永續發展貢獻

2023年

一、本系符合SDG 3良好健康與福祉

(一)中文說明

在2020年春天，全球迎來了一場前所未有的危機。那時，一種神秘的病毒悄然蔓延，迅速擴大成為全球大流行。這種病毒的致死率極高，人們對其充滿了恐懼和不安，許多國家紛紛封城鎖國，社會生活、交通運輸都受到了前所未有的衝擊。這種病毒被正式命名為新冠病毒（COVID-19）。

隨著疫情的擴散，截至2023年2月，全球已經報告超過7.56億例COVID-19病例和680萬例死亡。這場大流行被認為是近代歷史上最嚴重的一次。在美國，由於這種病毒導致的死亡人數已經超過上個世紀最致命的1918年H1N1流感大流行的死亡人數。為了對抗這種病毒，自2020年初病毒序列完成以來，各國科學家們就開始了疫苗的研發。到目前為止，許多疫苗已經在全球獲得批准，還有更多正在開發中，如美國的輝瑞-BioNTech和莫德納，英國的牛津-AstraZeneca，印度的Covaxin等。然而，儘管疫苗研發取得了進展，美國、日本、德國以及其他許多高收入國家的疫情仍在加劇。低收入國家雖然感染數量較少，但疫苗供應不足，疫情限制措施不得不延長。為了減輕這些問題的負面影響，持續開發COVID-19藥物顯得尤為重要。

 

 

 

在這樣的背景下，我們的研究團隊決定為藥物開發做出貢獻。我們針對喬治亞大學藥學院所提供的26,193種化合物進行了篩選，尋找對抗 PLpro（似木瓜素蛋白水解酶）的潛在候選藥物。PLpro是新冠病毒的重要靶點，抑制 PLpro 能夠阻斷病毒在宿主細胞內的複製和增殖。我們使用了分子對接技術，將這些化合物與PLpro進行了配體與蛋白質對接，並做詳細的分析。

結果顯示，F3077-0136、F2883-0639和F0514-5148是三個最佳的藥物候選者。特別是，F3077-0136和F2883-0639在藥物相似性方面表現尤為出色。我們預測了這些化合物的結合親和力，並進行了模擬來驗證這些預測。總體而言，本研究不僅為實驗室的進一步研究提供了有價值的線索，也有望加速COVID-19藥物的開發進程。

 

 

(二)英文說明

In the spring of 2020, the world faced an unprecedented crisis. A mysterious virus began spreading silently, rapidly escalating into a global pandemic. This virus had an extremely high mortality rate, instilling fear and anxiety in people worldwide. Many countries imposed lockdowns and travel restrictions, causing unprecedented disruptions to social life and transportation. This virus was officially named COVID-19.

As the pandemic spread, by February 2023, over 756 million cases and 6.8 million deaths from COVID-19 had been reported globally. This pandemic is considered one of the most severe in modern history. In the United States, the number of deaths caused by this virus has surpassed the death toll of the 1918 H1N1 influenza pandemic, making it the deadliest flu pandemic of the last century. In response to this virus, scientists worldwide began developing vaccines as soon as the virus sequence was completed in early 2020. To date, many vaccines have been approved globally, with more still under development, such as Pfizer-BioNTech and Moderna in the United States, Oxford-AstraZeneca in the United Kingdom, and Covaxin in India. However, despite progress in vaccine development, the pandemic continues to surge in the United States, Japan, Germany, and many other high-income countries. Low-income countries, while experiencing fewer infections, face insufficient vaccine supplies, resulting in extended pandemic restrictions. To mitigate the negative impact of these issues, continued development of COVID-19 treatments is crucial.

Against this backdrop, our research team decided to contribute to drug development. We screened a library of 26,193 compounds provided by the University of Georgia College of Pharmacy to identify potential drug candidates against PLpro (papain-like protease). PLpro is a critical target for the novel coronavirus, as inhibiting PLpro can block the virus's replication and proliferation within host cells. We employed molecular docking techniques to interface these compounds with PLpro and conducted detailed analyses.

The results revealed that F3077-0136, F2883-0639, and F0514-5148 were the top three drug candidates. Notably, F3077-0136 and F2883-0639 exhibited outstanding drug-likeness properties. We predicted the binding affinities of these compounds and conducted simulations to validate these predictions. Overall, this study not only provides valuable insights for further laboratory research but also holds the potential to accelerate the development of COVID-19 treatments.

