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[[alternative]]Social Media, Body Appreciation, and Appearance Comparison: A Cross-cultural Comparison Between Women in Taiwan and the United States
No full text[[abstract]]過去二十年來，社交媒體的使用在全球範圍內急遽擴展，大多數主要應用程式均具有照片分享功能。現今，每個人都能透過智慧型手機的相機不斷檢視自己的外貌，分享個人照片，並觀看朋友、同學和喜愛的名人在網路上分享的照片。關於社交媒體對身體形象滿意度和外貌比較的影響，尤其是在年輕女性和女孩中，相關研究已經伴隨著社交媒體普及化而存在了近二十年。然而，大部分有關社交媒體如何影響身體形象和身材鑑賞的研究僅限於西方地區，跨文化研究在此領域仍然相對未深入探討。為了解決這個問題，本論文以六種不同的量表作為比較基礎，並進行質性實證研究，旨在比較台灣和美國女性對社交媒體如何型塑身材鑑賞的觀念。研究對象為200名來自台灣（N=100）和美國（N=100）年齡介於18至35歲的Instagram使用者，完成匿名的線上問卷調查。研究結果顯示，兩組之間在身材鑑賞、外貌比較和照片編輯方面存在顯著差異。然而，在社交媒體上的外貌比較、媒體素養以及對整容手術的接受程度方面則沒有統計上的差異。當參與者依年齡分為不同群體時，年齡介於18至22歲的Z世代女性在兩組之間的身材鑑賞和照片編輯方面有顯著差異，而年齡介於23至35歲的Y世代女性則僅在外貌比較方面存在顯著差異。綜合以上結果，似乎在美國，身材鑑賞和外貌比較是一個較為嚴重的問題，而在台灣則較為輕微。在Z世代參與者中，美國女性的身材鑑賞程度較台灣女性低，而在Y世代參與者中，美國女性的外貌比較程度則較台灣女性高。有趣的是，Z世代參與者在照片編輯方面也存在顯著差異，台灣參與者較美國參與者更常編輯照片。此論文為跨文化身材鑑賞研究領域做出了貢獻，希望未來能繼續納入台灣進行相關研究。[[abstract]]The use of social media has exploded over the last twenty years around the globe, with most major apps having a photo sharing component. Everyone now has the ability to constantly check their appearance in their smartphone’s camera, post photos of themselves, and see what photos their friends, classmates, and favorite celebrities post online. Research on how social media affects body image satisfaction and appearance comparison, especially in young women and girls, has existed for almost as long as social media has been widely used by the public. Despite the large amounts of research on how social media influences body image and body appreciation, most research takes place exclusively in a Western setting, and cross-cultural research in this topic is still relatively underexplored. To address this concern, this thesis, with six different scales as a basis of comparison, along with qualitative empirical research, aimed to compare women in Taiwan and the United States on how social media shapes their thoughts on body appreciation. A sample of 200 Instagram users from Taiwan (N=100) and the United States (N=100) ranging in age from 18 to 35 completed an anonymous online survey. Results showed that there were significant differences in body appreciation, appearance comparison, and photo editing between the two groups. There was no statistical difference in appearance comparison on social media, media literacy, or acceptance of cosmetic surgery. When divided into different age groups, Gen Z woman ranging in age from 18 to 22 had significant differences in body appreciation and photo editing between the two groups, while Gen Y women ranging in age from 23 to 35 only had significant differences in appearance comparison between the two groups. Based on these results, it appears that body appreciation and appearance comparison is a larger issue in the United States than in Taiwan. There were statistically significant differences in body appreciation in Gen Z participants, and in appearance comparison in Gen Y participants. Gen Z American women had lower body appreciation than Gen Z Taiwanese women, and Gen Y American had higher appearance comparison than Gen Y Taiwanese women. Interestingly, Gen Z also had statistically significant differences in photo editing, with Taiwanese participants editing photos more than American participants. This thesis contributes to the growing field of cross-cultural research on body appreciation, which will hopefully continue to include Taiwan in the future
[[alternative]]A Study on Growth Crops by Controlling Different Wavelengths and Illuminance of Light Sources with Informatization
