過去五十年來，科技的飛速發展對我們的生活產生了重大影響。日益迅速的發展，要求下 一代專業人員能夠以更靈活的方式，應對快速進步的挑戰。例如，太空時代在Neil Armstrong在 月球上留下足跡的那一刻達到了頂點。 再比如，固態矽電子技術的發展方向路線圖，很快就會走到 盡頭。 年輕的專業人士將如何適應和引領未來？

技術發展的歷史，在很大程度上，是一條 S-形曲線。新技術的早期階段通常需要很長時間並且進展速度很慢。當到達起飛點時，技術的影響會高速增長，但最終會達到一個平台期。從這裡開始，更新的技術將取代已經到達頂點的技術，並再次遵循類似的 S-形曲線趨勢進展。

雖然我們正在努力改進現有技術，該過程是可靠且可持續的，因為我們可以順應趨勢並預 測近期進展。當我們從一種舊技術轉變為新技術時，它總是具有激進(disruptive)的替代性。有能力面對新挑戰的領導者，需要具有先見，能夠渡過新舊交換的關鍵，並能在新 S-形趨勢上繼續引領前進，確保成功。

學習知識是專業人士的必經之路。但在「博」與「精」兩者之間的平衡，如何取捨？如何創造新的知識？愛因斯坦的名言：「想像力比知識更重要」，也許是下一代專業人士在瞬息萬變的未來，應對這些挑戰的指導原則。但是如何培養想像力？如何成為一位領袖？什麼是成功的領袖？什麼是在這個快速變化社會的道德準則？ 這些是我將在這次演講中與各位一起討論的話題。

“Medicine is a science of uncertainty and an art of probability,” said Sir William Osler (1849–1919), a father of modern medicine. Hundred years have passed and major advances have been made in sciences and techniques. However, a clinician still cannot quantitatively determine the drug and dose for optimized efficacy/toxicity before treating a patient.
William Osler爵士(1849-1919)，近代醫學之父，曾說：「醫學是一門不確定性的科學和機率性的藝術。」數百年過去了，科學和技術已經取得了重大的進展。然而，臨床醫生在醫治病人之前，仍然不能量化決定藥物和劑量，以取得最優化的療效或毒性。

With an inductive approach to experimental evidence, we discovered that the regimen is related to the patient phenotype through a phenotypic response surface (PRS), which is governed by a dynamic non-linear function. Without relying on the conventional trial-and-error approach, this AI-PRS platform enables clinicians to dynamically optimize the drug and dose customized to a specific patient. For the first time, personalized therapy becomes possible.
利用歸納法來處理實驗證據，我們發現給藥方案經由顯型反應表面(Phenotypic Response Surface，PRS)，關聯到病人的顯型(或稱表型)，該表面是由一動態非線性函數所支配。不須依賴傳統的嘗試和錯誤方式，這個AI-PRS(人工智慧-顯型反應表面)平台可讓臨床醫生能為特定病人，量身開出動態優化的藥物和劑量。首次讓個人化醫療變成可能。

The PRS platform is indication agnostic and has been successfully applied for more than 30 different diseases. During the past five years, clinical trials of more than 250 patients with cancers, infectious diseases, and organ transplants have been treated with the PRS platform with 0 misses. More than 400 patients are participating in ongoing clinical trials.
這個PRS平台是不定類型的，已成功應用於超過30種不同的疾病。在過去的五年間，已有逾250名病患的臨床實驗，包括癌症、感染性疾病、和器官移植，以PRS平台治療，結果零失誤。現有四百多名病患參與正在進行中的臨床實驗。

Optimization of mechanical properties in physical complex systems, e.g., material synthesis or manufacturing process, involves searches in a very large space. In fact, physical complex systems are also governed by the PRS functions. G.B. West has found many metrics, e.g., wages, and crime rate, of urban areas follow the universal scaling law. We re-processed West’s data and found that the PRS function also works in the social complex systems. The PRS function seems to be a unified input-output transfer function for complex systems in general.
在實體複雜系統中的機械性質優化，例如材料合成或製造過程，涉及在很大空間的研究。事實上，實體複雜系統仍受到PRS函數的支配。Geoffrey Brian West已經找到許多度量，例如都會地區的工資和犯罪率，遵守普適性的尺度比例律。我們重新處理West的數據，發現PRS函數也適用於社會複雜系統。對一般的複雜系統來說，PRS函數可說是一個統一性的輸入-輸出轉移函數。