 

二、本系符合SDG 14 Life below Water(水下生物)

(一)中文說明

在一個陽光燦爛的早晨，位於臺灣南部的漁村中，一位漁民張先生站在魚場邊，焦急地看著他那片碧藍的魚池。張先生一直面臨著養殖業的挑戰——老化的人力資源、日益高漲的飼料成本，以及如何讓魚群在最優的環境下成長。然而，他不知道的是，這些問題即將迎來一個突破性的解決方案。

自2022年起，一群熱衷於科技與養殖結合的研究人員開始對魚場管理效率進行深度研究。我們的目標是改變傳統養殖的方式，減少資源浪費，提升魚群健康，並降低養殖成本。這個團隊創建了一套名為“雲端智慧投餌系統”的先進技術，旨在透過影像辨識技術來實現精準的投餌。

 

 

 

系統的核心技術是影像辨識，它可以捕捉魚隻進食過程中的水花反應。當魚群進食時，水面會出現微小的波紋，這些波紋會被系統捕捉並分析。系統會根據這些水花的變化，智能判斷是否需要增加或減少飼料投放量。這樣，魚群就能得到充分的飼料，既不會因為飼料過多而造成水質污染，也不會因為飼料不足而影響魚的生長。

整個投餌過程分為三個階段。首先是試投餌階段，漁民們會設定預期水花的大小、投餌量和時長。這些參數將作為系統的目標指引。接著是主要投餌階段，系統會根據魚隻的進食狀況進行智能投餌，實時調整投餌量。最後是彈性調整階段，系統會生成一個投餌階段示意圖，幫助漁民了解每次投餌的實際情況。這些數據可以累積並繪製成熱力圖，讓漁民清楚了解魚群的食慾趨勢，進而調整養殖策略。

對於張先生來說，這套系統無疑是他的福音。系統的實施使他每年節省了146小時的工時，飼料成本也降低了7.35萬元。更重要的是，系統提供了科學化的養殖記錄分析，讓他能夠把自己的養殖經驗更好地傳承給下一代。隨著時間的推移，這套雲端智慧投餌系統不僅可在張先生的魚場中發揮了巨大作用，也將逐步擴展到其他養殖場。養殖業者們可以通過手機即時監控和遠端操控投餌狀況，這不僅提高了養殖效率，還有效促進了養殖經驗的傳承。臺灣的養殖業因此迎來了一個嶄新的時代，帶來了更高的經濟效益和可持續發展的希望。

 

(二)英文說明

On a sunny morning in a fishing village in southern Taiwan, Mr. Zhang, a local fisherman, stood by the edge of his fish pond, anxiously watching his clear blue waters. Mr. Zhang had been grappling with challenges in the aquaculture industry—aging labor resources, rising feed costs, and how to ensure his fish thrive in the optimal environment. Little did he know that a groundbreaking solution was on the horizon to address these issues.                                                                                                                                                                                   

Since 2022, a group of researchers passionate about combining technology with aquaculture has embarked on an in-depth study of fish farm management efficiency. Our goal is to transform traditional aquaculture practices, reduce resource waste, improve fish health, and lower farming costs. This team developed an advanced technology called the "Cloud Smart Feeding System," which aims to achieve precise feeding through image recognition technology.                                                                                                                                                                        

The core technology of the system is image recognition, which captures the ripples produced during the fish feeding process. When the fish feed, small ripples appear on the water surface; these ripples are detected and analyzed by the system. Based on the changes in these ripples, the system intelligently determines whether to increase or decrease the amount of feed being dispensed. This ensures that the fish receive adequate feed without causing water pollution from overfeeding or hindering their growth due to insufficient feed.                                                                                           

The entire feeding process is divided into three stages. First is the trial feeding stage, where the fishermen set parameters such as the expected size of the ripples, the amount of feed, and the duration. These parameters serve as the system’s target guidelines. Next is the main feeding stage, during which the system performs intelligent feeding based on the fish’s feeding conditions, adjusting the feed amount in real time. Finally, in the flexible adjustment stage, the system generates a feeding stage diagram to help fishermen understand the actual feeding conditions. This data can be accumulated and visualized as a heatmap, allowing fishermen to clearly see the fish’s appetite trends and adjust their aquaculture strategies accordingly.                                                                                

For Mr. Zhang, this system is undoubtedly a blessing. The implementation of the system has saved him 146 hours of labor each year and reduced feed costs by 73,500 NT dollars. More importantly, the system provides scientific analysis of aquaculture records, allowing him to better pass on his aquaculture experience to the next generation. Over time, this Cloud Smart Feeding System has not only made a significant impact on Mr. Zhang's fish farm but is also gradually being adopted by other aquaculture farms. Farmers can monitor and remotely control the feeding process via their phones, which not only improves farming efficiency but also effectively promotes the transfer of aquaculture experience. Thus, Taiwan's aquaculture industry is entering a new era, bringing greater economic benefits and promising sustainable development.