No full text[[abstract]]在無農業設施下種植要達到無農藥防治、化學肥料使用是不容易達成目標，以小型人造光源植物工廠栽種蔬菜，無農藥殘留且不受天候影響隨時可栽種可重覆使用不佔空間，因此本研究目的為打造一個居家使用小型植物工廠並達到節能、標準化及兼具安全便利等，提出智慧室內植物工廠系統架構包括四個模組分別是建立、採集數據、傳輸數據和數據分析系統，加入大數據和人工智慧實現最佳植物生長環境條件，為達成上述目的以一次一因子法，利用高頻率發光二極體(Light Emitting Diode, LED)提供不同波長的光源及不同光照週期，確認有利於作物生長光源型態，其中智慧植物工廠必須不斷收集植物工廠的數據並適時更新監測參數，以獲得最佳植物生長條件。本實驗使用萵苣(Lactuca satva L.)品系幼苗，實驗箱中皆使用栽培土填滿培養盤定植供給水，每日定量噴灑於根部(500 ml/日)連續為期30天至採收完成，分別以木製植物栽培箱種植每箱中9株，首先植物箱中照射時數為24小時全日照射設置白光、藍光與紅光不同顏色LED為比較，替代陽光讓作物行光合作用；後續間歇式循環及全照射12小時給予相同紅光測試不同光週期，夜間時段(18時至5時，總照射時數12個小時)、週期2小時(總照射時數6小時)、週期4小時 (總照射時數8小時)，白天(6時至17時)則給予自然日照行光合作用；最後植物箱中額外增加排風系統，以12小時開關通氣循環及24小時進行通氣。三種不同光源全日照連續30天培育期有顯著差異，分別以紅光組(660 nm高功率LED)為最佳，其次為藍光組、白光組為最差，不同光照時間週期試驗中發現，以2小時及4小時、12小時為循環，對萵苣植株重量及高度呈現正成長，但12小時週期植株生長高度低於另兩組生長效益較低，其中提供光照4小時效率最好；植物箱中額外增加排風系統，12小時及24小時試驗其中12小時開關通氣循環對於植株生長最佳。資訊安全對於智慧植物工廠也非常重要，如收集到偽造的數據會影響機器學習性能，因而本研究使用聯邦學習技術針對每個區域內部數據進行訓練，上傳梯度損失和在區塊鏈中產生區塊，監測參數並經過模型訓練驗證及統計確認數據完整性，而不採取直接上傳原始參數的方式，達到保證隱私和數據安全目的。[[abstract]]It is challenging to achieve pesticide-free pest control and minimize the use of chemical fertilizers when planting without agricultural facilities. By cultivating vegetables in small-scale artificial light-based plant factories, it is possible to eliminate pesticide residue and overcome weather limitations. These factories are pesticide-free, unaffected by weather conditions, and allow for continuous cultivation and space efficiency. Therefore, the purpose of this research is to develop a small-scale plant factory for home use that promotes energy efficiency, standardization, safety, and convenience. To achieve these goals, a smart indoor plant factory system architecture is proposed, consisting of four modules: establishment, data collection, data transmission, and data analysis system. By incorporating big data and artificial intelligence, the system aims to create optimal plant growth conditions. To determine the optimal light source conditions for crop growth, a one-factor-at-a-time approach is employed. High-frequency Light Emitting Diodes (LEDs) are utilized to provide different wavelengths of light and operate on different lighting cycles, confirming the light source patterns favorable for crop growth. In the intelligent plant factory, continuous data collection from the plant factory and timely updating of monitoring parameters are essential to obtain the best plant growth conditions.In the M-PFALs experiment, 9 lettuces (Lactuca satva L.) were planted in a plant box. We use potting soil for planting and give daily water (500 ml/day) to harvest for a total of 30 days. It is mainly divided into three experiments, the different color light test, different photoperiod test, and ventilation cycle test. In different color light tests, the plants were illuminated by white, blue, and red LEDs for 24 hours instead of sunlight. Different photoperiod experiments test different night (18:00 to 5:00) cycles in red light, the control group is normal photoperiod, the full-time cycle 12-hour group, the cycle 2-hour group (total 6 hours), the cycle 4-hour group (total 8 hours), the control group was normal photoperiod. Natural light was given to sunlight for photosynthesis from 6:00 to 17:00. Ventilation cycle test, the fan was turned on for 12 hours and turned off for 12 hours in the ventilation group, and the other group was turned on for 24 hours. In different color light tests, the red light group is the best, followed by the blue light group, and the white light group is the worst. Giving red light in different photoperiod experiments was better than the control group, among which the group with a period of 4 hours had the best effect, followed by the group with a period of 2 hours, and the group with a period of 12 hours was the worst. In the ventilation cycle test, the group with fans turned on for 12 hours and turned off for 12 hours is better than