Sustainable Operations and Computers最新文献

The recent unprecedented situations like the COVID-19 pandemic and the Russia-Ukraine war have severely impacted food security and grain production in emerging economies. These countries can try to import grains to enhance secure food security, but this will strain their dollar reserve and endanger their financial stability. Under such circumstances, the adoption of sustainable grain storage practices is essential to reducing the unusual gap between grain production and grain availability. This research, therefore, explores the key factors that may affect the stability of stored grains to promote agricultural sustainability and food security in emerging economies. First, the study identifies the significant factors that influence the stability of stored grains from an emerging economy perspective. Then, the study employs an integrated approach consisting of Pareto analysis, fuzzy-based Total Interpretive Structural Modeling (TISM), and Cross-Impact Matrix Multiplication Applied to Classification (MICMAC) analysis. Based on the literature review and expert feedback, nineteen factors were initially identified. After employing Pareto analysis, the top thirteen factors have been further analyzed using fuzzy TISM- fuzzy MICMAC to examine their interrelationships. The study findings indicate that “Proper training on advanced storage operations” is the most significant factor influencing sustainable grain storage operations. The study insights can help practitioners to focus more on the crucial aspects of the grain storage operation and can assist the policymakers and industry leaders of emerging economies in strategic decision-making to achieve agricultural sustainability and thus improve food security.

With the rising human population, travelling on static roads is insufficient to meet modern mobility demands. The global economy loses hundreds of billions of dollars yearly due to traffic inefficiencies, including accidents, pollution etc. We undertake an idea engineering exercise to propose a new concept of travelator roads for future sustainable transportation which has been unexplored for vehicular mobility and is thus the novelty of this work.

The proposal offers a paradigm shift from static roads and provides a more generic solution than other advanced transportation technologies. It caters to various United Nations Sustainable Development Goals and provides a safer, self-sustainable, energy-efficient & environment-friendly solution for overcoming traffic violations, jams, road accidents etc. Travelator roads offer easy & quick installation for transportation in rugged terrains & congested areas thereby finding applications in diverse setups like defence, urban & smart cities.

Design model and possible calculations are provided to evaluate it as a solution addressing various transportation-related issues. The research methodology in this work entails a review of the literature on current and contemporary transport technologies, followed by an idea engineering exercise to construct a new concept of Travelator roads from an interdisciplinary viewpoint. Further, we propose the application of Additive Manufacturing (AM) for adapting this concept into reality. An AM technology-based prototype for a travelator road can be manufactured to provide a better idea for its effective adaptation in transportation system because AM offers flexibility in design, the opportunity for shape optimization, simplicity in making modifications, shortened time to market, cheap capital needs. Due to material-efficient designs, less waste, and a decreased requirement for production tools, moulds, and dies, AM is projected to result in a significant reduction of raw materials and overall cost involved in actually realizing the concept of travelator roads for future sustainable transportation. Although, there are a number of potential technical issues with AM-based technologies, such as the durability of materials generated by AM for roads, the amount of time required to build sizable amounts of roads, and the upkeep and repair of these roads, which must be addressed.

The e-commerce industry has seen significant growth over the past decade as it focuses on convenience and accessibility, leading to a surge in online shopping with more and more consumers opting for it. At the same time, the e-commerce industry faces various challenges. In order to fully harness the potential of this industry, it is important to identify its benefits and challenges and focus on pathways to mitigate the challenges and harness its growth. This study utilizes the Delphi approach and involves experts from the e-commerce domain to get their opinions to identify the top ten benefits, challenges, and pathways for the e-commerce industry. Analytic Hierarchy Process (AHP) and Criteria Importance Through Intercriteria Correlation (CRITIC) methods are subsequently, employed to prioritize the identified factors. Results of the study revealed that factors such as affordable advertising & marketing; availability and product variety; and Global reachability are the most important benefits, while technological upgradation; returns or refunds; and counterfeit products posed the greatest challenges for the industry. Government compliance check; better relationship with delivery partners; and strong data privacy and online security policies emerged as the best pathways. This study also provides valuable insights to businesses, policymakers, and researchers in the e-commerce industry on how to navigate the benefits, challenges, and pathways of this rapidly growing sector.