This lecture will provide an overview of recent and projected advances in space propulsion. All chemical and non-chemical propulsion systems will be considered systematically, including liquid/solid rockets, hypersonic airbreathing engines, electric propulsion engines, nuclear rockets and advanced concepts such as solar sails and anti-matters. Emphasis will be placed on the present understanding of the physico-chemical processes involved, and contemporary research needs and challenges.
本演講概述近年和計畫中有關太空推進的進展。所有化學和非化學的推進系統將分類循序介紹，包括液體/固體火箭、超高音速的吸氣式引擎、電力推進引擎、核能火箭、以及先進構想如太陽帆和反物質推進等。演講將著重在當前對所涉及的物理化學過程的理解，以及現代研究的需要和挑戰。

Design is a strategic approach with an executable plan for achieving specific objectives. Design innovation empowers leaders to develop new solutions, add values, and envision new possibilities. At the highest level, design drives and inspires the advance of science and technology.
設計是一種實現特定目標，具有可行計畫的策略方法。設計創新使得領導者有能力開發出新的解決方案、增加價值、和設想新的可能性。在最高的層級上，設計能驅動並激發出科學和技術的進步。

This lecture addresses engineering design, innovation, and practice in the data era. Integration of data science with engineering science will allow architectures in which fully integrated modeling and simulations, data analytics, systems engineering, and design methodology can be exploited for the purposes of scientific discovery and the advancement of technology in complex systems. As a specific example, a new approach to aerospace propulsion engines is discussed. The effort involves design of experiments, high-fidelity simulation and experiment, reduced-order modeling, uncertainty quantification, machine learning, and testing and certification. The developed paradigm enables efficient surveys of the design space and identification of the key design attributes that dictate the system behavior. The unified approach starts with high fidelity modeling and simulations. Reduced-basis models and emulation then leverage the established database for physics-based data assimilation. Stochastic-based extraction of physics from complex flowfields provides faithful and interpretable representations of the underlying mechanisms. Combined with statistical methodologies and control theories, these techniques are integrated to allow for efficient design optimization and uncertainty quantification. Finally, a system-level model is developed for effective assessment of system behaviors.
本演講論述在數位時代的工程設計、創新、和實踐。結合資料科學和工程科學將可使建築學，其中完全整合模型和模擬、數據分析、系統工程、和設計方法，作為科學發現和複雜系統的技術提升之用。舉一個特定例子來說，討論航空噴射引擎的一項新想法。投入的努力包括實驗設計、高傳真模擬和實驗、降階模型、不準度量化、機器學習、和測試驗證。這套發展模式可以對主控系統行為的關鍵設計屬性，進行其設計空間和識別的有效調查。統一的做法是從高傳真模型和模擬開始。簡基模型和仿真可用於操控已建立的資料庫，作為實體數據的同化之用。從複雜的流場中隨機抽取的物理數據，提供了忠實和可解釋的基本機制表徵。結合統計方法學和控制理論，這些技巧整合起來可作為有效的設計最佳化和不準度量化。最後，一個系統級模型可發展作為系統行為的有效評量之用。

All engineering systems could be addressed following a similar paradigm. A big obstacle is the relative insularity of academic and research fields, as well as the divide between engineers/scientists with domain expertise and data science. Looking forward, a huge opportunity exists for the community in bringing together researchers in the engineering, data science, and computer modeling areas to collaboratively develop large-scale design systems.
所有的工程系統皆可依循類似的範例予以處理。一個大障礙是來自於學術和研究界的相對孤立，以及具有領域專長和資料科學的工程師和科學家之間的分歧。展望未來，一個巨大的機會正等待著將工程學、資料科學、和電腦模式領域的研究人員結合成一社群，共同合作發展大規模的設計系統。

