با سلام خدمت کاربران عزیز، به اطلاع می رساند ترجمه مقالاتی که سال انتشار آن ها زیر 2008 می باشد رایگان بوده و میتوانید با وارد شدن در صفحه جزییات مقاله به رایگان ترجمه را دانلود نمایید.
Applying emergy and decoupling analysis to assess the sustainability of China’s coal mining area
استفاده از تحلیل اضطراری و جداسازی برای ارزیابی پایداری منطقه استخراج زغال سنگ چین-2020
The sustainable development of coal mining area continues to be one of the most topical issues in the world. Taking Shainxi Province as a case, this study applies emergy and decoupling analysis to build a multi-index sustainability evaluation system and constructs an emergy decoupling index to investigate the sustainability of a coal mining area in China during 2006e2015. It overcomes the problem of the unification of the traditional evaluation index system and integrates the influence of economic development, resources, the environment, and energy. The study finds that the coal mining area still depends on its coal resources. The sustainability of the coal mining area is still at a low level, and it is not sustainable in the long term. The economic growth still has a strong negative decoupling from the environmental loss. Energy management system and circular economic system should be built to improve the coal mining area’s sustainability. In the long run, the coal mining industry should gradually be abandoned. Based on China’s growing energy consumption, the findings of this study may not only serve as a reference for management to improve the sustainability of the coal mining areas but also to address China’s energy shortage problem.
Keywords: Sustainability | Emergy analysis | Decoupling | Coal mining area
Shape-stabilized hydrated salt/paraffin composite phase change materials for advanced thermal energy storage and management
مواد تغییر فاز کامپوزیت نمک / پارافین هیدراته شده با فرم تثبیت شده برای ذخیره سازی و مدیریت انرژی پیشرفته حرارتی-2020
Thermal energy storage and management have attracted considerable interest in the field of sustainable control and utilization of energy. Thermal energy storage materials with excellent thermal properties and shape stability are in high demand. Herein, we developed a simple and effective method to fabricate hydrated salt / paraffin composite (HPC) shape-stabilized phase change materials (SSPCMs). Hydrated salt was emulsified into paraffin by an inverse emulsion template method to obtain HPC. Owing to its low volatility, paraffin enhanced the thermal stability of the hydrated salt by preventing its direct contact with the environment. Furthermore, after its crystallization, paraffin provided nucleation sites and functioned as a nucleating agent to promote the crystallization of the hydrated salt. The HPC was then simultaneously impregnated into cellulose sponge (CS), forming the SSPCMs, which exhibited excellent thermal stability, high energy storage density with a phase transition enthalpy of 227.3 J/g, and a reduced supercooling degree. Besides, there was negligible leakage during the test. The efficiency of the SSPCMs as temperature management materials was then tested by using them as a lining in a fully enclosed protective clothing.
Keywords: Hydrated salt | Paraffin | Phase change materials | Thermal stability | Supercooling degree
Turing instability induced by random network in FitzHugh-Nagumo model
ناپایداری تورینگ ناشی از شبکه تصادفی در مدل FitzHugh-Nagumo-2020
Although there is general agreement that the network plays an essential role in the biolog- ical system, how the connection probability of network affects the natural model(Especially neural network) is poorly understood. In this paper, we show the impact of the network on Turing instability in the FitzHugh-Nagumo(FN) model. Then we obtain the condition of how the Turing bifurcation, saddle-node bifurcation, and Turing instability occur. By using the Gershgorin circle theorem, we investigate the relationship between degree and eigenvalue of the network matrix, and obtain the approximate range of eigenvalue of the network matrix. Also, We derive the instability condition about diffusion and the connec- tion probability in the network-organized system. And then we obtain the estimated range of connection probability. Meanwhile we apply these results to explaining the spiking of neuron and find this system has rich dynamics behavior. Finally, the numerical simulation verifies our analytical results.
Keywords: Turing instability | Pattern formation | Random network | Connection probability
System architecture for blockchain based transparency of supply chain social sustainability
معماری سیستم برای شفافیت مبتنی بر بلاکچین پایداری اجتماعی زنجیره تأمین-2020
Social sustainability is a major concern in global supply chains for protecting workers from exploitation and for providing a safe working environment. Although there are stipulated standards to govern supply chain social sustainability, it is not uncommon to hear of businesses being reported for noncompliance issues. Even reputable firms such as Unilever have been criticized for production labor exploitation. Consumers now increasingly expect sellers to disclose information on social sustainability, but sellers are confronted with the challenge of traceability in their multi-tier global supply chains. Blockchain offers a promising future to achieve instant traceability in supply chain social sustainability. This study develops a system architecture that integrates the use of blockchain, internet-of-things (IoT) and big data analytics to allow sellers to monitor their supply chain social sustainability efficiently and effectively. System implementation cost and potential challenges are analyzed before the research is concluded.