Blockchain, one of these new digital technologies, has special qualities like immutability, decentralization, and transparency that can be helpful in many different areas including managing electronic medical data and access rights, as well as mobile health. We reviewed all COVID-19-related and unrelated blockchain applications in the healthcare industry. MEDLINE, SpringerLink, Institute of Electrical and Electronics Engineers Xplore, ScienceDirect, arXiv, and Google Scholar were searched for pertinent reports up to July 29, 2021. There were articles with both technical and clinical designs, with or without prototype development. A total of 85 375 articles were assessed, and 415 full-length reports—37 of which were connected to COVID-19 and 378 of which were unrelated—were ultimately incorporated into the study. The three primary COVID-19-related applications that were reported were contact tracing, monitoring of immunity or vaccination passports, and pandemic control and surveillance. Management of electronic medical records, internet of things (such as remote monitoring or mobile health), and supply chain monitoring were the top three non-COVID-19-related applications. The majority of publications (277 [667 %] of 415] focused on the technical performance of blockchain prototype systems, whereas nine (2 %) research indicated actual clinical use and uptake. Only technical studies (129 [311 %] of 415) made up the remaining investigations. The most popular platforms were Hyperledger and Ethereum. Numerous COVID-19-related and unrelated health care applications of blockchain technology are possible. The necessity to adapt fundamental blockchain technology for use in healthcare settings is highlighted by the fact that the majority of current research is still in the technical stage and only a small number offers practical clinical applications.

The heavy reliance of the transportation and power generation sector on fossil fuels is seriously impacting the environment. Transitioning towards more sustainable transportation modes is necessary to reduce this dependency on fossil fuels. Even though shifting toward electric vehicles (EVs) can reduce harmful emissions, due to the lack of adequate charging infrastructures, underdeveloped power transmission systems, and increased cost of power generation, it is difficult for a developing country to adopt and rely heavily on EVs. However, developing countries like Bangladesh can adopt a different strategy to address this issue. Harmful emission reduction is also possible by transitioning from conventional internal combustion engine (ICE) vehicles to hybrid electric vehicles (HEVs). The drivers that can promote the expansion of HEV adoption have not been extensively studied to date, which inspired the proposed study. This study explores the drivers for the growth of HEV adoption in emerging economies. First, the study identifies seventeen drivers from the literature review and expert feedback. Then the identified drivers were assessed using the Bayesian Best-Worst method (BWM). The study findings indicate that no requirement for a charging station, incentivizing consumers through policy measures, and enhanced fuel efficiency are the top three drivers influencing the growth of HEV adoption in developing or emerging economies. This study can help the decision-makers and end users in developing counties to gradually shift towards a low-carbon emission-based economy and ensure a greener and more sustainable future.

In agriculture, one of the most challenging tasks is the early detection of plant diseases. It is essential to identify diseases early in order to boost agricultural productivity. This problem has been solved with machine learning and deep learning techniques using an automated method for detecting plant diseases on large crop farms which is beneficial because it reduces monitoring time. In this paper, we used the dataset "Plant Village" with 17 basic diseases, with a display of four bacterial diseases, two viral illnesses, two mould illnesses, and one mite-related disease. A total of 12 crop species are also shown with images of unaffected leaves. The machine learning approaches viz support vector machines (SVMs), gray-level co-occurrence matrices (GLCMs), and convolutional neural networks (CNNs) are used for the development of prediction models. With the development of backpropagation ANNs, artificial intelligence for classification has also evolved. A K-mean clustering operation is also used to detect disease based on the real-time leaf images collected.

Solar energy is the energy discharged by the sun in the form of radiation of light which is then utilized by human beings using a diversity of method such as photovoltaic cells. It is unlimited source of energy such as solar energy does not belongs to anybody and so it is at no cost. The quantity of solar energy acknowledged by the world was considered to be 3000–50,000 EJ, which is much superior to the total world energy utilization of 600 EJ. Maximum Power Point Tracking (MPPT) can be integrated in controlling charge and further used to take out highest extractable and obtainable output from photovoltaic cells depending on few circumstances. The particular input for Photovoltaic module is capable of generating highest possible output power is called MPP (Maximum power point) or highest voltage. Maximum power changes with Sun's energy parameter of required temperature of PV module. Along with dissimilar tracking technique with P-O methods etc. Furthermore, several components were used to compute input parameters which had their own uncertainty. This uncertainty was removed by using devices equipped with sensors comprising of industry 4.0 techniques. The values were delivered back by sensors enabling error free solar energy estimation. This delivers admirable outcome and hence are employed. This system can be developed for charge controller by employing a microcontroller-based circuit for DC-DC buck converter and introducing MPPT.