the group with fans turned on for 24 hours.Information security is also crucial for smart plant factories. For instance, the collection of falsified data can affect machine learning performance. Therefore, this study employs federated learning techniques to train the data within each region. It involves uploading gradient losses and generating blocks in a blockchain, monitoring parameters, and verifying data integrity through model training, validation, and statistical confirmation. This approach avoids directly uploading raw parameters, ensuring privacy and data security
[[alternative]]Study on Johari Window of Judo Recreational Sports－A Comparison of Visually Impaired and Non-Visually Impaired Perspectives
No full text[[abstract]]本論文研究目的為瞭解一般大眾 (非選手) 對柔道的看法，比較和選手(視障、非視障)與教練的看法有何不同。本研究架構主要針對研究目的想要了解柔道運動、國中體育班柔道選手、視障選手和教練為標題的不同認知比較，以周哈里窗為理論架構、啟發式探究為研究設計，使用訪談進行資料收集，將整理並提供有用資訊給柔道教練、柔道選手及對柔道休閒運動有興趣的大眾參考。本研究建議，現今社會大眾對於視障柔道並不熟悉，每年可以舉辦多場視障柔道體驗營來進行推展，並讓大眾更認識視障柔道，進而讓更多視障人士參與學習視障柔道的領域。而在適應體育方面希望能全面推展並加入至中、小學以下體育課程，能夠藉此推廣以及讓學生瞭解身心障礙人士的不方便以及擁有更多同理心。最後體育班推展除了學科基本能力外，可以再加入運動傷害防護、運動心理學等相關課程，協助選手擁有更好的觀念在面對傷害來臨或是提高心理層面。[[abstract]]The purpose of this research is to understand the perception and impression of judo by the general public (non-competitors), and compare the differences between the perceptions of judo players (visually impaired and non-visually impaired) and coaches. The research structure of this study is mainly aimed at the purpose of the research, which is to understand the different cognitive comparisons of judo sports, judo players in junior high school sports classes, visually impaired players and coaches. The Zhou Harry window is used as the theoretical framework and heuristic inquiry is used as the research design. Interviews are used to conduct data analysis. Collect, organize and provide useful information to judo coaches, judo players and the general public who are interested in judo leisure sports.This study suggests that the general public is not familiar with judo for the visually impaired, and that multiple judo camps for the visually impaired can be held every year to promote it, and let the public know more about judo for the visually impaired, so that more visually impaired people can participate in learning judo for the visually impaired field of. In terms of adapting to sports, I hope to fully promote and add it to the physical education courses below primary and secondary schools, so as to promote and let students understand the inconvenience of people with disabilities and have more empathy. Finally, in addition to basic academic and subject skills, the physical education class can also add related courses such as sports injury prevention and sports psychology to help players have a better concept in the face of injuries or improve their psychological level
[[alternative]]Research on the Value Cognition of Cabin Safety Based on the Method and Purpose Chain Model
No full text[[abstract]]航空產業可說是全球重要的運輸產業之一，客艙安全對於飛航安全十分重要，雖然航空安全管理此議題較受到重視，然而較少研究是針對客艙組員與客艙安全，因此本研究欲運用方法目的鏈來探討客艙組員處理客艙安全意外的結果及處理後所獲得之個人價值感。本研究之研究對象為客艙組員，透過階梯式一對一訪談來獲得受訪者的資訊，再經由內容分析、涵意矩陣表及價值階層圖來獲取訪談內的資訊並進行分析。研究分析結果發現：客艙組員面對客艙安全事件自我價值歸類出4項助益性價值，分別是經驗累積、提升敏銳度、情緒調節、堅定立場；最終價值為4項，包含飛安重要性、溝通重要性及成就感。最後本研究向航空公司、民航局以及後續研究者提出相對的建議，希望加以重視客艙組員面對客艙安全的價值及後續感受。