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Towards a circular economy in the aviation sector using eco-composites for interior and secondary structures. Results and recommendations from the EU/China project ECO-COMPASS
Full text linkFiber reinforced polymers play a crucial role as enablers of lightweight and high performing structures to increase efficiency in aviation. However, the ever-increasing awareness for the environmental impacts has led to a growing interest in bio-based and recycled ‘eco-composites’ as substitutes for the conventional synthetic con-stituents. Recently, the international collaboration of Chinese and European partners in the ECO-COMPASS pro-ject provided an assessment of different eco-materials and technologies for their potential application in aircraft interior and secondary composite structures. This project summary reports the main findings of the ECO-COM-PASS project and gives an outlook to the next steps necessary for introducing eco-composites as an alternative solution to fulfill the CLEAN SKY target
A genetic optimization resampling based particle filtering algorithm for indoor target tracking
Full text linkIn indoor target tracking based on wireless sensor networks, the particle filtering algorithm has been widely used because of its outstanding performance in coping with highly nonlinear problems. Resampling is generally required to address the inherent particle degeneracy problem in the particle filter. However, traditional resampling methods cause the problem of particle impoverishment. This problem degrades positioning accuracy and robustness and sometimes may even result in filtering divergence and tracking failure. In order to mitigate the particle impoverishment and improve positioning accuracy, this paper proposes an improved genetic optimization based resampling method. This resampling method optimizes the distribution of resampled particles by the five operators, i.e., selection, roughening, classification, crossover, and mutation. The proposed resampling method is then integrated into the particle filtering framework to form a genetic optimization resampling based particle filtering (GORPF) algorithm. The performance of the GORPF algorithm is tested by a one-dimensional tracking simulation and a three-dimensional indoor tracking experiment. Both test results show that with the aid of the proposed resampling method, the GORPF has better robustness against particle impoverishment and achieves better positioning accuracy than several existing target tracking algorithms. Moreover, the GORPF algorithm owns an affordable computation load for real-time applications
A stochastic programming model for an energy planning problem: formulation, solution method and application
Full text linkThe paper investigates national/regional power generation expansion planning for medium/long-term analysis in the presence of electricity demand uncertainty. A two-stage stochastic programming is designed to determine the optimal mix of energy supply sources with the aim to minimise the expected total cost of electricity generation considering the total carbon dioxide emissions produced by the power plants. Compared to models available in the extant literature, the proposed stochastic generation expansion model is constructed based on sets of feasible slots (schedules) of existing and potential power plants. To reduce the total emissions produced, two approaches are applied where the first one is performed by introducing emission costs to penalise the total emissions produced. The second approach transforms the stochastic model into a multi-objective problem using the ϵ-constraint method for producing the Pareto optimal solutions. As the proposed stochastic energy problem is challenging to solve, a technique that decomposes the problem into a set of smaller problems is designed to obtain good solutions within an acceptable computational time. The practical use of the proposed model has been assessed through application to the regional power system in Indonesia. The computational experiments show that the proposed methodology runs well and the results of the model may also be used to provide directions/guidance for Indonesian government on which power plants/technologies are most feasible to be built in the future
A blockchain based autonomous decentralized online social network
Full text linkOnline social networks (OSN) are becoming more important in people's daily life, however, all popular OSNs are centralized, and this raises a series of security, privacy and management issues. A decentralized architecture based on blockchain technology provides the ability to solve above issues. In this paper, an OSN service is developed based on blockchain technology in order to make it operate decentralized. Large volume of data normally required low-security requirements can be stored in Interplanetary Filesystem (IPFS) to make data decentralized. A decentralized autonomous organization is developed for user autonomy, users can self-manage the OSN in a democratic way
1-Phenyl-1H-tetrazol as corrosion inhibitor for pipeline steel in sulfuric acid solution
Full text link1-Phenyl-1H-tetrazol (PHT) has been studied as an efficient corrosion inhibitor for X65 steel in sulfuric acid corrosion environment. Atomic force microscope test results show that PHT can effectively inhibit the corrosion of X65 steel in 0.5 M sulfuric acid solution. Quantum chemical calculations and molecular dynamics simulations show that PHT has a small energy gap value and a large dipole moment value, which is an excellent corrosion inhibitor. In addition, the adsorption of PHT on the Fe(110) surface adopts parallel adsorption and a large binding energy value, which shows that PHT can effectively inhibit the corrosion of X65 steel. Potentiodynamic polarization test results show that as the PHT concentration increases, the value of the corrosion current density decreases significantly. When the PHT concentration is 1 mM, the corrosion inhibition efficiency can reach 92.1%. In addition, the adsorption of PHT on the surface of X65 steel conforms to Langmuir adsorption, and the adsorption process is spontaneous
Effect of hydrodynamic heterogeneity on micromixing intensification in a Taylor–Couette flow reactor with variable configurations of inner cylinder
