目前水冷散熱系統普遍應用在電腦CPU降溫中，雖然水的「比熱容」比空氣及大部分介質都高，但因水冷散熱系統內冷卻液的熱量，最終仍靠風扇送到機殼外，故CPU之最低溫度仍存在一個臨界值。本研究旨在對於「一體式」與「分離式」電腦水冷散熱系統及「熱電致（製）冷晶片」（Thermoelectric Cooling Module）結合進行模組開發設計，將此兩類相關元件搭配結合，以突破傳統水冷式散熱所無法降達的溫度。本研究將「致（製）冷晶片」之致冷端及水冷系統作結合，利用致冷端作為吸收CPU主要熱量，結果發現：與單純只利用風扇將熱量帶走的方式相比，本研究所開發之『第一代』一體式散熱模組與『第二代』分離式散熱模組皆成功地將頂級CPU之工作溫度再壓低，使電腦工作效率維持在最佳範圍。

本研究利用合成生物學技術開發的創新基因組篩檢平台PRISM(v2.0)，採用合成轉錄因子(crispr-TF)辨認DNA但不進行剪切，透過修改CRISPR/Cas的PAM-interacting (PI) domain序列打破其辨認限制，由原本NGG序列變成可以結合NAG的序列，提升crispr-TF在高通量基因組的篩選能力，期待未來可以做出辨認NNN序列，以此技術初步成功建構Chk2/Rad53所參與的基因網絡，讓失去Chk2/Rad53功能的酵母菌能在DNA損傷的情況生存。Chk2/Rad53蛋白磷酸酶在DNA修復的訊息傳遞中調控龐大的基因網絡，其中潛在大量不同調控路徑聯結的組合性，過去的研究都無法有效地分析且重建完整的DNA修復機制。未來將進一步探討Chk2/Rad53調控的下游基因組，並以此新技術和研究應用於複雜腫瘤與老化疾病的預防及治療。

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.

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.

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.

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

The number of natural disasters has risen significantly in recent years, and with climate change there is no end in sight. Consequently, the demands on rescue forces around the world are increasing. For this reason, I asked myself what I can do to improve the work of rescue teams. Advances in artificial intelligence and drone technology enable new possibilities for problem solving. Based on the technological advances mentioned above, an autonomous Search and Rescue drone was developed as part of this project. The system assists rescue workers in searching for survivors of natural disasters or missing people. This paper also suggests a method for prioritizing survivors based on their vitality. The system was implemented using a commercial Parrot ANAFI drone and Python. The software was tested on a simulated drone. To simplify the development, the whole system was divided into the following subsystems: Navigation System, Search System and Mission Abort System. These subsystems were tested independently. The testing of solutions and new concepts were performed using smaller test programs on the simulated drone and finally on the physical drone. The Search and Rescue system was successfully developed. The person detection system can detect humans and distinguish them from the environment. Furthermore, based on the movements of a person, the system can distinguish whether the person is a rescuer or a victim. In addition, an area to be flown over can be defined. If something goes wrong during the mission, the mission can be aborted by the Mission Abort System. In the simulation, the predefined area can successfully be flown over. Unfortunately, controlling the physical drone does not work. It stops in the air after takeoff due to the firmware of the drone. It does not change the flight state of the drone, which results in all subsequent commands from the system being ignored. This paper shows that artificial intelligence and drone technologies can be combined to deliver better rescue services. The same system can be applied to other applications.

Inhibitory effects of the secondary metabolite of actinomycete were examined on cell cycle of the yeasts of S. pombe and S. cerevisiae. The secondary metabolite was obtained from cultivation of the actinomycete isolated from the soil of Owakudani in Hakone, Japan. The fifth fraction of the secondary metabolite by ODS column separation (HK-T5), which was soluble to pure methanol, was used in the present experiments. The HK-T5 brought about the delay of forming colonies of S. pombe for about 11 days compared to that cultivated without the HK-T5. The delay of the colony formation was longer for the S. pombe cultivated with more amount of the HK-T5. The cultivation with HK-T5 also brought about the extension of the lifespan of the S. pombe for more than 10 weeks in a liquidus medium. The cell life recovered the ordinary manner by removal of the HK-T5, meaning that the activities of the HK-T5 is reversible. These facts confirm the suppression of cell cycle, and the delay of cell growth by the HK-T5. These phenomena were similarly observed for S. cerevisiae. Comparison of the action of HK-T5 with hydroxyurea, which is an anticancer drug inhibiting the cell cycle at S phase, clarified that the inhibitory action of HK-T5 worked at the phase earlier than S phase. The combined effects of HK-T5 on the cell cycle were evaluated with triamcinolone acetonide (TA), or aspirin, the former of which is a drug synchronizing cancer cells in S phase, and the latter keeping human cells in G1/G0 phases. The combined use of HK-T5 with TA synchronized the cells at the phase slightly proceeding from G1 to S phase without toxicity. On the other hand, the combined use with aspirin made the inhibitory effect of HK-T5 inactive. Hence, the HK-T5 is attractive as a drug for the extension of cell lifespan, and anticancer therapy.