[[abstract]]The aviation industry can be said to be one of the most important transportation industries in the world. Although the issue of aviation safety management has received more attention, there are few studies on cabin crew and cabin safety.Therefore, this study intends to use the method The objective chain is used to discuss the results of handling cabin safety accidents and the sense of personal value obtained by cabin crew members after handling them. The research object of this study is the cabin crew. The information of the interviewees is obtained through one- by-one, and then the information in the interviews is obtained and analyzed through content analysis, meaning matrix and value hierarchy diagram.The results of the research and analysis found that the self-worth of the cabin crew in the face of cabin safety incidents is helpful values, which are accumulation of experience, improvement of acuity, emotional regulation, and firm stand; the final value is including the importance of flight safety , communication importance and sense of accomplishment. Finally, this study puts forward relative suggestions to airlines, CAA and follow-up researchers, hoping to pay more attention to the value and follow-up feelings of cabin crew members in the face of cabin safety
[[alternative]]A Study of the Legal Risk Management on Long Term Care service
No full text[[abstract]]由於接受長期照顧的人口迅速增加，照顧疏忽及其訴訟案件也隨之增加，近年來，長照服務訴訟案件層出不窮。本研究收集2002年至2021年164件法院判決文，採用量性與質性整合性研究方法，從長照接受者（即原告）的角度，分析照顧疏失類型及其風險因素。 結果發現：1、照顧疏失以住宿型機構案例為多，而居家式、社區式自2017年起呈增加趨勢；2、提出訴訟者以子女和家屬最多(77.68%)；被告對象以機構雇主最多(98.17%)，其次是照服員、護理人員；受照顧者傷害嚴重度死亡(59.15%)最多；原告勝訴率33.54%；3、疏失類型前五項為：跌倒、哽咽、病情變化、感染、高處墜落；4、整體而言，技術性照顧疏失(64.02%)高於組織性照顧疏失(35.98%)；(1)技術性照顧實務(34.20%)，次類型：照顧處置、不適當的照顧干預、進食/餵食/管灌技術；(2)技術性照顧支援(29.82%)，次類型：專業警覺性不足、延誤送醫和溝通；(3)組織性照顧實務(6.29%)，次類型：照顧流程未落實、個人照顧衛生不良；(4)組織性照顧支援(29.69%)，次類型：人員管理、環境設施安全和清潔、照顧相關記錄等。 研究結果可供長照機構管理者與長照人員警惕。建議(1)強化法律風險教育訓練；(2)施行強制性異常/意外事件報告制度；(3)機構應確實建立標準作業流程與管理，主管機關應列為評鑑必要項目；(4)應定期查核與修復設施與環境清潔，以提高長照服務安全和照顧品質。[[abstract]]Due to the rapid increase in the population receiving long-term care. so has the incidence of nursing negligence and its litigation. In recent years, long-term care service lawsuits have emerged in endlessly. The purpose of this study is to from the perspective of long-term care recipients (namely plaintiffs), analysis the types of neglect and their risk factors. with judgments for 164 cases spanning from 2002 to 2021 collected. This study adopts a integrative research method combining quantitative and qualitative methods. Finding:1. Long-term care failures are mostly cases of accommodation institutions. The home-based or community-based have increased since 2017; 2.The plaintiffs are mostly children and family members (77.68%); The majority of defendants are employers (agencies or institutions) (98.17%), secondly are nurse aid and nursing staff; The rate of plaintiffs obtaining satisfaction of a claim is 33.54%, 3. The top five primary types of negligence are: falls, choking, changes in medical conditions, infections, and fall from height (FFH). Overall, technical care negligence (64.02%) is higher than organizational care negligence (35.98%); 1.technical care negligence (34.20%), with subtypes neglect: care practices , inappropriate care interventions, feeding or tube-feeding technique neglect, 2.technical care support (29.82%), with subtypes : insufficient professional alertness, delayed hospitalization, and communication errors. 3.Organizational care negligence(6.29%), with subtypes like failure to implement care processes and poor hygiene; 4. Organizational Care Support (29.69%), with subtypes including personnel management, environmental facility safety and cleanliness, and omissions in care-related records. The findings can provide long-term care institutions as references in management and practices. This Study suggests the following conducts: (1) legal education and training shall be strengthened; (2) mandatory exception/incident reporting systems shall be implemented; establish their standard operation procedure and managements, and competent authorities shall include these establishments as items required under evaluations; and (4) regular inspections and immediate recovery of environmental cleanliness and facilities as well as caregiver alertness and sensitivity enhancement shall be conducted to improve the safety and quality of long- term care services
