Terms Of System Cost Research Articles

3D-Printed Poly(3-hydroxybutyrate-co-3-hydroxyhexanoate)-Cellulose-Based Scaffolds for Biomedical Applications.

While biomaterials have become indispensable for a wide range of tissue repair strategies, second removal procedures oftentimes needed in the case of non-bio-based and non-bioresorbable scaffolds are associated with significant drawbacks not only for the patient, including the risk of infection, impaired healing, or tissue damage, but also for the healthcare system in terms of cost and resources. New biopolymers are increasingly being investigated in the field of tissue regeneration, but their widespread use is still hampered by limitations regarding mechanical, biological, and functional performance when compared to traditional materials. Therefore, a common strategy to tune and broaden the final properties of biopolymers is through the effect of different reinforcing agents. This research work focused on the fabrication and characterization of a bio-based and bioresorbable composite material obtained by compounding a poly(3-hydroxybutyrate-co-3-hydroxyhexanoate) (PHBH) matrix with acetylated cellulose nanocrystals (CNCs). The developed biocomposite was further processed to obtain three-dimensional scaffolds by additive manufacturing (AM). The 3D printability of the PHBH-CNC biocomposites was demonstrated by realizing different scaffold geometries, and the results of in vitro cell viability studies provided a clear indication of the cytocompatibility of the biocomposites. Moreover, the CNC content proved to be an important parameter in tuning the different functional properties of the scaffolds. It was demonstrated that the water affinity, surface roughness, and in vitro degradability rate of biocomposites increase with increasing CNC content. Therefore, this tailoring effect of CNC can expand the potential field of use of the PHBH biopolymer, making it an attractive candidate for a variety of tissue engineering applications.

Hybrid Smart Strategies to Predict Amine Thermal Degradation in Industrial CO2 Capture Processes.

CO2 emission reduction is an essential step to achieve the climate change targets. Solvent-based post-combustion CO2 capture (PCC) processes are efficient to be retrofitted to the existing industrial operations/installations. Solvent degradation (and/or loss) is one of the main concerns in the PCC processes. In this study, the thermal degradation of monoethanolamine (MEA) is investigated through the utilization of hybrid connectionist strategies, including an artificial neural network-particle swarm optimization (ANN-PSO), a coupled simulated annealing-least squares support vector machine (CSA-LSSVM), and an adaptive neuro-fuzzy inference system (ANFIS). Moreover, gene expression programming (GEP) is employed to generate a correlation that relates the solvent concentration to the operating variables involved in the adverse phenomenon of solvent thermal degradation. The input variables are the MEA initial concentration, CO2 loading, temperature, and time, and the output variable is the remaining/final MEA concentration after the degradation phenomenon. According to the training and testing phases, the most accurate model is ANFIS, and the reliability/performance of its optimal network is assessed by the coefficient of determination (R2), mean squared error, and average absolute relative error percentage, which are 0.992, 0.066, and 2.745, respectively. This study reveals that the solvent initial concentration has the most significant impact, and temperature plays the second most influential effect on solvent degradation. The developed models can be used to predict the thermal degradation of any solvent in a solvent-based PCC process regardless of the complicated reactions involved in the degradation phenomenon. The models introduced in this study can be employed for the development of more accurate hybrid models to optimize the proposed systems in terms of cost, energy, and environmental prospects.

On the modal characteristics and stability of translating cable: Application to an overall line of cable transport systems

Moving cables are used for many applications in engineering structures such as cable-supported transport systems that involves mobile cables suspended between supports for moving attached vehicles. Advantages of such systems in terms of cost, footprint and energy efficiency make its urban integration as one of the solutions to meet the need for new mobilities. Because of the inherent cable’s flexibility and the modification of dynamic properties during the cable movement, such systems are sensitive to the effects of perturbations and self-sustaining oscillations. The methods commonly used to describe cable dynamics are restricted to small static sag case with a predominant vertical dynamics. To accurately analyse a multi-segment mobile cable, this paper proposes an hybrid analytical–numerical strategy to deal with a general mechanical elementary model of a cable in unsteady translation formulated in curvilinear abscissa derived from the formalism of continuum mechanics. An assembly procedure permits to deal with a more complex system considering a cable loop in contact with intermediary supports and punctual loads according to different boundary conditions, considering the coupling between the vertical dynamics activated at the local scale of a span and the longitudinal dynamics activated at the scale of the whole cable loop. The translation speed and acceleration gyroscopic effects on the system’s complex modes are studied and design rules are provided to guide an optimal conception avoiding strong oscillations of the system.

