This paper presents a simplified but mostly accurate model for the acoustic mechanism of Seebeck sirens. We investigate the impact of key parameters, including the number and size of holes, as well as the angular speed of the disk, on the characteristics of the produced sound. The disk is fabricated using fused deposition modelling 3D printing, and we used a brushless motor, an air compressor, and a shotgun microphone to capture the generated sound. An order of magnitude analysis was conducted on the Navier-Stokes equation to formulate a simplified version. These simplifications allowed for a low computational intensity model relating volume flow rate to sound pressure level, which is used to predict the waveform of sound produced. Our findings reveal that the fundamental frequency of the sound can be precisely predicted by only the rotational frequency of the disk and the number of holes, a relationship validated experimentally. Notably, observed asymmetry in the waveform was attributed to skin drag effects, and this hypothesis was experimentally verified. Our model computes a solution in less than half a second on average: far less than the 21h 47min needed for a k−ω turbulent model to compute the same phenomenon. The research presents and verifies a simplified model of acoustic mechanics for the sound generated by rotating systems that require little computational resources, which can prove useful in situations where absolute precision is not required, in exchange for ease of computation. For more precise systems, this model serves as a foundation for quickly generating an initial design, paving the way for subsequent iterations using more comprehensive models. The developed model not only serves as a foundation for efficient preliminary designs but also contributes valuable insights into the intersection of fluid dynamics and sound production.

Examination of the predation behavior of polyclad flatworms is extremely rare. This study collects Paraplanocera oligoglena, the most common species in Taiwan. Tank-based feeding experiments reveal that Paraplanocera oligoglena can prey on several species of gastropods, such as sea snails and sea hares. Predation behavior encompasses attack, invasion and ingestion periods. This research pioneers the use of stained clam and static image analysis to observe the highly branched digestive system of flatworms. The sequentially bidirectional flow of gastrovascular cavity is first found in polyclad flatworms by the post-stain active tracking technique. Measuring peristalsis movement in inward and outward directions and segmented movement, the contraction frequencies are roughly the same in subsequent order of given branches. Confirmation is provided that the circular membrane-like muscles within the digestive tract are the main driving force for transporting and mixing food. The food dyeing technology used in this experiment also provides the possibility of future research on food chains in the wild.

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.

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.

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.

The following issues associated with soil affected by wildfires were identified: First, there was a significant decline in the populations of anaerobic and aerobic soil bacteria, which play a critical role in the decomposition and cycling of organic matter. This decline resulted in reduced water retention capacity and porosity of the soil, leading to poor moisture retention and increased evaporation compared to unaffected soil. Moreover, the organic matter content in the soil was significantly depleted, inhibiting plant growth. Additionally, there was a notable proliferation of methane-producing bacteria, which contribute to the greenhouse effect. It was further observed that fire-damaged soils exhibit limited natural recovery, even over prolonged periods. .An investigation into the underlying causes of these problems revealed that actinomycetes, the primary microorganisms responsible for producing watersoluble aromatic compounds in soil, are particularly sensitive to heat compared to other bacterial species. Research demonstrated that the population and diversity of actinomycetes are significantly diminished in soils exposed to wildfires. To mitigate these issues, water-soluble aromatic compounds produced by actinomycetes were extracted and introduced into wildfire-affected soil. This intervention promoted the restoration of actinomycetes populations, enabling their normal growth in the affected soil. Consequently, various wildfire-induced soil problems were effectively resolved. These outcomes were confirmed through the study...Key Words: Actinomycetes, anaerobic and aerobic soil bacteria, methane-producing bacteria, wildfires, water-soluble aromatic compounds.

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.

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.

本研究探討將連續正整數1至𝑛依大小交錯排列所形成的「波浪數列」，其定義如下：除了首項與末項外，每一項皆大於或小於其相鄰兩項。我們分析在最大數為 𝑛 時，波浪數列的各種情形與其在數量上的對應關係，從中歸納出排列的規律，並推導當首項為 𝑘 時的排法總數，進而求出所有波浪數列的排法總數。

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].

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.