 

三、本系符合SDG 14 Life below Water(水下生物)

(一)中文說明

在一片波光粼粼的水面下，魚群的生命故事悄然上演。魚隻的死亡原因錯綜複雜，主要可以歸結為三大類：環境因素、污染物，以及疾病因素。疾病因素中，細菌、病毒、寄生蟲等微小敵人會悄悄侵襲，導致魚隻生病甚至死亡。如果能及早發現並迅速處理，便能大大減少魚隻的損失。

但問題是，如何從魚隻的行為中察覺它們的健康狀況呢？在養殖專家的經驗中，生病的魚隻常常會顯示出異常的行為，例如離群或游動方式異常。此外，魚群的密度也會對魚隻的健康產生影響。若養殖密度過高，魚隻可能因為擁擠而生長緩慢；若密度過低，則會增加人力和空間上的成本。

養殖業者通常依賴肉眼觀察魚隻的狀況和養殖密度，但這種方法存在限制。水的透明度、觀察距離等因素使得他們只能看到水面附近的魚隻，難以全面掌握魚群的狀態。尤其是深水區域的魚隻情況往往被忽略，使得對整體魚群的了解不夠全面。

為了解決這一問題，我們的研究團隊投入了新技術。通過成像聲納系統，我們利用深度學習技術來分析養殖場內的魚群狀況。這一系統不僅能夠透過圖表等視覺化方式呈現分析結果，還能從水平寬度和垂直深度兩個方面觀察魚群狀態。

在水平寬度上，當掠食者出現或魚群因為疾病而沿著箱網邊緣游動時，魚群的分布會顯示出與以往不同的狀況。系統能夠及時發現這些異常，幫助養殖業者迅速將生病的魚隻撈起或驅趕掠食者。

在垂直深度上，由於魚類是外溫動物，當寒流等因素使水溫下降時，魚群會傾向於分布在較深的水域。我們的系統可以視覺化呈現這些變化，幫助業者將箱網調整到較溫暖的水域，或在飼料中添加不飽和脂肪，幫助魚隻安全度過寒冬。

我們的研究系統提供了關鍵的資訊，協助業者判斷養殖密度、魚群分布等，幫助他們採取相應的措施，減少經濟損失，實現最大的經濟效益。我們通過2D聲納影像構建3D點雲圖，並計算點雲圖中的箱網容積與魚群容積，業者能夠深入了解魚群的水平與垂直分布狀況。這項技術的應用，不僅提升了養殖業者的監控能力，也為養殖業的未來發展鋪平了道路。

 

 

 

 

 

 

 

 

(二)英文說明

Beneath the shimmering surface of the water, the life stories of fish unfold quietly. The causes of fish deaths are complex, generally categorized into three main types: environmental factors, pollutants, and diseases. Among these, diseases caused by bacteria, viruses, and parasites can silently invade, leading to illness or even death in fish. Early detection and swift intervention can significantly reduce fish losses.

The challenge, however, is how to detect fish health issues from their behavior. According to aquaculture experts, sick fish often exhibit unusual behaviors, such as isolating themselves or showing abnormal swimming patterns. Additionally, fish density can impact their health. High stocking density may cause overcrowding and stunted growth, while low density increases labor and spatial costs.

Farmers typically rely on visual observation to assess fish health and stocking density, but this method has limitations. Factors like water transparency and observation distance mean that only the fish near the water surface are visible, making it difficult to comprehensively understand the condition of the entire fish population. Deep-water fish often remain unnoticed, leading to an incomplete picture of the fish community.

To address this issue, our research team has deployed new technology. Using an imaging sonar system, we analyze the condition of fish in aquaculture settings through deep learning techniques. This system not only visualizes analysis results through charts and graphs but also observes fish status from both horizontal and vertical perspectives.

Horizontally, when predators appear or fish move along the edges of the net due to disease, their distribution will differ from usual patterns. The system can promptly detect these anomalies, helping farmers quickly remove sick fish or drive away predators.

Vertically, since fish are ectotherms, they tend to move to deeper waters when cold currents or temperature drops occur. Our system visualizes these changes, assisting farmers in relocating nets to warmer waters or adding unsaturated fats to feed, helping fish survive the winter.

Our research system provides crucial information to aid farmers in determining stocking density and fish distribution, enabling them to take appropriate measures to reduce economic losses and maximize economic benefits. By constructing 3D point cloud maps from 2D sonar images and calculating the volumes of net cages and fish schools, farmers can gain a deeper understanding of both horizontal and vertical fish distribution. This technology not only enhances monitoring capabilities for farmers but also paves the way for the future development of aquaculture.