Correlation-aided method for identification and gradation of periodicities in hydrologic time series

Identification of periodicities in hydrological time series and evaluation of their statistical significance are not only important for water-related studies, but also challenging issues due to the complex variability of hydrological processes. In this article, we develop a “Moving Correlation Coefficient Analysis” (MCCA) method for identifying periodicities of a time series. In the method, the correlation between the original time series and the periodic fluctuation is used as a criterion, aiming to seek out the periodic fluctuation that fits the original time series best, and to evaluate its statistical significance. Consequently, we take periodic components consisting of simple sinusoidal variation as an example, and do statistical experiments to verify the applicability and reliability of the developed method by considering various parameters changing. Three other methods commonly used, harmonic analysis method (HAM), power spectrum method (PSM) and maximum entropy method (MEM) are also applied for comparison. The results indicate that the efficiency of each method is positively connected to the length and amplitude of samples, but negatively correlated with the mean value, variation coefficient and length of periodicity, without relationship with the initial phase of periodicity. For those time series with higher noise component, the developed MCCA method performs best among the four methods. Results from the hydrological case studies in the Yangtze River basin further verify the better performances of the MCCA method compared to other three methods for the identification of periodicities in hydrologic time series

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

Fiber 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

Synthesis of an isomer of lycoplanine a via cascade cyclization to construct the spiro-N,O-acetal moiety

An isomer of lycoplanine A with a 6/10/5/5 tetracyclic skeleton was synthesized using D–A reaction and cascasde reaction to respectively construct the [9.2.2] pentadecane skeleton and the challenging 1-oxa-6-azaspiro[4.4]nonane spirocenter. Morever, detailed DFT calculations were conducted to explain the selectivity in the D–A reaction. This study may provide sufficient experience for the total synthesis of lycoplanine A and other alkaloids with similar spiro-N,O-acetal cores

The foreign direct investment-environment nexus: does emission disaggregation matter?

This paper examines the effect of foreign direct investment (FDI) on CO2 emissions by using disaggregated emissions data; territorial-based and consumption-based emissions. FDI is measured in three ways; inflow, net inflow, and stock. Employing data over the period 1995–2014 and a number of estimators, the results indicate FDI (whether measured as inflow or net inflow) has negative impact on emissions (irrespective of the measurement). However, the impact is generally found to be greater for the territorial-based emissions. The results of the FDI flow variables largely support the pollution halo hypothesis. Thus, the results are supportive of the robust effect of FDI’s positive effect. Regarding the stock measure, the negative effect of FDI is only found for the territorial-based CO2 emissions. Since the territorial-based emissions capture emissions in the domestic economy only, it is not surprising that the plausible efficiency of FDI stock is found to reduce these emissions rather the consumption-based. FDI stock is now considered part of the local economy. The results of the paper are largely not parallel with previous studies that did not disaggregate CO2 emissions. This we believe is an indication that the measure of CO2 matters for the analyses of the FDI-emissions nexus

Artificial neural network system for cell classification using single cell RNA expression

We implemented an automated system for single-cell classification using artificial neural networks (ANN). Our system takes single-cell gene expression sparse matrices and trains ANN to classify cell types and subtypes. The assemblies of ANNs predict cell classes by voting. We tested the system in a case study where we trained ANNs with a dataset containing approximately 120,000 single cells and tested the resulting model using an independent data set of 13,000 single cells. The overall accuracy of the 5-class classification was 95%. We trained and tested a total of 100 ANNs in 10 cycles. The prediction system demonstrated excellent reproducibility. The analysis of misclassifications indicated that 2% were likely classification errors, while the remaining 3% were likely due to mislabeled types and subtypes in the test set

Influence of environmental values on the typhoon risk perceptions of high school students: a case study in Ningbo, China

Typhoon is a severe natural disaster that would bring huge economic losses and casualties to society. High school students are more vulnerable compared with adults during typhoon. Improving risk perception of typhoon can assist high school students effectively respond to typhoon and reduce related losses. Environmental values play an important role in human’s perceptions and actions. Although typhoon is related with environment, few studies have investigated the influence of environmental values on typhoon risk perception of high school students. This study investigates typhoon risk perception of high school students in Ningbo, China, and further analyzes the influence of environmental values on the perception with structural equations model. Results reveal that environmental values have significantly positive impacts on the typhoon risk perception. The findings also demonstrate that disaster threats and the disaster management ability of the government have significant positive impacts on the typhoon risk perception. This study proposes suggestions and measures to improve the typhoon risk perception among high school students and provides a reference for typhoon prevention and reduction education in China