Economic and technological approach to the design of offshore field development. Review of foreign practices

Relevance. The high profitability potential of hydrocarbon production on the Russian Arctic shelf is one of the significant factors for the further development of the Russian oil and gas complex. For decades, international oil and gas companies (OGCs) have been successfully implementing large capital-intensive projects to develop offshore fields in underdeveloped regions with severe climatic conditions, including at unfavorable oil prices. The purpose of the research is to identify the features of the methodology used by international oil and gas companies to implement projects that ensure their success. Methodology. Systematic approach, methods of analysis, comparison, differentiation. Results. In the course of the study, the content of a systematic approach to the development of offshore fields was disclosed as the basis of the methodology in terms of the optimal choice and integration of technologies in the field development system in terms of cost and efficiency. The concepts and relationships between the system, subsystem and technology are defined. It is shown that the profitability of field development is ensured by compliance with the necessary criteria for the applicability of technologies as part of the development system. At the same time, the technologies must be confirmed by the practice of performing a similar production task under comparable conditions and are available for purchase in a competitive market, and they must also meet the requirements for the efficiency of performing a production task and restrictions on the cost of developing a field. The theoretical aspects of the methodology are illustrated with practical examples of the assessment and selection of technologies according to the specified criteria, including the implemented system of offshore field development in a poorly developed region. Based on the results of the study, recommendations were formulated on the selection of offshore fields for priority development and exploitation, the choice of technologies for field construction, as well as the application of accepted international regulations for more active implementation of oil and gas projects on the Russian Arctic shelf. Conclusions. Based on the review of foreign practices, it is recommended to: 1) focus primarily on projects using proven and available technologies; 2) clearly form a set of requirements for the developed projects; 3) apply the principles and rules laid down in the design regulations of foreign oil and gas companies and EPC companies.

Privacy-preserving energy trading management in networked microgrids via data-driven robust optimization assisted by machine learning

Trading mechanism design, uncertainty treatment and privacy protection are the main issues in the energy management of networked microgrids (MGs). To address these issues in a comprehensive manner, this paper proposes a data-driven two-level transactive energy management framework, where the upper-level determines the optimal strategies of internal scheduling within MGs and external trading between MGs while the lower-level formulates a Nash bargaining game model for the fair allocation of trading benefits. The uncertainties of renewable energy sources are fully captured by an adjustable data-driven robust optimization approach with an uncertainty set constructed using the robust kernel density estimation (RKDE) as a machine learning technique. The resulting uncertainty set can provide robust scheduling and trading schemes even when the power generation data of wind turbines and photovoltaic systems are contaminated with anomalous samples, whereas conventional sets are not reliable in the case of contaminated data. To preserve the operational independence and information privacy of MGs, the proposed model is solved in a distributed manner by the alternating direction method of multipliers (ADMM) and the augmented Lagrange-based alternating direction inexact Newton (ALADIN) algorithms. ADMM is commonly used in previous studies, but it has low computational efficiency for handling the consensus process among a large number of MGs. This paper applies ADMM and ALADIN to enhance the applicability of the proposed model when the size and complexity of the networks increase. Numerical tests show the effectiveness of the proposed framework and solution methodology in terms of system cost, solution robustness, and convergence speed.