Keywords: Blockchain | Social sustainability | Multi-tier supply chain | Supply chain sustainability | Traceability
Designing a short-term load forecasting model in the urban smart grid system
طراحی یک مدل پیش بینی بار کوتاه مدت در سیستم شبکه هوشمند شهری-2020
The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermittency of RE, it is challenging to design an accurate and reliable short-term load forecasting model. Recently, machine learning (ML) based forecasting models have been applied for short-term load forecasting whereas most of them ignore the importance of characteristics mining, parameters fine-tuning, and forecasting stability. To dissolve the above issues, a short-term load forecasting model is proposed that incorporates thorough data mining and multi-step rolling forecasting. To alleviate the chaos of short-term load, a de-noising method based on decomposition and reconstruction is used. Then, a phase space reconstruction (PSR) method is employed to dynamically determine the train-test ratios and neurons settings of the artificial neural network (ANN). Further, a multi-objective grasshopper optimization algorithm (MOGOA) is applied to optimize the parameters of ANNs. Case studies are conducted in the urban smart grid systems of Victoria and New South Wales in Australia. Simulation results show that the proposed model can forecast short-term load well with various measurement metrics. Multiple criterion and statistical evaluation also show the good performance of the proposed forecasting model in terms of accuracy and stability. To conclude, the proposed model achieves high accuracy and robustness, which will provide references to RE transitions and smart grid optimization, and offer guidance to sustainable city development.
Keywords: Smart grid | Short-term load forecasting | Neural networks | Multi-objective optimization algorithm | Urban sustainability
Role of big data and social media analytics for business to business sustainability: A participatory web context
نقش تجزیه و تحلیل داده های بزرگ و رسانه های اجتماعی برای پایداری تجارت از مشاغل: زمینه وب مشارکتی-2020
The digital transformation is an accumulation of various digital advancements, such as the transformation of the web phenomenon. The participatory web that allows for active user engagement and gather intelligence has been widely recognised as a value add tool by organisations of all shapes and sizes to improve business productivity and efficiency. However, its ability to facilitate sustainable business-to-business (B2B) activities has lacked focus in the business and management literature to date. This qualitative research is exploratory in nature and fills this gap through findings arising from interviews of managers and by developing taxonomies that highlight the capability of participatory web over passive web to enable different firms to engage in business operations. For this purpose, two important interrelated functions of business i.e. operations and marketing have been mapped against three dimensions of sustainability. Consequently, this research demonstrates the ability of big data and social media analytics within a participatory web environment to enable B2B organisations to become profitable and remain sustainable through strategic operations and marketing related business activities. The research findings will be useful for both academics and managers who are interested in understanding and further developing the business use of participatory web tools to achieve business sustainability. Hence, this may be considered as a distinct way of attaining sustainability.
Keywords: Participatory web | Marketing and operations | Big data | Social media analytics | Business sustainability | Business-to-business (B2B)
Blockchain-based life cycle assessment: An implementation framework and system architecture
ارزیابی چرخه زندگی مبتنی بر بلاکچین: چارچوب پیاده سازی و معماری سیستم-2020
Life cycle assessment (LCA) is widely used for assessing the environmental impacts of a product or service. Collecting reliable data is a major challenge in LCA due to the complexities involved in the tracking and quantifying inputs and outputs at multiple supply chain stages. Blockchain technology oﬀers an ideal solution to overcome the challenge in sustainable supply chain management. Its use in combination with internet-of-things (IoT) and big data analytics and visualization can help organizations achieve operational excellence in con- ducting LCA for improving supply chain sustainability. This research develops a framework to guide the im- plementation of Blockchain-based LCA. It proposes a system architecture that integrates the use of Blockchain, IoT, and big data analytics and visualization. The proposed implementation framework and system architecture were validated by practitioners who were experienced with Blockchain applications. The research also analyzes system implementation costs and discusses potential issues and solutions, as well as managerial and policy implications.