[[alternative]]Research on Prevention of Occupational Accidents and Legal Risks
No full text[[abstract]]勞資關係中，經常有勞工與雇主因各種爭議而對簿公堂，如：加班費，工資，解僱等糾紛。但對勞工而言，前述之爭議至多就是權益上之受損，依然可正常工作，不至於發生生計問題。但若因職災事件而訴訟於公堂，那麼事情就不能等閒視之，畢竟職災之情節可大可小，受災情況有重有輕，輕者住院開刀，重者可能終生癱瘓抑或者死亡。所以勞工發生職災，工作能力有可能因此而喪失，所以筆者認為，在各類型的勞資爭議中，屬職災爭議對勞工權益之影響最為巨大。從過去經驗觀察，無論勞工或雇主，對於職災之法令相關規定多半為一知半解，因此，在職災事故發生時，雇主往往給付不足或未能即時給付，而勞工因未獲得足額補償而認定雇主刻意規避責任，進而訴諸訴訟以求紛爭解決。一般民眾對於不同法規適用的情形無法全面理解，更無法熟悉法律救濟制度之行使，於是往往喪失自身權益而不自知；相同地，經營者或受雇工作者亦應就勞動法中關於職災所衍生之議題詳加認識。勞動基準法所規範的職業災害補償制度，對於勞工來說就是最即時的經濟上支持，惟當勞資雙方對於職業災害補償，產生認知上之差異時，司法訴訟雖然係屬最後的紛爭解決手段，但漫長的審理過程，勞工需要額外負擔經濟及時間上之成本，也使得勞工視司法訴訟程序為畏途。勞資爭議調解則係屬現行實務上最廣泛被使用的訴訟外紛爭解決機制，希冀透過設置此制度，能夠使勞工權益能夠更快速及更便捷的獲得應有的補償。除此以外，風險預防是避免災害最根本解決之道。故本文特別提出法令修正及行政措施調整之建議，期盼能夠使遭遇職業災害之勞工，權利能夠即時獲得救濟，並撫平傷痛。[[abstract]]In labor-management relations, disputes between workers and employers often end up in court due to various issues, such as overtime pay, wages, and dismissal. For laborers, such disputes may only bring about damages to their rights and interests, and they can still work normally without livelihood problems. However, if a lawsuit is filed in court due to an occupational accident, the matter cannot be disregarded. After all, the severity of workplace accidents can vary greatly, with some requiring hospitalization and surgery, while others can result in lifelong paralysis or even death. The working ability of laborers would be lost on account of workplace accidents. Therefore, occupational accident disputes are believed to have the greatest impact on workers' rights and interests among all types of labor disputes. Based on past experience and observation, both workers and employers often only partially understand the relevant laws and regulations regarding workplace accidents. Accordingly, when workplace accidents occur, employers often underpay or fail to pay compensation promptly. Workers may then believe that their employers are intentionally evading responsibility and resort to litigation to resolve disputes due to not receiving adequate compensation. Owing to the insufficient understanding of the application of different regulations and the exercise of legal remedies, the general public forfeits their own rights unconsciously. As a result, employers or employees should also have a thorough comprehension of the issues arising from occupational accidents under labor laws. The occupational accident compensation system regulated by the Labor Standards Act is the most immediate economic support for workers. When the understanding of occupational accident compensation between employers and employees is inconsistent, judicial litigation is the final means of resolution. Nonetheless, the lengthy trial process imposes additional economic and time costs on laborers, making the laborers take the judicial litigation process as a daunting task. Mediation of labor disputes is currently the most widely used litigation alternative for resolving disputes outside the court system. By establishing this system, it is hoped that workers' rights and interests can be compensated more quickly and conveniently. In addition, risk prevention is the most fundamental solution for damage avoidance. Therefore, the current study presents suggestions for legal revisions and administrative measures adjustment, with the expectation that workers who suffer from occupational accidents can receive immediate relief and healing of their injuries