Comparison of Characteristics of Gamma-Ray Imager Based on Coded Aperture by Varying the Thickness of the BGO Scintillator

Background: The conventional cerium-doped Gd2Al2Ga3O12 (GAGG(Ce)) scintillator-based gamma-ray imager has a bulky detector, which can lead to incorrect positioning of the gammaray source if the shielding against background radiation is not appropriately designed. In addition, portability is important in complex environments such as inside nuclear power plants, yet existing gamma-ray imager based on a tungsten mask tends to be weighty and therefore difficult to handle. Motivated by the need to develop a system that is not sensitive to background radiation and is portable, we changed the material of the scintillator and the coded aperture.Materials and Methods: The existing GAGG(Ce) was replaced with Bi4Ge3O12 (BGO), a scintillator with high gamma-ray detection efficiency but low energy resolution, and replaced the tungsten (W) used in the existing coded aperture with lead (Pb). Each BGO scintillator is pixelated with 144 elements (12 × 12), and each pixel has an area of 4 mm × 4 mm and the scintillator thickness ranges from 5 to 20 mm (5, 10, and 20 mm). A coded aperture consisting of Pb with a thickness of 20 mm was applied to the BGO scintillators of all thicknesses.Results and Discussion: Spectroscopic characterization, imaging performance, and image quality evaluation revealed the 10 mm-thick BGO scintillators enabled the portable gamma-ray imager to deliver optimal performance. Although its performance is slightly inferior to that of existing GAGG(Ce)-based gamma-ray imager, the results confirmed that the manufacturing cost and the system’s overall weight can be reduced.Conclusion: Despite the spectral characteristics, imaging system performance, and image quality is slightly lower than that of GAGG(Ce), the results show that BGO scintillators are preferable for gamma-ray imaging systems in terms of cost and ease of deployment, and the proposed design is well worth applying to systems intended for use in areas that do not require high precision.

Maximizing the Production of Recombinant Proteins in Plants: From Transcription to Protein Stability.

The production of therapeutic and industrial recombinant proteins in plants has advantages over established bacterial and mammalian systems in terms of cost, scalability, growth conditions, and product safety. In order to compete with these conventional expression systems, however, plant expression platforms must have additional economic advantages by demonstrating a high protein production yield with consistent quality. Over the past decades, important progress has been made in developing strategies to increase the yield of recombinant proteins in plants by enhancing their expression and reducing their degradation. Unlike bacterial and animal systems, plant expression systems can utilize not only cell cultures but also whole plants for the production of recombinant proteins. The development of viral vectors and chloroplast transformation has opened new strategies to drastically increase the yield of recombinant proteins from plants. The identification of promoters for strong, constitutive, and inducible promoters or the tissue-specific expression of transgenes allows for the production of recombinant proteins at high levels and for special purposes. Advances in the understanding of RNAi have led to effective strategies for reducing gene silencing and increasing recombinant protein production. An increased understanding of protein translation, quality control, trafficking, and degradation has also helped with the development of approaches to enhance the synthesis and stability of recombinant proteins in plants. In this review, we discuss the progress in understanding the processes that control the synthesis and degradation of gene transcripts and proteins, which underlie a variety of developed strategies aimed at maximizing recombinant protein production in plants.

Green Parallel Online Offloading for DSCI-Type Tasks in IoT-Edge Systems

In order to meet people’s demands for intelligent and user-friendly Internet of Things (IoT) services, the amount of computation is increasing rapidly and the requirements of task delay are becoming increasingly more stringent. However, the constrained battery capacity of IoT devices greatly limits the user experience. Energy harvesting technologies enable green energy to provide continuous energy support for devices in the IoT environment. Together with the maturity of the mobile edge computing technology and the development of parallel computing, it provides a strong guarantee for the normal operation of resource-constrained IoT devices. In this article, we design a parallel offloading strategy based on Lyapunov optimization, which is conducive to efficiently finding the optimal decision for delay-sensitive and compute-intensive tasks. We establish a stochastic optimization problem on a discrete-time slot system and propose a green parallel online offloading algorithm (GPOOA). By decoupling the target problem three times, the joint optimization of green energy, task division factor, CPU frequency, and transmission power is realized. Experimental results demonstrate that under the constraints of strict task deadlines and limited server computing resources, GPOOA performs well in terms of system cost and task drop ratio, far superior to several existing offloading algorithms.

