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.

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.

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.

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.

本研究開發一種新穎孔洞性吸附材料：金屬有機骨架 (MOF)。MOF 在反應溶液中自組裝形成孔洞結構，透過物理吸附有效捕捉氣相乙酸分子。研發出綠色、快速可在常溫常壓下大量合成三種MOF（HKUST-1(Cu)、UTSA-280(Ca) 及 A520(Al)）方法。此外，為提升材料機械強度和應用價值，採用 PVA 聚合技術製備 MOF 複合物，使其造粒型化更易處理，提升商業和環境應用價值。吸附實驗結果顯示，HKUST-1(Cu) 粉末對乙酸移除率高達98%，而HKUST-1(Cu) PVA 複合物達93%，對比活性碳及其PVA複合物（移除率分別為85%和78%）表現更優異。MOF憑藉優異吸附性能和可大量生產低成本優勢，成為極具潛力有機無機氣體吸附劑，可為半導體產業提供一種維持高標準製程環境精密且簡便解決方案。

Nano additives is becoming popular trends nowadays due to its nanosize (1-100 nm). Incorporating nano additives in polymer could increase different properties such as mechanical, physical, electrical and thermal stability (1, 2). Different nano additives has been used such as nano copper oxide, nano silica, nano zinc oxide, nano titanium dioxide but most of these come from synthetic or metal oxides that considered as non-environmentally friendly and harmful to human when exposed or inhaled (3). One of the green materials that become attention by researchers is nano cellulose. Nano cellulose can be extracted in different methods and sources such as from wood, and non-woody resources such as kenaf, jute, bamboo as well as from bacteria such as Acetobacter species(4). This making nano cellulose abundantly available in resources. Nano cellulose can be in the form of nano crystalline cellulose (CNC) or NCC or can be in form of nano fibrillated cellulose (NFC) and bacterial nanocellulose (BNC)(5). This nanocellulose has many advantages that can give improvement in different applications such as mechanical, physical, thermal and improving the biodegradation when added together in different matrices (6, 7). Polymers have a problem in thermal stability while processing. It hard to control and maintain the thermal stability of polymer during processing and most polymers considered to have low in thermal stability except for thermosetting polymers such as epoxy. Epoxy has been widely used in many fields such as coating, adhesive, laminates, castings and many more (8). But the drawbacks of epoxy while using is hard to maintain and controll the thermal properties when processing of this materials and used for long period due to aging and attack by free radicals causing by UV radiation (9, 10). In this study we are incorporating nano additives into epoxy as polymer matrix to enhance and improve the thermal stability of composite by crosslinking the polymer chains with the nano additives. Furthermore, the nano additive used is come from nano cellulose extracted from date palm waste and thus to create an environmentally friendly and sustainable nano additives products.

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。

The human gut microbiome is integral to digestion, overall health, and metabolic disorder imbalances. Recent advancements in fecal microbiota transplantation (FMT) have highlighted the therapeutic promise of restoring healthy gut microbiota in populations with high incidences of diseases. Focusing on fecal DNA samples from healthy Asian individuals, this study examines the potential of novel Akkermansia strains, specifically Akkermansia muciniphila (Z62) and Akkermansia massiliensis (IR119), as next-generation probiotics for mitigating metabolic syndrome. A key aspect of the study is the investigation of short-chain fatty acids (SCFAs), which are produced and play a crucial role in regulating metabolic processes. SCFAs such as butyrate, acetate, and propionate are essential for energy provision to colon cells and exerting anti-inflammatory effects. The methodology involves selecting two Akkermansia strains, analyzing them through 16S rRNA and WGS, evaluating their growth and survival rates under acidic and bile-salt conditions, alongside their cell adhesion capabilities. The study focuses on the production of key short-chain fatty acids (SCFAs) and tryptophan derivatives by bacteria in regulating metabolic processes, as well as their anti-inflammatory effects on colon cells. Through in vitro assays, both strains exhibited survival in acidic/bile-rich conditions, though Z62 demonstrated superior adhesion to Caco-2 cells, suggesting a higher colonization potential. Metabolomic analysis revealed both strains produce SCFAs, including propionic and acetic acids, and indole metabolites, such as indole-3-propionic acid and indole-3-acetic acid, which are known to influence lipid metabolism and insulin sensitivity. In adipocyte cell models, IR119 significantly reduced lipid accumulation, while Z62 increased lipid presence. Furthermore, IR119 reduced pro-inflammatory cytokine levels, including IL-6 and TNF-α, suggesting potential for inflammation mitigation. The future potential of IR119 as a therapeutic probiotic is extraordinary in addressing complex metabolic and inflammatory diseases, which open new avenues for managing chronic inflammatory conditions like type 2 diabetes and cardiovascular disease. Future clinical trials could refine IR119’s efficacy, positioning it as a leading probiotic in preventive and therapeutic contexts.

One of the main challenges with today’s batteries is their relatively low volumetric and specific capacities. The highest specific capacity can be achieved with lithium-air batteries, which use metallic lithium as the anode and typically some form of porous carbon as the cathode. To enhance performance, aerogels—among the world’s lightest solid materials—are ideal candidates for cathodes. Resorcinol-formaldehyde (RF)-based carbon aerogels, for example, serve this purpose well. In my work, I utilized two types of carbon aerogels as cathode materials: one derived from pyrolyzed resorcinol-formaldehyde polymer and the other a graphene-oxide-modified version of this carbon gel. I integrated the carbon aerogels I had pyrolyzed into lithium-air batteries to improve the cell’s performance, energy density, and capacity compared to cells using activated carbon. In my research, I examined the pore structure and surface properties of these materials in aqueous media using NMR (nuclear magnetic resonance) relaxometry and cryoporometry, exploring their impact on battery efficiency. I found that the graphene-oxide-containing sample's pores filled with water in a layered manner, indicating a more hydrophilic surface, which suggests a denser arrangement of oxygen-containing functional groups compared to the unmodified carbon aerogel. The pore sizes were reduced after adding graphene oxide, resulting in an increased specific surface area for the sample. Incorporating the reduced graphene-oxide-containing carbon aerogel enabled the creation of a more efficient, higher-capacity battery than with the RF carbon aerogel. This improved performance is likely due to the aerogel’s higher oxygen content and altered morphology. The increased oxygen content provides more active sites for oxygen reduction, meaning that a greater specific power output can be obtained from the battery.

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.

Carbon dioxide (CO2) is an important greenhouse gas that helps trap heat in our atmosphere; without it, our planet would be inhospitably cold [1]. It is the fourth most abundant gas in the Earth's atmosphere. It is a byproduct of normal cell function when breathed out of the body, and produced when fossil fuels and organic wood compounds are burned [2]. However, an increase in CO2 concentration in the atmosphere can contribute to climate change and ocean acidification, and exposure to high levels of CO2 can produce a variety of health effects [3]. Human progress and economic innovation have led to increased emissions, causing climate change and affecting all living creatures. Current levels are 36.8 Gt CO2 in 2023 and are expected to reach 54-56 Gt CO2 by 2030 [4]. Figure 1 displays the current atmospheric CO2 measurements at Mauna Loa Observatory without seasonal variations [5].

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.