Keywords: Blockchain | Life cycle assessment | Supply chain sustainability | Environmental sustainability | Operational excellence
Big data analytics as an operational excellence approach to enhance sustainable supply chain performance
تجزیه و تحلیل داده های بزرگ به عنوان یک رویکرد برتری عملیاتی برای افزایش عملکرد پایدار زنجیره تأمین-2020
Operations management is a core organizational function involved in the management of activities to produce and deliver products and services. Appropriate operations decisions rely on assessing and using information; a task made more challenging in the Big Data era. Effective management of data (big data analytics; BDA), along with staff capabilities (the talent capability in the use of big data) support firms to leverage big data analytics and organizational learning in support of sustainable supply chain management outcomes. The current study uses dynamic capability theory as a foundation for evaluating the role of BDA capability as an operational excellence approach in improving sustainable supply chain performance. We surveyed mining executives in the emerging economy of South Africa and received 520 valid responses (47% response rate). We used Partial Least Squares Structural Equation Modelling (PLS-SEM) to analyze the data. The findings show that big data analytics management capabilities have a strong and significant effect on innovative green product development and sustainable supply chain outcomes. Big data analytics talent capabilities have a weaker but still significant effect on employee development and sustainable supply chain outcomes. Innovation and learning performance affect sustainable supply chain performance, and supply chain innovativeness has an important moderating role. A contribution of the study is identifying two pathways that managers can use to improve sustainable supply chain outcomes in the mining industry, based on big data analytics capabilities.
Keywords: Big data analytics | Operational excellence | Dynamic capability view | Supply chain sustainability | Learning performance
Achieving sustainable performance in a data-driven agriculture supply chain: A review for research and applications
دستیابی به عملکرد پایدار در زنجیره تأمین کشاورزی داده محور: مروری بر تحقیقات و کاربردها-2020
The lack of industrialization, inadequacy of the management, information inaccuracy, and inefficient supply chains are the significant issues in an agri-food supply chain. The proposed solutions to overcome these challenges should not only consider the way the food is produced but also take care of societal, environmental and economic concerns. There has been increasing use of emerging technologies in the agriculture supply chains. The internet of things, the blockchain, and big data technologies are potential enablers of sustainable agriculture supply chains. These technologies are driving the agricultural supply chain towards a digital supply chain environment that is data-driven. Realizing the significance of a data-driven sustainable agriculture supply chain we extracted and reviewed 84 academic journals from 2000 to 2017. The primary purpose of the review was to understand the level of analytics used (descriptive, predictive and prescriptive), sustainable agriculture supply chain objectives attained (social, environmental and economic), the supply chain processes from where the data is collected, and the supply chain resources deployed for the same. Based on the results of the review, we propose an application framework for the practitioners involved in the agri-food supply chain that identifies the supply chain visibility and supply chain resources as the main driving force for developing data analytics capability and achieving the sustainable performance. The framework will guide the practitioners to plan their investments to build a robust data-driven agri-food supply chain. Finally, we outline the future research directions and limitations of our study.
Keywords: Agriculture supply chain | Food supply chain | Sustainability | Sustainable performance | Supply chain visibility | Big data | Blockchain | Data analytics | Supply chain resources
New criteria for global stability of neutral-type Cohen–Grossberg neural networks with multiple delays
معیارهای جدید برای ثبات جهانی شبکه های عصبی کوهن-گروسبرگ از نوع خنثی با تأخیرهای متعدد-2020
The significant contribution of this paper is the addressing the stability issue of neutral-type Cohen– Grossberg neural networks possessing multiple time delays in the states of the neurons and multiple neutral delays in time derivative of states of the neurons. By making the use of a novel and enhanced Lyapunov functional, some new sufficient stability criteria are presented for this model of neutraltype neural systems. The obtained stability conditions are completely dependent of the parameters of the neural system and independent of time delays and neutral delays. A constructive numerical example is presented for the sake of proving the key advantages of the proposed stability results over the previously reported corresponding stability criteria for Cohen–Grossberg neural networks of neutral type. Since, stability analysis of Cohen–Grossberg neural networks involving multiple time delays and multiple neutral delays is a difficult problem to overcome, the investigations of the stability conditions of the neutral-type the stability analysis of this class of neural network models have not been given much attention. Therefore, the stability criteria derived in this work can be evaluated as a valuable contribution to the stability analysis of neutral-type Cohen–Grossberg neural systems involving multiple delays.
Keywords: Neutral systems | Delayed neural networks | Stability analysis | Lyapunov stability theorems