Since the discovery of the first exoplanet, Pegasi 51b, in 1995, the field of exoplanet study has made tremendous progress. It has become one of the main streams of modern-day astronomy and astrophysics. With the high-precision photometric measurements carried out by the Kepler space telescope mission, we learned that the number of solid-surface exoplan-ets called super-Earths is very high in our Galaxy. A good fraction of them could be habita-ble in the sense that their surface temperature is like that of our Earth such that liquid water and ocean might exist. A major effort in exoplanet study is therefore trying to unwrap the evolutionary histories of habitable super-Earths orbiting around different types of host stars. The big question is, of course, whether life and even high-order intelligence (like humans) could emerge in these extraterrestrial environments. In particular, we are interested in the interaction of the exoplanetary atmospheres with the stellar winds and energetic coronal ma-terials ejected from the central stars. Inadvertently our Earth has been often used as the la-boratory to test theories on climate evolution and space weather effects. From using a set of parameters (such as the time taken for primitive life to evolve into Intelligent bio-systems) and the recent exoplanet statistics, it is possible to estimate the number of exoplanets host-ing high-tech entities at the present time. But just like the humankind again, they need to survive the compound threats of the Four Horsemen, namely, climate change, thermo-nuclear war, pandemics and artificial intelligence (or ET). From the famous Drake equation, it has been calculated that the total number of the so-called “communicating extraterrestrial intelligent civilizations (CETI) may be very small. This alarming result, though highly un-certain, serves as a reminder to us that we should learn everything we can and to do every-thing we can to keep the world in peace and its long-term development sustainable.
自從在1995年第一顆系外行星(Pegasi 51b)的發現，系外行星的研究突飛猛進，今己成天文學和天文物理學的主流之一。因為克普勒太空望遠鏡任務的精準亮度測量，我們知道如地球大小相似的超級地球，在銀河星系中多如恒河沙數。其中部份表面溫度可容液態水存在，所以說不定有生物圈在上滋生。系外行星研究當前的一個重要課題便是這些在所謂適居帶的超級地球的來源和演變歷史，如有生命則是否容許從低等生物進化成高等智慧生物和具有科技文明。關鍵問題便在於這些適居系外行星的大氣會否因為恒星風和恒星風暴作用而消失，因而無法提供生態系统成長的環境。亦因如此，地球便成為系外行星氣候變遷及太空天氣研究最理想的實驗室，從中可以應證各種理論和模型。再有利用一些有關地球生物圈演化過程的資料以及系外行星的统計數據，我們可以估計在銀河星系裏會在多少高等文明存在。又有多少個能夠用無線電波作星際之間的通訊？這個數目原則上由此種科技文明的生命期而定。譬如說地球文明從無線電的發現到今天不過百年左右，但己經自我產生全球暖化，熱核戰爭，瘟疫以及人工智慧的威脅，如無法克服便會到臨全人類生死存亡的瓶頸。從這個觀點來看，可以有通訊能力的外星科技文明可能會是寥寥無幾。雖然這個估計有着很大的不確定，但可以用來鼓勵大家盡力學習，以用所學盡力服務社會，並以尋求世界和平和永續發展為人生目的。

The full name of TTSS is “Taiwan Top Science Student” which Chinese name is “ 台灣科學特殊人才提昇計畫 “. Its origin can be traced to my first participation in the Wu-Chien-Shiung Science Camp about 20 years ago. This talk may therefore be considered as a report card on my journey on the road of science education from the point of view of a pe-destrian (路人甲) to a practitioner (實踐者). I will briefly describe the main components of TTSS with special attention given to the interaction and dialogs between high school teach-ers and university professors with a view to strengthening the learning and teaching activi-ties in Earth Science within the framework of the 108 curriculum. In addition, I will also in-troduce the “physics mentor program (PMP)” and the “philosophy, politics and economics (PPE)” course program that have been partly inspired by my conversations with Prof. Ming-Juey Lin. Your comments and suggestions are most welcome.
「台灣科學特殊人才提昇計劃」是由教育部支持的一個科學教育倡議，英文名字是「Taiwan Top Science Student」簡稱為TTSS。這計畫的由來可以追遡到我在20多年前初次參加吳健雄科學營的經驗。所以這個演講可以說是一個成果報告，描述我如何從一個科學教育的路人甲成為實踐者。在這裡我將簡單介紹TTSS的幾個主要項目和活動，特別是如何利用高中教師和大學教授的積極對話和討論，改善108課綱中地科課程的教學。此外，我亦會介紹才剛剛開始的以高中生為導生及大學教授為導師的「物理良師益友計畫」或稱「PMP」，以及強調通才教育的「哲學、政治和經濟」或稱「PPE」的大學部學分學程。這些倡議或多或少都得益於林明瑞教授的指引，所以在此特致謝意。