本研究源於 2016 年數學雜誌 Crux Mathematicorum 的一個三角形定性問題[1]，我們將這個問題進行推廣且創新探討其定量與定性性質。我們先討論任意三角形與任意凸四邊形、凹四邊形，分別針對不同連線情形下的兩個衍伸圖形的有向面積之和與有向面積之差進行完整討論，再巧妙利用平移不變性處理行列式級數和，最後給出一般化的不變量關係式與刻劃其幾何意義。此外，我們也特殊化探討其衍伸圖形恆為正三角形、正方形等有趣優美的定性性質。最後，系統性推廣到平面上任意封閉的凸四邊形、凹多邊形，先給出不同連線方式之間的重要輪換對稱性質，再分為奇多邊形與偶多邊形進行討論而得出任意連線構造的衍伸圖形之有向面積不變量的一般式。

Currently, no method can completely eliminate the human immunodeficiency virus (HIV) in an infected person. HIV employs an accessory protein called Nef that forms a complex with cellular AP-1, preventing detection of HIV-infected cells. Lovastatin has been recently identified to inhibit the formation of said Nef-AP-1 complex, but its effective concentration is remarked to be far higher than other Nef inhibitors. This study aims to develop a modified lovastatin molecule exhibiting higher binding affinity to the HIV-1 Nef protein than lovastatin in silico. Modified lovastatin molecules based on the interaction map of lovastatin with Nef were modeled, and flexible ligand-flexible receptor docking to the Nef binding site was performed using AutoDock Vina. Residues within the Nef binding site identified by Liu et al. (2019) to be crucial (Glu-63, Val-66, Phe-68, Asp-108, Leu-112, Tyr-115) were set as flexible. Fragment-based drug design was utilized to append molecular fragments to lovastatin in order to maximize its interactions with said crucial residues. From the fragment-based approach, molecule F4 ((1S,3S)‐8‐{2‐[(2R,4R)‐4‐chloro‐6‐oxooxan‐2‐yl]ethyl}‐3‐(hydroxymethyl)‐7‐methyl‐1,2,3,4‐tetrahydronaphthalen‐1‐yl 4‐aminobenzoate) exhibited a binding affinity of -9.0 kcal/mole, and its estimated IC50 ranges between 0.25-0.51 μM which is at least 7.5 times lower than the reported IC50 of lovastatin from literature. This study presents insights on the key modifications to improve lovastatin as an HIV-1 Nef inhibitor and pertinent information about the Nef binding site for future drug development studies.

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.

目的：在台灣，胰臟癌雖非癌症發生數最高，但85%為末期，五年存活率小於5%，有癌王之稱。如何早期偵測胰臟癌風險，一直是醫療上的重大議題。本研究運用時間矩陣搭配深度學習進行大量變數之胰臟癌風險偵測。 方法：健保資料庫收集國人疾病、用藥等結構化資料，可藉此反映每人健康狀態。將三年內診斷與藥物碼轉為時間矩陣，以卷積神經網路訓練，訓練組與測試組比例為9:1。 結果：共計案例組1095名及對照組10950名，訓練後測試組表現之AUROC(area under the receiver operating characteristiccurve)為0.937，六十歲以上及以下AUROC分別為0.846及0.897。 結論：結論：能將診斷、藥物、時間轉成矩陣，是以疾病軌跡預測胰臟癌風險，且能找到新特徵，未來搭配健康存摺，為低成本、快速、大量的胰臟癌數位快篩。