Sustainable Operations and Computers最新文献

{"title":"Understanding the role of digital technologies in education: A review","authors":"Abid Haleem ,&nbsp;Mohd Javaid ,&nbsp;Mohd Asim Qadri ,&nbsp;Rajiv Suman","doi":"10.1016/j.susoc.2022.05.004","DOIUrl":"10.1016/j.susoc.2022.05.004","url":null,"abstract":"<div><p>One of the fundamental components of the United Nations’ sustainable development 2030 agenda is quality education. It aims to ensure inclusive and equitable quality education for all. Digital technologies have emerged as an essential tool to achieve this goal. These technologies are simple to detect emissions sources, prevent additional damage through improved energy efficiency and lower-carbon alternatives to fossil fuels, and even remove surplus greenhouse gases from the environment. Digital technologies strive to decrease or eliminate pollution and waste while increasing production and efficiency. These technologies have shown a powerful impact on the education system. The recent COVID-19 Pandemic has further institutionalised the applications of digital technologies in education. These digital technologies have made a paradigm shift in the entire education system. It is not only a knowledge provider but also a co-creator of information, a mentor, and an assessor. Technological improvements in education have made life easier for students. Instead of using pen and paper, students nowadays use various software and tools to create presentations and projects. When compared to a stack of notebooks, an iPad is relatively light. When opposed to a weighty book, surfing an E-book is easier. These methods aid in increasing interest in research. This paper is brief about the need for digital technologies in education and discusses major applications and challenges in education.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 275-285"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000137/pdfft?md5=e1216576e9826b915c1f78175f993819&pid=1-s2.0-S2666412722000137-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72827989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A parameter tuned hybrid algorithm for solving flow shop scheduling problems with parallel assembly stages","authors":"Mona Jabbari ,&nbsp;Madjid Tavana ,&nbsp;Parviz Fattahi ,&nbsp;Fatemeh Daneshamooz","doi":"10.1016/j.susoc.2021.09.002","DOIUrl":"https://doi.org/10.1016/j.susoc.2021.09.002","url":null,"abstract":"","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412721000362/pdfft?md5=8cb71b4a6a84e59cfedeb19396ddea43&pid=1-s2.0-S2666412721000362-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72243599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytics of machine learning-based algorithms for text classification","authors":"Sayar Ul Hassan ,&nbsp;Jameel Ahamed ,&nbsp;Khaleel Ahmad","doi":"10.1016/j.susoc.2022.03.001","DOIUrl":"10.1016/j.susoc.2022.03.001","url":null,"abstract":"<div><p>Text classification is the most vital area in natural language processing in which text data is automatically sorted into a predefined set of classes. The application of text classification is wide in commercial works like spam filtering, decision making, extracting information from raw data, and many other applications. Text classification is more significant for many enterprises since it eliminates the need for manual data classification, a more expensive and time-consuming mechanism. In this paper, a comparative analysis of text classification is done in which the efficiency of different machine learning algorithms on different datasets is analyzed and compared. Support Vector Machine (SVM), k-Nearest Neighbor (k-NN), Logistic Regression (LR), Multinomial Naïve Bayes (MNB), and Random Forest (RF) are Machine Learning based algorithms used in this work. Two different datasets are used to make a comparative analysis of these algorithms. This paper further analyzes the machine learning techniques employed for text classification on the basis of performance metrics viz accuracy, precision, recall and f1- score. The resullltsss reveals that Logistic Regression and Support Vector Machine outperforms the other models in the IMDB dataset, and kNN outperforms the other models for the SPAM dataset as per the results obtained from the proposed system.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 238-248"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000101/pdfft?md5=d84d8e2a79ecabbe3f21ae207e2de5b9&pid=1-s2.0-S2666412722000101-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73665692","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analytical research of artificial intelligent models for machining industry under varying environmental strategies: An industry 4.0 approach","authors":"Mohammad Seraj ,&nbsp;Osama Khan ,&nbsp;Mohd Zaheen Khan ,&nbsp;Mohd Parvez ,&nbsp;Bhupendra Kumar Bhatt ,&nbsp;Amaan Ullah ,&nbsp;Md Toufique Alam","doi":"10.1016/j.susoc.2022.01.006","DOIUrl":"10.1016/j.susoc.2022.01.006","url":null,"abstract":"<div><p>Since the introduction of Industry 4.0, manufacturing industries have adopted smarter automation systems enabling better interconnection amongst various aspects of the production industry. Application of industry 4.0 furnishes better performance and efficiency with improved reliability and robustness. The present research provides a novel framework which takes in consideration the complexity and flexibility of the working environment within the factory premises, previously not explored. Smart systems equipped with sensors and