Sustainable Operations and Computers最新文献

COVID-19 pandemic expedites the development of digital technologies to tackle the spread of the virus. Several digital interventions have been deployed to reduce the catastrophic impact of the pandemic and observe preventive measures. However, the adoption and utilization of these technologies by the affected populace has been a daunting task. Therefore, this study carried out exploratory investigation of the factors influencing the behavioural intention (BI) of people to accept COVID-19 digital tackling technologies (CDTT) using the UTAUT (Unified Theory of Acceptance and Use of Technology) framework. The study applied principal components analysis and multiple regression analysis for hypotheses testing. The study revealed that performance expectancy (PE), facilitating conditions (FC) and social influence (SI) are the best predictors of people's BI to accept CDTT. Also, organizational

influence and benefit (OIB) and government expectancy and benefits (GEB) influence the people's BI. However, variables such as age, gender and voluntariness to use CDTT have no significance to influence BI because the CDTT is still nascent and not easily accessible. The results show that the decision-makers and regulators should consider inciting variables such as PE, FC, SI, OIB and GEB, that motivate the acceptance and use of CDTT. Furthermore, the populace must be sensitized to the availability and use of CDTT in all communities. Also, the path diagram and hypothesis testing results for CDTT acceptance and use, will help government and private organizations in planning and responding to the digitalization of COVID-19 protective measures and hence revise the COVID-19 health protection regulation.

Breast cancer detection based on the deep learning approach has gained much interest among other conventional-based CAD systems as the conventional based CAD system's accuracy results seems to be inadequate. The convolution neural network, a deep learning approach, has emerged as the most promising technique for detecting cancer in mammograms. In this paper we delve into some of the CNN classifiers used to detect breast cancer by classifying mammogram images into benign, cancer, or normal class. Our study evaluated the performance of various CNN architectures such as AlexNet, VGG16, and ResNet50 by training some of them from scratch and some using transfer learning with pre-trained weights. The above model classifiers are trained and tested using mammogram images from the mini-DDSM dataset which is publicly available. The medical dataset contains limited samples of data due to low patient volume; this can lead to overfitting issue, so to overcome this limitation data augmentation process is applied. Rotation and zooming techniques are applied to increase the data volume. The validation strategy used here is 90:10 ratio. AlexNet showed an accuracy of 65 percent, whereas VGG16 and ResNet50 showed an accuracy of 65% and 61%, respectively when fine-tuned with pre-trained weights. VGG16 performed significantly worse when trained from scratch, whereas AlexNet outperformed others. VGG16 and ResNet50 performed well when transfer learning was applied.

In the modern era, computers using artificial intelligence (AI) and industry 4.0 have found acceptance since its application in renewable energy sectors thereby optimising the cost and efficiency of the equipment. Despite its importance, lack of comprehensive literature has been reported in the past highlighting its relationship with consumer behaviour (CB) in the market considering the modern women's in the sustainable energy field. Findings from 10 studies furnish that physiological, social, personal and economical aspects significantly impact women consumer behaviour when categorized on the perception for intention to buy, acceptance and need for recognition. The current review paper is the first distinguishable review highlighting the importance of stipulating the relationship between artificial intelligence and characteristics of consumer behaviour in the field of sustainable energies. The paper synthesises previous findings by developing a model with the aid of meta-analysis. The review and organization procedure were simultaneously verified. Eventually, outcomes of the review stipulated intention to buy area, which requires utmost importance in order to establish and maintain a healthy attitude of consumers towards women entrepreneurs and industry 4.0. In future, this review will establish a roadmap to researchers, thereby guiding to collect technology information and analyse the applications in sustainability and CB. This paper aims to enhance our expertise and simultaneously develop a feasible relationship between consumer behaviour and computer based renewable technologies by addressing different concerns related to implementation of robots at home and outlining the investigation programs for the future experiments.

Climate change has become a major global challenge. At present, few studies have reviewed the application practices and theoretical research of carbon neutrality. This paper summarizes the practical progress of carbon neutrality, the realization path of carbon neutrality, and the carbon neutrality research in typical fields, and concludes that the previous research has made some progress in the carbon neutrality goal domestic and overseas, the pathways to carbon neutrality, and the carbon neutrality issues in various fields. However, this paper also points out existing problems. Firstly, more studies should be carried out on the quantitative evaluation of carbon neutrality by adopting empircal datas and tools in various fields; Secondly, the correlation between paths and industries should be taken more attention; Additionally, how to measure carbon neutral capability, d potential and costis of great significance in subsequent studies.