Given that the previous industrial revolution brought about significant and occasionally unanticipated changes in the “economy,” “the environment,” and “society,” industry 4.0’s sustainability effects deserve all of academia's attention. The study of the start-up operations 4.0 sustainability effects is in its infancy, and more research is needed to fully understand the sustainability implication of start-up operation 4.0 in terms of the influence of digitization on the economy, the environment, and society. Though research on sustainability in industry 4.0 has been performed, a study on the factors influencing start-up operations 4.0 to achieve sustainability has not received the necessary attention. To address this issue and gap, the current study models the factors influencing start-up operations 4.0 to achieve sustainability. Through review of literatures and experts’ opinion, ten factors have been identified. To identify how the factors interact, the “Modified-Total Interpretive Structural Modelling (M-TISM)” technique is employed, and the “MICMAC method” is used to “rank and categorize” the factors. The findings shows that the key importance should be given to the “management support for sustainability adoption,” “decentralized system,” “green design,” and “machine learning system.” The developed hierarchical link between variables provides a comprehensive understanding of how sustainability helps start-ups competitiveness and what elements are responsible for this impact. The management of the start-ups can utilize this framework to enhance start-up operations 4.0 since our study uses factors often studied separately but not combined. This study will help academics, and key stakeholders understand the aspects that lead to sustainability in start-up operations 4.0.

Additive Manufacturing (AM) has tremendous applications in this decade, which speeding up various design, engineering, and manufacturing processes. AM encompasses 3D Printing, 3D Scanning, and software support for designing, printing, and post-processing. It is helpful to the specific stages of the product development cycle by allowing manufacturers to produce better sustainable products than those previously limited by the limitations of traditional production techniques. This technological platform enables engineers and scientists to develop more robust and lighter functional geometries. It triggers an innovative surge in design. AM allows firms to produce complicated components that could not be produced through conventional technologies. 3D printers speed up tool cycles, improve measures and tests, and deliver customised solutions in every element of the development process. 3D printing lays the material in several layers until the product is manufactured per the requirements. This study provides a quick overview of AM, its substantial benefits, and the various types of AM that have been researched. Materials from the three polymers, ceramics, and metals classes have been utilised in all AM processes. This study also discussed the latest developments, featured AM-based process classes, and identified materials used in AM technologies. The authors have identified and discussed seventeen significant potentials of AM and finally discussed AM's potential for Sustainability. 3D printing offers incredible design flexibility, enabling us to build passageways that enhance performance, giving customers and operations great value. The benefits of AM for sustainability are evident in the manufacturing environment today. Readers should be able to access the knowledge structure of the subject through this study, which will assist them in recognising past research, the most active research clusters, and the strength of research relationships.

In e-commerce, Industry 4.0 is all about combining analytics, artificial intelligence, and machine learning to simplify procedures and enable product quality review. In addition, the importance of anticipating client behavior in the context of e-commerce is growing as individuals migrate from visiting physical businesses to shopping online. By providing a more personalized purchasing experience, it can increase consumer satisfaction and sales, leading to improved conversion rates and competitive advantage. Using data from e-commerce platforms such as Flipkart and Amazon, it is possible to build models for forecasting customer behavior. This study examines machine learning techniques for product quality prediction and gives an insight into the performance differences of machine learning-based models by doing descriptive data analysis and training each model separately on three datasets viz Mobile, Health Equipments, and Book Datasets. Support Vector Machine, Nave Bayes, k-Nearest Neighbors, Random Forest, and Random Tree were the machine learning methods utilized in this work. The results indicate that a Support Vector Machine Model provides the greatest fit for the prediction task, with the best performance, reasonable latency, comprehensibility, and resilience for the first two datasets, but Random Forest provides the highest performance for the Book dataset.