communicators are responsible for monitoring information and detecting malfunctions pre-hand which eventually boosts the system performance. Furthermore, the research explores the concept of predictive maintenance in industry 4.0 setup which apprehends any system failure based on atmospheric related changes. A novel algorithm is explored in this research which takes in consideration multisource diverse dataset based on varying environmental conditions and simultaneously furnishing inputs for predictive maintenance in Industry 4.0 implementation, thereby providing a transparent and effective manufacturing method. The framework for Industry 4.0 is validated and deemed feasible with quantitative comparison with previous prediction models which can further predict any future malfunctions in the industrial machines. The productivity values are validated with models developed with the help of intelligent hybrid prediction techniques such as adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM). The input parameters considered are atmospheric conditions whereas the required output response is productivity of the machines. Error rates were evaluated lowest error rate for triangular membership functions for both machining models.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 176-187"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266641272200006X/pdfft?md5=91af88bc80535824c0596322cd82db51&pid=1-s2.0-S266641272200006X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84370675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Emerging technologies for the management of COVID19: A review","authors":"Nadiya Zafar,&nbsp;Jameel Ahamed","doi":"10.1016/j.susoc.2022.05.002","DOIUrl":"10.1016/j.susoc.2022.05.002","url":null,"abstract":"<div><p>The outbreak of COVID19 has put a halt on life over the globe. For a while, everything was stopped except the spread of disease and mortality rate. This has become the greatest challenge of decade to deal with it. Globally, scientists and researchers were busy in finding a way to deal with this deadly pandemic. As this pandemic breaks out a huge demand for healthcare equipment, medicinal facilities has been rises and Industry 4.0 seems to be a hope during this pandemic which has potential to satisfy all these needs. In the battle, against this pandemic branches of computer science: Artificial Intelligence(AI), Internet of Things(IoT), Robotics, Machine Learning(ML) and Deep Learning(DL) played very important roles. Without the help of IoT and Robotics it would be impossible for frontline warriors to remain contactless with an infected person. Meanwhile, rapid testing, prediction of disease, sentiment analysis of population and many more would be only possible due to presence ML and DL algorithms. Undoubtedly, if this pandemichappened before the emergence of AI, IoT, ML, DL and Robotics; then the aftermath will surely be something else. This paper will highlight the contribution of these technologies in handling this pandemic from its treatment to management. This paper will give idea about the role of technologies, their affects, solutions provided by them, improvement needed in healthcare facilities, their role in managing sentiments of public during pandemic. The innovative part of this paper is that we are exploring each field of industry 4.0 and observing which plays the most important role.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 249-257"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000113/pdfft?md5=1e08aff9302e39d4cc595e8e4d7c42b8&pid=1-s2.0-S2666412722000113-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89944507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systems theory-based abstractions and decision schemes for corporate environmental compliance management","authors":"Heiko Thimm","doi":"10.1016/j.susoc.2022.01.007","DOIUrl":"10.1016/j.susoc.2022.01.007","url":null,"abstract":"<div><p>Even though corporate sustainability management has become a major topic of research and of the business world, its digitalization is still in an infancy stage. One of the reasons for this situation is the fact that domain-specific abstractions, which are a major underpinning of digitalization in general, have so far received very little attention. This is particularly a major obstacle for the digitalization of the work area of corporate environmental compliance management (CECM), which is intended to ensure that the firm complies with environmental legislation. Using general system theory, the CECM area is analyzed, and a set of domain-specific abstractions is proposed. Further contributions of the article are focused on two crucial CECM decision tasks: the relevance assessment of environmental regulations and the determination of compliance enforcement measures. Based on the abstractions, decision schemes and tailored domain-specific templates of decision trees for these tasks are suggested and demonstrated through a running example. It is expected that through these novel building blocks, this research contributes to an advancement of the digitalization of CECM processes and of sustainability management in general. For example, the proposed building blocks may serve as a foundation for the development of compliance management information systems that offer smart assistance for CECM tasks. Digital assistance may mitigate deficiencies in CECM expertise and CECM experience, which are often imposed in practice by tight budget constraints.