一等獎

Due to widespread water shortages, there is an increasing need for innovative water conservation strategies, such as reusing greywater from laundry. The World Health Organization (WHO) recognizes greywater as suitable for plant irrigation, but commercial laundry detergents contain synthetic chemicals that can harm both the environment and plant health. Soap nuts, derived from the Sapindus mukorossi tree, offer a natural alternative. Their pericarp is rich in triterpenoid saponins, amphiphilic compounds, composed of hydrophilic sugar group and hydrophobic triterpenoid sapogenins. These saponins mimic the chemical structure of surfactants in detergents, allowing soap nuts to act as natural foaming and surface-active agents in water. As a result, soap nuts have long been used as a sustainable option for shampoo and laundry detergent in many Asian countries (Sochacki & Vogt, 2022). Greywater, an often overlooked resource, is generated from household activities like laundry, showers, and basins. Unlike blackwater, it contains lower levels of pathogens and bacteria. However, due to a lack of awareness, greywater is frequently mixed with blackwater and directed to the same sewage treatment systems (Greywater Systems: From Recycling to Filtration, n.d.). Greywater accounts for 50-80% of a household’s daily wastewater (Wong, 2011). Repurposing greywater offers a promising and sustainable solution to address water conservation challenges.

Problem • Rice is the primary cereal crop consumed by nearly half the population worldwide • By 2050, there will be a 50% increase in demand for rice • The world’s poor populations depend more on rice, both for income and consumption, than any other food. Rice is the single-largest source of employment and income for rural people • Worldwide, 51–82% of agricultural crop yield is lost annually due to abiotic stress due to climate change • Climate change causes extreme temperatures, erratic rainfall, dangerous droughts, and increased salinity from rising sea levels Solution • To adapt to abiotic stress, rice has intricate signaling pathways, particularly those mediated by the phytohormone abscisic acid (ABA), that cause an increase in stress tolerance • Clade A genes of the Protein Phosphatase 2C (PP2C) gene family are known to be negative regulators of the ABA signaling pathway. • “Deleting” these genes activates the ABA pathway and increases stress tolerance in rice without inducing stress CRISPR gene editing technology is the ideal solution Research Goal • While the role of PP2C genes in stress response is recognized, there is a gap in understanding the specific genes within this family that contribute significantly to stress signaling. Furthermore, there is a need for a detailed investigation into the effects of targeted CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) genome editing on rice stress response pathways.

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。

We invented the Midori which means green Bioreactor (MBR), beads of euglena and other microalgae fixed in calcium alginate that absorbs carbon dioxide (CO2). We examined the effect of 19 different solutions and two different organisms on MBR cultivation. Surprisingly, when the MBR was supplied with carbon dioxide or cultured with yeast, they became drastically darker green. Chromatography revealed this green color to be that of microalgae such as green algae or Euglena because chlorophyll a and chlorophyll b were detected. Under sunlight, MBR absorbed CO2 and the absorption rate was 1.5 L CO2/day/1L of MBR. Furthermore, when we put MBR in the water tank, they increased the amount of dissolved oxygen without polluting the environment. These results indicate that MBR can absorb CO2 by photosynthesizing without leaking out the inside microalgae.

This study dives into the benefits of a heart rate (HR) monitoring application to aid families in identifying mood changes in children diagnosed with neurodivergence. Children with neurodivergence often struggle with communicating their emotions, which often results in tantrums or emotional outbursts, and this study plans to address this by creating an app that detects users’ HR to calculate heart rate variability (HRV) and detect when the user’s heart rate variability (HRV) levels become higher than usual. Heart-rate variability is defined as a small variation of the interval between every heartbeat, it’s calculated with the formula of . By looking at the developments of these small variations, it will be 60/𝐵𝑃𝑀 × 1000 easier to notice then the interval for heartbeats are shorter, meaning the body is in need of more blood pumped quickly for support. The app is connected to a heart rate sensor that is worn by the user. The heart-rate sensor frequently uploads data to the app which the app processes and carefully observes while looking for any sudden, dramatic change. The sensor and app was tested and proved to meet the expected requirements of functionality. Four participants with neurodivergence were asked to equip the heart-rate sensor and results showed that different developments of heart-rate variability were able to be detected by the app, these participants varied in their type of neurodivergence as well as their age. As an example, the third participant showed the purpose of the app most visibly, having a resting heart-rate of 86 BPM (697.67 ms) turning into a high 107 BPM (561.68ms) after changing activities. When the user’s sensor detects a sudden spike in heart-rate variability, the app notified the parent account about this change in emotion. This study has supported the relevance of using heart-rate variability to observe changes in mood.

