex mathematical techniques and advanced physical concepts. In the following, I will analyze to what extent a quantum mechanical two-state model can be used to describe the Coulomb interaction between two charged particles. To do so, I will exclusively focus on the electrostatic interaction, leaving dynamics aside. Furthermore, the analysis is nonrelativistic and does not consider the spin of the particles. Finally, using discrete state theory allows to explore the strength of the basic concepts of early quantum mechanics. In this sense, I will try to develop a simpli ed model for the quantum mechanical description of the electrostatic force. However, the analysis is not simplistic, since the traditional formalism of quantum mechanics will be used, including Dirac's Bra-ket notation, probability amplitudes, the Hamiltonian matrix as well as the Schrödinger equation. To understand the framework of my project, it may be helpful to take a look at the source of inspiration for my analysis: In Chapter 10 of the third volume of the well-known textbook series The Feynman Lectures on Physics[4], the force holding the hydrogen molecular ion together is explained in terms of a two-state system. The electron of the molecular ion can be either at the rst proton or at the second one. The exchange of the electron between both protons leads to an attractive force between them. It is known from QED that the electrostatic interaction between two charged particles is due to the exchange of a virtual photon which acts as force carrier. The idea of my work is to explore whether the electrostatic force can be described by a very similar model, replacing the electron acting as force carrier in the molecular ion by a virtual photon for the description of the electrostatic force between two charged particles. To describe a system consisting of charged particles, I will make the assumption that a charged particle can appear in two states. Either it is in state e where it can emit a photon or it is in state a which enables it to absorb a photon. Upon emission or absorption of a photon the charged particle transitions to the respective other state. This makes the approach analyzed in my work an element of discrete state theory, since two di erent states of the particle are used to store information about it. Of course such a model cannot be compared to the sophisticated theory of Quantum Electrodynamics. The point is, however, that it is interesting to explore the power of the most fundamental concepts of quantum mechanics and to show that such an analysis can lead to inspiring results.

Road safety remains a global concern, with driver-related factors like distraction, drowsiness, and medical conditions such as stroke being leading causes of accidents. In this paper, we propose a real-time ensemble learning framework that leverages transfer learning for the detection of stroke, drowsiness, and distracted driving. Our model integrates multiple Convolutional Neural Networks (CNNs) fine-tuned for each specific task, and employs a stacking method to combine the predictions of these models using a meta-classifier. Notably, the model is optimized to enhance stroke detection, minimizing false negatives— an essential aspect for timely medical intervention. Experimental evaluations on diverse datasets demonstrate the efficacy of our approach, achieving an overall accuracy of 92.5%. The results emphasize the model’s potential for real-time driver monitoring, offering critical safety features that could reduce accidents and save lives.