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 188-202"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000083/pdfft?md5=83fcadfc2b8e3f6e1663be958ac00acb&pid=1-s2.0-S2666412722000083-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72915594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systems approach to environment, social and governance (ESG): Case of Reliance industries","authors":"Dr. Monica Singhania ,&nbsp;Dr. Neha Saini","doi":"10.1016/j.susoc.2021.11.003","DOIUrl":"10.1016/j.susoc.2021.11.003","url":null,"abstract":"<div><p>In the 21st Century, societal welfare has captured global imagination and companies are encouraged to participate in this process. The paper aims to study how professional acculturation and appropriate capital is leading to the advance agenda of sustainability through the actions and initiatives of corporates considering the ESG initiatives and gauge the effectiveness of resulting responsible actions by all social actors through an assessment based on national ranking system for ESG disclosures and sustainability in Indian context. Reliance industries Limited (RIL) has been chosen to study system approach because it has been aligning its initiatives with regulations on ESG and such investments made by the companies attract socially conscious investors and create value within their portfolios. Systems approach has been adopted to highlight environmental and socially responsible initiatives undertaken by Reliance Industries, a large Indian multinational. Considering the ESG structure as a <em>processing</em> framework, we have tried to propose how an initiative (<em>input</em>) can be turned as an outcome (<em>output</em>) to benefit the society at large, through a case study. We propose system input and output methodology through efficient strategies to conquer good health, education, development, safety, energy, environment and climate protection. RIL has undertaken various ESG initiatives such as energy and water conservation, upliftment of community and initializing educational programs, adhering to various legal compliance and health and safety measures. On comparing RIL performance with top ten companies highlights that RIL has to go a long way in terms of environmental performance. Adopting system approach using ESG structures will serve RIL as a potential benchmark to the Indian corporate sector and emerging world towards spreading awareness and achievement of sustainable development goals (SDGs).</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 103-117"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412721000477/pdfft?md5=cc4ca533a16b56a9f3ba85264c9dc48c&pid=1-s2.0-S2666412721000477-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73296206","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"On the Sustainable Nexus between Oil Prices and Aviation Stocks","authors":"Arif Billah Dar","doi":"10.1016/j.susoc.2022.01.004","DOIUrl":"https://doi.org/10.1016/j.susoc.2022.01.004","url":null,"abstract":"<div><p>Aviation stock sensitivity to crude oil prices has raised a suspicion of nexus between aviation stocks and crude oil prices. In this paper, the sustainable nexus between oil Prices and aviation stocks in India are analysed. In particular, it is tested whether there is any sustainable long-term co-movement between the stocks of three aviation companies listed on the Indian stock market and crude oil. The oil prices and aviation stocks are decomposed into different time horizons using the methodology of wavelets. The nexus is studied at shorter, medium, and longer time horizons. Broadly, results show that there is some nexus in the short-run, however, sustainable long-run nexus is missing. The results are found to be robust and approximately similar when data from three Indian aviation stocks are used. It is inferred that sustainable long-run movements in aviation stocks should be attributed to the reasons not related to crude oil price changes.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 168-175"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000046/pdfft?md5=761c81830a0922e338b9490cd2c4e1fa&pid=1-s2.0-S2666412722000046-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136552628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fabrication of personalized lithophane via additive manufacturing","authors":"Adarsh Kumar Singh ,&nbsp;Ankit Nayak ,&nbsp;Narendra Kumar ,&nbsp;Ravi Pratap Singh ,&nbsp;Prashant K Jain","doi":"10.1016/j.susoc.2021.09.001","DOIUrl":"https://doi.org/10.1016/j.susoc.2021.09.001","url":null,"abstract":"","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412721000350/pdfft?md5=8cfebb08042ae4983ad30409d4b09767&pid=1-s2.0-S2666412721000350-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72243598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic","authors":"Mirpouya Mirmozaffari ,&nbsp;Reza Yazdani ,&nbsp;Elham Shadkam ,&nbsp;Seyed Mohammad Khalili ,&nbsp;Meysam Mahjoob ,&nbsp;Azam Boskabadi","doi":"10.1016/j.susoc.2022.01.003","DOIUrl":"10.1016/j.susoc.2022.01.003","url":null,"abstract":"<div><p>The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&amp;D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.</p></div>","PeriodicalId":101201,"journal":{"name":"Sustainable Operations and Computers","volume":"3 ","pages":"Pages 156-167"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666412722000022/pdfft?md5=1b7a465ff21c47c0a03ad7fe93f5e7c3&pid=1-s2.0-S2666412722000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91352301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}