LIDU Liquor: A Time-honored Baijiu Distiller's Digitalization

数字经济时代，白酒老字号企业无一例外地面临着拥抱数字化的“阵痛”，虽然白酒老字号企业融入数字化的难度较大，但这种数字浪潮势不可挡，头部的白酒老字号企业已纷纷涌入这一赛道。而处于资源和能力双重劣势的中小型地方白酒老字号企业站在数字化转型的十字路口，应如何抉择？ 本案例以江西省知名的地方性白酒老字号企业江西李渡酒业有限公司为对象，从数字化转型的视角，分析自2014年起，李渡酒业董事长汤向阳带领实现企业的数字化转型的过程。本案例从数字化转型的驱动因素、举措、评价标准等方面切入，旨在帮助学员了解老字号企业是否需要进行数字化转型、是否能够进行数字化转型以及是否能够成功实现数字化转型等一系列问题，从而为身处数字经济时代的企业提供数字化转型的借鉴经验。In the second decade of the 21st century, many time-honored baijiu distillers faced challenges from the digital economy. Going digital was undoubtedly tricky for them, but the mega-trend of digitalization was unavoidable, and the time-honored baijiu distillers had no choice but to adapt. Leading time-honored baijiu distillers were the first in the industry to jump into this trend. However, the road ahead seemed more challenging for small and medium baijiu distillers. This case study focuses on digitalizing a time-honored baijiu distiller in Jiangxi - LIDU Liquor (hereinafter referred to as LIDU). The case describes LIDU's digital journey starting in 2014, focusing on defining digitization, influencing factors, digital measures, and evaluating indicators. It aims to help students understand why and how time-honored baijiu distillers start digitalization. It also provides suggestions on how to assess its digitalization performance. This case offers insights and references for traditional manufacturers and companies in the digital era

熱電晶片發電效能的探討及應用

[[abstract]]由於化石燃料大量被利用當作主力能源，造成能源枯竭及環境遭受嚴重暖化衝擊，人類及地球上各物種面臨到存亡危機，再生能源以及節能減碳已是各國迫切需要去執行的問題。熱電材料對於當今能源問題是個很好的解決方案，可以同時達到節能及提高能源利用效率的目的。 半導體熱電材料是藉由Seebeck、Peltier熱電效應，由施加電壓或是由冷熱面之溫差產生電子電洞的遷移，形成致冷效應及發電效應，這兩種效應於提高工廠、運輸工具、能源利用之效能是相當有幫助的。實驗是以蘇聯Kryotherm溫差發電專用晶片，了解單一晶片以及以不同方式串聯及並聯後，晶片的開路電壓(Vo)及閉路電流(Is)對溫度差之變化，並藉此計算其電阻及發電功率的關係，藉此來了解發電晶片的發電特性，以及在實際應用時的最佳化條件。 由於實驗設備簡陋，本組克服了種種問題並反覆驗證以獲得可靠的結果。實驗結果中發現，蘇聯溫差發電專用晶片每片所測得電壓及電流都隨溫度呈現良好的線性關係且再現性非常好，表示晶片品質穩定性高，尤其在150C-200C區間，一般致冷晶片就會發生異常現象。在實驗過程中發現溫差發電晶片之電阻不隨溫度差而改變，但是串聯後會是使電阻略為上升。發電功率與溫差呈現拋物線上升的關係，串聯的功率效益比並聯要高。而溫差晶片的功率只受溫差影響，冷面熱面的溫度並不會影響到結果。 此外，也將溫差發電晶片結合超級電容製作成「零碳排放熱驅動概念船」，可利用各種熱源(如太陽熱能、廢熱等)驅動馬達使船在水中行走，多餘的電力則可儲存在超級電容中，在無熱源狀態下則可利用超級電容中的電能驅動馬達

JUNO Sensitivity on Proton Decay p→νˉK+p\to \bar\nu K^+ Searches

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this paper, the potential on searching for proton decay in $p\to \bar\nu K^+$ mode with JUNO is investigated.The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits to suppress the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar\nu K^+$ is 36.9% with a background level of 0.2 events after 10 years of data taking. The estimated sensitivity based on 200 kton-years exposure is $9.6 \times 10^{33}$ years, competitive with the current best limits on the proton lifetime in this channel

JUNO sensitivity on proton decay p → ν K + searches*

The Jiangmen Underground Neutrino Observatory (JUNO) is a large liquid scintillator detector designed to explore many topics in fundamental physics. In this study, the potential of searching for proton decay in the $p\to \bar{\nu} K^+$ mode with JUNO is investigated. The kaon and its decay particles feature a clear three-fold coincidence signature that results in a high efficiency for identification. Moreover, the excellent energy resolution of JUNO permits suppression of the sizable background caused by other delayed signals. Based on these advantages, the detection efficiency for the proton decay via $p\to \bar{\nu} K^+$ is 36.9% ± 4.9% with a background level of $0.2\pm 0.05({\rm syst})\pm$$0.2({\rm stat})$ events after 10 years of data collection. The estimated sensitivity based on 200 kton-years of exposure is $9.6 \times 10^{33}$ years, which is competitive with the current best limits on the proton lifetime in this channel and complements the use of different detection technologies