[[alternative]]A Study on the Mechanisms of Creating Crypto-Generated Art in the Generation of AI
No full text[[abstract]]隨著人工智慧和區塊鏈等新興科技的快速發展，生成藝術和加密藝術已成為藝術創作領域的熱門話題。全球知名刊物《經濟學人》以AI生成圖像作為雜誌封面，並探討了人工智慧領域的突破性新技術。這表明生成藝術和加密藝術正在逐漸改變藝術市場的格局。因此，本研究旨在探討「AI世代加密生成藝術創作之機制」，運用紮根理論和半結構式訪談等研究方法，深入探討這些新興藝術形式對藝術創作和市場的影響，並分析其發展趨勢和挑戰。具體研究目的包括：（1）探討加密藝術和生成藝術對藝術創作發展的影響。（2）分析生成藝術影像呈現能力及協作可能。（3）提出AI世代下加密生成藝術創作機制。 本研究採用四個階段進行：第一階段對區塊鏈、加密藝術、生成藝術內容進行分析和比較，以界定初步範疇理論；第二階段了解紮根理論建構流程；第三階段實測提示碼工程之生成藝術平臺，包括DALL-E 2、Midjourney、Stable Diffusion與NOVEL AI，了解平臺的影像呈現能力及協作性。同時以半結構式訪談國內設計技術領域及藝術產業管理專家，了解專家對加密藝術及生成藝術之想法與觀察。第四階段使用紮根理論對兩位專家的論述進行譯碼分析，建立典範模型，再輔以第一階段之理論從中彙整共同脈絡。 研究結果顯示生成藝術和加密藝術產業正在改變藝術創作和藝術市場，但AI生成技術仍存在限制，例如缺乏創作者的個人風格和感性溫度感，以及生成過程難以控制。加密藝術產業和NFT技術的出現為藝術界帶來了重大變革，從展示平臺到藝術價值的民主化，從藝術作品的多載體可能到藝術創作者和收藏者的互動性提升，都為藝術創作和藝術消費帶來了新的可能性。透過智能合約和版權登記證明的保障，加密藝術和NFT的價值得到了更好的體現和保護。最終，本研究提出了AI世代下加密生成藝術之創作機制，提供給傳統藝術創作者過渡到AI生成藝術創作之參考。[[abstract]]With the rapid development of emerging technologies such as artificial intelligence and blockchain, generative art and crypto art have become hot topics in the field of art creation. The globally renowned publication The Economist used AI-generated images as a magazine cover and explored breakthrough new technologies in the field of artificial intelligence. This indicates that generative art and crypto art are gradually changing the pattern of the art market. Therefore, this study aims to explore the mechanisms of crypto-generated art in the generation of AI by using grounded theory and semi-structured interviews and other research methods to deeply explore the impact of these emerging art forms on art creation and the market, and analyze their development trends and challenges. The specific research objectives include: (1) exploring the impact of crypto art and generative art on art creation development, (2) analyzing the image presentation ability and collaboration potential of generative art, (3) proposing the mechanism for the creation of crypto-generated art in the AI generation.This study is conducted in four stages: the first stage analyzes and compares blockchain, crypto art, and generative art content to define the preliminary theoretical scope; the second stage understands the grounded theory construction process; the third stage tests the generative art platforms of DALL-E 2, Midjourney, Stable Diffusion, and NOVEL AI, to understand the image presentation ability and collaboration potential of the platforms. At the same time, semi-structured interviews were conducted with domestic design technology and art industry management experts to understand their thoughts and observations on crypto art and generative art. In the fourth stage, grounded theory was used to translate the theories of the two experts into a paradigm model. This model was then supplemented with the theories from the first stage in order to compile the common threads from both sources.The results show that the crypto art and generative art industries are changing art creation and the art market, but AI generation technology still has limitations, such as a lack of the creator's personal style and emotional warmth, and difficulty controlling the generation process. The emergence of the crypto art industry and NFT technology has brought significant changes to the art world, from the democratization of display platforms to the democratization of art value, from the possibility of multiple carriers of artworks to the increased interactivity between art creators and collectors, which has brought new possibilities for art creation and art consumption. Through the protection of smart contracts and copyright registration certificates, the value of crypto art and NFT has been better reflected and protected. Finally, this study proposes a mechanism for the creation of crypto-generated art in the AI generation, providing a reference for traditional art creators to transition to AI-generated art creation