The beauty and sadness of nitrogen fertilizers--The use of nitrogen fertilizers has increased the world's food production, creating an opportunity for the world's population to grow significantly after 1960. However, the nitrogen use efficiency of crops is very poor, and half or even two-thirds of nitrogen fertilizers remain in the farmland, leading to the production of greenhouse gases N2O and eutrophication of rivers and oceans. Nitrate is the most important nitrogen source for plants, so plant scientists have delved into how plants absorb, transport, and use nitrate. The whole research originated from the discovery of the first nitrate transporter gene CHL1, which opened the door to the study of nitrate utilization in the field of molecular biology. The quest for high-yield breeding has resulted in current cultivars relying on high amounts of fertilizers, posing a great threat to the environment. Through the study of the nitrate transporter homologues of CHL1, we have learned about the mechanisms of nitrate distribution into various parts of plants, and these basic research findings help us to identify new strategies to improve the nitrogen use efficiency of crops.
氮肥的美麗與哀愁，氮肥的利用讓世界糧食的產量增加，以造就世界人口在1960年之後大幅成長的契機。但是作物的氮利用效率很差，有一半甚至是三分之二的氮肥殘留在農田，造成溫室氣體及河川海洋的優養化。硝酸鹽是植物最主要的氮源，因此植物學家們深入探究植物如何吸收、運送、利用硝酸鹽。整個研究起源於找到第一個硝酸鹽轉運蛋基因CHL1，開啟進入分子生物領域研究硝酸鹽利用之門。尋求高產量的育種過程，造成目前的栽培種都仰賴高量的肥料，對環境造成極大的威脅。我們透過對CHL1的同源硝酸鹽轉運蛋白的研究了解到將硝酸鹽分佈到植物各個部位的機制，也從基礎科學的研究中，找到改善農作物氮利用效率的新策略。

Plants do not have nervous systems like animals, so how do immobile plants perceive changes in their surroundings and respond quickly? How do plants sense physical damage, how do they sense insects, how do they sense heat and cold, and how do they sense the amount of nutrients? Except for carbon, hydrogen and oxygen, plants must absorb most of the nutrients from the soil. Sensing whether the nutrients in the soil are sufficient is critical process for plant growth, because plants need to make the most efficient allocation of limited energy in order to assimilate different nutrient raw materials into required building blocks for growth. We found that plants use the transporter CHL1 responsible for absorbing nitrate to detect whether there are sufficient or under-optimal levels of nitrate in the soil. The multi-tasking CHL1 is not only a transporter of nitrate, but also a gatekeeper. We called this kind of protein "transceptor" with combined function of transporter and receptor. Using CHL1, plants can do the math, reckoning external nitrate levels to initiate downstream metabolic reactions to varying degrees. Another transceptor, NRT1.13, monitors the amount of nitrate in the plants, which in turn regulates flowering time and shoot shape. How immovable plants can achieve optimal growth and survival by responding to ever-changing environments is an interesting topic of research.
植物不像動物有神經系統，那不能移動的植物又是如何感知周遭的變化而快速應變呢？植物如何感知物理傷害，如何感知昆蟲，如何感知冷熱，還有如何感知養分的多寡？碳氫氧之外，植物要從土壤中吸取絕大部分的養分，感知土壤中養分是否充裕影響植物生長，因為植物需要對有限的能源做最有效的分配，將不同的養分原料製造成提供生長所需的建材。我們發現植物利用負責吸收硝酸鹽的轉運蛋白(transporter) CHL1來偵測土壤中硝酸鹽的含量是多還是少，身兼多職的CHL1不僅是硝酸鹽的搬運工，還是守門員。我們給這類蛋白一個新的名詞『transceptor』，不僅是transporter還是receptor。Transceptor CHL1還幫助植物做數學，算清楚外在硝酸鹽的多寡而啟動不同程度的下游代謝反應。另一個transceptor NRT1.13則監看植物內部的硝酸鹽多寡，進而調控開花時間及枝條的型態。不能動的植物如何以不動應萬變來求得最佳的生長與存活是很有趣的研究主題。