Development of titanium bipolar plates fabricated by additive manufacturing for PEM fuel cells in electric vehicles

Bipolar plates (BPs) are one of the main parts of proton exchange membrane (PEM) fuel cell stacks, which constitute a significant percentage of a PEM fuel cell system in terms of cost, weight, and structural strength. Although frequently used graphite BPs have low density, high conductivity, and high corrosion resistance, machining the desired flow channels on these plates is challenging. On the other hand, BPs made of various materials rather than graphite can be also fabricated by additive manufacturing methods. These methods can be considered as a reasonable alternative to conventional machining for the fabrication of graphite BPs in PEM fuel cells regarding material cost, fabrication of flow channels, and some post-processes in which the large-scale manufacturing of graphite BPs is more complex. This study offers a comparison of formed stainless-steel, additive manufactured titanium and machined composite graphite plates having the same flow-field geometry as a bipolar plate. In addition, titanium BPs are coated with gold and their performances are compared. Among the cells tested, the highest peak power of 639 mWcm−2 is measured from the cell with 450 nm gold coated titanium BP, whereas those of the cell with conventional graphite and stainless-steel BP are only around 322 mWcm−2 and 173 mWcm−2, respectively. Moreover, a new titanium bipolar plate design providing high specific power density is also presented.

On Optimization of Automation Systems: Integrating Modular Learning and Optimization

<italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Compositional Optimization</i> (CompOpt) was recently proposed for optimization of discrete-event systems of systems. A modular optimization model allows CompOpt to divide the optimization into separate sub-problems, mitigating the state space explosion problem. This paper presents the Modular Optimization Learner (MOL), a method that interacts with a simulation of a system to automatically learn these modular optimization models. MOL uses a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">modular learning</i> that takes as input a hypothesis structure of the system and uses the provided structural information to split the acquired learning into a set of modules, and to prune parts of the search space. Experiments show that modular learning reduces the state space by many orders of magnitude compared to a monolithic learning, which enables learning of much larger systems. Furthermore, an integrated greedy search heuristic allows MOL to remove many sub-optimal paths in the individual modules, speeding up the subsequent optimization. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —Automation systems are becoming increasingly large and complex and the automation of more and more advanced tasks often requires the coordination of multiple subsystems. Optimization can have a great impact on the efficiency of these systems in terms of cost and operation speed. Finding optimal solutions is, however, a difficult task. As the number of tasks and subsystems of the automation system increases, the search space of the optimization problems tends to grow exponentially. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Compositional Optimization</i> (CompOpt) is a method specifically designed for the optimization of large-scale automation systems. A challenge with the application of CompOpt is that it takes as input a specific type of optimization model that divides the system into subsystems; like machines, vehicles, etc. Formulating these models requires a high level of expertise and system knowledge. This paper addresses this challenge with an algorithm that learns these models from a simulation of the system. The simulation can be implemented in any software as long as a suitable interface exists or can be constructed. To divide the learning into subsystems, the algorithm uses an initial <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">plant structure hypothesis</i> (PSH). This can be viewed as a meta-model that includes known structural information, such as the number of subsystems and which actions that affect each subsystem. The more structural information that is added to PSH, the more efficient the learning and subsequent optimization will be. The purpose of this is to reduce the level of expertise and system knowledge needed in the application of optimization, to simplify the transition to Industry 4.0.

Implications of heating sector electrification on the Irish power system in view of the Climate Action Plan

Electrifying the heating sector, which is energy and carbon intensive, and shifting electricity supply to renewable technologies is one of the main policy options being pursued for decarbonisation of the heating sector. Heat pumps are widely adopted for this purpose. However, the impact of this policy on existing electrical systems, both from the perspectives of supply and demand, is understudied in the literature. This paper examines the system-wide impacts of integrating high quantities of heat pumps, in line with government targets, in Ireland. We provide a broad discussion of the ramifications of heating sector electrification in terms of system costs and expansion planning under renewable energy targets. Results reveal significant changes in generation and increases in associated costs with increasing levels of electrification. On the flip side, the heating sector electrification leads to more efficient utilisation of renewable energy and the transmission network. We also explore alternative policy pathways to minimize impacts on the system. These include determining optimal locations for heat pump integration and a technology-neutral emission reduction target. Optimal distribution of heat pumps has a low impact on system metrics whereas pursuing an emission reduction target makes a large impact on system costs.

