Cardiac Arrhythmia is one of the conditions in the group of heart and blood vessel diseases that can lead to sudden cardiac arrest (sudden death) and other conditions if not diagnosed quickly and accurately. According to research, heart and blood vessel diseases are the most common diseases and have a mortality rate of one-half of all non-communicable diseases. According to WHO statistics in 2012, it was found that there were 7.4 million deaths from heart and blood vessel diseases, and in 2017, the number of deaths increased to 177 million people, or about 94,444 people per day. Diagnosis of heart and blood vessel diseases can be done by measuring the electrical activity of the heart, and after the examination, a specialized physician will read and analyze the graph to find abnormal patterns. Currently, the shortage of qualified heart specialists to read the graph and screen for heart disease is a medical position shortage, which requires transferring data to hospitals with specialists, resulting in delays in diagnosis and treatment and even death. The project "EIPCA: Electrocardiogram Interpretation Pattern for Cardiovascular Abnormalities prediction" is an application program that assists in screening for fatal diseases that arise from abnormal heart rhythm. It employs artificial intelligence to aid in the screening and analysis of the electrical waveforms generated by an ECG machine, thus reducing diagnosis time and addressing the shortage of cardiology experts. EIPCA is comprised of two systems: (1) a system for screening and analyzing ECG waveforms using artificial intelligence to solve the problem of a shortage of specialized cardiology physicians, and (2) a system for risk assessment of fatal diseases by analyzing the ECG waveform data. The target group of the project is Rural hospitals, as well as health-related agencies. The project team hopes that the development of this project will significantly improve the efficiency and speed of screening for heart-related diseases, ultimately reducing the mortality rate from these diseases in the future.

本實驗為了探究在常壓、真空、負壓和高壓…等不同條件下對食物醃製效果的影響，以白蘿蔔為醃製對象，利用各種裝置改變氣壓進行一系列研究。 由實驗得知白蘿蔔的醃製效果為6塊紅磚和7塊紅磚高壓醃製＞負壓8cm-Hg＞負壓4cm-Hg＞真空＞常壓。高壓醃製相對於其他氣壓條件的表現為最佳，僅需6塊紅磚重壓在保鮮袋上即可。至於負壓醃製的效果隨著負壓值的增加，白蘿蔔醃製面積有增加趨勢。 本實驗的高壓醃製相對於負壓醃製所需要的技術門檻、費用皆較低，又可有效提升醃製效果，是不錯的醃製選擇。不建議購買「市售真空抽氣棒」，因功率小、吸力差，未如產品說明書上所說可使保鮮罐達真空狀態。如果對醃製效果沒有太過苛求，真空保鮮袋的醃製操作更為簡單方便。

本研究針對農業害蟲辨識數據集的優化進行探討，旨在提高深度學習模型的辨識準確度。隨著科技進步，人工智慧在害蟲識別中愈發重要，但現有數據集面臨數據品質不良、樣本偏差和錯誤標註等問題，影響模型的可靠性。研究主要分為三個部分：首先分析現有數據集，探討樣本數對分類準確度的影響；其次優化數據集，通過刪除拼接和錯誤圖片、重複圖片等，提升數據質量；最後實際應用模型，開發一套害蟲辨識系統。實驗結果顯示，增加樣本數和改善數據質量顯著提升了模型的準確率，並且開發的介面可協助用戶即時識別害蟲。整體而言，本研究不僅提高了農作物的病蟲 害防治效率，還為未來的害蟲識別技術提供了有價值的參考。

本研究主要目的為改變風力渦輪機的結構，將原始的單層葉片設計改為雙層葉片，當風流過第一層葉片時將產生尾流效應，使葉片後氣流產生偏轉，若將第二層葉片更具此氣流偏轉區域調整，將能有效的提升發電效率。根據實驗結果顯示，葉片後的尾流造成氣流偏轉的角度約為 15 度，因此當渦輪機的兩層葉片位於理論最高的相位夾角時，應再減去 15 度的尾流偏轉角，該夾角才為最大的發電效率配置，基於此結果所設計的渦輪機，儘管與單層葉片渦輪機具有相同的葉片數量，卻能提升發電效率約 40%，同時相關文獻指出，葉片的成本約佔整體發電機總成本之 14%，因此透過使用本研究之作法，預估可降低約 18%的成本，整體而言，本研究之設計不僅能提高風能發電效率，同時將有效的減少成本，未來具高度前瞻性。

