2022年

To investigate the reproduction success and natural recruitment of several Cyprinidae fish and Claridae fish species in the Allemanskraal Dam. The purpose of the project included investigating whether each individual fish species studied has a successful 2020/2021 spawning season in comparison with each other. Sections of the seine net were measured along with a distance of 10 along the shoreline. The ends of the seine nets were attached to one foot and the top of the net was held by hand. Both volunteers moved in unison while covering the 10m. The volunteer in the “deep end” moved towards the shoreline creating a semi-circle while the other volunteer remained stationary. The two ends of the net were then pulled onto the shore and the fish were collected. The results found that the Labeo Umbratus and Cyprinus carpio had the most successful spawning seasons with the highest recorded numbers. These high numbers of the Labeo Umbratus can be due to the fact that the species lays a large number of eggs. The high numbers of the Cyprinus carpio is due to the lower numbers of the other fish species as previous studies have shown that the species negatively impacts the environment which could in turn negatively impact the other fish species. The Claridae gariepinus and Labeobarbus aeneusas were the lowest. The low numbers of the Labeobarbus aneusas may be due to their slow growth and late maturity rate. The Labeo capensis had an average number relative to the other species and this is due to the fact that during the sampling period the dam was at 100% capacity as this is essential for the survival of the juvenile fish. The hypothesis was accepted as the Labeo Umbratus, Cyprinus carpio and Labeo capensis all have a successful spawning season. However, due to the size of the Cyprinus carpio, they would be most suited for a small scale fishery.

在這個資訊發達的時代，網路充滿著五花八門的資訊，導致我們在查詢資料時會因為這些雜亂且未經過濾的資料浪費許多時間，其中最為氾濫的便是點擊誘餌（clickbait），此種新聞常常有著吸引人的標題，而內容卻不會與主題相符，人們也常常在讀完整篇文章後才意識到自己浪費了許多時間在無意義的資訊上面。解決此問題很常用的方法之一便是運用摘要演算法來讓讀者先對新聞有一個大概的理解，不過，雖然摘要演算法越來越普及，但產生出來的摘要仍會和人為判斷的結果有所差距，進而造成閱讀理解上的錯誤以及偏差，所以我們想要藉由這次研究，從一個嶄新的角度切入，探討摘要演算法和句型分析之間的關係，融合原本向量建構的方式以及語句結構的分析來測試摘要的準確度，並且由結果研發出一個可以產生出更為精確的主旨之摘要演算法，除此之外，我們也會融合實地調查以及搜集意見的方式來更進一步探討人們思模式與產生出的摘要之關聯性。

The aim of this research was to investigate whether the ecology and biology of the Orange River mudfish Labeo capensis were suitable for the species to be used in fisheries. Three fleets of the gill nets were set, parallel to the shore. One fleet was lifted, and the fish were collected by hand. The two remaining fleets were lifted the next day. The seine net was pulled for 10 metres within the littoral zone. The net was then pulled towards the shore of the dam and the procedure was repeated four times. The four fyke nets were set parallel to the shore and were left for two nettings nights and then lifted. All fish caught were collected by hand and placed into buckets. The majority (82.93%) of the fish caught were within the 0-100 mm size class. The 101-200mm and 201-300mm size classes contain similar numbers of fish, while no fish were caught in the 301-400mm size class. The hypothesis was accepted. Allemanskraal Dam, as of the study period, has a very small juvenile fish population of L. capensis, as only 7 out of 41 fish individuals caught were within the 101- 300mm fork length size class. These results show that the population of L. capensis is not established as of yet, as the research did was right after their breeding season. Historical research has shown that sexually mature individuals of the L. capensis species tend to be a minimum of 300mm SL, 4-6 years after hatching. The population was largely young-of-the-year and may develop into an established population in 3-4 years (after sexual maturity). The L. capensis population in Allemanskraal Dam has the potential to be a fishery species if suitable conditions are maintained. Establishing this species’ potential will therefore allow economically viable fisheries to utilise them sustainably and to their full economic potential.

本研究針對學習圖論演算法的需求，設計一套使用者友善的繪圖軟體Graphene。Graphene繪圖程式除了提供高可讀性的繪製結果，作為輔助繪圖的工具外，也可直接輸入競賽題目的文字格式測試資料產生繪圖結果，並結合現有繪圖演算法，改善、優化樹與類樹圖的繪製結果。此外，也加入時間軸、自訂外觀、參數調整、匯出圖片等功能，幫助學習者理解圖論演算法，亦可幫助教師製作教材，有助於圖論演算法教學。 Graphene採用的繪圖演算法以force-directed graph drawing演算法為基礎，實作節點的分布。然而初始的節點分布會影響繪圖結果，因此我們利用biconnected component、block-cut tree等圖論結構對圖的繪製進行優化。首先找出圖的biconnected component及關節點，重新定義block-cut tree裡的block，接著利用radial tree的布局方式配置每個block，再套用force-directed graph drawing演算法，得到最後的布局結果。如此可以減少不同block之間的交錯，得到較佳的結果。

Triage is the process by which nurses manage hospital emergency departments by assigning patients varying degrees of urgency. While triage algorithms such as the Emergency Severity Index (ESI) have been standardized worldwide, many of them are highly inconsistent, which could endanger the lives of thousands of patients. One way to improve on nurses’ accuracy is to use machine learning models (ML), which can learn from past data to make predictions. We tested six ML models: random forest, XGBoost, logistic regression, support vector machines, k-nearest neighbors, and multilayer perceptron. These models were tasked with predicting whether a patient would be admitted to the intensive care unit (ICU), another unit in the hospital, or be discharged. After training on data from more than 30,000 patients and testing using 10-fold cross-validation, we found that all six models outperformed ESI. Of the six, the random forest model achieved the highest average accuracy in predicting both ICU admission (81% vs. 69% using ESI; p<0.001) and hospitalization (75% vs. 57%; p<0.001). These models were then added to an Android application, which would accept patient data, predict their triage, and then add them to a priority-ordered waiting list. This approach may offer significant advantages over conventional triage: mainly, it has a higher accuracy than nurses and returns predictions instantaneously. It could also stand-in for triage nurses entirely in disasters, where medical personnel must deal with a large influx of patients in a short amount of time.