Effect of Proper Treatment on Mineral Oils

For over a century, a transformer act as a crucial part of the high voltage system. This also acts as an energy alteration device that is used to step-up and step-down voltage for power distribution from power generation to the consumers. Maintaining a transformer is important since a single mistake can lead to a vast amount of problems in a high voltage system in terms of cost. Thus, nowadays, many researchers study the important parts of a transformer, making it to function more effective. One of the studies involves improving treatment in the insulation medium of transformers to improve its function and performance. Here, the focus of this experiment was to study the effect of proper treatment on mineral oil in terms of electrical properties as an insulator in the transformer. The drying process was the main concern in this experiment. Parameters such as AC breakdown voltage, Tan Delta value, Raman measurement, resistivity, and relative permittivity were particularly measured. Origin software was used to analyze the data obtained from the experiment. The results expected from the experiment is that the proper preparation plays the role in tuning the AC breakdown voltage.

Newly developed γ-NaTiOPO4 by simple solid-state synthesis for anode material of Na-ion batteries in both nonaqueous and aqueous electrolytes

Sodium-ion batteries (SIBs) are a promising next-generation energy storage system in terms of cost due to the abundance of sodium. However, obtaining good anode materials for SIBs remains a challenge. NaTiOPO4 has been investigated as an anode not only for SIBs but also for aqueous SIBs. Despite the various NaTiOPO4 polymorphs, only the β-NaTiOPO4 phase has been investigated as an anode for SIBs due to the limited synthesis process. In this study, we successfully stabilized γ-NaTiOPO4 via our newly developed solid-state synthesis process. In addition, its electrochemical properties as an anode for SIBs were investigated in this study. The synthesized material demonstrates a high voltage of 1.7 and ∼1.5 V vs Na/Na+ with 120 mAh/g and good capacity retention of 64% for up to 500 cycles at 0.5C in a nonaqueous electrolyte. In an aqueous electrolyte, Na0·44MnO2//γ-NaTiOPO4 full cell achieves excellent stable capacity retention with a high Coulombic efficiency for 175 cycles. It shows high cycling stability with a three-dimensional framework despite the relatively high redox potential. Thus, it demonstrates that γ-NaTiOPO4 is a promising anode material for both nonaqueous and aqueous rechargeable SIBs.

Detection of unbalance and looseness faults in a ventilation turbine using vibration signature analysis

Heavy industry, which generally uses turbomachines, often uses both technologies to perform high-performance vibration monitoring of its production tool. Vibration analysis is one of the means used to monitor the health of rotating machinery in operation. This is part of a policy of forecast maintenance of the industrial production tool. This work is part of the monitoring and diagnosis of rotating machines by vibration analysis taking as an example the X205 circulation fan. Numerical simulation was done to test the capabilities and limitations of a dynamic simulation. The modelling of vibratory phenomena is developed using SolidWorks software to perform a dynamic simulation of the 3D model. The numerical simulations were performed to find the effect of different types of defects, such as defect of unbalance fin fan and bolt joint looseness, on the output of system. The numerical results were confirmed either by experimentally using employing results available in the open literature that existent at factory of cement plant (SCHS) and a good agreement was observed. The proposed and derived model has demonstrated the viability of dynamic simulation approach to rotating machines by vibration analysis and serves as a significant alternative approach to the direct experimentation on the same systems in terms of cost and time.