"Blindness keeps you from things, deafness keeps you from people" (Helen Keller) Wibrazz is a communication tool that can be placed inside sportswear. Two versions have been developed. The simpler one allows hearing-impaired footballers to compete in the league with other athletes. The referee is given an additional device to give a signal when he blows his whistle. The hearing-impaired footballer then senses the signal from the device he is wearing and knows that he must pay attention to the referee. The complex version speeds up communication between the coach and the players during training sessions. It allows the coach to send simple messages to his players using his smart device. The athlete senses the signal from the device and acts on what has been previously discussed (e.g. a long signal means, "Everyone come to me!") With over 70 million deaf people worldwide, and 2-4 out of 1000 people in the United States who are functionally deaf, this can affect an individual's mental and physical well-being, and it is therefore a pressing issue to provide these athletes with the means to develop their talents in a traditional team environment. In addition to the organisations within countries, the ICSD is present on the international stage. Their importance is demonstrated by the fact that the 2023 Deaf Football World Cup featured teams from countries such as the United States, Germany, England and Japan.

首先以濕度儀測量花生樣品之水活性，需時超過264分鐘，而水活性儀僅約22分鐘，經實驗證明，較小的密閉空間可加速平衡相對濕度的穩定，進而縮短測試時間。因此，本研究設計自製 DIY裝置，樣品槽上部空間採用圓錐狀設計可縮小空間，同時促進水氣向上集中，提高測量穩定性。進而利用六種飽和鹽液進行校正，得到趨勢線方程式應用於DIY裝置之校正，使其測量結果與專業水活性儀無顯著差異。就經濟成本分析，濕度儀與水活性儀的購置成本分別為新台幣9,500元與103,500元，而 DIY裝置的材料成本僅為1,151元。本研究開發之DIY裝置在提升測量準確性與時間效益的同時，亦具備高度成本效益，未來頗具商品化潛力。

風洞是重要的流體觀測設備，生活中許多物件都需要經過「風洞」研究空氣流經物體所產生的氣動效應；但風洞設備體積大又昂貴，小學生難以接觸到，因此，本研究針對微型風洞的製作條件進行探討。 本研究針對微型風洞的風速大小、整流段孔徑大小及長度、收縮段延長長度等變因，進行氣流穩定性觀測，運用水煙及紅色點狀雷射光點輔助氣流的呈現；結果顯示風速及整流段孔徑過大或過小都不利於層流的產生，整流段較長及收縮段適當長度有利於層流的穩定呈現。 研究發現使用孔徑9mm大小、長度20cm的整流段，組合收縮段延長長度10cm，搭配風速1m/s，是自製「微型風洞」較佳的組合條件，可以清楚觀測氣流在微型風洞中層流的產生，適用於中小學生對於流體的觀測應用。

本研究從濕地篩選出可能為新種的耐鹽菌Oceanobacillus sp.，暫命名為OC2，其在無植物相伴狀態下不會降低土壤含鹽量，但卻在與植物共存後誘發特殊機轉，促使土壤含鹽量降低約，並提升植物耐鹽能力，顯示 OC2與植物存在特殊交互作用。深入研究發現，OC2能產生IAA，並吸引植物根部向其生長以利其進入根部，並在鹽逆境下分泌代謝物以刺激植物合成脯胺酸 (增加達98.5%)提升根部滲透壓、增加葉片類胡蘿蔔素及類黃酮含量以提升植物抗氧化力。植物方面，鹽逆境下植物分泌的化學物質會觸發OC2產生更多的IAA(約17%)，藉以刺激植物根系發展以利水分吸收，而OC2的存在會促進根部澱粉酶活性上升達88%，以分解澱粉產生可溶性醣類供OC2使用，推測兩者存在共生關係。本研究展示新種耐鹽菌與植物的交互作用，期待透過此菌改善鹽化農地並能提升作物產量。