[[alternative]]The Effect of GRI Framework Adoption in CSR Disclosure on Firm Performance With Gender Diversity and CEO Busyness as Moderator
No full text[[abstract]]本研究的目的在探討印尼上市公司企業社會責任披露(CSR)與公司績效的相關性，並檢視這種關係是否受到性別多樣化和公司CEO忙碌程度的影響。本研究採用2017-2021年間印尼證券交易所（IDX）上市公司的181個企業年度資料，以多元線性回歸來檢驗所提出的假設。實證結果顯示，在企業社會責任CSR披露中，應用全球報告倡議組織（GRI）框架程度較高的公司傾向於有較好的企業績效表現。 此外，隨著董事會中女性董事數量的增加，CSR揭露與公司績效之間的關係會變得更為強化。相反地，公司總經理的忙碌程度則會減弱這種關係。因此，董事會結構如果策略性地注重招募女性董事，並限制公司CEO的兼職數目，可以促進有效的CSR披露，並進而對公司績效產生正面影響。[[abstract]]The aim of this research is to explore the link between Corporate Social Responsibility (CSR) disclosures and firm performance in Indonesian listed companies, examining whether this relationship is influenced by gender diversity and CEO busyness (CEO's workload). Employing data from 181 firm-year observations for listed firms on the Indonesian Stock Exchange (IDX) from 2017 to 2021, this study uses multiple linear regression to validate the proposed hypotheses. The findings suggest that firms employing a higher degree of the Global Reporting Initiative (GRI) framework in their CSR disclosures are likely to demonstrate superior firm performance. Moreover, the relationship between CSR disclosure and firm performance strengthens with the increase in the number of female directors on the board. In contrast, the CEO's workload seems to diminish this relationship. Therefore, a strategic focus on recruiting female directors and limiting the number of CEO's concurrent roles can enhance effective CSR disclosure, which can positively impact firm�performance
[[alternative]]Assessing a Circular Supply Chain Management Model Using Fuzzy Synthetic Method-Decision Making Trial and Evaluation Laboratory Method: Food Industry in Vietnam
No full text[[abstract]]全球食物浪費問題亟待解決。 因此，每個國家的政府都在努力減少食物浪費，例如越南政府對食品工業進行投資以減少浪費。 越南的食品行業正在實施（循環供應鏈管理）CSCM 以實現零浪費，但是，實施它會在快速變化的商業環境中帶來挑戰，因為服務已經變得複雜，與相關的社會、經濟和環境條件相關 產品的生產和分銷。 本研究通過使用模糊綜合方法決策試驗和評估實驗室方法 (FSM-DEMATEL) 來評估 CSCM 性能，以解決層次結構問題，並分別使用定性和定量措施跟踪屬性的重要性和性能水平。 此外，本研究應用探索性因素分析 (EFA) 來消除不太重要的屬性，並確認所提出措施的有效性和可靠性。 從經濟效益、環境影響、社會發展、生產經營風險四個方面提出了二十二條標準。 本研究表明，員工在健康、安全和環境方面的熟練程度 (C18)，使用可回收或生物分解包裝減少塑料使用量 (C10)，在技術創新中找到支持資源 (C7)，提高需求利用率 和供應的不確定性以提高運營效率（C19），優化生產效率以減少訂單提前期（C17），並提高副產品的有效利用（C6）是提高 CSCM 績效的關鍵成功屬性。[[abstract]]Global food waste problem is very essential to be solved. Thus, each country’s government has strived for reducing the food wastes, such as Vietnam government giving investment to the Food Industry to reduce their waste. Food Industry in Vietnam is implementing (circular supply chain management) CSCM to achieve zero-waste, however, implementing it brings the challenges in a fast-changing business environment as services have become complex related to the social, economic, and environmental conditions associated with the production and distribution of products. This study assesses CSCM performance by using the fuzzy synthetic method-decision making trial and evaluation laboratory method (FSM-DEMATEL) to address the hierarchical structure and trace the attributes’ importance and performance levels using qualitative and quantitative measures, respectively. In addition, this study applies exploratory factor analysis (EFA) to eliminate less important attributes and confirm the validity and reliability of the proposed measures. There are four aspects, including economic benefits, environmental impacts, social development, production and operation risks with twenty-two criteria are proposed. This study shows that the proficiency of employees in health, safety, and environment (C18), use recyclable or bio-discompose packaging for reducing the plastic usage (C10), find support resources in technological innovation (C7), increase the utilization of demand and supply uncertainty to enhance the operational efficiency (C19), optimize the production efficiency for reducing the order lead times (C17), and enhance the effective use of by-products (C6) are the key success attributes to improve the CSCM performance