Priorities for social science and humanities research on the challenges of moving beyond animal-based food systems

Increasingly high-profile research is being undertaken into the socio-environmental challenges associated with the over-production and consumption of food from animals. Transforming food systems to mitigate climate change and hidden hunger, ensure food security and good health all point to reducing animal-based foods as a key lever. Moving beyond animal-based food systems is a societal grand challenge requiring coordinated international research by the social sciences and humanities. A ‘selective openness’ to this range of disciplines has been observed within multi-discipline research programmes designed to address societal grand challenges including those concerned with the sustainability of food systems, inhibiting the impact of social sciences and humanities. Further, existing research on animal-based foods within these disciplines is largely dispersed and focused on particular parts of food systems. Inspired by the ‘Sutherland Method’ this paper discusses the results of an iterative research prioritisation process carried out to enhance capacity, mutual understanding and impact amongst European social sciences and humanities researchers. The process produced 15 research questions from an initial list of 100 and classified under the following five themes: (1) debating and visioning food from animals; (2) transforming agricultural spaces; (3) framing animals as food; (4) eating practices and identities; and (5) governing transitions beyond animal-based food systems. These themes provide an important means of making connections between research questions that invite and steer research on key challenges in moving beyond animal-based food systems. The themes also propose loci for future transdisciplinary research programmes that join researchers from the natural sciences, social sciences, and humanities and stakeholders from beyond academia to develop cooperative research and implementation initiatives. The experiences gained from the prioritisation process draw attention to the value of spending time to discuss and collaboratively steer research enquiry into emergent and controversial matters of concern. Fundamental, ethical questions around the continuation or complete cessation of the use of animals for food was a key tension. The positioning of research towards these questions affects not only the framing of the research area but also the partners with whom the research can be carried out and for whom it may be of benefit

Intelligent handover triggering mechanism in 5G ultra-dense networks via clustering-based reinforcement learning

Ultra-dense networks (UDNs) are considered as key 5G technologies. They provide mobile users a high transmission rate and efficient radio resource management. However, UDNs lead to the dense deployment of small base stations (BSs) that can cause stronger interference and subsequently increase the handover management complexity. At present, the conventional handover triggering mechanism of user equipment (UE) is only designed for macro mobility and thus could result in negative effects such as frequent handovers, ping-pong handovers, and handover failures on the handover process of UE at UDNs. These effects degrade the overall network performance. In addition, a massive number of BSs significantly increase the network maintenance system workload. To address these issues, this paper proposes an intelligent handover triggering mechanism for UE based on Q-learning frameworks and subtractive clustering techniques. The input metrics are first converted to state vectors by subtractive clustering, which can improve the efficiency and effectiveness of the training process. Afterward, the Q-learning framework learns the optimal handover triggering policy from the environment. The trained Q table is deployed to UE to trigger the handover process. The simulation results demonstrate that the proposed method can ensure the stronger mobility robustness of UE that is improved by 60%–90% compared to the conventional approach with respect to the number of handovers, ping-ping handover rate, and handover failure rate while maintaining other key performance indicators (KPIs), that is, a relatively high level of throughput and network latency. In addition, through integration with subtractive clustering, the proposed mechanism is further improved by an average of 20% in terms of all the evaluated KPIs

Cu-ZrO2 catalysts with highly dispersed Cu nanoclusters derived from ZrO2@ HKUST-1 composites for the enhanced CO2 hydrogenation to methanol

In this study, a series of Cu-ZrO2 catalysts with highly dispersed Cu nanoclusters were prepared via the calcination and reduction of ZrO2@HKUST-1 precursors. These catalysts demonstrated an outstanding selectivity in the yield of methanol during CO2 hydrogenation. The space-time yield (STY) of methanol is 5.2 times higher than that of those similar catalysts reported by other researchers, which were prepared using conventional method and tested under the same testing conditions. Density functional theory (DFT) study revealed that the activation of CO2 occurs at the Cu-ZrO2 interfaces and facilitates the hydrogenation of CO2 to methanol. It is concluded that the controlled formation of the highly dispersed Cu nanoclusters not only provides a large number of highly efficient active centers for CO2 hydrogenation, but also leads the generation of more Cu-ZrO2 interfaces. These two effects contribute to the superior catalytic performance of the nano Cu-ZrO2 catalyst in CO2 hydrogenation