East-west oriented photovoltaic power systems: model, benefits and technical evaluation

This paper presents an evaluation of east–west oriented photovoltaic power system. The evaluation is done based on a simplified model for east–west oriented photovoltaic system. This model is developed mathematically then implemented by using Matlab. The model is also validated by a commercial software and experimental data. After that, a comparison between east–west oriented photovoltaico system and south oriented photovoltaic system in terms of cost of energy and technical requirement is conducted. In addition to that, the benefits of using east–west oriented photovoltaic system are discussed in this paper. Results show that the proposed model is accurate in predicting the output power of east–west oriented photovoltaic system. It is also found that east–west oriented photovoltaic system requires less land area. Moreover, it is found that east–west oriented photovoltaic system requires less cost for mounting piles and steel structure, and less costs of the interfacing power substation especially in case of photovoltaic systems slanted at high tilt angle. However, it is found as well that the south oriented photovoltaic system produces more energy than east–west oriented photovoltaic system. It is also found in this research that there is no significant difference between the costs of energy for both systems. Finally it is concluded that for direct grid interfacing east–west oriented PV system can provide smoother power injection to the grid with fewer harmonic and less reverse power. On the other hand, south oriented photovoltaic system is preferred when high power injection is required.

A scalable laser system at 461 nm for laser cooling and trapping of Sr atoms

The realization of compact high-power narrow-linewidth laser sources in the visible range has been a long-standing interest for the atomic physics community, especially in the field of laser cooling and trapping of alkaline-earth and alkaline-earth-like atoms. We report a simple master-slave injection-locked laser system capable of generating 500 mW at 460.86 nm with a linewidth of 1.6 MHz to realize magneto-optical trapping of strontium atoms on the 5s^{2} ^{1}mathrm {S}_{0}-5s5p ^{1}mathrm {P}_{1} transition. The master laser used is an external cavity diode laser built with an anti-reflection coated laser diode in a Littrow configuration and the slave laser is built with a recently launched single mode high power GaN laser diode. We characterized the frequency noise, injection stability, spectral purity and relative intensity noise of the laser system. The reported laser system is used routinely to produce 10^7 strontium atoms at a temperature of 10 mK in a magneto-optical trap. The laser system offers a prominent alternative to the existing frequency-doubled laser systems in terms of cost, design flexibility, electrical power consumption, ease of scaling, intensity stability, and frequency tunability. The proposed system is also an effective solution for space-based experiments where a laser system’s size, weight, and electrical power consumption are crucial design parameters.

Optimal Use of Lignocellulosic Biomass for the Energy Transition, Including the Non-Energy Demand: The Case of the Belgian Energy System

Biomass is a key renewable resource for energy transition and climate change mitigation. It can be used for either energy purposes (production of heat, electricity, and fuel) or non-energy demand (e.g., chemicals). This raises the question of the optimal use of biomass in energy systems. In the literature, this optimal use is often determined for one specific situation in terms of greenhouse gas emissions and rarely considering the non-energy demand. The non-energy demand is defined as the demand for energy products used as raw materials. Given the expected simultaneous changes in all industrial sectors, it is important to include the non-energy demand in the models of energy systems as they will share common resources. This paper 1) studies the optimal use of lignocellulosic biomass within an energy system including the non-energy demand and 2) analyzes the evolution of its role throughout the energy transition. Belgium is taken as a case study as it presents a non-energy demand corresponding to ∼15% of its primary energy mix. The energy system is modeled with EnergyScope TD which optimizes whole-energy systems in terms of costs under greenhouse gas emission constraints. Local and imported biomass is considered with two potential scenarios. Fourteen biomass-converting technologies are included in the model. It is shown that high-temperature heat remains a significant application for biomass in all scenarios and increases when moving toward carbon neutrality. For greenhouse gas savings below 50%, biomass is largely used for low-temperature heat. However, when aiming at reducing greenhouse gas further (&amp;gt;50% reduction), biomass is substantially exploited for the non-energy demand. Electricity from biomass also appears, to a lesser extent, for large greenhouse gas savings only. The integration of the non-energy demand in the simulations impacts the allocation of biomass in the system, depending on the scenario of potential considered.