本研究旨在設計基於毫米波雷達的數位物理實驗系統，用於精確量化彈簧簡諧運動。傳統物理實驗易受肉眼觀察與手動測量的誤差影響，本系統利用24GHz毫米波雷達結合自製電路板，進行即時、無接觸的運動測量。透過設計電路板、撰寫韌體訊號轉換程式，並進行數位數據分析，成功開發了靈敏的毫米波雷達系統。我們利用彈簧簡諧運動實驗驗證了該系統，觀察不同質量砝碼對彈簧運動頻率的影響。實驗結果顯示，考慮彈簧質量後，測量數據與理論結果的均方根誤差從0.62Hz降低至0.35Hz，顯示出系統的高度精確性及穩定性。本研究成功解決了傳統實驗中的量測誤差問題，以毫米波雷達技術實現了精確觀測。開源設計有助於推廣至學校的物理實驗室，為學生提供先進的實驗工具與數據分析經驗。這展示了毫米波雷達在物理實驗中的應用潛力，並為未來教學實驗提供了高效、低成本的解決方案。

本研究探討人類與生成式人工智慧（Artificial Intelligence,AI）在幽默創作上的表現和兩者幽默感內涵的差異。本研究以人類、ChatGPT及DeepSeek為研究對象，分別創作四種類型笑話（諧音梗、雙關語、禁忌、反常識），再請受試者對這些笑話進行量化的幽默評價，並輔以質性分析 了解其評價原因。量化結果顯示，人類的諧音梗與反常識笑話創作顯著優於AI；而雙關語及禁忌笑話創作則皆無顯著差異。另外，質性分析更指出受試者認為AI創作常有「笑點生硬」或「情緒不足」問題；人類創作則貼近日常情境，容易引起共鳴，在整體節奏布局跟反差感方面也較為自然流暢。本研究認為，AI的語言生成雖已有相當水準，但在非文字層面，人類的敏感和靈活度仍較具有創意優勢。

Identifyingthespeciesorsexofabirdbasedonafeatherfoundinnatureisoftenchallenging,evenwith the help of reference books. However, determining the presence of a rare species in a habitat using an indirectpresenceindicator,suchasafeather,canhelpinimplementingspecificmeasuresforpreserving the species. The aim of this study is to investigate whether DNAgenotyping is better than specialized books when identifying bird feathers. Toanswerthisquestion,Icollectedfeathersinthewildand,withthehelpoftwobooks,triedtoidentify theirspeciesandsex.Then,assistedbyDrGwenaëlJacob(UNIFR),Iisolatedtwogenesinnineselected feathers. The investigated genes were the CHD gene for sexing and the COI gene for species identification.Todothis,theDNAwasfirstextractedfromthefeathers,purified,andamplifiedbyPCR. Subsequently,anelectrophoresis wasperformedtosexthe samples andcheckthatthe PCRamplification hadworkedproperly.Finally,thesamplesweresequencedbytheMicrosynthlaboratory(St-Gall),and the obtained sequences were entered into the NCBI database. Acomparisonoftheresultsobtainedwitheachofthetwodifferentmethodsshowsthattheidentification with specialized books was fairly successful. 56% of the species identification made with the books were indeed confirmed by genotyping. DNAanalysis provided a different result only for feather #16. However,33%ofgeneticidentificationfailed,eitherduetogeneticmaterialqualityorlaboratoryerrors. Asitwaspossibletoidentifythesexofonlyonesample(feather#14)withthebooks,itwasnotpossible tomakeatruecomparisonofthetwoapproaches.However,asgeneticsexingworkedwell(onefailure, feather #28), it can be inferred that genetic sexing is more effective than using books. This work demonstrated that DNAis not infallible and that sometimes books are equally effective in identifyingbirdspeciesfromafeather.However,insexingbird,DNAremainsmoreefficient.Thus,one can conclude that DNAgenotyping is not superior but rather complementary to specialized books for identifying bird feathers.

本作品結合積木、馬達機械結構與AI技術自製「圍棋AI自動分類機」，將黑白子分類收納解決下完棋後沒人收拾的問題，我們以自動麻將桌概念為發想，設計9項不同實驗，首先以不同裝置測試棋子掉落方式，發現賓果滾筒能有效控制棋子一個一個掉落，其次大量拍攝不同燈光環境下黑白子照片，以Label-Studio進行影像標註，撰寫Python程式訓練AI影像辨識模式並控制伺服馬達轉向，使黑白子依辨識結果正確分類至棋盒。結果顯示「圍棋AI自動分類機」分類正確率高達99%，此外我們也應用於盲用圍棋，協助視障學生收棋，提升下棋便利性與學習意願。本作品不僅展現AI應用於日常生活創意實踐，也呼應SDGs中促進優質教育目標。