Recent developments in urbanization and e-commerce have pushed businesses to deploy efficient systems to decrease their supply chain cost. Vendor Managed Inventory (VMI) is one of the most widely used strategies to effectively manage supply chains with multiple parties. VMI implementation asks for solving the Inventory Routing Problem (IRP). This study considers a multi-product multi-period inventory routing problem, including a supplier, set of customers, and a fleet of heterogeneous vehicles. Due to the complex nature of the IRP, we developed a Modified Adaptive Genetic Algorithm (MAGA) to solve a variety of instances efficiently. As a benchmark, we considered the results obtained by Cplex software and an efficient heuristic from the literature. Through extensive computational experiments on a set of randomly generated instances, and using different metrics, we show that our approach distinctly outperforms the other two methods. In this way, we created a decision support and computer-based approach to assist policy and decision-makers in the pathway of constructing a sustainable society.

Industry 4.0 technologies provide critical perspectives for future innovation and business growth. Technologies like Artificial Intelligence (AI), Internet of Things (IoT), Big data, Machine Learning (ML), and other advanced upcoming technologies are being used to implement Industry 4.0. This paper explores how Industry 4.0 technologies help create a sustainable environment in manufacturing and other industries. Industry 4.0 technologies and the crucial interrelationships through advanced technologies should impact the environment positively. In the age of Industry 4.0, manufacturing is tightly interlinked with information and communication systems, making it more scalable, competitive, and knowledgeable. Industry 4.0 provides a range of principles, instructions, and technology for constructing new and existing factories, enabling consumers to choose different models at production rates with scalable robotics, information, and communications technology. This paper aims to study the significant benefits of Industry 4.0 for sustainable manufacturing and identifies tools and elements of Industry 4.0 for developing environmental sustainability. This literature review-based research is undertaken to identify how Industry 4.0 technologies can help to improve environmental sustainability. It also details the capabilities of Industry 4.0 in dealing with environmental aspects. Twenty major applications of Industry 4.0 to create a sustainable environment are identified and discussed. Thus, it gives a better understanding of the production environment, the supply chains, the delivery chains, and market results. Overall, Industry 4.0 technology seems environmentally sustainable while manufacturing goods with better efficiency and reducing resource consumption.

Scheduling is a decision-making process that plays an important role in the service and production industries. Effective scheduling can assist companies to survive in the competitive market. Single machine scheduling is an important optimization problem in the scheduling research area. It can be found in a wide range of real-world engineering problems, from manufacturing to computer science. Due to the high complexity of single machine scheduling problems, developing approximation methods, particularly metaheuristic algorithms, for solving them have absorbed considerable attention. In this study, a Lion Optimization Algorithm (LOA) is employed to solve a single machine with maintenance activities, where the objective is to minimize the Total Absolute Deviation of Compilation Times (TADC). In the scheduling literature, TADC as an objective function has hardly been studied. To evaluate the performance of the LOA, it was compared against a set of well-known metaheuristics. Therefore, a set of problem was generated, and a comprehensive experimental analysis was conducted. The results of computational experiments indicate the superiority of the proposed optimization method.

Supply chains are getting more and more complex with addition of new sustainability paradigms in highly fragile and vulnerable environments as the world is transforming faster and faster due to the acceleration of activities, operations and new technologies. To date, few efforts have been made to systematically explore the status of sustainable supply chains networks models as a few research includes sustainable development as a main attribute of the problem considered. This review is the outcome of several papers under the year frame from 2010 to 2021 delivering the role of sustainability in supply chain network with identification of strategies and various methodologies used by the academicians. A new framework of sustainable supply chain network design dimensions with inclusion of indicators and the parameters have been introduced. Moreover, future paths and research directions are provided for researchers and practitioners to explore the concepts of sustainability and new avenues of research to include sustainability aspects more effectively.

With increment in the utilization of Internet, the pace of increment of social networks is getting ubiquitous in recent years. This paper focuses on the job portal websites. The research objective of this paper is that the recommender framework takes the abilities from the website and makes suggestion to the candidates with the jobs whose descriptions are coordinating with their profiles the most. This paper additionally presents a short presentation on recommender framework and talks about different categories of this framework. From the start, information is cleaned by expelling the filthy information as extra space and duplicates. Then the job recommendations are made to the target applicants on the basis of their preferences. It utilizes different Machine Learning procedures which results show that Random Forest Classifier (RFC) gives the most noteworthy expectation accuracy when contrasted with different procedures. Finally, the optimization technique is utilized to get the most exact outcome. The advantage of recommender framework in career orientation is expressed. Geo-area based recommendation framework is utilized to find the organization's position which can assist the ideal applicants with reaching their destination. This examination shows that the utilization of job recommender system can assist with improving the recommendation of appropriate employment for work searchers.

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.