Virtualized Network Function Forwarding Graph Placing in SDN and NFV-Enabled IoT Networks: A Graph Neural Network Assisted Deep Reinforcement Learning Method

With an ambitious increase in the number of Internet of Things (IoT) terminals, IoT networks face a huge challenge which is providing diverse and complex network services with different requirements on a common infrastructure. To solve this challenge, Software Defined Network (SDN) and Network Function Virtualization (NFV) are adopted to build next-generation IoT networks which are softwarized and virtualized. This way, network functions are virtualized as Virtualized Network Functions (VNFs) and a network service consists of a set of VNFs. One of the main challenges for realizing this paradigm is the optimal resource allocation for VNFs. Most existing works assumed that services are represented as Service Function Chains (SFCs) which are chains. However, network services in IoT networks are more complex and diverse, therefore, more appropriate representations are Virtualized Network Function Forwarding Graphs (VNF-FGs) which are Directed Acyclic Graphs (DAGs). Previous works failed to exploit this special graph structure, which makes them sub-optimal or non-applicable for IoT networks. In this paper, we investigate the VNF-FG placing problem in dynamic IoT networks where DAG-represented services arrive and depart. To fully exploit the graph structures of services and handle the complexity of dynamic IoT networks, we combine a novel neural network structure Graph Neural Network (GNN) with Deep Reinforcement Learning (DRL) and propose an efficient algorithm for VNF-FG placing, which is called Kolin. Extensive simulation results suggest that Kolin outperforms the state-of-the-art solutions in terms of system cost, acceptance ratio, and computation complexity.

An Intelligent Resource Reservation for Crowdsourced Live Video Streaming Applications in Geo-Distributed Cloud Environment

Crowdsourced live video streaming (livecast) services such as Facebook Live, YouNow, Douyu, and Twitch are gaining more momentum recently. Allocating the limited resources in a cost-effective manner while maximizing the quality of service (QoS) through real-time delivery and the provision of the appropriate representations for all viewers is a challenging problem. In our article, we introduce a machine-learning based predictive resource allocation framework for geo-distributed cloud sites, considering the delay and quality constraints to guarantee the maximum QoS for viewers and the minimum cost for content providers. First, we present an offline optimization that decides the required transcoding resources in distributed regions near the viewers with a tradeoff between the QoS and the overall cost. Second, we use machine learning to build forecasting models that proactively predict the approximate transcoding resources to be reserved at each cloud site ahead of time. Finally, we develop a greedy nearest and cheapest algorithm (GNCA) to perform the resource allocation of real-time broadcasted videos on the rented resources. Extensive simulations have shown that GNCA outperforms the state-of-the-art resource allocation approaches for crowdsourced live streaming by achieving more than 20% gain in terms of system cost while serving the viewers with relatively lower latency.

Model-based energy flexibility analysis of a dry room HVAC system in battery cell production

Heating, ventilation and air conditioning (HVAC) systems in a production environment ensure a variety of functions, such as providing a pleasant room climate, ensuring defined process conditions and maintaining clean air in the workplace. In fulfilling these functions, they contribute significantly to the overall energy demand in industry. With the rise of fluctuating renewable energies, HVAC systems are interesting in the context of energy flexibility measures to ensure grid stability. In this context, model-based, quantitative analyses are of interest in order to evaluate the effects of energy flexibility measures on the indoor climate or process conditions. In the Battery LabFactory Braunschweig (BLB), research is being conducted on the production of batteries on a laboratory to pilot plant scale. In the BLB the semi-automated cell production takes place in a large drying room, which is supplied by a specially designated HVAC system, which requires more than 70 % of the total energy demand of the BLB. In addition, by using a continuous data acquisition system, the HVAC system records over 90 data points in high temporal resolution. Using a validated physical simulation model of the HVAC system different alternative, energy flexible operating modes can be examined. As alternative modes of operation, a simple rule-based method is compared with a mathematical optimization, which optimizes the overall system in terms of cost and air quality. Mathematical optimization is preferable in terms of the amount of energy potential that can be flexibilized. However, since the optimization involves a higher computational as well as implementation effort and the results of the rule-based method are very similar in terms of cost savings, the rule-based method can also be interesting when the cost-benefit trade-off is taken into account.