蚊蟲呼吸透過氣管系統，本研究假設底棲孑孓與搖蚊幼蟲具備血紅蛋白系統以擷取氧氣。戶外採集8種孑孓和1種搖蚊幼蟲，錄影觀察白腹叢蚊、竹生翠蚊和鹽埕搖蚊幼蟲長時間停留底層；家蚊、斑蚊則反覆上下至水面呼吸。顯微鏡下發現白腹叢蚊幼蟲體壁內側充滿紅點，色澤隨著富氧或缺氧而變化；然二種斑蚊則不明顯。PCR增殖6種蚊蟲和1種搖蚊血紅蛋白基因片段並建構親緣關係樹。定量RT-PCR顯示，白腹叢蚊與鹽埕搖蚊在缺氧條件下，血紅蛋白相對表現量分別增加5倍和150倍，上層埃及斑蚊和白線斑蚊則增加3.9倍和1.5倍。西方墨點法證實孑孓缺氧條件下，17-kDa血紅蛋白皆有大量表現。本研究首次確定6種蚊蟲具血紅蛋白基因，證明孑孓血紅蛋白基因在缺氧下的表現量增加，包括mRNA與蛋白質。此等反應氣候變遷，暖化導致水溫升高與低溶氧條件下，蚊蟲可能有因應環境改變的呼吸機制。未來將進一步標幟血紅蛋白在蚊幼蟲體內組織之表現與其他環境變因之影響。

本研究以二氯化鐵和三氯化鐵所合成之磁性奈米粒子為基底，包覆上以蝦紅素為模板修飾的二氧化矽，製作出具有蝦紅素專一性的磁性奈米粒子，應用於萃取福壽螺卵中之蝦紅素。 利用干擾物證明磁性奈米萃取粒子具有蝦紅素的專一性，再探討奈米粒子合成及萃取條件的影響。福壽螺卵經過打碎離心後，依序加入0.2 M氫氧化鈉及1 mM十二烷基硫酸鈉，使蝦紅素水解並去脫去蛋白質轉換為游離態。當模板濃度為0.059 mg/mL、TEOS濃度為1.892 mg/mL和蝦紅素的濃度為0.07 mg/mL時，會有最佳的萃取率達60.7 %。此磁性奈米粒子在萃取步驟後，再以丙酮進行脫附，至少重複使用3次。此技術可減少福壽螺的農害，也極具經濟價值，很值得研究。

本研究區域為蘭陽溪口溼地與五十二甲溼地，各選擇5和6個採樣點，檢測水體中的溶氧度、pH值、導電度、總固體溶解量(TDS)、水溫以及濁度並記錄當時氣溫。 在蘭陽溪口溼地中我們發現越靠近出海口，導電度、TDS越高。採樣點4濁度為最高，猜想可能與位置有關。五十二甲溼地中則以採樣點6的導電度、TDS為最高，濁度、pH值及溶氧量則是採樣點3最高。我們還藉由五十二甲濕地分區使用圖，比較人為因素對水質的影響。採樣點3為遊客休憩區，測得的濁度、pH值皆較高，採樣點6為生態區，測值相對較小，推測人為因素與水質有關聯。最後，在10月10日的數據中，發現蘭陽溪口溼地的導電度特別高，推測潮汐現象為可能造成此現象的因素，也是未來研究的方向。

台灣火災地點以建築物占最大比例，在火災現場時常為了逃生及救火而打破門窗，造成建築物內部濃煙流動面積擴大，而釀成更大的災害，故本作品目的為研究「方形結構四周開口大小與數量對火焰與煙霧燃燒狀況的影響」，本研究從熱力學、流體力學、結構學三個角度探究此議題，並以煙囪效應與煙層逆流效應為理論基礎，使用壓克力搭建方形盒子來模擬建築物，並以四面壓克力上的開口大小及數量進行實驗，使用火焰及煙霧作為實驗介質，火焰方面以高度與溫度作為應變變因，煙霧方面則以MQ-2煙霧氣體感測器於結構內部進行濃度測量，同時也使用CFS-MODEL的FDS進行結構內部模擬，綜合實體實驗及各結果可發現，與一般想像不同，並不是開啟門窗就能撲滅火勢，甚至可能因為湧進過多氧氣而導致火勢更加嚴重，開口面積與火焰強度關係成二次函數曲線，本研究可供建築物搭建時的火災防範參考以及防災的宣導，並有進階研究的可能性。

平常聽音樂時經常有種似曾相識的感覺。為了描述這種感覺，我們展開了複音音樂片段相似性度量的研究。因為曾經使用過最長公共子序列實作卻效果不如預期，我們將音樂片段正規化後，視為座標平面上的時間、音高點對的集合，使用點對應與二分圖匹配的方法，定義兩個複音音樂片段的相似度為最大權重匹配的平均邊權。我們計算了資料集(JKUPDD)中相同、相異的音樂片段的相似性，調整算法中的參數，找出最適合的參數組合，並且透過音符之間的權重，畫出自相似度矩陣，發現樂曲中的重複片段。