Full text linkEffect of hydrodynamic heterogeneity on micromixing intensification in a Taylor–Couette flow (TC) reactor with variable configurations of inner cylinder has beeninvestigated by adoption of a parallel competing iodide-iodate reaction system. Twotypes of inner cylinder, circular inner cylinder and lobed inner cylinder (CTC andLTC), were used to generate hydrodynamic heterogeneity, focusing on the effects ofthe Reynolds number, the acid concentration, and the feeding time on the micro-mixing performance. Segregation index (Xs) was employed to evaluate the micro-mixing efficiency. It is revealed thatXs decreases with the increase of Reynoldsnumber and feeding time but increases with the increase of acid concentration forboth the CTC and LTC. However, the LTC does present a better micromixing perfor-mance at various operating conditions than that of the CTC as affirmed by both theexperimental and computational fluid dynamics simulation results
Federated learning algorithm based on knowledge distillation
Full text linkFederated learning is a new scheme of distributed machine learning, which enables a large number of edge computing devices to jointly learn a shared model without private data sharing. Federated learning allows nodes to synchronize only the locally trained models instead of their own private data, which provides a guarantee for privacy and security. However, due to the challenges of heterogeneity in federated learning, which are: (1) heterogeneous model architecture among devices; (2) statistical heterogeneity in real federated dataset, which do not obey independent-identical-distribution, resulting in poor performance of traditional federated learning algorithms. To solve the problems above, this paper proposes FedDistill, a new distributed training method based on knowledge distillation. By introducing personalized model on each device, the personalized model aims to improve the local performance even in a situation that global model fails to adapt to the local dataset, thereby improving the ability and robustness of the global model. The improvement of the performance of local device benefits from the effect of knowledge distillation, which can guide the improvement of global model by knowledge transfer between heterogeneous networks. Experiments show that FedDistill can significantly improve the accuracy of classification tasks and meet the needs of heterogeneous users
Pharmacoeconomics of obesity in China: a scoping review
Full text linkBackground: With the growing rate of obesity and associated chronic conditions in China, there is a need to assess the health and economic burdens of obesity and examine the effectiveness of pharmaceutical, medical, and comprehensive weight-loss interventions. Areas covered: This article reviewed publications retrieved from PubMed and Google Scholar during 2010–2020 on pharmacoeconomic studies related to overweight and obesity in China. We identified five cost-of-illness studies and four cost-effectiveness analyses of weight-loss interventions, including bariatric surgeries and a comprehensive intervention program. Expert opinion: There is a lack of pharmacoeconomic analyses of obesity in China. Existing studies have often taken the health system perspective without accounting for productivity loss. Cohort studies and studies based on electronic health records or claims data are needed to provide the epidemiologic parameters required for homegrown economic evaluations of the health and economic burdens of obesity in China, as well as the cost-effectiveness of interventions to reduce obesity and its sequela
An investigation into the impact of variations of ambient air pollution and meteorological factors on lung cancer mortality in Yangtze River Delta
Full text linkLung cancer (LC) mortality, as one of the top cancer deaths in China, has been associated with increased levels of exposure to ambient air pollutants. In this study, different lag times on weekly basis were applied to study the association of air pollutants (PM2.5, PM10, and NO2) and LC mortality in Ningbo, and in subpopulations at different age groups and genders. Furthermore, seasonal variations of pollutant concentrations and meteorological variables (temperature, relative humidity, and wind speed) were analysed. A generalised additive model (GAM) using Poisson regression was employed to estimate the effect of single pollutant model on LC mortality in Yangtze River Delta using Ningbo as a case study. It was reported that there were statistically significant relationships between lung cancer mortality and air pollutants. Increases of 6.2% (95% confidence interval [CI]: 0.2% to 12.6%) and 4.3% (95% CI: 0.1% to 8.5%) weekly total LC mortality with a 3-week lag time were linked to each 10 μg/m3 increase of weekly average PM2.5 and PM10 respectively. The association of air pollutants (PM2.5, PM10 and NO2) and LC mortality with a 3-week lag time was also found statistically significant during periods of low temperature (T < 18 °C), low relative humidity (H < 73.7%) and low wind speed (u < 2.8 m/s), respectively. The female population was found to be more susceptible to the exposure to air pollution than the male population. In addition, the population with an age of 50 years or above was shown to be more sensitive to ambient air pollutant. These outcomes indicated that increased risk of lung cancer mortality was evidently linked to exposure to ambient air pollutant on a weekly basis. The impact of weekly variation on the LC mortality and air pollutant levels should be considered in air pollution-related health burden analysis
The COVID-19 vaccines: recent development, challenges and prospects
Full text linkThe highly infectious coronavirus disease 2019 (COVID-19) associated with the pathogenic severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has spread to become a global pandemic. At present, the world is relying mainly on containment and hygiene-related measures, as well as repurposed drugs to control the outbreak. The development of COVID-19 vaccines is crucial for the world to return to pre-pandemic normalcy, and a collective global effort has been invested into protection against SARS-CoV-2. As of March 2021, thirteen vaccines have been approved for application whilst over 90 vaccine candidates are under clinical trials. This review focuses on the development of COVID-19 vaccines and highlights the efficacy and vaccination reactions of the authorised vaccines. The mechanisms, storage, and dosage specification of vaccine candidates at the advanced stage of development are also critically reviewed together with considerations for potential challenges. Whilst the development of a vaccine is, in general, in its infancy, current progress is promising. However, the world population will have to continue to adapt to the “new normal” and practice social distancing and hygienic measures, at least until effective vaccines are available to the general public
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