[[alternative]]Application of Machine Learning Algorithms in Prediction of Dementia Staging
No full text[[abstract]]本研究旨在探討以機器學習為基礎的方法，應用於區分失智症的不同程度。失智症是一種複雜的疾病，包括阿茲海默症、額顳葉失智症、路易體失智症及血管性失智症等，其症狀在很多方面有不同。失智症的診斷主要是依靠病人的臨床表現、一系列的心智行為測驗評估、再輔以一些臨床的特殊檢查才能正確診斷出來。一旦失智的診斷確定以後，需要將失智分級以訂定後續的治療照顧計畫。失智分級很依賴評估人員的經驗及測試的技巧，還有病人本身的配合度，因此有可能產生人為因素的誤判。本研究採用機器學習、深度學習及集成學習算法，並結合資料清洗、資料前處理和特徵篩選等步驟，以提高分辨失智症等級的準確性。研究選擇了來自豐原醫院失智共同照護中心登錄的病人資料，並使用單純貝氏演算法、決策樹、K近鄰算法、支持向量機、多層感知器及3種集成學習等機器學習模型進行分析。在進行機器學習之前，研究採取了資料清洗的步驟，去除了缺失值和異常值。接著，進行了資料前處理，包括數據標準化、正規化縮放，以確保不同特徵間的數值範圍一致。最後，進行特徵篩選，選擇對於失智症等級分類有關聯的最具信息量的特徵。研究結果顯示，在經過資料清洗、資料前處理和特徵篩選後，機器學習算法在分辨失智症等級方面取得了很高的準確性。同時選擇重要的特徵可以解釋80%以上的模型分類效果。基本機器學習方以支持向量機分類效果最好(F1值為0.831)，其次是多層感知器(F1值為0.811)，而集成學習器可以進一步提升模型的準確度，最高的是堆疊法(F1值為0.907)。這些發現有助於我們更深入地了解失智症的分級及特徵篩選的重要性，同時提供了對於未來進一步改進和優化機器學習模型的指導方向。[[abstract]]This study aims to explore the application of machine learning-based methods for distinguishing different stages of dementia. Dementia is a complex disease that includes Alzheimer's disease, frontotemporal dementia, Lewy body dementia, and vascular dementia, each with various symptoms. The diagnosis of dementia relies primarily on the patient's clinical presentation, a series of cognitive and behavioral tests, and additional clinical examinations. Once the diagnosis of dementia is confirmed, it is important to classify the severity of dementia to establish appropriate treatment and care plans. However, the accuracy of dementia classification is often subject to human factors, relying on the experience and skills of evaluators, as well as patient cooperation, which may lead to misjudgments. In this study, machine learning, deep learning, and ensemble learning algorithms were employed, along with data cleaning, preprocessing, and feature selection steps, to improve the accuracy of dementia classification.The study utilized patient data from the Dementia Care Center at Fengyuan Hospital. Five machine learning models, including Naive Bayes, Decision Tree, K-Nearest Neighbors, Support Vector Machine, and Multilayer Perceptron, were employed for analysis. Prior to machine learning, data cleaning was performed to remove missing values and outliers. Subsequently, data preprocessing was conducted, including data standardization, normalization, and scaling to ensure consistent value ranges across different features. Finally, feature selection was performed to identify the most informative features related to dementia classification.The results demonstrated that after data cleaning, preprocessing, and feature selection, machine learning algorithms achieved high accuracy in distinguishing different stages of dementia. Selecting important features accounted for over 80% of the model's classification performance. Among the basic machine learning algorithms, Support Vector Machine exhibited the best classification performance (F1-score of 0.831), followed by Multilayer Perceptron (F1-score of 0.811). Furthermore, ensemble learning techniques improved the accuracy of the models, with the highest performance achieved using the stacking method (F1-score of 0.907). These findings provide insights into the importance of dementia classification and feature selection and offer guidance for further improvement and optimization of machine learning models in the future