Fuzzy Interactive Approach for a Multi-objective Supplier Selection Problem under Robust Uncertainty

In this paper, the authors proposed a multi-objective Mixed Integer Linear Programming (MILP) model for supplier selection problems. The main aim of the system under the investigation is to plan the companies to supply goods to achieve financial benefit by minimizing the total costs and satisfying the customers with on-time delivery and minimizing rejected items. In this case, some restrictions such as multi-product and multi-period conditions, shortage inventory constraints, and discount circumstances simultaneously are considered. Despite these efforts, due to the uncertainty nature of the problem, some parameters are considering as uncertainty data. For this aim, applying robust counterparts for uncertain parameters plays an essential role in real-world applications of this case. It is concluded that the feasibility and optimality properties of the usual solutions of real-world LPs can be severely affected by small changes of the data and that the robust optimization (RO) methodology can be successfully used to overcome this phenomenon

A Case Study of the Integration of Mine water into Smart Cooling and Heating Network systems

Minewater presents a significant opportunity as an energy source and store in the UK and elsewhere. This research investigates the feasibility and factors necessary to successfully integrate minewater into smart cooling and heating network systems that can support acceleration towards the UK’s net zero target. Heat recovery from minewater offers a lowcarbon source of energy for either heating or cooling and can provide thermal storage, potentially valuable for inter-seasonal demand. The work builds on a feasibility study in Barnsley, Yorkshire, which explored the design of a heat network that integrates heat, power, and mobility and uses waste heat from a glass factory. This work focusses on analyzing the subsurface factors including flowrate, yield, mine void volume, and interconnectivity, which affect the flow and consequently thermal behavior of the available minewater. A 3D model using Petrel and Groundhog have been created combining data from the available boreholes and Coal Authority maps to characterise the subsurface conditions

Diameter-dependent elastic properties of carbon nanotube-polymer composites: Emergence of size effects from atomistic-scale simulations

We propose a computational procedure to assess size effects in nonfunctionalized single-walled carbon nanotube (CNT)-polymer composites. The procedure upscales results obtained with atomistic simulations on a composite unit cell with one CNT to an equivalent continuum composite model with a large number of CNTs. Molecular dynamics simulations demonstrate the formation of an ordered layer of polymer matrix surrounding the nanotube. This layer, known as the interphase, plays a central role in the overall mechanical response of the composite. Due to poor load transfer from the matrix to the CNT, the reinforcement effect attributed to the CNT is negligible; hence the interphase is regarded as the only reinforcement phase in the composite. Consequently, the mechanical properties of the interface and the CNT are not derived since their contribution to the elastic response of the composite is negligible. To derive the elastic properties of the interphase, we employ an intermediate continuum micromechanical model consisting of only the polymer matrix and a three-dimensional fiber representing the interphase. The Young’s modulus and Poisson’s ratio of the equivalent fiber, and therefore of the interphase, are identified through an optimization procedure based on the comparison between results from atomistic simulations and those obtained from an isogeometric analysis of the intermediate micromechanical model. Finally, the embedded reinforcement method is employed to determine the macroscopic elastic properties of a representative volume element of a composite with various fiber volume fractions and distributions. We then investigate the role of the CNT diameter on the elastic response of a CNT polymer composite; our simulations predict a size effect on the composite elastic properties, clearly related to the interphase volume fraction

Mine water utilization as a secondary heat source and heat storage in a smart local heating and cooling distribution system

Mine water has been gaining increasing attention in recent years as a potential source for heat recovery and storage. This is due to its unique properties that make it an ideal medium for capturing and storing large amounts of thermal energy. Mine water is naturally heated by the earth's geothermal energy and typically has a constant temperature throughout the year, making it an excellent source of renewable energy. Additionally, the water's high thermal conductivity and large volumes provide an effective means for storing and transferring heat. With the increasing demand for sustainable energy sources and the need to reduce greenhouse gas emissions, the utilization of mine water for heat recovery and storage has become an attractive option for many industries and communities. In this project, we will explore the benefits of using mine water for heat recovery and storage, as well as some of the risks and challenges that need to be overcome to fully realize its potential as a renewable energy source

Sudden Unexpected Natural Death in the Youth; an Iranian Single Center Investigation

Background: Sudden unexpected natural death (SUND) has not been studied in Iran. Herein we investigated its main causes in our country.Methods: Records of 80 cases registered to a single referral center were investigated to determine the distribution of sex, age, and etiology of death.Results: Fifty eight (72.5%), 6 (7.5%), 6 (7.5%) and 4 (5%) of our cases have died due to various types of heart diseases, cerebral events, pulmonary emboli and gastrointestinal bleeding (GIB), respectively. Moreover, men are victims of SUND more that women (83.7% vs.16.3%, respectively).Conclusion: Policies should be planned by the governments to prevent youth mortality in societies. These attempts should especially target ischemic heart disease

{\mu}-DDRL: A QoS-Aware Distributed Deep Reinforcement Learning Technique for Service Offloading in Fog computing Environments

Fog and Edge computing extend cloud services to the proximity of end users, allowing many Internet of Things (IoT) use cases, particularly latency-critical applications. Smart devices, such as traffic and surveillance cameras, often do not have sufficient resources to process computation-intensive and latency-critical services. Hence, the constituent parts of services can be offloaded to nearby Edge/Fog resources for processing and storage. However, making offloading decisions for complex services in highly stochastic and dynamic environments is an important, yet difficult task. Recently, Deep Reinforcement Learning (DRL) has been used in many complex service offloading problems; however, existing techniques are most suitable for centralized environments, and their convergence to the best-suitable solutions is slow. In addition, constituent parts of services often have predefined data dependencies and quality of service constraints, which further intensify the complexity of service offloading. To solve these issues, we propose a distributed DRL technique following the actor-critic architecture based on Asynchronous Proximal Policy Optimization (APPO) to achieve efficient and diverse distributed experience trajectory generation. Also, we employ PPO clipping and V-trace techniques for off-policy correction for faster convergence to the most suitable service offloading solutions. The results obtained demonstrate that our technique converges quickly, offers high scalability and adaptability, and outperforms its counterparts by improving the execution time of heterogeneous services

Statistical analysis of historic hydrocarbon production data from Gulf of Mexico oil and gas fields and application to dynamic capacity assessment in CO2 storage

Bureau of Economic Geolog