Climate changes that include ocean acidification and global warming are serious problems in the ecosystem, affecting marine phytoplankton, including Nannochloropsis oceanica. In the effort to further explore the impact of rising temperature and carbon dioxide (CO₂) concentrations on oceanic ecosystems, the phytoplankton Nannochloropsis oceanica was used as a model organism. This study explored the effect of temperature change and CO₂ concentration on the growth of Nannochloropsis oceanica, achieving 243 samples that were tested with three different temperatures (24 degrees Celsius (°C), 28°C, 32°C) and CO₂ concentrations (0 milliliter (ml)/min, 0.4 ml/min, 0.6 ml/min), utilizing a hemocytometer counting method. Results indicate that the CO₂ concentration has a significant effect on the population of Nannochloropsis oceanica. But the temperature doesn't affect a lot. The Nannochloropsis oceanica in the lowest temperature and highest concentration of CO₂ in its environment had the highest population growth, and in the highest temperature and lowest concentration of CO₂, it had the lowest population growth. Results show the serious negative effect of climate change on the cosystem and the importance of environmental protection. Population blooms due to excess CO₂ taking up ocean resources causing dangerous ecological imbalances.

量子電腦是近年來新發展的科技，利用量子糾纏態的量子位元進行計算。本文希望可以利用量子電腦計算諧振子隨時間演化算符。而這也是我第一次在量子電腦上模擬諧振子隨時間演化系統。首先我找出可以用於諧振子算符的合適算符矩陣大小、空間步長(Δ𝑥)、質量(m)、角頻率(ω)並且在位置基底下表現時間演化算符矩陣。設計並簡化量子電路後，使用IBM公司提供的量子電腦模擬並計算數值。我透過矩陣修正減少修正輸出錯誤產生的誤差，達到較精確的結果。模擬出在一個時間單位內的數值與理論值大致相符，未來希望可以利用此量子電路尋找矩陣的特徵值或是模擬更大型的系統。

本研究設計與合成一系列的電子供體分子，以研究分子單體的化學結構對於所形成的激發複合體光物理性質的影響。 五個所設計的供體分子已被成功的合成並確定均具有分子內電子轉移的性質 其躍遷偶級距變化分布範圍在17.6-28.6D之間。 將此五個供體分子分別與兩種電子受體分子在溶液聚集在一起，利用在長波長處所新生的螢光發光，推測激發複合體的形成。研究的成果並顯示，具有類似三角形結構的供體分子將更容易形成激發複合體，而具有棒狀結構的分子則較不易形成之。此成果有效的提供有關於單體分子結構的設計對於所需激發複合體光物理性質的影響，形成可快速地提供各式不同發光波長的材料，將可作為在發光二極體發光層材料、螢光感測器、生物成像等領域需求時的分子設計藍圖與指引。

Understanding species abundance is critical to managing and conserving planetary biodiversity. Pacific salmon (Oncorhynchus spp.) are keystone species of cultural, economic, and ecological importance in Alaska and especially Southwest Alaska. Traditional methods of enumerating salmon such as weirs and visual surveys are often costly, time-intensive, and reliant on taxonomic expertise. Environmental DNA (eDNA), which identifies and quantifies species based on DNA they shed in their habitats, is a potential cost- and time- saving alternative. The relative ease of collecting eDNA samples also enables citizen scientist involvement, expanding research coverage. Currently, more research is required to define eDNA’s potential and limits. This project investigates whether quantitative eDNA metabarcoding can accurately quantify the abundances of six fish species: the five Pacific salmon species plus rainbow trout. Water samples were collected from eight creeks in the Wood River watershed of Southwest Alaska. eDNA metabarcoding and subsequent bioinformatics processing produced a read count for each species. These were compared to visual survey counts, taken to be the true counts for the purposes of this study. Data analyses showed a positive, linear relationship between visual survey count and eDNA count for sockeye salmon. The regressions were significant for both the early (p = 0.089) and late (p = 0.030) sampling dates when 𝛼 = 0.10. eDNA detections of non-sockeye species generally corresponded to visual survey observations of species presence or absence. Overall, the results of this study support eDNA’s potential to be an alternative or supplement to standard methods for the enumeration of fish species.

AI (Artificial Intelligence) technology has developed very rapidly in recent years, to the point where it can make fake videos or photos called "deepfake''. According to Sumsub Identity Fraud Report 2023 just in the past year, in the APAC region the number of deepfakes has grown 1530%, in the philippines an astounding 4500% and in 馬來西亞 a 1000% increase, these numbers will continue to rise without a proper defense against them, With this rapidly developing technology, there are several threats from misuse deepfake, namely making fraud via video calls, fake videos to blame innocent people, and so on. Therefore, in this research project, an algorithm architecture will be created, namely a system and method used to detect "deepfake" images. The architecture of this algorithm involves convolution functions, neural networks, convolutional neural networks, data normalization functions, namely ReLu and SoftMax, and pooling. This architecture will then be trained over and over with 140,000 scrambled images, which then will make the architecture ready to be used. By researching and combining this algorithm architecture, a system is produced without a cost and with a final result of up to 90% accuracy and detection of 32 images faster than a human can blink.